system

Abstract

The system according to this embodiment aims to integrate the user's past data and current goals and generate a video message from their future self. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a comprehension unit, and a generation unit. The collection unit collects the user's past data. The analysis unit analyzes the data collected by the collection unit to identify the user's strengths and weaknesses. The comprehension unit understands the user's current goals and situation. The generation unit generates a future video message from the user based on the information obtained by the analysis unit and the comprehension unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot 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 conventional technology, a system that integrates the user's past data and current goals and uses them for future self-improvement has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to integrate the user's past data and current goals and generate a video message from the future self.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data understanding unit, and a data generation unit. The data collection unit collects the user's past data. The analysis unit analyzes the data collected by the data collection unit to identify the user's strengths and weaknesses. The data understanding unit understands the user's current goals and situation. The data generation unit generates a future video message from the user based on the information obtained by the analysis unit and the data understanding unit. [Effects of the Invention]

[0007] The system according to this embodiment can integrate the user's past data and current goals and generate a video message from their future self. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 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. [[ID=...]]

[0017] [[ID=...]] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The LifeMap Journey system according to an embodiment of the present invention is a system that visualizes a user's past, present, and future self as a series of narratives. The LifeMap Journey system learns from the user's past data, reflects current goals and circumstances, and generates guidance and video messages for the future. This provides a consistent life map for long-term self-growth, in addition to self-improvement and increased motivation. For example, the LifeMap Journey system collects the user's past data. This includes emails, social media posts, voice memos, diaries, etc. Next, the AI ​​analyzes this data to learn from the user's past actions and experiences. For example, it analyzes past successes and failures to identify the user's strengths and weaknesses. Next, it grasps the current goals and circumstances. The user inputs their current goals and circumstances, which the AI ​​analyzes. For example, this may include career goals, academic goals, health goals, etc. Based on this information, the AI ​​provides advice to optimize the current situation. Furthermore, it generates guidance and video messages for the future. Based on past data and current goals, the AI ​​generates video messages from the future self. For example, these may include encouraging messages and specific advice from the future self. This allows users to have concrete plans for the future, which can be used for self-improvement and increased motivation. The LifeMap Journey system targets young and middle-aged people interested in self-improvement, students, career-oriented individuals, and those who want to clarify their life direction. It is particularly expected to be used by many digital natives interested in self-improvement. The LifeMap Journey system analyzes the user's past data to understand their current situation and goals, and generates a video message from their future self. This allows them to learn from the past, optimize the present, and present a consistent growth plan for the future. All data is managed securely and with respect for privacy. In this way, the LifeMap Journey system visualizes the user's past, present, and future self as a continuous narrative, which can be used for self-improvement and increased motivation.

[0029] The LifeMap Journey system according to this embodiment comprises a collection unit, an analysis unit, a comprehension unit, and a generation unit. The collection unit collects the user's past data. For example, the collection unit collects data such as the user's emails, social media posts, voice memos, and diaries. The collection unit manages this data securely and respects privacy. The analysis unit analyzes the data collected by the collection unit to identify the user's strengths and weaknesses. For example, the analysis unit analyzes past successes and failures to identify the user's strengths and weaknesses. The analysis unit uses AI to analyze the collected data in detail. The comprehension unit grasps the user's current goals and situation. For example, the comprehension unit analyzes career goals, academic goals, health goals, etc., entered by the user, and provides optimal advice. The comprehension unit uses AI to grasp the user's current goals and situation in detail. The generation unit generates a video message from the user's future self based on the information obtained by the analysis unit and the comprehension unit. The generation unit generates a video message containing encouraging messages and specific advice from the user's future self, based on past data and current goals, for example. The generation unit uses AI to generate a detailed video message from the user's future self. As a result, the LifeMap Journey system according to this embodiment visualizes the user's past, present, and future self as a series of stories, which can be used for self-improvement and motivation enhancement.

[0030] The data collection unit collects users' past data. For example, it collects data such as users' emails, social media posts, voice memos, and diaries. Specifically, it extracts users' communication patterns and important events from the content of emails, and analyzes users' interests, concerns, and emotional fluctuations from social media posts. Voice memos collect thoughts and ideas that users record on a daily basis, and diaries help understand users' long-term goals and emotional changes. This data is managed by the data collection unit in a secure and privacy-respecting manner. Specifically, the data is encrypted, and access rights are strictly controlled. Furthermore, the data collection unit collects data only with the user's consent and handles it in accordance with the privacy policy. In addition, the data collection unit allows users to set the frequency and scope of data collection, protecting user privacy to the fullest extent. This enables the data collection unit to efficiently collect diverse user data and improve the accuracy and reliability of the entire system.

