system

Abstract

The system according to this embodiment aims to propose a step-by-step plan based on the user's values ​​and goals. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives information about the user's values ​​or goals. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes a step-by-step plan based on the information analyzed by the analysis unit.

Description

Technical Field

[0006] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, 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 prior art, a step-by-step plan based on the user's values and goals has not been sufficiently proposed, and there is room for improvement.

[0005] The system according to the embodiment aims to propose a step-by-step plan based on the user's values and goals.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives information regarding the user's values or goals. The analysis unit analyzes the information received by the reception unit. The proposal unit proposes a step-by-step plan based on the information analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can propose a step-by-step plan based on the user's values ​​and goals. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 system according to an embodiment of the present invention is a system that creates a life vision based on the user's values ​​and goals and proposes a step-by-step plan. This system allows the user to input information about their values ​​and goals, and the AI ​​analyzes this information to propose a step-by-step plan ranging from short-term goals to a long-term vision. For example, if a user inputs specific goals such as "I want to own my own business in five years" or "I want to live a healthy life," the AI ​​will use this information to propose steps such as creating a business plan, securing funding, and launching the business. For the goal of living a healthy life, the AI ​​will propose steps such as improving diet, establishing exercise habits, and regular health checkups. This plan is designed to make it easy for the user to take action, setting specific tasks and goals and providing concrete action plans to achieve them. Furthermore, it includes a function to monitor progress and modify the plan as needed. This allows the user to follow the AI's guidance on a path of self-growth and enjoy a sense of accomplishment at each stage of life. For example, after launching a business, the user can experience its success and further grow towards their next goal. Additionally, by leading a healthy life, the quality of daily life improves, and long-term health can be maintained. In this way, AI proposes a step-by-step plan based on the user's values ​​and goals, and supports the user in taking action towards that plan. This allows the user to experience personal growth and enjoy a sense of accomplishment at each stage of life. The system proposes a step-by-step plan based on the user's values ​​and goals, making it easier for the user to take action.

[0029] The system according to this embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives information about the user's values ​​and goals. The information entered by the user includes, but is not limited to, specific goals such as "I want to own my own business in 5 years" or "I want to live a healthy life." The reception unit can receive information by methods such as text input, voice input, or image input. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using techniques such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes the information based on the user's values ​​and goals and generates basic data for proposing a step-by-step plan. The proposal unit proposes a step-by-step plan based on the information analyzed by the analysis unit. The proposal unit proposes a step-by-step plan, such as short-term goals, medium-term goals, and long-term goals. The proposal unit sets specific tasks and goals to make it easier for the user to take action and provides specific action plans to achieve them. For example, the proposal unit proposes steps such as creating a business plan, raising funds, and launching a business. Furthermore, the suggestion function can also propose steps such as improving diet, establishing exercise habits, and regular health checkups. This allows the system according to the embodiment to propose a step-by-step plan based on the user's values ​​and goals, making it easier for the user to take action.

[0030] The reception desk receives information about users' values ​​and goals. This information may include, but is not limited to, specific goals such as "I want to own my own business in five years" or "I want to live a healthy life." The reception desk can accept information through methods such as text input, voice input, or image input. Specifically, with text input, users can use a keyboard to describe their goals and values ​​in detail. With voice input, users speak their thoughts through a microphone, which is then converted into text using speech recognition technology. With image input, users upload images or photos related to their goals, which are then interpreted using image analysis technology. This allows the reception desk to offer diverse input methods, enabling users to provide information in the most user-friendly way. Furthermore, the reception desk can centrally manage the information entered by users and collaborate with other departments as needed. For example, user-entered information can be stored in a database and made accessible to the analysis department. Additionally, the reception desk can monitor user input in real time and ask additional questions or confirmations as needed. This allows the reception desk to accurately and efficiently collect information about users' values ​​and goals, improving the overall system performance.

[0031] The analysis unit analyzes the information received by the reception unit. The analysis unit uses techniques such as data mining, statistical analysis, and machine learning algorithms to analyze the information. Specifically, it uses data mining techniques to extract useful patterns and trends from user input data. Statistical analysis techniques are used to identify key factors related to the user's goal achievement and to evaluate how these factors influence the outcome. Machine learning algorithms are used to generate an optimal plan based on the user's past behavioral data and data from other users. For example, natural language processing techniques can be used to analyze the user's text input and automatically classify goals and values. Speech recognition techniques can be used to convert speech input into text and analyze its content. Furthermore, image analysis techniques can be used to extract information related to the user's goals from image input. This allows the analysis unit to analyze information based on the user's values ​​and goals and generate foundational data for proposing a step-by-step plan. Additionally, the analysis unit continuously updates the generated foundational data to adapt to changes in the user's situation and environment. For example, if a user sets a new goal or changes an existing one, the analysis unit immediately incorporates the new data and updates the analysis results. This allows the analysis unit to provide highly accurate analysis based on the latest information at all times, supporting users in achieving their goals.

[0032] The proposal department proposes a step-by-step plan based on the information analyzed by the analysis department. For example, the proposal department proposes a plan with short-term, medium-term, and long-term goals. Specifically, short-term goals include concrete tasks that the user can immediately tackle. Medium-term goals are intermediate objectives to be achieved over several months to several years. Long-term goals are the ultimate goals the user wants to achieve. The proposal department sets specific tasks and goals to make it easy for the user to take action and provides concrete action plans to achieve them. For example, it proposes steps such as creating a business plan, securing funding, and launching a business. The proposal department can also propose steps such as improving diet, establishing exercise habits, and regular health checkups. This allows the proposal department to propose a step-by-step plan based on the user's values ​​and goals, making it easier for the user to take action. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can collect feedback on how the user felt about the proposed plan and to what extent they were able to implement it, and revise the proposal based on this feedback. The proposal department can also monitor the user's progress and modify the plan as needed. This allows the proposal department to provide users with the optimal plan and support them in achieving their goals.

