system

Abstract

The system according to this embodiment aims to provide customized feedback and concrete steps toward future goals by utilizing the user's past data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a generation unit. The collection unit collects the user's past data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides customized feedback based on the analysis results obtained by the analysis unit. The generation unit generates specific steps toward future goals and video messages based on the feedback provided by the provision unit.

Description

Technical Field

[0006] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully done to utilize the past data of users to provide individually customized feedback or specific steps for future goals, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize the past data of users to provide customized feedback or specific steps for future goals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a generation unit. The collection unit collects the user's past data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides customized feedback based on the analysis results obtained by the analysis unit. The generation unit generates specific steps toward future goals and video messages based on the feedback provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can utilize the user's past data to provide customized feedback and concrete steps toward future goals. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The LifeMap Journey agent system according to an embodiment of the present invention is a system that uses AI to analyze a user's past data and generates future guidance and video messages based on the user's current goals and circumstances. The LifeMap Journey agent system promotes self-improvement and supports increased motivation and personal growth by analyzing the user's past data and generating future guidance and video messages based on the user's current goals and circumstances. Specifically, it consists of the following steps. First, the LifeMap Journey agent system collects the user's past data and the AI ​​analyzes that data. To understand past behavioral patterns and experiences, it collects data such as the user's behavioral history and goal achievement status. For example, the LifeMap Journey agent system analyzes the user's past goals achieved and experiences of failure. Next, the LifeMap Journey agent system provides customized feedback based on the user's current goals and circumstances. The AI ​​understands the user's current goals and circumstances and generates specific advice and feedback based on that. For example, the LifeMap Journey agent system advises the user on how to proceed with the projects and goals they are currently working on. Furthermore, the LifeMap Journey agent system generates specific steps and video messages for future goals. The AI ​​proposes specific action plans and steps to help users achieve their future goals. The LifeMap Journey agent system also generates personalized video messages to maintain user motivation. For example, it provides encouraging messages and specific advice as users work towards their goals. This system allows users to deepen their self-understanding and improve their goal achievement rate. It also contributes to improved mental well-being. The AI ​​agent continuously learns from user feedback and improves the service, resulting in personalized life coaching tailored to the user's needs.The LifeMap Journey agent system is a useful tool for individuals who desire personal growth, those who want to deepen their self-awareness and self-actualization, and those who aim to improve their quality of life. Through personalized feedback and progress tracking powered by AI, users can achieve personal growth in a concrete and effective manner. Furthermore, it provides motivational support and concrete action plans through video messages. This enables users to experience personal growth and contribute to a society where they can actively take action toward self-actualization. In this way, the LifeMap Journey agent system can encourage user self-development and support increased motivation and personal growth.

[0029] The LifeMap Journey agent system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a generation unit. The collection unit collects the user's past data. The collection unit collects data such as the user's behavior history and goal achievement status. The collection unit can also collect data such as goals the user has achieved or failed to achieve in the past. The collection unit can also collect data such as the user's feedback history. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using statistical analysis or machine learning algorithms, for example. The analysis unit can also analyze the user's past behavior patterns and experiences, for example. The analysis unit can also analyze the user's goal achievement status, for example. The provision unit provides customized feedback based on the analysis results obtained by the analysis unit. The provision unit provides specific advice based on the user's current goals and situation, for example. The provision unit can also provide feedback that points out areas for improvement in the user's behavior, for example. The provision unit can also provide hints for the user to achieve their goals, for example. The generation unit generates specific steps and video messages toward future goals based on the feedback provided by the provision unit. The generation unit generates, for example, a concrete action plan for the user's future goals. The generation unit can also generate, for example, video messages to maintain the user's motivation. The generation unit can also improve the service based on, for example, user feedback. As a result, the LifeMap Journey agent system according to the embodiment can encourage the user's self-development and support improved motivation and personal growth.

[0030] The data collection unit collects users' past data. Specifically, it collects a wide range of data, including users' behavioral history, goal achievement status, failures, and feedback history. For example, it collects detailed information such as goals set by users in the past and their achievement status, reasons and obstacles if they were not achieved, daily behavioral patterns, tools and resources used, and the content of feedback. This data may be collected automatically from the devices and applications used by the user, or it may be entered manually by the user. The data collection unit centrally manages this data and stores it in a database. Data collection is carried out with respect for user privacy and appropriate security measures in place. The collected data is used for processing in subsequent analysis, delivery, and generation units, so accurate and detailed data collection is required. The data collection unit can collect user behavior and feedback in real time and always maintain up-to-date information. This makes it possible to quickly grasp changes and progress of users and provide appropriate support. Furthermore, the data collection unit can anonymize user data and use it for statistical analysis and comparison with other users. This makes it possible to build foundational data to provide more personalized support to individual users.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, it uses statistical analysis and machine learning algorithms to analyze in detail users' past behavioral patterns, experiences, and goal achievement status. For example, by analyzing what goals users have set in the past and how they achieved or failed to achieve them, the analysis department identifies users' strengths and weaknesses, as well as factors for success and failure. Machine learning algorithms are used to predict future behavior and the likelihood of goal achievement based on user behavioral data. For example, they learn patterns of past success and generate advice to increase the probability of success in similar situations. In addition, by analyzing users' feedback history, the analysis department understands what kind of support users need and provides feedback to the provision and generation departments. The analysis department can also use data visualization tools to present analysis results to users in an easy-to-understand manner. This allows users to objectively understand their own behavior and goal achievement status and find concrete steps for self-improvement. Furthermore, the analysis department provides benchmarks by comparing user data with data from other users, allowing users to compare their progress with others. This allows users to feel a sense of their own growth and maintain motivation.

