system

Abstract

The system according to this embodiment aims to create action plans for realizing an individual's dreams and goals and to optimize progress. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, an analysis unit, and a matching unit. The reception unit receives input from the user about their dreams and goals. The generation unit creates an action plan based on the dreams and goals entered by the reception unit. The analysis unit analyzes the progress of the action plan created by the generation unit and optimizes it in real time. The matching unit matches users who have the same goals.

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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there was a problem that it was difficult to efficiently create an action plan for realizing an individual's dreams and goals and optimize the progress.

[0005] The system according to the embodiment aims to create an action plan for realizing an individual's dreams and goals and optimize the progress.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, an analysis unit, and a matching unit. The reception unit receives the user's dreams and goals. The generation unit creates an action plan based on the dreams and goals entered by the reception unit. The analysis unit analyzes the progress of the action plan created by the generation unit and optimizes it in real time. The matching unit matches users who have the same goals. [Effects of the Invention]

[0007] The system according to this embodiment can create an action plan to realize an individual's dreams and goals and optimize progress. [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 a plurality of 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 action plan creation system according to an embodiment of the present invention is a system in which an AI agent deeply understands an individual's dreams and goals and creates a personalized action plan. This action plan creation system uses a generating AI to analyze the progress of the plan, optimize the plan in real time, and make improvement suggestions. It also matches users with the same goals and promotes community formation, thereby maintaining motivation and providing mutual support. This makes the realization of dreams and goals more certain. For example, a user interacts with the generating AI and inputs their dreams and goals. The generating AI analyzes the user's input and creates a personalized action plan based on the individual's dreams and goals. For example, if a user inputs "I want to be able to speak English fluently," the generating AI automatically creates specific tasks to achieve that goal. Next, the generating AI analyzes the progress of the plan in real time and optimizes the plan as needed. For example, if a user fails to complete tasks as planned, the generating AI automatically adjusts the schedule and tasks. It also conducts regular reviews, and the generating AI proposes improvement suggestions. For example, every week the generating AI asks the user, "How was your progress this week?" and optimizes the plan for the following week based on the user's response. Furthermore, the system matches users with similar goals, promoting community building. This allows users to share information and support each other. For example, users aiming to learn English can form a community and share learning methods and progress, making it easier to maintain motivation. This system allows users to concretize their dreams and goals through the generating AI and create action plans to achieve them. The generating AI also analyzes and optimizes the progress of the plan in real time, increasing the probability of achieving the goal. Moreover, community building among users with similar goals makes it easier to maintain motivation and enables mutual support. In this way, the action plan creation system can provide support to efficiently realize users' dreams and goals.

[0029] The action plan creation system according to this embodiment comprises a reception unit, a generation unit, an analysis unit, and a matching unit. The reception unit receives input from the user regarding their dreams and goals. For example, the user can input their dreams and goals in text format into the reception unit. The reception unit can also receive input from the user using voice input. Furthermore, the reception unit can receive input from the user using images or videos. The generation unit uses a generation AI to create an action plan based on the dreams and goals entered by the reception unit. For example, if the user inputs "I want to be able to speak English fluently," the generation unit uses the generation AI to automatically create specific tasks toward that goal. The generation unit can use the generation AI to create a personalized action plan based on the user's dreams and goals. For example, the generation AI analyzes the user's input and creates an action plan based on individual dreams and goals. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. The analysis unit analyzes the progress of the action plan created by the generation unit and optimizes it in real time. The analysis unit, for example, automatically adjusts schedules and tasks using generative AI if a user fails to complete tasks as planned. The analysis unit can analyze the progress of plans in real time using generative AI and optimize the plans as needed. The analysis unit, for example, uses generative AI to analyze the progress of plans in real time and optimize the plans as needed. Some or all of the above processes in the analysis unit may be performed using generative AI or not. The matching unit matches users with the same goals and promotes community formation. The matching unit, for example, helps users aiming to learn English to form a community and maintain motivation by sharing learning methods and progress. The matching unit can match users with the same goals and promote community formation. The matching unit, for example, promotes information sharing and mutual support among users. Some or all of the above processes in the matching unit may be performed using AI or not.As a result, the action plan creation system according to this embodiment enables efficient input of the user's dreams and goals, plan creation, progress analysis, and matching.

[0030] The reception desk is where users input their dreams and goals. For example, users can input their dreams and goals in text format. The reception desk also allows users to input their dreams and goals using voice input. Furthermore, users can input their dreams and goals using images and videos. Specifically, with text input, users can use a keyboard to describe their dreams and goals in detail. With voice input, users simply speak their dreams and goals into a microphone, and the system converts their voice into text. When using images and videos, users can upload photos and videos related to their dreams and goals, and the system analyzes these media to understand their content. For example, if a user's goal is to "complete a marathon," they can input this in text, speak it aloud, or upload photos and videos of past runs. The reception desk can also combine these input methods to maximize user convenience. Furthermore, the reception desk centrally manages the input data, making it accessible to the generation and analysis departments. This facilitates the creation of action plans based on users' dreams and goals.

[0031] The generation unit uses a generation AI to create an action plan based on the dreams and goals entered by the reception unit. For example, if a user enters "I want to be able to speak English fluently," the generation unit will use the generation AI to automatically create specific tasks to achieve that goal. The generation AI uses natural language processing technology to analyze the user's input and breaks down the steps to achieve the goal. For example, for the goal of learning English, it will suggest tasks such as daily vocabulary study, weekly conversation practice, and monthly mock tests. The generation unit can also adjust the priority and timing of tasks according to the user's schedule and learning style. Furthermore, the generation unit creates a more effective action plan by referring to the user's past data and the success stories of other users. For example, it can analyze data from users who have achieved the same goal in the past and reflect those success patterns in the new user's plan. Some or all of the above processing in the generation unit may be performed using the generation AI or not. This allows the generation unit to create a personalized action plan based on the user's dreams and goals and provide the user with specific steps to achieve those goals.