[0033] The suggestion unit can set specific tasks or goals to make it easier for users to take action. For example, the suggestion unit can help users set daily tasks. For instance, it can list tasks that users should do every day and provide a specific action plan to achieve them. The suggestion unit can also help users set weekly or monthly goals. For example, it can set weekly goals that users should achieve and suggest steps to achieve them. It can also set monthly goals that users should achieve and provide a specific action plan to achieve them. In this way, the suggestion unit can set specific tasks and goals to make it easier for users to take action. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user data into a generating AI and have the generating AI set specific tasks and goals.

[0034] The proposal unit can monitor the user's progress and modify the plan as needed. For example, the proposal unit can periodically check the progress of tasks and goals set by the user. For example, the proposal unit can monitor the user's daily task completion status and evaluate the progress. The proposal unit can also monitor the user's weekly and monthly goal completion status and evaluate the progress. For example, the proposal unit can check the user's progress towards weekly goals and modify the plan as needed. The proposal unit can also check the user's progress towards monthly goals and modify the plan as needed. In this way, the proposal unit can monitor the user's progress and modify the plan as needed. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input user progress data into a generating AI and have the generating AI perform plan modifications.

[0035] The reception desk can analyze the user's past input history of values ​​and goals and select an appropriate input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can store the user's past input history in a database and identify frequently used input methods. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze data entered by the user during specific time periods in the past and suggest an input method suitable for that time period. The reception desk can also automatically generate relevant questions based on the values ​​and goals the user has entered in the past. For example, the reception desk can use an algorithm that analyzes the user's past input data and generates relevant questions. This allows the reception desk to analyze the user's past input history of values ​​and goals and select an appropriate input method. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0036] The reception unit can filter the input of values ​​and goals based on the user's current life situation and areas of interest. For example, the reception unit can prioritize inputting values ​​and goals related to the project the user is currently working on. For example, the reception unit can store the user's current life situation in a database and identify relevant values ​​and goals. The reception unit can also suggest relevant values ​​and goals based on the user's areas of interest. For example, the reception unit can collect data on the user's areas of interest and filter values ​​and goals based on that data. The reception unit can also input appropriate values ​​and goals according to the user's life situation (work, family, health, etc.). For example, the reception unit can collect data on the user's life situation and filter values ​​and goals based on that data. This allows the reception unit to filter based on the user's life situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's life situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0037] The reception desk can prioritize inputting highly relevant information when users input their values ​​and goals, taking into account their geographical location. For example, if a user lives in a specific region, the reception desk will prioritize inputting values ​​and goals related to that region. For example, the reception desk can obtain the user's geographical location using GPS data or location services and identify values ​​and goals related to that region. The reception desk can also prioritize inputting values ​​and goals related to the user's travel destination if the user is traveling. For example, the reception desk can obtain the geographical location of the user's travel destination and suggest values ​​and goals related to that region. The reception desk can also prioritize inputting values ​​and goals related to the user's new residence if the user is planning to move. For example, the reception desk can obtain the geographical location of the user's new residence and suggest values ​​and goals related to that region. This allows the reception desk to prioritize inputting highly relevant information, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into the generating AI, and have the AI ​​prioritize inputting highly relevant information.

[0038] The reception unit can analyze the user's social media activity and input relevant information when values ​​and goals are entered. For example, the reception unit can input values ​​and goals related to topics that the user frequently mentions on social media. For example, the reception unit can analyze the content of the user's social media posts and identify relevant values ​​and goals. The reception unit can also input values ​​and goals shared by the user's social media friends. For example, the reception unit can analyze the content of the user's social media friends and identify relevant values ​​and goals. The reception unit can also analyze the frequency of the user's social media activity and input relevant values ​​and goals. For example, the reception unit can store the frequency of the user's social media activity in a database and identify values ​​and goals related to frequently mentioned topics. This allows the reception unit to analyze the user's social media activity and input relevant information. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media post data into a generating AI and have the generating AI input relevant information.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of values ​​and goals during the analysis. For example, the analysis unit performs a detailed analysis for values ​​and goals of high importance. For example, the analysis unit evaluates the importance of values ​​and goals entered by the user and performs a detailed analysis for those of high importance. The analysis unit can also perform a concise analysis for values ​​and goals of low importance. For example, the analysis unit evaluates the importance of values ​​and goals entered by the user and performs a concise analysis for those of low importance. The analysis unit can also adjust the display order of the analysis results according to their importance. For example, the analysis unit prioritizes displaying values ​​and goals of high importance and postpones displaying those of low importance. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of values ​​and goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user input data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of values ​​and goals during analysis. For example, the analysis unit can apply a business analysis algorithm to business-related values ​​and goals. For example, the analysis unit evaluates the categories of values ​​and goals entered by the user and applies a business analysis algorithm to those that are business-related. The analysis unit can also apply a health analysis algorithm to health-related values ​​and goals. For example, the analysis unit evaluates the categories of values ​​and goals entered by the user and applies a health analysis algorithm to those that are health-related. The analysis unit can also apply an education analysis algorithm to education-related values ​​and goals. For example, the analysis unit evaluates the categories of values ​​and goals entered by the user and applies an education analysis algorithm to those that are education-related. In this way, the analysis unit can apply different analysis algorithms depending on the category of values ​​and goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user input data into a generating AI and have the generating AI execute the application of analysis algorithms according to the categories.

[0041] The analysis unit can determine the priority of analysis based on the submission date of values ​​and goals during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted values ​​and goals. For instance, the analysis unit may store the submission dates of recently entered values ​​and goals in a database and prioritize the analysis of those with the most recent submission dates. The analysis unit may also postpone the analysis of older values ​​and goals. For example, the analysis unit may evaluate the submission dates of values ​​and goals previously entered by the user and postpone the analysis of older ones. The analysis unit may also adjust the display order of the analysis results according to the submission date. For example, the analysis unit may prioritize the display of newly submitted values ​​and goals and postpone the display of older ones. This allows the analysis unit to determine the priority of analysis based on the submission date of values ​​and goals. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit may input user input data into a generating AI and have the generating AI perform the determination of analysis priority based on submission date.