[0032] The service provider provides customized feedback based on the analysis results obtained by the analysis department. Specifically, it provides specific advice, areas for improvement in behavior, and hints for achieving goals, based on the user's current goals and situation. For example, it provides specific advice based on past success and failure cases in relation to the goals set by the user. It also analyzes the user's behavior patterns, points out areas that need improvement, and proposes specific improvement measures. The service provider can receive user feedback in real time and adjust the content of the feedback accordingly. This ensures that users always receive the latest information and advice, and can take concrete steps for self-improvement. The service provider can also provide positive feedback and encouraging messages to maintain user motivation. For example, when a user achieves a goal, it sends a congratulatory message to boost motivation for the next goal. Also, when a user faces difficulties, it sends an encouraging message to provide support. Based on user feedback, the service provider can continuously improve the accuracy and effectiveness of the feedback. This allows the service provider to provide optimal support to users and promote their self-growth.

[0033] The generation unit generates specific steps and video messages toward future goals based on feedback provided by the service provider. Specifically, it generates a concrete action plan toward the user's future goals and provides a step-by-step guide for the user to achieve those goals. For example, for a goal set by the user, it lists and presents the timeframe for achievement, necessary resources, and specific action items. It also generates video messages that include encouragement and advice to maintain the user's motivation. The video messages can showcase the user's past success stories and the success stories of other users to give the user a concrete image. The generation unit can also continuously improve the service based on user feedback. For example, if a user provides feedback on the provided action plan, the unit adjusts the action plan based on that feedback to provide more effective support. The generation unit uses AI to analyze user data and generate optimal action plans and video messages. This ensures that users always receive the latest information and support, promoting their self-growth. The generation unit anonymizes user data and compares it with data from other users to provide benchmarks, allowing users to compare their progress with others. This allows users to feel their own growth and maintain their motivation.

[0034] The generation unit can generate a concrete action plan for the user's future goals. For example, the generation unit can suggest daily tasks and weekly goals for the user's future goals. The generation unit can also provide a concrete action plan for the user's future goals. The generation unit can also set success criteria for the user's future goals. By generating a concrete action plan for the user's future goals, it can provide concrete steps for achieving those goals. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input a concrete action plan for the user's future goals into a generation AI, and the generation AI can generate the action plan.

[0035] The generation unit can generate video messages to maintain user motivation. For example, the generation unit can generate a video containing an encouraging message when a user is working towards a goal. The generation unit can also generate a video containing specific advice when a user is working towards a goal. The generation unit can also generate a video showcasing success stories when a user is working towards a goal. In this way, by generating video messages to maintain user motivation, it is possible to support the user in achieving their goals. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input a video message to maintain user motivation into a generation AI, and the generation AI can generate the video message.

[0036] The service provider can provide specific advice based on the user's current goals and circumstances. For example, the service provider can provide advice that points out specific areas for improvement in the user's actions toward their current goals. The service provider can also provide appropriate feedback depending on the user's current situation. The service provider can also provide hints for the user to achieve their goals. In this way, by providing specific advice based on the user's current goals and circumstances, the service provider can support the user in achieving their goals. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input specific advice based on the user's current goals and circumstances into a generative AI, and the generative AI can generate the advice.

[0037] The analysis unit can analyze the user's past behavioral patterns and experiences. For example, the analysis unit can analyze the user's past successes. For example, the analysis unit can also analyze the causes of the user's past failures. For example, the analysis unit can also analyze the user's behavioral patterns. By analyzing the user's past behavioral patterns and experiences, it is possible to understand the user's behavior and provide appropriate feedback. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the user's past behavioral patterns and experiences into a generative AI, which can then perform the analysis.

[0038] The data collection unit can collect data such as the user's behavior history and goal achievement status. For example, the data collection unit can collect the user's daily activity records. For example, the data collection unit can also collect the user's progress toward achieving their goals. For example, the data collection unit can also collect the user's feedback history. By collecting data such as the user's behavior history and goal achievement status, it is possible to provide a basis for analyzing the user's past data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data such as the user's behavior history and goal achievement status into a generative AI, and the generative AI can collect the data.

[0039] The generation unit can improve the service based on user feedback. For example, the generation unit can analyze user feedback and identify areas for service improvement. The generation unit can also add features to the service based on user feedback. The generation unit can also improve the usability of the service based on user feedback. By improving the service based on user feedback, personalized life coaching tailored to user needs can be realized. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user feedback into a generation AI, which can then identify areas for service improvement.

[0040] The service provider can track the user's progress. For example, the service provider can periodically check the user's progress toward achieving their goals. The service provider can also track the progress of the user's tasks. The service provider can also provide feedback based on the user's progress. This allows the service provider to support the user in achieving their goals by tracking their progress. Some or all of the above processes in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the user's progress into a generative AI, which can then track the progress.

[0041] The data collection unit can analyze the user's past data submission history and select the optimal data collection method. For example, the data collection unit can analyze the frequency of data previously submitted by the user and set the optimal collection interval. The data collection unit can also analyze the format of data previously submitted by the user and select the optimal collection format. The data collection unit can also analyze the content of data previously submitted by the user and select the optimal content to collect. By analyzing the user's past data submission history, the optimal data collection method can be selected, enabling efficient data collection. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input the user's past data submission history into a generating AI, which can then select the optimal data collection method.

[0042] The data collection unit can filter data based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. The data collection unit can also prioritize collecting data related to the user's areas of interest. For example, the data collection unit can filter and collect necessary data according to the user's living situation. This allows for the collection of highly relevant data by filtering data based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input filtering requirements based on the user's current living situation and areas of interest to the generative AI, which can then filter the data.

[0043] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also prioritize the collection of data related to places the user has visited in the past. The data collection unit can also prioritize the collection of data related to places the user plans to visit in the future. By collecting highly relevant data based on the user's geographical location information, the data collection unit can provide the user with useful data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then prioritize the collection of highly relevant data.