[0032] The analysis unit analyzes the progress of action plans created by the generation unit and optimizes them in real time. For example, if a user fails to complete a task as planned, the analysis unit automatically adjusts the schedule and tasks using the generation AI. Specifically, if a user fails to complete a task within the deadline set by the user, the generation AI analyzes the cause and adjusts the difficulty of the task or reorganizes the schedule. For example, if a user planned to study English vocabulary but did not make progress as planned, the generation AI would suggest simplifying the learning content or increasing the study time. Furthermore, the analysis unit collects user feedback and uses it to improve the plan. By having users report the progress and achievement level of tasks, the generation AI optimizes the plan based on that data. For example, if a user provides feedback that "this task is too difficult," the generation AI will simplify the task or suggest an alternative approach. Some or all of the above processes in the analysis unit may be performed using the generation AI or not. This allows the analysis unit to monitor the progress of the user's action plan in real time and optimize the plan as needed, making it easier for users to achieve their goals.

[0033] The matching function matches users with the same goals and promotes community building. For example, the matching function helps users aiming to learn English to form a community and maintain motivation by sharing learning methods and progress. Specifically, the matching function recommends the most suitable partners and groups based on the user's goals and interests. For example, it introduces users aiming to learn English to other users with the same level and goals, providing opportunities for them to learn together. The matching function uses AI to analyze user profiles and activity history to make optimal matches. For example, it recommends compatible partners based on the user's learning history and feedback. The matching function also provides a platform that makes it easy for users to share information. For example, through chat functions and forums, users can share learning methods and progress and encourage each other. Furthermore, the matching function regularly holds online events and workshops to promote interaction among users. In this way, the matching function can support community building among users and increase motivation toward achieving goals. Some or all of the above processes in the matching function may be performed using AI or not. This allows the matching system to effectively connect users with similar goals, promote community building, and ultimately support users in achieving their objectives.

[0034] The generation unit can create personalized action plans based on the user's dreams and goals using a generation AI. For example, if the user inputs "I want to be able to speak English fluently," the generation unit will use the generation AI to automatically create specific tasks toward that goal. The generation unit can create personalized action plans based on the user's dreams and goals using the generation AI. For example, the generation unit's generation AI analyzes the user's input and creates action plans based on individual dreams and goals. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. This makes it possible to personalize action plans by using the generation AI.

[0035] The analysis unit can analyze the progress of the plan in real time using generative AI and optimize the plan as needed. For example, if a user fails to complete a task according to the plan, the analysis unit can automatically adjust the schedule and tasks using generative AI. The analysis unit can analyze the progress of the plan in real time using generative AI and optimize the plan as needed. For example, the analysis unit uses generative AI to analyze the progress of the plan in real time and optimize the plan as needed. Some or all of the above processing in the analysis unit may be performed using generative AI or not. This makes it possible to analyze and optimize the progress of the plan in real time by using generative AI.

[0036] The matching unit can match users with the same goals and promote community formation. For example, the matching unit can help users aiming to learn English to form a community and maintain motivation by sharing learning methods and progress. The matching unit can match users with the same goals and promote community formation. For example, the matching unit can promote information sharing and mutual support among users. Some or all of the above processing in the matching unit may be performed using AI or not. This promotes community formation through matching users with the same goals.

[0037] The generation unit can automatically adjust the schedule and tasks using the generation AI if the user fails to complete tasks as planned. For example, if the user fails to complete tasks as planned, the generation unit will automatically adjust the schedule and tasks using the generation AI. The generation unit can automatically adjust the schedule and tasks using the generation AI if the user fails to complete tasks as planned. For example, the generation unit will use the generation AI to analyze the user's progress and adjust the schedule and tasks if the user fails to complete tasks as planned. Some or all of the above processing in the generation unit may be performed using the generation AI or not. This improves the flexibility of the plan by allowing the generation AI to automatically adjust the schedule and tasks even if tasks are not completed as planned.

[0038] The analysis department can conduct regular reviews and propose improvement plans using generative AI. For example, the analysis department's generative AI can ask the user "How was your progress this week?" every week and optimize the plan for the following week based on the user's response. The analysis department can use generative AI to conduct regular reviews and propose improvement plans. For example, the analysis department's generative AI analyzes the user's progress and proposes improvement plans on a regular basis. Some or all of the above processes in the analysis department may be performed using generative AI or not. This improves the accuracy of the plan through regular reviews and proposals for improvement.

[0039] The matching unit can facilitate information sharing and mutual support among users. For example, the matching unit can facilitate information sharing and mutual support among users. The matching unit can match users with the same goals and facilitate community formation. For example, the matching unit can facilitate information sharing and mutual support among users. Some or all of the above-described processes in the matching unit may be performed using AI or not. This will revitalize the community through information sharing and mutual support among users.

[0040] The reception desk can analyze the user's past input history of dreams and goals and select the optimal input method. For example, the reception desk can analyze patterns of dreams and goals the user has entered in the past and suggest the most efficient input method. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest input methods to be used at specific times based on the user's past input history. In this way, the optimal input method can be selected by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI or not.

[0041] The reception desk can filter the user's dreams and goals based on their current living situation and areas of interest when the user inputs them. For example, the reception desk can consider the user's current work and family situation and suggest realistic dreams and goals. The reception desk can prioritize displaying dreams and goals relevant to the user's areas of interest. The reception desk can suggest achievable dreams and goals that match the user's lifestyle. By filtering based on the user's living situation and areas of interest, it can suggest more realistic dreams and goals. Some or all of the above processing in the reception desk may be performed using AI or not.

[0042] The reception desk can prioritize the input of highly relevant goals when users input their dreams and goals, taking into account their geographical location. For example, the reception desk can suggest achievable dreams and goals based on the user's current location. Based on the user's geographical location, the reception desk can prioritize the input of dreams and goals related to local events and activities. The reception desk can also suggest dreams and goals related to places the user has visited in the past, taking into account their travel history. This allows for the suggestion of more relevant goals by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not.

[0043] The reception desk can analyze the user's social media activity when they input their dreams and goals, and input related goals. For example, the reception desk can analyze the content of the user's social media posts and suggest related dreams and goals. The reception desk can input related dreams and goals based on the activity of accounts the user follows. The reception desk can analyze the user's interests on social media and suggest related dreams and goals. In this way, by analyzing social media activity, it is possible to suggest more relevant goals. Some or all of the above processing in the reception desk may be performed using AI, or not using AI.