[0042] The analysis unit can adjust the order of analysis based on the relevance of values ​​and goals during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant values ​​and goals. For instance, the analysis unit evaluates the relevance of values ​​and goals entered by the user and prioritizes the analysis of highly relevant ones. The analysis unit can also postpone the analysis of less relevant values ​​and goals. For example, the analysis unit evaluates the relevance of values ​​and goals entered by the user and postpones the analysis of less relevant ones. The analysis unit can also adjust the display order of the analysis results according to their relevance. For example, the analysis unit can prioritize the display of highly relevant values ​​and goals and postpone the display of less relevant ones. In this way, the analysis unit can adjust the order of analysis based on the relevance of values ​​and goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user input data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0043] The proposal unit can adjust the level of detail in its proposals based on the importance of values ​​and goals. For example, it can provide detailed proposals for values ​​and goals of high importance. For instance, it can evaluate the importance of values ​​and goals entered by the user and provide detailed proposals for those of high importance. It can also provide concise proposals for values ​​and goals of low importance. For example, it can evaluate the importance of values ​​and goals entered by the user and provide concise proposals for those of low importance. Furthermore, the proposal unit can adjust the display order of proposal content according to importance. For example, it can prioritize the display of values ​​and goals of high importance and postpone those of low importance. This allows the proposal unit to adjust the level of detail in its proposals based on the importance of values ​​and goals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input user input data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0044] The proposal unit can apply different proposal algorithms depending on the category of values ​​and goals when making a proposal. For example, the proposal unit can apply a business proposal algorithm to business-related values ​​and goals. For example, the proposal unit can evaluate the categories of values ​​and goals entered by the user and apply a business proposal algorithm to those that are business-related. The proposal unit can also apply a health proposal algorithm to health-related values ​​and goals. For example, the proposal unit can evaluate the categories of values ​​and goals entered by the user and apply a health proposal algorithm to those that are health-related. The proposal unit can also apply an education proposal algorithm to education-related values ​​and goals. For example, the proposal unit can evaluate the categories of values ​​and goals entered by the user and apply an education proposal algorithm to those that are education-related. In this way, the proposal unit can apply different proposal algorithms depending on the category of values ​​and goals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user input data into a generating AI and have the generating AI execute the application of a proposal algorithm according to the category.

[0045] The proposal department can prioritize proposals based on the submission timing of values ​​and goals. For example, the proposal department can prioritize recently submitted values ​​and goals. For instance, it can store the submission timing of recently entered values ​​and goals in a database and prioritize proposals based on the most recent submission timing. The proposal department can also postpone proposals based on older submission timings. For example, it can evaluate the submission timing of values ​​and goals previously entered by the user and postpone older submissions. The proposal department can also adjust the display order of proposals based on their submission timing. For example, it can prioritize the display of recently submitted values ​​and goals and postpone older submissions. This allows the proposal department to prioritize proposals based on the submission timing of values ​​and goals. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input user input data into a generating AI and have the generating AI determine the priority of proposals based on submission timing.

[0046] The proposal unit can adjust the order of proposals based on the relevance of values ​​and goals during the proposal process. For example, the proposal unit can prioritize proposing highly relevant values ​​and goals. For instance, it can evaluate the relevance of values ​​and goals entered by the user and prioritize proposing those that are highly relevant. The proposal unit can also postpone proposing less relevant values ​​and goals. For example, it can evaluate the relevance of values ​​and goals entered by the user and postpone those that are less relevant. The proposal unit can also adjust the display order of proposals according to their relevance. For example, it can prioritize displaying highly relevant values ​​and goals and postpone those that are less relevant. In this way, the proposal unit can adjust the order of proposals based on the relevance of values ​​and goals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input user input data into a generating AI and have the generating AI perform the adjustment of the order of proposals based on relevance.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The suggestion function can also propose learning resources to support the user's skill and knowledge improvement based on the user's values ​​and goals. For example, if a user enters "I want to improve my business skills," the suggestion function will propose relevant online courses, seminars, books, and other learning resources. If a user enters "I want to live a healthy life," the suggestion function can also propose learning resources related to nutrition and fitness. Furthermore, if a user enters "I want to learn a new language," the suggestion function can propose language learning apps and online lessons. In this way, the suggestion function can provide specific learning resources to support the improvement of skills and knowledge based on the user's values ​​and goals.

[0049] The suggestion function can also propose communities and groups that users can join based on their values ​​and goals. For example, if a user enters "I want to expand my business network," the suggestion function will suggest relevant business communities and networking events. If a user enters "I want to live a healthy life," the suggestion function can also suggest fitness groups and online forums related to health. Furthermore, if a user enters "I want to find a new hobby," the suggestion function can also suggest communities and clubs related to that hobby. In this way, the suggestion function can suggest communities and groups that users can join based on their values ​​and goals, and help users build social connections.

[0050] The suggestion department can also propose support services that users can utilize based on their values ​​and goals. For example, if a user enters "I want to improve my mental health," the suggestion department will suggest counseling services or mental health apps. If a user enters "I want to advance my career," the suggestion department can also suggest career coaching or mentorship programs. Furthermore, if a user enters "I want to improve my home life," the suggestion department can also suggest support services and resources related to home life. In this way, the suggestion department can propose available support services based on the user's values ​​and goals, and provide support to help users achieve their goals.

[0051] The suggestion function can also propose volunteer activities and social contribution opportunities that users can participate in, based on their values ​​and goals. For example, if a user enters "I want to contribute to my local community," the suggestion function will propose local volunteer activities and social contribution projects. If a user enters "I am interested in environmental protection," the suggestion function can also propose environmental protection activities and eco-projects. Furthermore, if a user enters "I am interested in supporting education," the suggestion function can also propose educational support activities and mentoring programs. In this way, the suggestion function can propose volunteer activities and social contribution opportunities that users can participate in, based on their values ​​and goals, and provide users with a path to contribute to society.