[0044] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also collect data related to accounts followed by the user on social media. The data collection unit can also collect data related to topics the user has shown interest in on social media. This allows for the collection of relevant data and the provision of feedback tailored to the user's interests by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity into a generative AI, which can then collect relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can perform an analysis with a moderate level of detail on data of moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the data, a detailed analysis can be performed on important data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral data. For example, the analysis unit can also apply a sentiment analysis algorithm to sentiment data. For example, the analysis unit can also apply a goal achievement analysis algorithm to goal achievement data. By applying different analysis algorithms depending on the data category, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into a generative AI, and the generative AI can apply different analysis algorithms.

[0047] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. The analysis unit may also postpone the analysis of previously submitted data. The analysis unit may also dynamically adjust the analysis priority according to the submission date. This allows for the prioritization of the latest data by determining the analysis priority based on the data submission date. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the data submission date into a generative AI, which can then determine the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, which can then adjust the order of analysis.

[0049] The service provider can adjust the level of detail of the feedback based on the user's current goals and situation when providing feedback. For example, the service provider can provide specific advice regarding the user's current goals. The service provider can also provide appropriate feedback depending on the user's current situation. The service provider can also adjust the level of detail of the feedback based on the user's progress toward achieving their goals. By adjusting the level of detail of the feedback based on the user's current goals and situation, the service provider can provide appropriate feedback to the user. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's current goals and situation into a generative AI, which can then adjust the level of detail of the feedback.

[0050] The service provider can apply different feedback algorithms depending on the user's past behavior patterns when providing feedback. For example, the service provider can provide positive feedback based on the user's past successes. For example, the service provider can also provide feedback that points out areas for improvement based on the user's past failures. For example, the service provider can analyze the user's past behavior patterns and apply the most suitable feedback algorithm. This allows the service provider to provide the user with the most suitable feedback by applying different feedback algorithms depending on the user's past behavior patterns. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's past behavior patterns into a generative AI, and the generative AI can apply different feedback algorithms.

[0051] The service provider can determine the priority of feedback based on the user's progress when providing feedback. For example, the service provider can prioritize providing the most important feedback according to the user's progress. The service provider can also dynamically adjust the priority of feedback based on the user's progress. The service provider can also track the user's progress and provide feedback at the appropriate time. This allows the service provider to prioritize providing the most important feedback to the user by determining the priority of feedback based on the user's progress. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the user's progress into a generative AI, which can then determine the priority of feedback.

[0052] The feedback provider can adjust the order of feedback based on the user's relevant data when providing feedback. For example, the provider can prioritize providing the most important feedback based on the user's relevant data. The provider can also dynamically adjust the order of feedback according to the user's relevant data. The provider can also analyze the user's relevant data to determine the optimal order of feedback. This allows the provider to prioritize providing the most important feedback to the user by adjusting the order of feedback based on the user's relevant data. Some or all of the above processing in the provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input the user's relevant data into a generative AI, which can then adjust the order of feedback.

[0053] The generation unit can adjust the level of detail in a video message based on the user's future goals when generating the video message. For example, if the user's future goals are specific, the generation unit can generate a video message that includes a detailed action plan. If the user's future goals are vague, the generation unit can also generate a video message that includes general advice. If the user's future goals are short-term, the generation unit can also generate a video message that includes short-term steps. By adjusting the level of detail in the message based on the user's future goals, the generation unit can provide the user with the most suitable video message. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's future goals into a generation AI, which can then adjust the level of detail in the message.

[0054] The generation unit can apply different generation algorithms to video messages depending on the user's past behavior patterns. For example, the generation unit can generate a video containing a positive message based on the user's past successes. For example, the generation unit can also generate a video pointing out areas for improvement based on the user's past failures. For example, the generation unit can analyze the user's past behavior patterns and apply the optimal generation algorithm. This allows the system to provide the user with the most suitable video message by applying different generation algorithms depending on the user's past behavior patterns. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past behavior patterns into a generation AI, which can then apply different generation algorithms.

[0055] The generation unit can prioritize messages based on the user's future goals when generating video messages. For example, if the user's future goals are important, the generation unit will prioritize generating messages related to those goals. For example, if the user's future goals are urgent, the generation unit can also prioritize generating messages related to those goals. For example, if the user's future goals are long-term, the generation unit can postpone generating messages related to those goals. By prioritizing messages based on the user's future goals, the most important video messages for the user can be provided preferentially. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's future goals into a generation AI, which can then determine the message priorities.

[0056] The generation unit can adjust the order of messages based on the user's relevant data when generating video messages. For example, the generation unit can prioritize generating the most important messages based on the user's relevant data. The generation unit can also dynamically adjust the order of messages according to the user's relevant data. For example, the generation unit can analyze the user's relevant data and determine the optimal order of messages. This allows the system to prioritize providing the user with the most important video messages by adjusting the order of messages based on the user's relevant data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's relevant data into a generation AI, which can then adjust the order of messages.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can analyze a user's past data submission history and select the optimal collection method. For example, it can analyze the frequency of data previously submitted by the user and set the optimal collection interval. It can also analyze the format of data previously submitted by the user and select the optimal collection format. It can also analyze the content of data previously submitted by the user and select the optimal content to collect. In this way, by analyzing a user's past data submission history, the optimal collection method can be selected, enabling efficient data collection.

[0059] The data collection unit can filter data based on the user's current lifestyle and areas of interest during the data collection process. For example, it can prioritize collecting data related to projects the user is currently working on. It can also prioritize collecting data related to the user's areas of interest. It can also filter and collect necessary data according to the user's lifestyle. This allows for the collection of highly relevant data by filtering data based on the user's current lifestyle and areas of interest.