[0044] The generation unit can adjust the level of detail in an action plan based on the importance of the objectives when creating the plan. For example, the generation unit can create a detailed action plan for high-importance objectives, and a simplified action plan for low-importance objectives. The generation unit can also adjust the number of steps in the action plan according to the importance of the objectives. This allows for more efficient plan creation by adjusting the level of detail according to the importance of the objectives. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not be performed using AI.

[0045] The generation unit can apply different planning algorithms depending on the category of the goal when creating an action plan. For example, the generation unit can apply a learning planning algorithm to learning goals. For health goals, it can apply a health management algorithm. For career goals, it can apply a career planning algorithm. By applying different planning algorithms depending on the category of the goal, it becomes possible to create a more appropriate plan. Some or all of the above processing in the generation unit may be performed using AI or not.

[0046] The generation unit can determine the priority of action plans based on the deadlines for submitting goals when creating action plans. For example, if the deadline for submitting a goal is approaching, the generation unit will prioritize creating the action plan. If the deadline for submitting a goal is far away, the generation unit can postpone creating the action plan. The generation unit can adjust the number of steps in the action plan according to the submission deadline. This allows for more efficient plan creation by determining the priority of plans based on the deadline for submitting goals. Some or all of the above processing in the generation unit may be performed using AI or not.

[0047] The generation unit can adjust the order of the action plan based on the relevance of the goals when creating the action plan. For example, the generation unit can prioritize incorporating highly relevant goals into the action plan. The generation unit can also postpone incorporating less relevant goals into the action plan. The generation unit can adjust the number of steps in the action plan according to the relevance of the goals. This allows for more efficient plan creation by adjusting the order of the plan based on the relevance of the goals. Some or all of the above processing in the generation unit may be performed using AI or not.

[0048] The analysis unit can improve the accuracy of its progress analysis by considering the interrelationships between goals. For example, the analysis unit can improve the accuracy of its progress analysis by considering the dependencies between goals. The analysis unit can adjust the criteria for progress analysis based on the interrelationships of goals. The analysis unit can optimize the results of its progress analysis by considering the relationships between goals. This improves the accuracy of the progress analysis by considering the interrelationships of goals. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0049] The analysis department can perform progress analysis while considering the attribute information of goal submitters. For example, the analysis department can improve the accuracy of progress analysis by considering the age and gender of goal submitters. The analysis department can adjust the criteria for progress analysis based on the occupation and lifestyle of goal submitters. The analysis department can optimize the results of progress analysis by considering the past performance of goal submitters. This improves the accuracy of progress analysis by considering the attribute information of goal submitters. Some or all of the above processes in the analysis department may be performed using AI or not.

[0050] The analysis unit can perform progress analysis while considering the geographical distribution of targets. For example, the analysis unit can improve the accuracy of progress analysis based on the geographical distribution of targets. The analysis unit can adjust the criteria for progress analysis while considering geographical distribution. The analysis unit can optimize the results of progress analysis based on geographical distribution. This improves the accuracy of progress analysis by considering the geographical distribution of targets. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0051] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the objectives during progress analysis. For example, the analysis unit can improve the accuracy of progress analysis by referring to literature related to the objectives. The analysis unit can adjust the criteria for progress analysis based on the relevant literature. The analysis unit can optimize the results of progress analysis by considering the relevant literature. As a result, the accuracy of progress analysis is improved by referring to relevant literature on the objectives. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0052] The matching unit can improve the accuracy of matching by considering the interrelationships between targets during the matching process. For example, the matching unit can improve the accuracy of matching by considering the dependencies between targets. The matching unit can adjust the matching criteria based on the interrelationships between targets. The matching unit can optimize the matching results by considering the relationships between targets. This improves the accuracy of matching by considering the interrelationships between targets. Some or all of the above processes in the matching unit may be performed using AI or not.

[0053] The matching unit can perform matching while considering the attribute information of the goal submitter. For example, the matching unit can improve the accuracy of matching by considering the age and gender of the goal submitter. The matching unit can adjust the matching criteria based on the occupation and lifestyle of the goal submitter. The matching unit can optimize the matching results by considering the past performance of the goal submitter. This improves the accuracy of matching by considering the attribute information of the goal submitter. Some or all of the above processes in the matching unit may be performed using AI or not.

[0054] The matching unit can perform matching while considering the geographical distribution of the target. For example, the matching unit can improve the accuracy of matching based on the geographical distribution of the target. The matching unit can adjust the matching criteria while considering the geographical distribution. The matching unit can optimize the matching results based on the geographical distribution. As a result, the accuracy of matching is improved by considering the geographical distribution of the target. Some or all of the above processing in the matching unit may be performed using AI or not.

[0055] The matching unit can improve the accuracy of matching by referring to relevant literature on the target during the matching process. For example, the matching unit can refer to literature related to the target to improve the accuracy of matching. The matching unit can adjust the matching criteria based on the relevant literature. The matching unit can optimize the matching results by considering the relevant literature. This improves the accuracy of matching by referring to relevant literature on the target. Some or all of the above processes in the matching unit may be performed using AI or not.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The reception desk analyzes user input and, considering the user's past behavior history and achievements, can propose more realistic goals. For example, if a user re-enters a goal they previously failed to achieve, the reception desk analyzes the cause and resets the goal to a more achievable level. It can also suggest similar goals based on the user's past successes. Furthermore, the reception desk can propose an optimal schedule for achieving the goal based on the user's behavior history. This allows for the setting of more realistic and achievable goals by considering the user's past behavior history.

[0058] The generation unit can create action plans by taking into account the user's past successes. For example, it can refer to methods that have worked for the user in the past and incorporate similar methods into the action plan. It can also adjust the plan to avoid methods that have failed for the user in the past. Furthermore, it can include motivational messages in the plan based on the user's past successes. As a result, by considering the user's past successes, it becomes possible to create more effective action plans.

[0059] The generation unit can create action plans while taking the user's lifestyle into consideration. For example, if the user has a morning-oriented lifestyle, tasks can be concentrated in the morning hours. If the user has a night-owl lifestyle, tasks can be concentrated in the evening hours. Furthermore, rest periods can be incorporated into the plan to match the user's lifestyle. This makes it possible to create more realistic and actionable action plans by considering the user's lifestyle.