[0052] The suggestion team can also propose feedback systems that users can use based on their values ​​and goals. For example, if a user enters "I want to improve my business plan," the suggestion team can suggest an online platform that provides feedback on the business plan. Similarly, if a user enters "I want to live a healthy life," the suggestion team can suggest an app that provides feedback on diet and exercise. Furthermore, if a user enters "I want to improve my learning outcomes," the suggestion team can suggest an online tool that provides feedback on learning outcomes. This allows the suggestion team to propose usable feedback systems based on the user's values ​​and goals, helping users identify areas for improvement to achieve their objectives.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The reception desk receives information about the user's values ​​and goals. The information the user enters may include, but is not limited to, specific goals such as "I want to own my own business in 5 years" or "I want to live a healthy life." The reception desk can receive information through methods such as text input, voice input, or image input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using techniques such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes the information based on the user's values ​​and goals and generates basic data to propose a step-by-step plan. Step 3: The proposal team proposes a step-by-step plan based on the information analyzed by the analysis team. The proposal team proposes a step-by-step plan, such as short-term goals, medium-term goals, and long-term goals. The proposal team sets specific tasks and goals to make it easy for users to take action and provides specific action plans to achieve them. For example, the proposal team proposes steps such as creating a business plan, securing funding, and launching a business. The proposal team can also propose steps such as improving diet, establishing exercise habits, and regular health checkups.

[0055] (Example of form 2) The system according to an embodiment of the present invention is a system that creates a life vision based on the user's values ​​and goals and proposes a step-by-step plan. This system allows the user to input information about their values ​​and goals, and the AI ​​analyzes this information to propose a step-by-step plan ranging from short-term goals to a long-term vision. For example, if a user inputs specific goals such as "I want to own my own business in five years" or "I want to live a healthy life," the AI ​​will use this information to propose steps such as creating a business plan, securing funding, and launching the business. For the goal of living a healthy life, the AI ​​will propose steps such as improving diet, establishing exercise habits, and regular health checkups. This plan is designed to make it easy for the user to take action, setting specific tasks and goals and providing concrete action plans to achieve them. Furthermore, it includes a function to monitor progress and modify the plan as needed. This allows the user to follow the AI's guidance on a path of self-growth and enjoy a sense of accomplishment at each stage of life. For example, after launching a business, the user can experience its success and further grow towards their next goal. Additionally, by leading a healthy life, the quality of daily life improves, and long-term health can be maintained. In this way, AI proposes a step-by-step plan based on the user's values ​​and goals, and supports the user in taking action towards that plan. This allows the user to experience personal growth and enjoy a sense of accomplishment at each stage of life. The system proposes a step-by-step plan based on the user's values ​​and goals, making it easier for the user to take action.

[0056] The system according to this embodiment comprises a reception unit, an analysis unit, and a proposal unit. The reception unit receives information about the user's values ​​and goals. The information entered by the user includes, but is not limited to, specific goals such as "I want to own my own business in 5 years" or "I want to live a healthy life." The reception unit can receive information by methods such as text input, voice input, or image input. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using techniques such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes the information based on the user's values ​​and goals and generates basic data for proposing a step-by-step plan. The proposal unit proposes a step-by-step plan based on the information analyzed by the analysis unit. The proposal unit proposes a step-by-step plan, such as short-term goals, medium-term goals, and long-term goals. The proposal unit sets specific tasks and goals to make it easier for the user to take action and provides specific action plans to achieve them. For example, the proposal unit proposes steps such as creating a business plan, raising funds, and launching a business. Furthermore, the suggestion function can also propose steps such as improving diet, establishing exercise habits, and regular health checkups. This allows the system according to the embodiment to propose a step-by-step plan based on the user's values ​​and goals, making it easier for the user to take action.

[0057] The reception desk receives information about users' values ​​and goals. This information may include, but is not limited to, specific goals such as "I want to own my own business in five years" or "I want to live a healthy life." The reception desk can accept information through methods such as text input, voice input, or image input. Specifically, with text input, users can use a keyboard to describe their goals and values ​​in detail. With voice input, users speak their thoughts through a microphone, which is then converted into text using speech recognition technology. With image input, users upload images or photos related to their goals, which are then interpreted using image analysis technology. This allows the reception desk to offer diverse input methods, enabling users to provide information in the most user-friendly way. Furthermore, the reception desk can centrally manage the information entered by users and collaborate with other departments as needed. For example, user-entered information can be stored in a database and made accessible to the analysis department. Additionally, the reception desk can monitor user input in real time and ask additional questions or confirmations as needed. This allows the reception desk to accurately and efficiently collect information about users' values ​​and goals, improving the overall system performance.

[0058] The analysis unit analyzes the information received by the reception unit. The analysis unit uses techniques such as data mining, statistical analysis, and machine learning algorithms to analyze the information. Specifically, it uses data mining techniques to extract useful patterns and trends from user input data. Statistical analysis techniques are used to identify key factors related to the user's goal achievement and to evaluate how these factors influence the outcome. Machine learning algorithms are used to generate an optimal plan based on the user's past behavioral data and data from other users. For example, natural language processing techniques can be used to analyze the user's text input and automatically classify goals and values. Speech recognition techniques can be used to convert speech input into text and analyze its content. Furthermore, image analysis techniques can be used to extract information related to the user's goals from image input. This allows the analysis unit to analyze information based on the user's values ​​and goals and generate foundational data for proposing a step-by-step plan. Additionally, the analysis unit continuously updates the generated foundational data to adapt to changes in the user's situation and environment. For example, if a user sets a new goal or changes an existing one, the analysis unit immediately incorporates the new data and updates the analysis results. This allows the analysis unit to provide highly accurate analysis based on the latest information at all times, supporting users in achieving their goals.

[0059] The proposal department proposes a step-by-step plan based on the information analyzed by the analysis department. For example, the proposal department proposes a plan with short-term, medium-term, and long-term goals. Specifically, short-term goals include concrete tasks that the user can immediately tackle. Medium-term goals are intermediate objectives to be achieved over several months to several years. Long-term goals are the ultimate goals the user wants to achieve. The proposal department sets specific tasks and goals to make it easy for the user to take action and provides concrete action plans to achieve them. For example, it proposes steps such as creating a business plan, securing funding, and launching a business. The proposal department can also propose steps such as improving diet, establishing exercise habits, and regular health checkups. This allows the proposal department to propose a step-by-step plan based on the user's values ​​and goals, making it easier for the user to take action. Furthermore, the proposal department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can collect feedback on how the user felt about the proposed plan and to what extent they were able to implement it, and revise the proposal based on this feedback. The proposal department can also monitor the user's progress and modify the plan as needed. This allows the proposal department to provide users with the optimal plan and support them in achieving their goals.