[0060] The analysis department can adjust the level of detail in the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with a moderate level of detail on moderately important data. This allows for detailed analysis of important data by adjusting the level of detail based on the data's importance.

[0061] The feedback provider can adjust the level of detail in the feedback based on the user's current goals and situation. For example, it can provide specific advice regarding the user's current goals. It can also provide appropriate feedback depending on the user's current situation. It can also adjust the level of detail in the feedback based on the user's progress toward achieving their goals. By adjusting the level of detail in the feedback based on the user's current goals and situation, it is possible to provide feedback that is appropriate for the user.

[0062] The generation unit can adjust the level of detail in a video message based on the user's future goals. For example, if the user's future goals are specific, it can generate a video message that includes a detailed action plan. If the user's future goals are vague, it can also generate a video message that includes general advice. If the user's future goals are short-term, it can also generate a video message that includes short-term steps. By adjusting the level of detail in the message based on the user's future goals, it is possible to provide the user with the most suitable video message.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The data collection unit collects the user's past data. For example, it collects data such as the user's behavior history, goal achievement status, past goals achieved and failures, and feedback history. Step 2: The analysis department analyzes the data collected by the data collection department. For example, they analyze the data using statistical analysis and machine learning algorithms to analyze users' past behavior patterns, experiences, and goal achievement status. Step 3: The service provider provides customized feedback based on the analysis results obtained by the analysis provider. For example, they provide specific advice, suggestions for improvement in actions, and hints for achieving goals based on the user's current goals and situation. Step 4: The generation unit generates specific steps toward future goals and video messages based on the feedback provided by the delivery unit. For example, it generates specific action plans toward the user's future goals and video messages to maintain motivation.

[0065] (Example of form 2) The LifeMap Journey agent system according to an embodiment of the present invention is a system that uses AI to analyze a user's past data and generates future guidance and video messages based on the user's current goals and circumstances. The LifeMap Journey agent system promotes self-improvement and supports increased motivation and personal growth by analyzing the user's past data and generating future guidance and video messages based on the user's current goals and circumstances. Specifically, it consists of the following steps. First, the LifeMap Journey agent system collects the user's past data and the AI ​​analyzes that data. To understand past behavioral patterns and experiences, it collects data such as the user's behavioral history and goal achievement status. For example, the LifeMap Journey agent system analyzes the user's past goals achieved and experiences of failure. Next, the LifeMap Journey agent system provides customized feedback based on the user's current goals and circumstances. The AI ​​understands the user's current goals and circumstances and generates specific advice and feedback based on that. For example, the LifeMap Journey agent system advises the user on how to proceed with the projects and goals they are currently working on. Furthermore, the LifeMap Journey agent system generates specific steps and video messages for future goals. The AI ​​proposes specific action plans and steps to help users achieve their future goals. The LifeMap Journey agent system also generates personalized video messages to maintain user motivation. For example, it provides encouraging messages and specific advice as users work towards their goals. This system allows users to deepen their self-understanding and improve their goal achievement rate. It also contributes to improved mental well-being. The AI ​​agent continuously learns from user feedback and improves the service, resulting in personalized life coaching tailored to the user's needs.The LifeMap Journey agent system is a useful tool for individuals who desire personal growth, those who want to deepen their self-awareness and self-actualization, and those who aim to improve their quality of life. Through personalized feedback and progress tracking powered by AI, users can achieve personal growth in a concrete and effective manner. Furthermore, it provides motivational support and concrete action plans through video messages. This enables users to experience personal growth and contribute to a society where they can actively take action toward self-actualization. In this way, the LifeMap Journey agent system can encourage user self-development and support increased motivation and personal growth.

[0066] The LifeMap Journey agent system according to this embodiment comprises a collection unit, an analysis unit, a provision unit, and a generation unit. The collection unit collects the user's past data. The collection unit collects data such as the user's behavior history and goal achievement status. The collection unit can also collect data such as goals the user has achieved or failed to achieve in the past. The collection unit can also collect data such as the user's feedback history. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using statistical analysis or machine learning algorithms, for example. The analysis unit can also analyze the user's past behavior patterns and experiences, for example. The analysis unit can also analyze the user's goal achievement status, for example. The provision unit provides customized feedback based on the analysis results obtained by the analysis unit. The provision unit provides specific advice based on the user's current goals and situation, for example. The provision unit can also provide feedback that points out areas for improvement in the user's behavior, for example. The provision unit can also provide hints for the user to achieve their goals, for example. The generation unit generates specific steps and video messages toward future goals based on the feedback provided by the provision unit. The generation unit generates, for example, a concrete action plan for the user's future goals. The generation unit can also generate, for example, video messages to maintain the user's motivation. The generation unit can also improve the service based on, for example, user feedback. As a result, the LifeMap Journey agent system according to the embodiment can encourage the user's self-development and support improved motivation and personal growth.

[0067] The data collection unit collects users' past data. Specifically, it collects a wide range of data, including users' behavioral history, goal achievement status, failures, and feedback history. For example, it collects detailed information such as goals set by users in the past and their achievement status, reasons and obstacles if they were not achieved, daily behavioral patterns, tools and resources used, and the content of feedback. This data may be collected automatically from the devices and applications used by the user, or it may be entered manually by the user. The data collection unit centrally manages this data and stores it in a database. Data collection is carried out with respect for user privacy and appropriate security measures in place. The collected data is used for processing in subsequent analysis, delivery, and generation units, so accurate and detailed data collection is required. The data collection unit can collect user behavior and feedback in real time and always maintain up-to-date information. This makes it possible to quickly grasp changes and progress of users and provide appropriate support. Furthermore, the data collection unit can anonymize user data and use it for statistical analysis and comparison with other users. This makes it possible to build foundational data to provide more personalized support to individual users.