[0060] The generation unit can create action plans while taking the user's health condition into consideration. For example, if the user is in good health, challenging tasks can be included in the plan. If the user is in poor health, a plan including rest can be proposed. Furthermore, tasks related to health management can be included in the plan based on the user's health condition. This makes it possible to create more realistic and actionable action plans by considering the user's health condition.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives the user's dreams and goals. For example, the reception desk allows users to enter their dreams and goals in text format. The reception desk also allows users to enter their dreams and goals using voice input. Furthermore, the reception desk allows users to enter their dreams and goals using images or videos. Step 2: The generation unit uses a generation AI to create an action plan based on the dreams and goals entered by the reception unit. For example, if the user enters "I want to be able to speak English fluently," the generation unit uses the generation AI to automatically create specific tasks to achieve that goal. The generation unit can use the generation AI to create a personalized action plan based on the user's dreams and goals. For example, the generation unit uses the generation AI to analyze the user's input and create an action plan based on individual dreams and goals. Some or all of the above processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. Step 3: The analysis unit analyzes the progress of the action plan created by the generation unit and optimizes it in real time. For example, if a user fails to complete a task as planned, the analysis unit automatically adjusts the schedule and tasks using the generation AI. The analysis unit can use the generation AI to analyze the progress of the plan in real time and optimize the plan as needed. For example, the analysis unit uses the generation AI to analyze the progress of the plan in real time and optimize the plan as needed. Some or all of the above processing in the analysis unit may be performed using the generation AI or not. Step 4: The matching unit matches users with the same goals and promotes community building. For example, the matching unit helps users aiming to learn English to form a community and share learning methods and progress, making it easier to maintain motivation. The matching unit can match users with the same goals and promote community building. For example, the matching unit promotes information sharing and mutual support among users. Some or all of the above processes in the matching unit may be performed using AI or not.

[0063] (Example of form 2) The action plan creation system according to an embodiment of the present invention is a system in which an AI agent deeply understands an individual's dreams and goals and creates a personalized action plan. This action plan creation system uses a generating AI to analyze the progress of the plan, optimize the plan in real time, and make improvement suggestions. It also matches users with the same goals and promotes community formation, thereby maintaining motivation and providing mutual support. This makes the realization of dreams and goals more certain. For example, a user interacts with the generating AI and inputs their dreams and goals. The generating AI analyzes the user's input and creates a personalized action plan based on the individual's dreams and goals. For example, if a user inputs "I want to be able to speak English fluently," the generating AI automatically creates specific tasks to achieve that goal. Next, the generating AI analyzes the progress of the plan in real time and optimizes the plan as needed. For example, if a user fails to complete tasks as planned, the generating AI automatically adjusts the schedule and tasks. It also conducts regular reviews, and the generating AI proposes improvement suggestions. For example, every week the generating AI asks the user, "How was your progress this week?" and optimizes the plan for the following week based on the user's response. Furthermore, the system matches users with similar goals, promoting community building. This allows users to share information and support each other. For example, users aiming to learn English can form a community and share learning methods and progress, making it easier to maintain motivation. This system allows users to concretize their dreams and goals through the generating AI and create action plans to achieve them. The generating AI also analyzes and optimizes the progress of the plan in real time, increasing the probability of achieving the goal. Moreover, community building among users with similar goals makes it easier to maintain motivation and enables mutual support. In this way, the action plan creation system can provide support to efficiently realize users' dreams and goals.

[0064] The action plan creation system according to this embodiment comprises a reception unit, a generation unit, an analysis unit, and a matching unit. The reception unit receives input from the user regarding their dreams and goals. For example, the user can input their dreams and goals in text format into the reception unit. The reception unit can also receive input from the user using voice input. Furthermore, the reception unit can receive input from the user using images or videos. The generation unit uses a generation AI to create an action plan based on the dreams and goals entered by the reception unit. For example, if the user inputs "I want to be able to speak English fluently," the generation unit uses the generation AI to automatically create specific tasks toward that goal. The generation unit can use the generation AI to create a personalized action plan based on the user's dreams and goals. For example, the generation AI analyzes the user's input and creates an action plan based on individual dreams and goals. Some or all of the above-described processes in the generation unit may be performed using the generation AI or not. The analysis unit analyzes the progress of the action plan created by the generation unit and optimizes it in real time. The analysis unit, for example, automatically adjusts schedules and tasks using generative AI if a user fails to complete tasks as planned. The analysis unit can analyze the progress of plans in real time using generative AI and optimize the plans as needed. The analysis unit, for example, uses generative AI to analyze the progress of plans in real time and optimize the plans as needed. Some or all of the above processes in the analysis unit may be performed using generative AI or not. The matching unit matches users with the same goals and promotes community formation. The matching unit, for example, helps users aiming to learn English to form a community and maintain motivation by sharing learning methods and progress. The matching unit can match users with the same goals and promote community formation. The matching unit, for example, promotes information sharing and mutual support among users. Some or all of the above processes in the matching unit may be performed using AI or not.As a result, the action plan creation system according to this embodiment enables efficient input of the user's dreams and goals, plan creation, progress analysis, and matching.

[0065] The reception desk is where users input their dreams and goals. For example, users can input their dreams and goals in text format. The reception desk also allows users to input their dreams and goals using voice input. Furthermore, users can input their dreams and goals using images and videos. Specifically, with text input, users can use a keyboard to describe their dreams and goals in detail. With voice input, users simply speak their dreams and goals into a microphone, and the system converts their voice into text. When using images and videos, users can upload photos and videos related to their dreams and goals, and the system analyzes these media to understand their content. For example, if a user's goal is to "complete a marathon," they can input this in text, speak it aloud, or upload photos and videos of past runs. The reception desk can also combine these input methods to maximize user convenience. Furthermore, the reception desk centrally manages the input data, making it accessible to the generation and analysis departments. This facilitates the creation of action plans based on users' dreams and goals.