[0060] The suggestion unit can set specific tasks or goals to make it easier for users to take action. For example, the suggestion unit can help users set daily tasks. For instance, it can list tasks that users should do every day and provide a specific action plan to achieve them. The suggestion unit can also help users set weekly or monthly goals. For example, it can set weekly goals that users should achieve and suggest steps to achieve them. It can also set monthly goals that users should achieve and provide a specific action plan to achieve them. In this way, the suggestion unit can set specific tasks and goals to make it easier for users to take action. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user data into a generating AI and have the generating AI set specific tasks and goals.

[0061] The proposal unit can monitor the user's progress and modify the plan as needed. For example, the proposal unit can periodically check the progress of tasks and goals set by the user. For example, the proposal unit can monitor the user's daily task completion status and evaluate the progress. The proposal unit can also monitor the user's weekly and monthly goal completion status and evaluate the progress. For example, the proposal unit can check the user's progress towards weekly goals and modify the plan as needed. The proposal unit can also check the user's progress towards monthly goals and modify the plan as needed. In this way, the proposal unit can monitor the user's progress and modify the plan as needed. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input user progress data into a generating AI and have the generating AI perform plan modifications.

[0062] The reception desk can estimate the user's emotions and adjust the timing of value and goal input based on the estimated emotions. For example, if the user is stressed, the reception desk will prompt for value and goal input at a time when the user can relax. For example, the reception desk can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. The reception desk calculates an emotion score based on changes in facial expressions and detects when the user is relaxed. The reception desk can also request detailed value and goal input when the user is concentrating. For example, the reception desk can record the user's voice and estimate emotions using voice analysis technology. The reception desk analyzes the tone and speed of the voice to detect when the user is concentrating. The reception desk can also ask for value and goal input in the form of simple questions when the user is tired. For example, the reception desk can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. The reception desk calculates an emotion score based on fluctuations in heart rate and detects when the user is tired. This allows the reception desk to adjust the input timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 area may be performed using AI or not using AI. For example, the reception area can input user image data captured by a camera into a generative AI and have the generative AI perform emotion estimation of the user.

[0063] The reception desk can analyze the user's past input history of values ​​and goals and select an appropriate input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can store the user's past input history in a database and identify frequently used input methods. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze data entered by the user during specific time periods in the past and suggest an input method suitable for that time period. The reception desk can also automatically generate relevant questions based on the values ​​and goals the user has entered in the past. For example, the reception desk can use an algorithm that analyzes the user's past input data and generates relevant questions. This allows the reception desk to analyze the user's past input history of values ​​and goals and select an appropriate input method. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0064] The reception unit can filter the input of values ​​and goals based on the user's current life situation and areas of interest. For example, the reception unit can prioritize inputting values ​​and goals related to the project the user is currently working on. For example, the reception unit can store the user's current life situation in a database and identify relevant values ​​and goals. The reception unit can also suggest relevant values ​​and goals based on the user's areas of interest. For example, the reception unit can collect data on the user's areas of interest and filter values ​​and goals based on that data. The reception unit can also input appropriate values ​​and goals according to the user's life situation (work, family, health, etc.). For example, the reception unit can collect data on the user's life situation and filter values ​​and goals based on that data. This allows the reception unit to filter based on the user's life situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input data on the user's life situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0065] The reception system can estimate the user's emotions and, based on those emotions, prioritize the values ​​and goals to be entered. For example, if the user is stressed, the reception system will prioritize inputting values ​​and goals that promote relaxation. For instance, the reception system can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The reception system calculates an emotion score based on changes in facial expressions and detects when the user is relaxed. The reception system can also prioritize inputting important values ​​and goals when the user is focused. For example, the reception system can record the user's voice and estimate their emotions using voice analysis technology. The reception system analyzes the tone and speed of the voice to detect when the user is focused. The reception system can also prioritize inputting easily achievable values ​​and goals when the user is tired. For example, the reception system can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The reception system calculates an emotion score based on fluctuations in heart rate and detects when the user is tired. This allows the reception desk to determine the priority of values ​​and goals based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 desk may be performed using AI, or not using AI. For example, the reception desk can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0066] The reception desk can prioritize inputting highly relevant information when users input their values ​​and goals, taking into account their geographical location. For example, if a user lives in a specific region, the reception desk will prioritize inputting values ​​and goals related to that region. For example, the reception desk can obtain the user's geographical location using GPS data or location services and identify values ​​and goals related to that region. The reception desk can also prioritize inputting values ​​and goals related to the user's travel destination if the user is traveling. For example, the reception desk can obtain the geographical location of the user's travel destination and suggest values ​​and goals related to that region. The reception desk can also prioritize inputting values ​​and goals related to the user's new residence if the user is planning to move. For example, the reception desk can obtain the geographical location of the user's new residence and suggest values ​​and goals related to that region. This allows the reception desk to prioritize inputting highly relevant information, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information into the generating AI, and have the AI ​​prioritize inputting highly relevant information.

[0067] The reception unit can analyze the user's social media activity and input relevant information when values ​​and goals are entered. For example, the reception unit can input values ​​and goals related to topics that the user frequently mentions on social media. For example, the reception unit can analyze the content of the user's social media posts and identify relevant values ​​and goals. The reception unit can also input values ​​and goals shared by the user's social media friends. For example, the reception unit can analyze the content of the user's social media friends and identify relevant values ​​and goals. The reception unit can also analyze the frequency of the user's social media activity and input relevant values ​​and goals. For example, the reception unit can store the frequency of the user's social media activity in a database and identify values ​​and goals related to frequently mentioned topics. This allows the reception unit to analyze the user's social media activity and input relevant information. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media post data into a generating AI and have the generating AI input relevant information.