[0068] The analysis department analyzes the data collected by the data collection department. Specifically, it uses statistical analysis and machine learning algorithms to analyze in detail users' past behavioral patterns, experiences, and goal achievement status. For example, by analyzing what goals users have set in the past and how they achieved or failed to achieve them, the analysis department identifies users' strengths and weaknesses, as well as factors for success and failure. Machine learning algorithms are used to predict future behavior and the likelihood of goal achievement based on user behavioral data. For example, they learn patterns of past success and generate advice to increase the probability of success in similar situations. In addition, by analyzing users' feedback history, the analysis department understands what kind of support users need and provides feedback to the provision and generation departments. The analysis department can also use data visualization tools to present analysis results to users in an easy-to-understand manner. This allows users to objectively understand their own behavior and goal achievement status and find concrete steps for self-improvement. Furthermore, the analysis department provides benchmarks by comparing user data with data from other users, allowing users to compare their progress with others. This allows users to feel a sense of their own growth and maintain motivation.

[0069] The service provider provides customized feedback based on the analysis results obtained by the analysis department. Specifically, it provides specific advice, areas for improvement in behavior, and hints for achieving goals, based on the user's current goals and situation. For example, it provides specific advice based on past success and failure cases in relation to the goals set by the user. It also analyzes the user's behavior patterns, points out areas that need improvement, and proposes specific improvement measures. The service provider can receive user feedback in real time and adjust the content of the feedback accordingly. This ensures that users always receive the latest information and advice, and can take concrete steps for self-improvement. The service provider can also provide positive feedback and encouraging messages to maintain user motivation. For example, when a user achieves a goal, it sends a congratulatory message to boost motivation for the next goal. Also, when a user faces difficulties, it sends an encouraging message to provide support. Based on user feedback, the service provider can continuously improve the accuracy and effectiveness of the feedback. This allows the service provider to provide optimal support to users and promote their self-growth.

[0070] The generation unit generates specific steps and video messages toward future goals based on feedback provided by the service provider. Specifically, it generates a concrete action plan toward the user's future goals and provides a step-by-step guide for the user to achieve those goals. For example, for a goal set by the user, it lists and presents the timeframe for achievement, necessary resources, and specific action items. It also generates video messages that include encouragement and advice to maintain the user's motivation. The video messages can showcase the user's past success stories and the success stories of other users to give the user a concrete image. The generation unit can also continuously improve the service based on user feedback. For example, if a user provides feedback on the provided action plan, the unit adjusts the action plan based on that feedback to provide more effective support. The generation unit uses AI to analyze user data and generate optimal action plans and video messages. This ensures that users always receive the latest information and support, promoting their self-growth. The generation unit anonymizes user data and compares it with data from other users to provide benchmarks, allowing users to compare their progress with others. This allows users to feel their own growth and maintain their motivation.

[0071] The generation unit can generate a concrete action plan for the user's future goals. For example, the generation unit can suggest daily tasks and weekly goals for the user's future goals. The generation unit can also provide a concrete action plan for the user's future goals. The generation unit can also set success criteria for the user's future goals. By generating a concrete action plan for the user's future goals, it can provide concrete steps for achieving those goals. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input a concrete action plan for the user's future goals into a generation AI, and the generation AI can generate the action plan.

[0072] The generation unit can generate video messages to maintain user motivation. For example, the generation unit can generate a video containing an encouraging message when a user is working towards a goal. The generation unit can also generate a video containing specific advice when a user is working towards a goal. The generation unit can also generate a video showcasing success stories when a user is working towards a goal. In this way, by generating video messages to maintain user motivation, it is possible to support the user in achieving their goals. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input a video message to maintain user motivation into a generation AI, and the generation AI can generate the video message.

[0073] The service provider can provide specific advice based on the user's current goals and circumstances. For example, the service provider can provide advice that points out specific areas for improvement in the user's actions toward their current goals. The service provider can also provide appropriate feedback depending on the user's current situation. The service provider can also provide hints for the user to achieve their goals. In this way, by providing specific advice based on the user's current goals and circumstances, the service provider can support the user in achieving their goals. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input specific advice based on the user's current goals and circumstances into a generative AI, and the generative AI can generate the advice.

[0074] The analysis unit can analyze the user's past behavioral patterns and experiences. For example, the analysis unit can analyze the user's past successes. For example, the analysis unit can also analyze the causes of the user's past failures. For example, the analysis unit can also analyze the user's behavioral patterns. By analyzing the user's past behavioral patterns and experiences, it is possible to understand the user's behavior and provide appropriate feedback. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the user's past behavioral patterns and experiences into a generative AI, which can then perform the analysis.

[0075] The data collection unit can collect data such as the user's behavior history and goal achievement status. For example, the data collection unit can collect the user's daily activity records. For example, the data collection unit can also collect the user's progress toward achieving their goals. For example, the data collection unit can also collect the user's feedback history. By collecting data such as the user's behavior history and goal achievement status, it is possible to provide a basis for analyzing the user's past data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input data such as the user's behavior history and goal achievement status into a generative AI, and the generative AI can collect the data.

[0076] The generation unit can improve the service based on user feedback. For example, the generation unit can analyze user feedback and identify areas for service improvement. The generation unit can also add features to the service based on user feedback. The generation unit can also improve the usability of the service based on user feedback. By improving the service based on user feedback, personalized life coaching tailored to user needs can be realized. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input user feedback into a generation AI, which can then identify areas for service improvement.