[0066] The generation unit uses a generation AI to create an action plan based on the dreams and goals entered by the reception unit. For example, if a user enters "I want to be able to speak English fluently," the generation unit will use the generation AI to automatically create specific tasks to achieve that goal. The generation AI uses natural language processing technology to analyze the user's input and breaks down the steps to achieve the goal. For example, for the goal of learning English, it will suggest tasks such as daily vocabulary study, weekly conversation practice, and monthly mock tests. The generation unit can also adjust the priority and timing of tasks according to the user's schedule and learning style. Furthermore, the generation unit creates a more effective action plan by referring to the user's past data and the success stories of other users. For example, it can analyze data from users who have achieved the same goal in the past and reflect those success patterns in the new user's plan. Some or all of the above processing in the generation unit may be performed using the generation AI or not. This allows the generation unit to create a personalized action plan based on the user's dreams and goals and provide the user with specific steps to achieve those goals.

[0067] The analysis unit analyzes the progress of action plans created by the generation unit and optimizes them in real time. For example, if a user fails to complete a task as planned, the analysis unit automatically adjusts the schedule and tasks using the generation AI. Specifically, if a user fails to complete a task within the deadline set by the user, the generation AI analyzes the cause and adjusts the difficulty of the task or reorganizes the schedule. For example, if a user planned to study English vocabulary but did not make progress as planned, the generation AI would suggest simplifying the learning content or increasing the study time. Furthermore, the analysis unit collects user feedback and uses it to improve the plan. By having users report the progress and achievement level of tasks, the generation AI optimizes the plan based on that data. For example, if a user provides feedback that "this task is too difficult," the generation AI will simplify the task or suggest an alternative approach. Some or all of the above processes in the analysis unit may be performed using the generation AI or not. This allows the analysis unit to monitor the progress of the user's action plan in real time and optimize the plan as needed, making it easier for users to achieve their goals.

[0068] The matching function matches users with the same goals and promotes community building. For example, the matching function helps users aiming to learn English to form a community and maintain motivation by sharing learning methods and progress. Specifically, the matching function recommends the most suitable partners and groups based on the user's goals and interests. For example, it introduces users aiming to learn English to other users with the same level and goals, providing opportunities for them to learn together. The matching function uses AI to analyze user profiles and activity history to make optimal matches. For example, it recommends compatible partners based on the user's learning history and feedback. The matching function also provides a platform that makes it easy for users to share information. For example, through chat functions and forums, users can share learning methods and progress and encourage each other. Furthermore, the matching function regularly holds online events and workshops to promote interaction among users. In this way, the matching function can support community building among users and increase motivation toward achieving goals. Some or all of the above processes in the matching function may be performed using AI or not. This allows the matching system to effectively connect users with similar goals, promote community building, and ultimately support users in achieving their objectives.

[0069] The generation unit can create personalized action plans based on the user's dreams and goals using a generation AI. For example, if the user inputs "I want to be able to speak English fluently," the generation unit will use the generation AI to automatically create specific tasks toward that goal. The generation unit can create personalized action plans based on the user's dreams and goals using the generation AI. For example, the generation unit's generation AI analyzes the user's input and creates action plans based on individual dreams and goals. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. This makes it possible to personalize action plans by using the generation AI.

[0070] The analysis unit can analyze the progress of the plan in real time using generative AI and optimize the plan as needed. For example, if a user fails to complete a task according to the plan, the analysis unit can automatically adjust the schedule and tasks using generative AI. The analysis unit can analyze the progress of the plan in real time using generative AI and optimize the plan as needed. For example, the analysis unit uses generative AI to analyze the progress of the plan in real time and optimize the plan as needed. Some or all of the above processing in the analysis unit may be performed using generative AI or not. This makes it possible to analyze and optimize the progress of the plan in real time by using generative AI.

[0071] The matching unit can match users with the same goals and promote community formation. For example, the matching unit can help users aiming to learn English to form a community and maintain motivation by sharing learning methods and progress. The matching unit can match users with the same goals and promote community formation. For example, the matching unit can promote information sharing and mutual support among users. Some or all of the above processing in the matching unit may be performed using AI or not. This promotes community formation through matching users with the same goals.

[0072] The generation unit can automatically adjust the schedule and tasks using the generation AI if the user fails to complete tasks as planned. For example, if the user fails to complete tasks as planned, the generation unit will automatically adjust the schedule and tasks using the generation AI. The generation unit can automatically adjust the schedule and tasks using the generation AI if the user fails to complete tasks as planned. For example, the generation unit will use the generation AI to analyze the user's progress and adjust the schedule and tasks if the user fails to complete tasks as planned. Some or all of the above processing in the generation unit may be performed using the generation AI or not. This improves the flexibility of the plan by allowing the generation AI to automatically adjust the schedule and tasks even if tasks are not completed as planned.

[0073] The analysis department can conduct regular reviews and propose improvement plans using generative AI. For example, the analysis department's generative AI can ask the user "How was your progress this week?" every week and optimize the plan for the following week based on the user's response. The analysis department can use generative AI to conduct regular reviews and propose improvement plans. For example, the analysis department's generative AI analyzes the user's progress and proposes improvement plans on a regular basis. Some or all of the above processes in the analysis department may be performed using generative AI or not. This improves the accuracy of the plan through regular reviews and proposals for improvement.

[0074] The matching unit can facilitate information sharing and mutual support among users. For example, the matching unit can facilitate information sharing and mutual support among users. The matching unit can match users with the same goals and facilitate community formation. For example, the matching unit can facilitate information sharing and mutual support among users. Some or all of the above-described processes in the matching unit may be performed using AI or not. This will revitalize the community through information sharing and mutual support among users.

[0075] The reception unit can estimate the user's emotions and adjust the timing of dream and goal input based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can prompt them to input their dreams and goals during a time when they can relax. The reception unit can also encourage the user to input their dreams and goals when they are highly motivated. If the user is tired, the reception unit can prompt them to input their dreams and goals after resting. By adjusting the input timing according to the user's emotions, dreams and goals can be entered at a more appropriate time. 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 reception unit may be performed using AI or not.

[0076] The reception desk can analyze the user's past input history of dreams and goals and select the optimal input method. For example, the reception desk can analyze patterns of dreams and goals the user has entered in the past and suggest the most efficient input method. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest input methods to be used at specific times based on the user's past input history. In this way, the optimal input method can be selected by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI or not.