[0068] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For instance, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit calculates an emotion score based on changes in facial expressions and detects when the user is relaxed. The analysis unit can also provide concise analysis results that get straight to the point if the user is in a hurry. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. The analysis unit analyzes the tone and speed of the voice and detects when the user is in a hurry. The analysis unit can also provide analysis results with visually stimulating effects if the user is excited. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The analysis unit calculates an emotion score based on fluctuations in heart rate and detects when the user is excited. This allows the analysis unit to adjust the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generative AI and have the generative AI perform emotion estimation of the user.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of values ​​and goals during the analysis. For example, the analysis unit performs a detailed analysis for values ​​and goals of high importance. For example, the analysis unit evaluates the importance of values ​​and goals entered by the user and performs a detailed analysis for those of high importance. The analysis unit can also perform a concise analysis for values ​​and goals of low importance. For example, the analysis unit evaluates the importance of values ​​and goals entered by the user and performs a concise analysis for those of low importance. The analysis unit can also adjust the display order of the analysis results according to their importance. For example, the analysis unit prioritizes displaying values ​​and goals of high importance and postpones displaying those of low importance. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of values ​​and goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user input data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0070] The analysis unit can apply different analysis algorithms depending on the category of values ​​and goals during analysis. For example, the analysis unit can apply a business analysis algorithm to business-related values ​​and goals. For example, the analysis unit evaluates the categories of values ​​and goals entered by the user and applies a business analysis algorithm to those that are business-related. The analysis unit can also apply a health analysis algorithm to health-related values ​​and goals. For example, the analysis unit evaluates the categories of values ​​and goals entered by the user and applies a health analysis algorithm to those that are health-related. The analysis unit can also apply an education analysis algorithm to education-related values ​​and goals. For example, the analysis unit evaluates the categories of values ​​and goals entered by the user and applies an education analysis algorithm to those that are education-related. In this way, the analysis unit can apply different analysis algorithms depending on the category of values ​​and goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user input data into a generating AI and have the generating AI execute the application of analysis algorithms according to the categories.

[0071] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For instance, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit calculates an emotion score based on changes in facial expressions and detects when the user is in a hurry. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. The analysis unit analyzes the tone and speed of the voice and detects when the user is relaxed. The analysis unit can also provide analysis results with visually stimulating effects if the user is excited. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The analysis unit calculates an emotion score based on fluctuations in heart rate and detects when the user is excited. This allows the analysis unit to adjust the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generative AI and have the generative AI perform emotion estimation of the user.

[0072] The analysis unit can determine the priority of analysis based on the submission date of values ​​and goals during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted values ​​and goals. For instance, the analysis unit may store the submission dates of recently entered values ​​and goals in a database and prioritize the analysis of those with the most recent submission dates. The analysis unit may also postpone the analysis of older values ​​and goals. For example, the analysis unit may evaluate the submission dates of values ​​and goals previously entered by the user and postpone the analysis of older ones. The analysis unit may also adjust the display order of the analysis results according to the submission date. For example, the analysis unit may prioritize the display of newly submitted values ​​and goals and postpone the display of older ones. This allows the analysis unit to determine the priority of analysis based on the submission date of values ​​and goals. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit may input user input data into a generating AI and have the generating AI perform the determination of analysis priority based on submission date.

[0073] The analysis unit can adjust the order of analysis based on the relevance of values ​​and goals during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant values ​​and goals. For instance, the analysis unit evaluates the relevance of values ​​and goals entered by the user and prioritizes the analysis of highly relevant ones. The analysis unit can also postpone the analysis of less relevant values ​​and goals. For example, the analysis unit evaluates the relevance of values ​​and goals entered by the user and postpones the analysis of less relevant ones. The analysis unit can also adjust the display order of the analysis results according to their relevance. For example, the analysis unit can prioritize the display of highly relevant values ​​and goals and postpone the display of less relevant ones. In this way, the analysis unit can adjust the order of analysis based on the relevance of values ​​and goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user input data into a generating AI and have the generating AI perform the adjustment of the analysis order based on relevance.

[0074] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The unit calculates an emotion score based on changes in facial expressions and detects when the user is relaxed. Furthermore, if the user is in a hurry, the suggestion unit can provide concise, to-the-point suggestions. For example, it might record the user's voice and estimate their emotions using voice analysis technology. The unit analyzes the tone and speed of the voice to detect when the user is in a hurry. Additionally, if the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The unit calculates an emotion score based on fluctuations in heart rate and detects when the user is excited. This allows the suggestion unit to adjust the way it presents suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed unit may be performed using AI or not using AI. For example, the proposed unit can input user image data captured by a camera into a generative AI and have the generative AI perform emotion estimation of the user.

[0075] The proposal unit can adjust the level of detail in its proposals based on the importance of values ​​and goals. For example, it can provide detailed proposals for values ​​and goals of high importance. For instance, it can evaluate the importance of values ​​and goals entered by the user and provide detailed proposals for those of high importance. It can also provide concise proposals for values ​​and goals of low importance. For example, it can evaluate the importance of values ​​and goals entered by the user and provide concise proposals for those of low importance. Furthermore, the proposal unit can adjust the display order of proposal content according to importance. For example, it can prioritize the display of values ​​and goals of high importance and postpone those of low importance. This allows the proposal unit to adjust the level of detail in its proposals based on the importance of values ​​and goals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input user input data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0076] The proposal unit can apply different proposal algorithms depending on the category of values ​​and goals when making a proposal. For example, the proposal unit can apply a business proposal algorithm to business-related values ​​and goals. For example, the proposal unit can evaluate the categories of values ​​and goals entered by the user and apply a business proposal algorithm to those that are business-related. The proposal unit can also apply a health proposal algorithm to health-related values ​​and goals. For example, the proposal unit can evaluate the categories of values ​​and goals entered by the user and apply a health proposal algorithm to those that are health-related. The proposal unit can also apply an education proposal algorithm to education-related values ​​and goals. For example, the proposal unit can evaluate the categories of values ​​and goals entered by the user and apply an education proposal algorithm to those that are education-related. In this way, the proposal unit can apply different proposal algorithms depending on the category of values ​​and goals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user input data into a generating AI and have the generating AI execute the application of a proposal algorithm according to the category.