[0077] The service provider can track the user's progress. For example, the service provider can periodically check the user's progress toward achieving their goals. The service provider can also track the progress of the user's tasks. The service provider can also provide feedback based on the user's progress. This allows the service provider to support the user in achieving their goals by tracking their progress. Some or all of the above processes in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the user's progress into a generative AI, which can then track the progress.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect data during a relaxed period. For example, if the user is concentrating, the data collection unit can collect data at that time. For example, if the user is tired, the data collection unit can collect data after they have rested. By adjusting the timing of data collection based on the user's emotions, the burden on the user can be reduced and efficient data collection can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input the user's emotions into a generative AI, which can estimate the emotions and adjust the timing of data collection.

[0079] The data collection unit can analyze the user's past data submission history and select the optimal data collection method. For example, the data collection unit can analyze the frequency of data previously submitted by the user and set the optimal collection interval. The data collection unit can also analyze the format of data previously submitted by the user and select the optimal collection format. The data collection unit can also analyze the content of data previously submitted by the user and select the optimal content to collect. By analyzing the user's past data submission history, the optimal data collection method can be selected, enabling efficient data collection. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the data collection unit can input the user's past data submission history into a generating AI, which can then select the optimal data collection method.

[0080] The data collection unit can filter data based on the user's current living situation and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. The data collection unit can also prioritize collecting data related to the user's areas of interest. For example, the data collection unit can filter and collect necessary data according to the user's living situation. This allows for the collection of highly relevant data by filtering data based on the user's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input filtering requirements based on the user's current living situation and areas of interest to the generative AI, which can then filter the data.

[0081] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit may prioritize collecting data related to stress reduction. For example, if the user is relaxed, the data collection unit may prioritize collecting data related to relaxation. For example, if the user is focused, the data collection unit may prioritize collecting data related to improved concentration. In this way, by prioritizing the data to collect based on the user's emotions, it is possible to prioritize the collection of data that is important to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input the user's emotions into a generative AI, which can estimate the emotions and determine the priority of data to collect.

[0082] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also prioritize the collection of data related to places the user has visited in the past. The data collection unit can also prioritize the collection of data related to places the user plans to visit in the future. By collecting highly relevant data based on the user's geographical location information, the data collection unit can provide the user with useful data. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI, which can then prioritize the collection of highly relevant data.

[0083] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also collect data related to accounts followed by the user on social media. The data collection unit can also collect data related to topics the user has shown interest in on social media. This allows for the collection of relevant data and the provision of feedback tailored to the user's interests by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity into a generative AI, which can then collect relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will use a simple and easy-to-understand presentation. For example, if the user is relaxed, the analysis unit may also use a presentation that includes detailed information. For example, if the user is focused, the analysis unit may also use a presentation that emphasizes specific data. By adjusting the presentation of the analysis based on the user's emotions, the analysis unit can provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input the user's emotions into a generative AI, which will estimate the emotions and adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can perform an analysis with a moderate level of detail on data of moderate importance. In this way, by adjusting the level of detail of the analysis based on the importance of the data, a detailed analysis can be performed on important data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a behavioral analysis algorithm to behavioral data. For example, the analysis unit can also apply a sentiment analysis algorithm to sentiment data. For example, the analysis unit can also apply a goal achievement analysis algorithm to goal achievement data. By applying different analysis algorithms depending on the data category, appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into a generative AI, and the generative AI can apply different analysis algorithms.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is focused, the analysis unit can also provide a longer analysis with specific data. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide the user with an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input the user's emotions into a generative AI, which can estimate the emotions and adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted data. The analysis unit may also postpone the analysis of previously submitted data. The analysis unit may also dynamically adjust the analysis priority according to the submission date. This allows for the prioritization of the latest data by determining the analysis priority based on the data submission date. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the data submission date into a generative AI, which can then determine the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This allows for the prioritization of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, which can then adjust the order of analysis.

[0090] The service provider can estimate the user's emotions and adjust the way feedback is presented based on the estimated emotions. For example, if the user is stressed, the service provider can provide simple and easy-to-understand feedback. For example, if the user is relaxed, the service provider can also provide detailed feedback. For example, if the user is focused, the service provider can also provide feedback that includes specific advice. By adjusting the way feedback is presented based on the user's emotions, the service provider can provide feedback that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input the user's emotions into a generative AI, which can estimate the emotions and adjust the way feedback is presented.

[0091] The service provider can adjust the level of detail of the feedback based on the user's current goals and situation when providing feedback. For example, the service provider can provide specific advice regarding the user's current goals. The service provider can also provide appropriate feedback depending on the user's current situation. The service provider can also adjust the level of detail of the feedback based on the user's progress toward achieving their goals. By adjusting the level of detail of the feedback based on the user's current goals and situation, the service provider can provide appropriate feedback to the user. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's current goals and situation into a generative AI, which can then adjust the level of detail of the feedback.

[0092] The service provider can apply different feedback algorithms depending on the user's past behavior patterns when providing feedback. For example, the service provider can provide positive feedback based on the user's past successes. For example, the service provider can also provide feedback that points out areas for improvement based on the user's past failures. For example, the service provider can analyze the user's past behavior patterns and apply the most suitable feedback algorithm. This allows the service provider to provide the user with the most suitable feedback by applying different feedback algorithms depending on the user's past behavior patterns. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or without a generative AI. For example, the service provider can input the user's past behavior patterns into a generative AI, and the generative AI can apply different feedback algorithms.

[0093] The service provider can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is stressed, the service provider can provide short, concise feedback. If the user is relaxed, the service provider can also provide detailed feedback. If the user is focused, the service provider can also provide longer feedback that includes specific advice. By adjusting the length of the feedback based on the user's emotions, the service provider can provide feedback of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using a generative AI or not. For example, the service provider can input the user's emotions into a generative AI, which can estimate the emotions and adjust the length of the feedback.