[0077] The reception desk can filter the user's dreams and goals based on their current living situation and areas of interest when the user inputs them. For example, the reception desk can consider the user's current work and family situation and suggest realistic dreams and goals. The reception desk can prioritize displaying dreams and goals relevant to the user's areas of interest. The reception desk can suggest achievable dreams and goals that match the user's lifestyle. By filtering based on the user's living situation and areas of interest, it can suggest more realistic dreams and goals. Some or all of the above processing in the reception desk may be performed using AI or not.

[0078] The reception unit can estimate the user's emotions and, based on the estimated emotions, determine the priority of dreams and goals to be entered. For example, if the user is feeling stressed, the reception unit will prioritize dreams and goals that promote relaxation. If the user is highly motivated, the reception unit can prioritize challenging dreams and goals. If the user is tired, the reception unit can prioritize dreams and goals that are easily achievable. This allows for more appropriate goal setting by prioritizing dreams and goals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not.

[0079] The reception desk can prioritize the input of highly relevant goals when users input their dreams and goals, taking into account their geographical location. For example, the reception desk can suggest achievable dreams and goals based on the user's current location. Based on the user's geographical location, the reception desk can prioritize the input of dreams and goals related to local events and activities. The reception desk can also suggest dreams and goals related to places the user has visited in the past, taking into account their travel history. This allows for the suggestion of more relevant goals by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI or not.

[0080] The reception desk can analyze the user's social media activity when they input their dreams and goals, and input related goals. For example, the reception desk can analyze the content of the user's social media posts and suggest related dreams and goals. The reception desk can input related dreams and goals based on the activity of accounts the user follows. The reception desk can analyze the user's interests on social media and suggest related dreams and goals. In this way, by analyzing social media activity, it is possible to suggest more relevant goals. Some or all of the above processing in the reception desk may be performed using AI, or not using AI.

[0081] The generation unit can estimate the user's emotions and adjust the way the action plan is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can present the action plan in a gentle manner. If the user is in a hurry, the generation unit can present the action plan in a concise and to-the-point manner. If the user is excited, the generation unit can present the action plan in a visually stimulating manner. By adjusting the way the action plan is presented according to the user's emotions, it becomes possible to present a more appropriate plan. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI or not using AI.

[0082] The generation unit can adjust the level of detail in an action plan based on the importance of the objectives when creating the plan. For example, the generation unit can create a detailed action plan for high-importance objectives, and a simplified action plan for low-importance objectives. The generation unit can also adjust the number of steps in the action plan according to the importance of the objectives. This allows for more efficient plan creation by adjusting the level of detail according to the importance of the objectives. Some or all of the above-described processes in the generation unit may be performed using AI, or they may not be performed using AI.

[0083] The generation unit can apply different planning algorithms depending on the category of the goal when creating an action plan. For example, the generation unit can apply a learning planning algorithm to learning goals. For health goals, it can apply a health management algorithm. For career goals, it can apply a career planning algorithm. By applying different planning algorithms depending on the category of the goal, it becomes possible to create a more appropriate plan. Some or all of the above processing in the generation unit may be performed using AI or not.

[0084] The generation unit can estimate the user's emotions and adjust the length of the action plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit can create an action plan that can be achieved in a short period of time. If the user is relaxed, the generation unit can create an action plan that spans a long period of time. If the user is excited, the generation unit can create an action plan that combines short-term and long-term goals. This allows for the creation of a more appropriate plan by adjusting the length of the action plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not.

[0085] The generation unit can determine the priority of action plans based on the deadlines for submitting goals when creating action plans. For example, if the deadline for submitting a goal is approaching, the generation unit will prioritize creating the action plan. If the deadline for submitting a goal is far away, the generation unit can postpone creating the action plan. The generation unit can adjust the number of steps in the action plan according to the submission deadline. This allows for more efficient plan creation by determining the priority of plans based on the deadline for submitting goals. Some or all of the above processing in the generation unit may be performed using AI or not.

[0086] The generation unit can adjust the order of the action plan based on the relevance of the goals when creating the action plan. For example, the generation unit can prioritize incorporating highly relevant goals into the action plan. The generation unit can also postpone incorporating less relevant goals into the action plan. The generation unit can adjust the number of steps in the action plan according to the relevance of the goals. This allows for more efficient plan creation by adjusting the order of the plan based on the relevance of the goals. Some or all of the above processing in the generation unit may be performed using AI or not.

[0087] The analysis unit can estimate the user's emotions and adjust the progress analysis criteria based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can analyze progress using flexible criteria. If the user is in a hurry, the analysis unit can analyze progress using strict criteria. If the user is excited, the analysis unit can analyze progress using visually stimulating criteria. This allows for more appropriate progress analysis by adjusting the progress analysis criteria according to the user's emotions. 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 analysis unit may be performed using AI or not.

[0088] The analysis unit can improve the accuracy of its progress analysis by considering the interrelationships between goals. For example, the analysis unit can improve the accuracy of its progress analysis by considering the dependencies between goals. The analysis unit can adjust the criteria for progress analysis based on the interrelationships of goals. The analysis unit can optimize the results of its progress analysis by considering the relationships between goals. This improves the accuracy of the progress analysis by considering the interrelationships of goals. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0089] The analysis department can perform progress analysis while considering the attribute information of goal submitters. For example, the analysis department can improve the accuracy of progress analysis by considering the age and gender of goal submitters. The analysis department can adjust the criteria for progress analysis based on the occupation and lifestyle of goal submitters. The analysis department can optimize the results of progress analysis by considering the past performance of goal submitters. This improves the accuracy of progress analysis by considering the attribute information of goal submitters. Some or all of the above processes in the analysis department may be performed using AI or not.

[0090] The analysis unit can estimate the user's emotions and adjust the order in which the progress analysis results are displayed based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can prioritize displaying detailed progress analysis results. If the user is in a hurry, the analysis unit can prioritize displaying concise progress analysis results. If the user is excited, the analysis unit can prioritize displaying visually stimulating progress analysis results. This allows for more appropriate result display by adjusting the order in which the progress analysis results are displayed according to the user's emotions. 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 analysis unit may be performed using AI or not.