[0077] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. For instance, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The suggestion unit can calculate an emotion score based on changes in facial expressions and detect when the user is in a hurry. The suggestion unit can also provide detailed suggestions if the user is relaxed. For example, the suggestion unit can record the user's voice and estimate their emotions using voice analysis technology. The suggestion unit can analyze the tone and speed of the voice and detect when the user is relaxed. The suggestion unit can also provide suggestions with visually stimulating effects if the user is excited. For example, the suggestion unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. The suggestion unit can calculate an emotion score based on fluctuations in heart rate and detect when the user is excited. This allows the suggestion unit to adjust the length of the suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposed unit may be performed using AI or not using AI. For example, the proposed unit can input user image data captured by a camera into a generative AI and have the generative AI perform emotion estimation of the user.

[0078] The proposal department can prioritize proposals based on the submission timing of values ​​and goals. For example, the proposal department can prioritize recently submitted values ​​and goals. For instance, it can store the submission timing of recently entered values ​​and goals in a database and prioritize proposals based on the most recent submission timing. The proposal department can also postpone proposals based on older submission timings. For example, it can evaluate the submission timing of values ​​and goals previously entered by the user and postpone older submissions. The proposal department can also adjust the display order of proposals based on their submission timing. For example, it can prioritize the display of recently submitted values ​​and goals and postpone older submissions. This allows the proposal department to prioritize proposals based on the submission timing of values ​​and goals. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input user input data into a generating AI and have the generating AI determine the priority of proposals based on submission timing.

[0079] The proposal unit can adjust the order of proposals based on the relevance of values ​​and goals during the proposal process. For example, the proposal unit can prioritize proposing highly relevant values ​​and goals. For instance, it can evaluate the relevance of values ​​and goals entered by the user and prioritize proposing those that are highly relevant. The proposal unit can also postpone proposing less relevant values ​​and goals. For example, it can evaluate the relevance of values ​​and goals entered by the user and postpone those that are less relevant. The proposal unit can also adjust the display order of proposals according to their relevance. For example, it can prioritize displaying highly relevant values ​​and goals and postpone those that are less relevant. In this way, the proposal unit can adjust the order of proposals based on the relevance of values ​​and goals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input user input data into a generating AI and have the generating AI perform the adjustment of the order of proposals based on relevance.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] The suggestion function can also propose learning resources to support the user's skill and knowledge improvement based on the user's values ​​and goals. For example, if a user enters "I want to improve my business skills," the suggestion function will propose relevant online courses, seminars, books, and other learning resources. If a user enters "I want to live a healthy life," the suggestion function can also propose learning resources related to nutrition and fitness. Furthermore, if a user enters "I want to learn a new language," the suggestion function can propose language learning apps and online lessons. In this way, the suggestion function can provide specific learning resources to support the improvement of skills and knowledge based on the user's values ​​and goals.

[0082] The suggestion function can also propose communities and groups that users can join based on their values ​​and goals. For example, if a user enters "I want to expand my business network," the suggestion function will suggest relevant business communities and networking events. If a user enters "I want to live a healthy life," the suggestion function can also suggest fitness groups and online forums related to health. Furthermore, if a user enters "I want to find a new hobby," the suggestion function can also suggest communities and clubs related to that hobby. In this way, the suggestion function can suggest communities and groups that users can join based on their values ​​and goals, and help users build social connections.

[0083] The suggestion department can also propose support services that users can utilize based on their values ​​and goals. For example, if a user enters "I want to improve my mental health," the suggestion department will suggest counseling services or mental health apps. If a user enters "I want to advance my career," the suggestion department can also suggest career coaching or mentorship programs. Furthermore, if a user enters "I want to improve my home life," the suggestion department can also suggest support services and resources related to home life. In this way, the suggestion department can propose available support services based on the user's values ​​and goals, and provide support to help users achieve their goals.

[0084] The suggestion function can also propose volunteer activities and social contribution opportunities that users can participate in, based on their values ​​and goals. For example, if a user enters "I want to contribute to my local community," the suggestion function will propose local volunteer activities and social contribution projects. If a user enters "I am interested in environmental protection," the suggestion function can also propose environmental protection activities and eco-projects. Furthermore, if a user enters "I am interested in supporting education," the suggestion function can also propose educational support activities and mentoring programs. In this way, the suggestion function can propose volunteer activities and social contribution opportunities that users can participate in, based on their values ​​and goals, and provide users with a path to contribute to society.

[0085] The suggestion team can also propose feedback systems that users can use based on their values ​​and goals. For example, if a user enters "I want to improve my business plan," the suggestion team can suggest an online platform that provides feedback on the business plan. Similarly, if a user enters "I want to live a healthy life," the suggestion team can suggest an app that provides feedback on diet and exercise. Furthermore, if a user enters "I want to improve my learning outcomes," the suggestion team can suggest an online tool that provides feedback on learning outcomes. This allows the suggestion team to propose usable feedback systems based on the user's values ​​and goals, helping users identify areas for improvement to achieve their objectives.

[0086] The suggestion function can estimate the user's emotions and, based on those estimates, suggest environments that can help the user relax. For example, if the user is feeling stressed, the suggestion function can suggest relaxing music or meditation apps. If the user is feeling tired, the suggestion function can also provide information on relaxing hot springs or spas. Furthermore, if the user is feeling anxious, the suggestion function can provide information on relaxing natural environments or parks. In this way, the suggestion function can suggest relaxing environments based on the user's emotions and help the user reduce stress.

[0087] The suggestion function can estimate the user's emotions and, based on those estimates, suggest activities the user might enjoy. For example, if the user is bored, the suggestion function can suggest entertainment or hobby-related activities. If the user is feeling lonely, the suggestion function can suggest social events or group activities. Furthermore, if the user is excited, the suggestion function can suggest adventure or sports-related activities. In this way, the suggestion function can suggest enjoyable activities based on the user's emotions and help the user have a fulfilling time.