[0094] The service provider can determine the priority of feedback based on the user's progress when providing feedback. For example, the service provider can prioritize providing the most important feedback according to the user's progress. The service provider can also dynamically adjust the priority of feedback based on the user's progress. The service provider can also track the user's progress and provide feedback at the appropriate time. This allows the service provider to prioritize providing the most important feedback to the user by determining the priority of feedback based on the user's progress. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input the user's progress into a generative AI, which can then determine the priority of feedback.

[0095] The feedback provider can adjust the order of feedback based on the user's relevant data when providing feedback. For example, the provider can prioritize providing the most important feedback based on the user's relevant data. The provider can also dynamically adjust the order of feedback according to the user's relevant data. The provider can also analyze the user's relevant data to determine the optimal order of feedback. This allows the provider to prioritize providing the most important feedback to the user by adjusting the order of feedback based on the user's relevant data. Some or all of the above processing in the provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider can input the user's relevant data into a generative AI, which can then adjust the order of feedback.

[0096] The generation unit can estimate the user's emotions and adjust the content of the video message it generates based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can generate a video containing an encouraging message. For example, if the user is relaxed, the generation unit can also generate a video with a relaxed atmosphere. For example, if the user is focused, the generation unit can also generate a video containing specific advice. This allows the system to provide the user with the most suitable video message by adjusting the content of the video message based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the user's emotions into a generation AI, which can estimate the emotions and adjust the content of the video message.

[0097] The generation unit can adjust the level of detail in a video message based on the user's future goals when generating the video message. For example, if the user's future goals are specific, the generation unit can generate a video message that includes a detailed action plan. If the user's future goals are vague, the generation unit can also generate a video message that includes general advice. If the user's future goals are short-term, the generation unit can also generate a video message that includes short-term steps. By adjusting the level of detail in the message based on the user's future goals, the generation unit can provide the user with the most suitable video message. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's future goals into a generation AI, which can then adjust the level of detail in the message.

[0098] The generation unit can apply different generation algorithms to video messages depending on the user's past behavior patterns. For example, the generation unit can generate a video containing a positive message based on the user's past successes. For example, the generation unit can also generate a video pointing out areas for improvement based on the user's past failures. For example, the generation unit can analyze the user's past behavior patterns and apply the optimal generation algorithm. This allows the system to provide the user with the most suitable video message by applying different generation algorithms depending on the user's past behavior patterns. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past behavior patterns into a generation AI, which can then apply different generation algorithms.

[0099] The generation unit can estimate the user's emotions and adjust the length of the video message it generates based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a short, concise video message. If the user is relaxed, the generation unit can also generate a longer video message with detailed explanations. If the user is focused, the generation unit can also generate a longer video message with specific advice. By adjusting the length of the video message based on the user's emotions, it is possible to provide the user with a video message of the optimal length. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, the generation unit can input the user's emotions into a generation AI, which can estimate the emotions and adjust the length of the video message.

[0100] The generation unit can prioritize messages based on the user's future goals when generating video messages. For example, if the user's future goals are important, the generation unit will prioritize generating messages related to those goals. For example, if the user's future goals are urgent, the generation unit can also prioritize generating messages related to those goals. For example, if the user's future goals are long-term, the generation unit can postpone generating messages related to those goals. By prioritizing messages based on the user's future goals, the most important video messages for the user can be provided preferentially. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's future goals into a generation AI, which can then determine the message priorities.

[0101] The generation unit can adjust the order of messages based on the user's relevant data when generating video messages. For example, the generation unit can prioritize generating the most important messages based on the user's relevant data. The generation unit can also dynamically adjust the order of messages according to the user's relevant data. For example, the generation unit can analyze the user's relevant data and determine the optimal order of messages. This allows the system to prioritize providing the user with the most important video messages by adjusting the order of messages based on the user's relevant data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's relevant data into a generation AI, which can then adjust the order of messages.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The system can estimate the user's emotions and adjust the timing of feedback based on those emotions. For example, if the user is stressed, feedback can be provided during a relaxed period. If the user is focused, feedback can be provided at that time. If the user is tired, feedback can be provided after they have rested. By adjusting the timing of feedback based on the user's emotions, the system can reduce the user's burden and provide more effective feedback.

[0104] The generation unit can estimate the user's emotions and adjust the tone of the video message it generates based on those emotions. For example, if the user is stressed, it can generate a video message in a gentle tone. If the user is relaxed, it can generate a video message in a relaxed tone. If the user is focused, it can generate a video message that includes specific advice. By adjusting the tone of the video message based on the user's emotions, it is possible to provide the user with the most suitable video message.

[0105] The analysis unit can estimate the user's emotions and adjust the visual representation of the analysis based on those emotions. For example, if the user is stressed, a simple and visually easy-to-understand graph can be used. If the user is relaxed, a graph with detailed data can be used. If the user is focused, a graph highlighting specific data can be used. By adjusting the visual representation of the analysis based on the user's emotions, the analysis results can be provided in a way that is easy for the user to understand.

[0106] The system can estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is stressed, it can provide feedback that includes encouraging messages. If the user is relaxed, it can provide feedback that includes detailed advice. If the user is focused, it can provide feedback that includes specific areas for improvement. By adjusting the content of the feedback based on the user's emotions, the system can provide the most appropriate feedback for the user.

[0107] The generation unit can estimate the user's emotions and adjust the background music of the generated video message based on those emotions. For example, if the user is stressed, relaxing music can be used as the background. If the user is relaxed, music that creates a relaxing atmosphere can be used as the background. If the user is concentrating, music that enhances concentration can be used as the background. In this way, by adjusting the background music of the video message based on the user's emotions, it is possible to provide the user with the most suitable video message.