[0091] The analysis unit can perform progress analysis while considering the geographical distribution of targets. For example, the analysis unit can improve the accuracy of progress analysis based on the geographical distribution of targets. The analysis unit can adjust the criteria for progress analysis while considering geographical distribution. The analysis unit can optimize the results of progress analysis based on geographical distribution. This improves the accuracy of progress analysis by considering the geographical distribution of targets. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0092] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the objectives during progress analysis. For example, the analysis unit can improve the accuracy of progress analysis by referring to literature related to the objectives. The analysis unit can adjust the criteria for progress analysis based on the relevant literature. The analysis unit can optimize the results of progress analysis by considering the relevant literature. As a result, the accuracy of progress analysis is improved by referring to relevant literature on the objectives. Some or all of the above processes in the analysis unit may be performed using AI or not.

[0093] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is relaxed, the matching unit can perform matching using flexible criteria. If the user is in a hurry, the matching unit can perform matching using strict criteria. If the user is excited, the matching unit can perform matching using visually stimulating criteria. By adjusting the matching criteria according to the user's emotions, more appropriate matching becomes possible. 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 matching unit may be performed using AI or not using AI.

[0094] The matching unit can improve the accuracy of matching by considering the interrelationships between targets during the matching process. For example, the matching unit can improve the accuracy of matching by considering the dependencies between targets. The matching unit can adjust the matching criteria based on the interrelationships between targets. The matching unit can optimize the matching results by considering the relationships between targets. This improves the accuracy of matching by considering the interrelationships between targets. Some or all of the above processes in the matching unit may be performed using AI or not.

[0095] The matching unit can perform matching while considering the attribute information of the goal submitter. For example, the matching unit can improve the accuracy of matching by considering the age and gender of the goal submitter. The matching unit can adjust the matching criteria based on the occupation and lifestyle of the goal submitter. The matching unit can optimize the matching results by considering the past performance of the goal submitter. This improves the accuracy of matching by considering the attribute information of the goal submitter. Some or all of the above processes in the matching unit may be performed using AI or not.

[0096] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, if the user is relaxed, the matching unit can prioritize displaying detailed matching results. If the user is in a hurry, the matching unit can prioritize displaying concise matching results. If the user is excited, the matching unit can prioritize displaying visually stimulating matching results. This allows for more appropriate result display by adjusting the order in which matching results are displayed according to the user's emotions. 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 matching unit may be performed using AI or not.

[0097] The matching unit can perform matching while considering the geographical distribution of the target. For example, the matching unit can improve the accuracy of matching based on the geographical distribution of the target. The matching unit can adjust the matching criteria while considering the geographical distribution. The matching unit can optimize the matching results based on the geographical distribution. As a result, the accuracy of matching is improved by considering the geographical distribution of the target. Some or all of the above processing in the matching unit may be performed using AI or not.

[0098] The matching unit can improve the accuracy of matching by referring to relevant literature on the target during the matching process. For example, the matching unit can refer to literature related to the target to improve the accuracy of matching. The matching unit can adjust the matching criteria based on the relevant literature. The matching unit can optimize the matching results by considering the relevant literature. This improves the accuracy of matching by referring to relevant literature on the target. Some or all of the above processes in the matching unit may be performed using AI or not.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception desk analyzes user input and, considering the user's past behavior history and achievements, can propose more realistic goals. For example, if a user re-enters a goal they previously failed to achieve, the reception desk analyzes the cause and resets the goal to a more achievable level. It can also suggest similar goals based on the user's past successes. Furthermore, the reception desk can propose an optimal schedule for achieving the goal based on the user's behavior history. This allows for the setting of more realistic and achievable goals by considering the user's past behavior history.

[0101] The generation unit can estimate the user's emotions and adjust the difficulty of the action plan based on those emotions. For example, if the user is stressed, the generation unit will prioritize including easy tasks in the plan. When the user is highly motivated, challenging tasks can be added to the plan. Furthermore, if the user is tired, the unit can suggest a plan that includes rest. This allows for the creation of more appropriate plans by adjusting the difficulty of the action plan according to the user's emotions.

[0102] The analytics department can estimate the user's emotions and adjust the frequency of progress reports based on those estimates. For example, if the user is relaxed, the frequency of progress reports will be reduced; if the user is in a hurry, progress reports will be given more frequently. If the user is excited, visually stimulating progress reports can be provided. This allows for more effective progress management by adjusting the frequency of progress reports according to the user's emotions.

[0103] The matching unit can estimate the user's emotions and suggest a role within the community based on those emotions. For example, if the user is relaxed, it can suggest a support role; if the user is highly motivated, it can suggest a leadership role. If the user is tired, it can suggest participating in the community as an observer. This allows for more appropriate community formation by suggesting roles within the community according to the user's emotions.

[0104] The generation unit can create action plans by taking into account the user's past successes. For example, it can refer to methods that have worked for the user in the past and incorporate similar methods into the action plan. It can also adjust the plan to avoid methods that have failed for the user in the past. Furthermore, it can include motivational messages in the plan based on the user's past successes. As a result, by considering the user's past successes, it becomes possible to create more effective action plans.

[0105] The reception desk can estimate the user's emotions and adjust the input interface based on those emotions. For example, if the user is relaxed, it can provide an interface with soft colors; if the user is in a hurry, it can provide a simple and intuitive interface; and if the user is excited, it can provide a visually stimulating interface. By adjusting the input interface according to the user's emotions, a more comfortable input experience can be provided.

[0106] The generation unit can create action plans while taking the user's lifestyle into consideration. For example, if the user has a morning-oriented lifestyle, tasks can be concentrated in the morning hours. If the user has a night-owl lifestyle, tasks can be concentrated in the evening hours. Furthermore, rest periods can be incorporated into the plan to match the user's lifestyle. This makes it possible to create more realistic and actionable action plans by considering the user's lifestyle.

[0107] The analysis department can estimate the user's emotions and adjust the content of progress reports based on those estimates. For example, if the user is relaxed, a detailed progress report can be provided; if the user is in a hurry, a concise progress report can be provided. If the user is excited, a visually stimulating progress report can be provided. By adjusting the content of progress reports according to the user's emotions, more appropriate progress management becomes possible.

[0108] The matching unit can estimate the user's emotions and adjust the matching timing based on those emotions. For example, if the user is relaxed, the matching timing can be delayed; if the user is in a hurry, matching can be performed quickly. If the user is excited, matching can be performed at a visually stimulating moment. By adjusting the matching timing according to the user's emotions, more appropriate matching becomes possible.