[0088] The suggestion function can estimate the user's emotions and, based on those estimates, suggest ways for the user to refresh themselves. For example, if the user is tired, the suggestion function can suggest a short nap or relaxing stretches. If the user is lacking concentration, the suggestion function can suggest a short walk or deep breathing exercises. Furthermore, if the user is irritable, the suggestion function can suggest relaxing aromatherapy or massage. In this way, the suggestion function can suggest refreshing ways to rest based on the user's emotions, helping the user to rest effectively.

[0089] The suggestion function can estimate the user's emotions and, based on those estimates, suggest ways to help the user maintain motivation. For example, if a user is feeling discouraged, the suggestion function can suggest positive messages or success stories to boost motivation. If a user is feeling anxious, the suggestion function can suggest relaxing music or meditation apps. Furthermore, if a user is feeling tired, the suggestion function can suggest nutritional supplements or rest methods to restore energy. In this way, the suggestion function can suggest ways to maintain motivation based on the user's emotions and help the user move forward towards their goals.

[0090] The suggestion function can estimate the user's emotions and, based on those estimates, suggest environments that can help the user relax. For example, if the user is feeling stressed, the suggestion function can suggest relaxing music or meditation apps. If the user is feeling tired, the suggestion function can also provide information on relaxing hot springs or spas. Furthermore, if the user is feeling anxious, the suggestion function can provide information on relaxing natural environments or parks. In this way, the suggestion function can suggest relaxing environments based on the user's emotions and help the user reduce stress.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The reception desk receives information about the user's values ​​and goals. The information the user enters may include, but is not limited to, specific goals such as "I want to own my own business in 5 years" or "I want to live a healthy life." The reception desk can receive information through methods such as text input, voice input, or image input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the information using techniques such as data mining, statistical analysis, and machine learning algorithms. The analysis unit analyzes the information based on the user's values ​​and goals and generates basic data to propose a step-by-step plan. Step 3: The proposal team proposes a step-by-step plan based on the information analyzed by the analysis team. The proposal team proposes a step-by-step plan, such as short-term goals, medium-term goals, and long-term goals. The proposal team sets specific tasks and goals to make it easy for users to take action and provides specific action plans to achieve them. For example, the proposal team proposes steps such as creating a business plan, securing funding, and launching a business. The proposal team can also propose steps such as improving diet, establishing exercise habits, and regular health checkups.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] For example, the reception unit is implemented by the reception device 38 of the smart device 14, and receives information about the user's values ​​and goals through methods such as text input, voice input, or image input. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the information using technologies such as data mining, statistical analysis, and machine learning algorithms. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12, and proposes a phased plan including short-term goals, medium-term goals, and long-term goals. The proposal unit can also be implemented by the control unit 46A of the smart device 14, for example, and sets specific tasks and goals and provides a specific action plan to achieve them. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, which receives information about the user's values ​​and goals via voice input. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the information using technologies such as data mining, statistical analysis, and machine learning algorithms. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12, which proposes a phased plan including short-term, medium-term, and long-term goals. The proposal unit can also be implemented by the control unit 46A of the smart glasses 214, which sets specific tasks and goals and provides a concrete action plan to achieve them. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, and receives information about the user's values ​​and goals via voice input. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, and analyzes the information using technologies such as data mining, statistical analysis, and machine learning algorithms. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12, and proposes a phased plan including short-term, medium-term, and long-term goals. The proposal unit can also be implemented by the control unit 46A of the headset terminal 314, and sets specific tasks and goals and provides a concrete action plan to achieve them. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] For example, the reception unit is implemented by the microphone 238 of the robot 414, which receives information about the user's values ​​and goals via voice input. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the information using technologies such as data mining, statistical analysis, and machine learning algorithms. The proposal unit is implemented by the specific processing unit 290 of the data processing device 12, which proposes a phased plan including short-term, medium-term, and long-term goals. The proposal unit can also be implemented by the control unit 46A of the robot 414, which sets specific tasks and goals and provides a concrete action plan to achieve them. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] (Note 1) A reception desk that receives information about the user's values ​​or goals, An analysis unit that analyzes the information received by the reception unit, The system includes a proposal unit that proposes a step-by-step plan based on the information analyzed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Set specific tasks or goals to make it easier for users to take action. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Monitor user progress and adjust the plan as needed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of inputting values ​​and goals based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Analyze the user's past values ​​and goal input history to select the appropriate input method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users input their values ​​or goals, the system filters the results based on their current life circumstances or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the values ​​and goals to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users input their values ​​and goals, the system prioritizes inputting information that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their values ​​or goals, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of values ​​and goals. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of values ​​and goals. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, prioritize the analysis based on the timing of value and goal submission. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of values ​​and goals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of your values ​​and goals. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of values ​​and goals. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When submitting proposals, prioritize them based on values ​​and the timing of goal submission. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of values ​​and goals. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0165] 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 desk that receives information about the user's values ​​or goals, An analysis unit that analyzes the information received by the reception unit, The system includes a proposal unit that proposes a step-by-step plan based on the information analyzed by the analysis unit. A system characterized by the following features.

2. The aforementioned proposal section is, Set specific tasks or goals to make it easier for users to take action. The system according to feature 1.

3. The aforementioned proposal section is, Monitor user progress and adjust the plan as needed. The system according to feature 1.

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

5. The aforementioned reception unit is Analyze the user's past values ​​and goal input history to select the appropriate input method. The system according to feature 1.

6. The aforementioned reception unit is When users input their values ​​or goals, the system filters the results based on their current life circumstances or areas of interest. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the values ​​and goals to be entered based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When users input their values ​​and goals, the system prioritizes inputting information that is highly relevant based on the user's geographical location. The system according to feature 1.

9. The aforementioned reception unit is When users enter their values ​​or goals, the system analyzes their social media activity and inputs relevant information. The system according to feature 1.

10. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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