[0108] The data collection unit can analyze a user's past data submission history and select the optimal collection method. For example, it can analyze the frequency of data previously submitted by the user and set the optimal collection interval. It can also analyze the format of data previously submitted by the user and select the optimal collection format. It can also analyze the content of data previously submitted by the user and select the optimal content to collect. In this way, by analyzing a user's past data submission history, the optimal collection method can be selected, enabling efficient data collection.

[0109] The data collection unit can filter data based on the user's current lifestyle and areas of interest during the data collection process. For example, it can prioritize collecting data related to projects the user is currently working on. It can also prioritize collecting data related to the user's areas of interest. It can also filter and collect necessary data according to the user's lifestyle. This allows for the collection of highly relevant data by filtering data based on the user's current lifestyle and areas of interest.

[0110] The analysis department can adjust the level of detail in the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with a moderate level of detail on moderately important data. This allows for detailed analysis of important data by adjusting the level of detail based on the data's importance.

[0111] The feedback provider can adjust the level of detail in the feedback based on the user's current goals and situation. For example, it can provide specific advice regarding the user's current goals. It can also provide appropriate feedback depending on the user's current situation. It can also adjust the level of detail in the feedback based on the user's progress toward achieving their goals. By adjusting the level of detail in the feedback based on the user's current goals and situation, it is possible to provide feedback that is appropriate for the user.

[0112] The generation unit can adjust the level of detail in a video message based on the user's future goals. For example, if the user's future goals are specific, it can generate a video message that includes a detailed action plan. If the user's future goals are vague, it can also generate a video message that includes general advice. If the user's future goals are short-term, it can also generate a video message that includes short-term steps. By adjusting the level of detail in the message based on the user's future goals, it is possible to provide the user with the most suitable video message.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The data collection unit collects the user's past data. For example, it collects data such as the user's behavior history, goal achievement status, past goals achieved and failures, and feedback history. Step 2: The analysis department analyzes the data collected by the data collection department. For example, they analyze the data using statistical analysis and machine learning algorithms to analyze users' past behavior patterns, experiences, and goal achievement status. Step 3: The service provider provides customized feedback based on the analysis results obtained by the analysis provider. For example, they provide specific advice, suggestions for improvement in actions, and hints for achieving goals based on the user's current goals and situation. Step 4: The generation unit generates specific steps toward future goals and video messages based on the feedback provided by the delivery unit. For example, it generates specific action plans toward the user's future goals and video messages to maintain motivation.

[0115] 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.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects data such as the user's behavior history and goal achievement status. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The provision unit is implemented by the control unit 46A of the smart device 14 and provides customized feedback based on the analysis results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates specific steps toward future goals and video messages. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0122] 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.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0124] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] 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.

[0126] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects data such as the user's behavior history and goal achievement status. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides customized feedback based on the analysis results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates specific steps toward future goals and video messages. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0138] 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.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0140] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] 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.

[0142] 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.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects data such as the user's behavior history and goal achievement status. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides customized feedback based on the analysis results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates specific steps toward future goals and video messages. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0154] 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.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0156] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] 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.

[0158] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] 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.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the collection unit, analysis unit, provision unit, and generation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects data such as the user's behavior history and goal achievement status. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The provision unit is implemented by the control unit 46A of the robot 414 and provides customized feedback based on the analysis results. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates specific steps toward future goals and video messages. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] 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.

[0169] Figure 9 shows the 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.

[0170] 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.

[0171] 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.

[0172] 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, and motorcycles, 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 based, for example, 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.

[0173] 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."

[0174] 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.

[0175] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] 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 other things 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.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A data collection unit that collects the user's past data, An analysis unit analyzes the data collected by the aforementioned collection unit, A providing unit that provides customized feedback based on the analysis results obtained by the aforementioned analysis unit, The system comprises a generation unit that generates specific steps toward future goals and a video message based on the feedback provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The generating unit is Generates concrete action plans for the user's future goals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generate video messages to maintain user motivation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide specific advice based on the user's current goals and circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyze the user's past behavioral patterns and experiences. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We collect data such as user behavior history and goal achievement status. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is We improve our services based on user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, Track user progress The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past data submission history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing feedback, adjust the level of detail based on the user's current goals and circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing feedback, different feedback algorithms are applied depending on the user's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing feedback, we prioritize feedback based on the user's progress. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing feedback, the order of feedback will be adjusted based on the user's relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates the user's emotions and adjusts the content of the generated video message based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is When generating video messages, adjust the level of detail in the message based on the user's future goals. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating video messages, different generation algorithms are applied depending on the user's past behavior patterns. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is It estimates the user's emotions and adjusts the length of the generated video message based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is When generating video messages, prioritize messages based on the user's future goals. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is When generating video messages, the message order is adjusted based on the user's relevant data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects the user's past data, An analysis unit analyzes the data collected by the aforementioned collection unit, A providing unit that provides customized feedback based on the analysis results obtained by the aforementioned analysis unit, The system comprises a generation unit that generates specific steps toward future goals and a video message based on feedback provided by the aforementioned provision unit. A system characterized by the following features.

2. The generating unit is Generates concrete action plans for the user's future goals. The system according to feature 1.

3. The generating unit is Generate video messages to maintain user motivation. The system according to feature 1.

4. The aforementioned supply unit is, Provide specific advice based on the user's current goals and circumstances. The system according to feature 1.

5. The aforementioned analysis unit is Analyze the user's past behavioral patterns and experiences. The system according to feature 1.

6. The aforementioned collection unit is We collect data such as user behavior history and goal achievement status. The system according to feature 1.

7. The generating unit is We improve our services based on user feedback. The system according to feature 1.

8. The aforementioned supply unit is, Track user progress The system according to feature 1.

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