[0109] The generation unit can create action plans while taking the user's health condition into consideration. For example, if the user is in good health, challenging tasks can be included in the plan. If the user is in poor health, a plan including rest can be proposed. Furthermore, tasks related to health management can be included in the plan based on the user's health condition. This makes it possible to create more realistic and actionable action plans by considering the user's health condition.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives the user's dreams and goals. For example, the reception desk allows users to enter their dreams and goals in text format. The reception desk also allows users to enter their dreams and goals using voice input. Furthermore, the reception desk allows users to enter their dreams and goals using images or videos. Step 2: The generation unit uses a generation AI to create an action plan based on the dreams and goals entered by the reception unit. For example, if the user enters "I want to be able to speak English fluently," the generation unit uses the generation AI to automatically create specific tasks to achieve that goal. The generation unit can use the generation AI to create a personalized action plan based on the user's dreams and goals. For example, the generation unit uses the generation AI to analyze the user's input and create an action plan based on individual dreams and goals. Some or all of the above processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. Step 3: The analysis unit analyzes the progress of the action plan created by the generation unit and optimizes it in real time. For example, if a user fails to complete a task as planned, the analysis unit automatically adjusts the schedule and tasks using the generation AI. The analysis unit can use the generation AI to analyze the progress of the plan in real time and optimize the plan as needed. For example, the analysis unit uses the generation AI to analyze the progress of the plan in real time and optimize the plan as needed. Some or all of the above processing in the analysis unit may be performed using the generation AI or not. Step 4: The matching unit matches users with the same goals and promotes community building. For example, the matching unit helps users aiming to learn English to form a community and share learning methods and progress, making it easier to maintain motivation. The matching unit can match users with the same goals and promote community building. For example, the matching unit promotes information sharing and mutual support among users. Some or all of the above processes in the matching unit may be performed using AI or not.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, generation unit, analysis unit, and matching unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which inputs the user's dreams and goals. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which creates an action plan using generation AI. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes and optimizes the progress of the plan in real time. The matching unit is implemented by the control unit 46A of the smart device 14, which matches users with the same goals and promotes community formation. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, generation unit, analysis unit, and matching unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user's dreams and goals are input. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where an action plan is created using a generation AI. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the progress of the plan is analyzed and optimized in real time. The matching unit is implemented by the control unit 46A of the smart glasses 214, where users with the same goals are matched together, promoting community formation. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, generation unit, analysis unit, and matching unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which inputs the user's dreams and goals. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which creates an action plan using a generation AI. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes and optimizes the progress of the plan in real time. The matching unit is implemented by the control unit 46A of the headset terminal 314, which matches users with the same goals and promotes community formation. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, generation unit, analysis unit, and matching unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from the user about their dreams and goals. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an action plan using a generation AI. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes and optimizes the progress of the plan in real time. The matching unit is implemented by the control unit 46A of the robot 414 and matches users with the same goals to promote community formation. 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception area where users input their dreams and goals, A generation unit creates an action plan based on the dreams and goals entered by the reception unit, An analysis unit analyzes the progress of the action plan created by the generation unit and optimizes it in real time. It includes a matching unit that matches users with the same goal. A system characterized by the following features. (Note 2) The generating unit is Generative AI creates personalized action plans based on the user's dreams and goals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The AI ​​generates data to analyze the progress of the plan in real time and optimize the plan as needed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is Matching users with similar goals and promoting community building. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is If a user fails to complete a task as planned, the AI ​​will automatically adjust the schedule and tasks. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is We conduct regular reviews and use AI to propose improvement suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The matching unit is Promoting information sharing and mutual support among users. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of inputting dreams and goals based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system analyzes the user's past input history of dreams and goals and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input their dreams and goals, the system filters them based on their current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the dreams and goals to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users enter their dreams and goals, the system prioritizes highly relevant goals by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When users enter their dreams and goals, the system analyzes their social media activity and inputs relevant goals. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts how the action plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When creating an action plan, adjust the level of detail in the plan based on the importance of the goals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When creating an action plan, apply different planning algorithms depending on the category of the goal. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is The system estimates the user's emotions and adjusts the length of the action plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When creating an action plan, prioritize the plan based on the deadline for submitting the goals. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When creating an action plan, adjust the order of the plan based on the relevance of the goals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is We estimate user sentiment and adjust progress analysis criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is When conducting progress analysis, consider the interrelationships between goals to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is When conducting progress analysis, the analysis should take into account the attribute information of those who submitted the goals. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is It estimates the user's emotions and adjusts the order in which progress analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is When conducting progress analysis, the geographical distribution of targets should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is When conducting progress analysis, referencing relevant literature related to the objectives improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The matching unit is During the matching process, the accuracy of the matching is improved by considering the interrelationships between targets. The system described in Appendix 1, characterized by the features described herein. (Note 28) The matching unit is During the matching process, the attribute information of the goal submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The matching unit is During the matching process, the geographical distribution of the target users is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The matching unit is During the matching process, we improve the accuracy of the matching by referring to relevant literature related to the target. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 reception area where users input their dreams and goals, A generation unit creates an action plan based on the dreams and goals entered by the reception unit, An analysis unit analyzes the progress of the action plan created by the generation unit and optimizes it in real time. It includes a matching unit that matches users with the same goal. A system characterized by the following features.

2. The generating unit is Generative AI creates personalized action plans based on the user's dreams and goals. The system according to feature 1.

3. The aforementioned analysis unit is The AI ​​generates data to analyze the progress of the plan in real time and optimize the plan as needed. The system according to feature 1.

4. The matching unit is Matching users with similar goals and promoting community building. The system according to feature 1.

5. The generating unit is If a user fails to complete a task as planned, the AI ​​will automatically adjust the schedule and tasks. The system according to feature 1.

6. The aforementioned analysis unit is We conduct regular reviews and propose improvement plans using AI-generated data. The system according to feature 1.

7. The matching unit is Promoting information sharing and mutual support among users. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of inputting dreams and goals based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is The system analyzes the user's past input history of dreams and goals and selects the optimal input method. The system according to feature 1.

10. The aforementioned reception unit is When users input their dreams and goals, the system filters them based on their current lifestyle and areas of interest. The system according to feature 1.

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