[0031] The analysis unit analyzes the data collected by the data collection unit to identify the user's strengths and weaknesses. For example, the analysis unit analyzes past successes and failures to identify the user's strengths and weaknesses. Specifically, it uses AI to analyze the content of emails and social media posts using natural language processing technology to extract the user's emotions and behavioral patterns. The content of voice memos and diaries is similarly analyzed to reveal the changes in the user's thoughts and emotions. The AI ​​integrates this data to identify the user's strengths and weaknesses in detail. For example, past successes can identify the situations in which the user performs best, and failures can reveal the situations in which challenges arise. Furthermore, the analysis unit can analyze the user's behavioral patterns and emotional fluctuations over time to grasp long-term trends and tendencies. This allows the analysis unit to identify the user's strengths and weaknesses in detail, improving the accuracy and reliability of the entire system.

[0032] The understanding unit grasps the user's current goals and situation. For example, it analyzes career goals, academic goals, and health goals entered by the user and provides optimal advice. Specifically, the AI ​​analyzes the goals and situation entered by the user to understand the user's current state and progress toward achieving those goals. For example, regarding career goals, it analyzes the user's work history and skill set to suggest the optimal career path and methods for skill development. Regarding academic goals, it analyzes the user's learning history and grades to provide effective learning methods and resources. Regarding health goals, it analyzes the user's health data and lifestyle habits to provide appropriate exercise and dietary advice. Furthermore, the understanding unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. As a result, the understanding unit can grasp the user's current goals and situation in detail and provide optimal advice.

[0033] The generation unit generates a video message from the future self based on the information obtained by the analysis and understanding units. For example, the generation unit generates a video message from the future self that includes encouraging messages and specific advice based on past data and current goals. Specifically, it uses AI to analyze the user's past data and current goals and generates a message from the future self. The AI ​​uses natural language generation technology to create the most effective message for the user. For example, it generates an encouraging message from the future self based on past successes to improve the user's motivation. It also generates a message that includes specific advice for current goals, providing the user with concrete steps to achieve those goals. Furthermore, the generation unit can collect user feedback and continuously improve the content and format of the messages. This allows the generation unit to visualize the user's past, present, and future selves as a series of narratives, which can be used for self-improvement and motivation enhancement.

[0034] The data collection unit can collect data such as user emails, social media posts, voice memos, and diary entries. For example, the data collection unit can automatically collect user emails and store them for analysis. The data collection unit can also periodically collect social media posts and use them to analyze user behavior patterns. The data collection unit can also analyze voice memos to understand user emotions and intentions. The data collection unit can also analyze diary entries to learn about the user's past behavior and experiences. This allows for more accurate analysis by collecting information from diverse user data sources. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emails into an AI, which can then automatically extract important information.

[0035] The analysis unit can analyze collected data, learn from the user's past behavior and experiences, and identify their strengths and weaknesses. For example, the analysis unit can analyze past successes and failures to identify the user's strengths and weaknesses. The analysis unit uses AI to analyze the collected data in detail. For example, the analysis unit can use natural language processing technology to analyze the user's past behavior and experiences as text data. The analysis unit can also use machine learning algorithms to analyze the user's behavior patterns and identify their strengths and weaknesses. The analysis unit can also use data mining technology to extract useful information from the user's past data. This allows for the identification of individual strengths and weaknesses by analyzing the user's past behavior and experiences. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected data into AI, which can then automatically identify strengths and weaknesses.

[0036] The understanding unit can analyze the user's current goals and situation and provide optimal advice. For example, the understanding unit can analyze the user's career goals, academic goals, health goals, etc., and provide optimal advice. The understanding unit uses AI to understand the user's current goals and situation in detail. For example, the understanding unit can use natural language processing technology to analyze the user's goals and situation as text data. The understanding unit can also use machine learning algorithms to provide optimal advice for the user to achieve their goals. The understanding unit can also use data mining technology to extract information to optimize the user's current situation. This allows the understanding unit to provide appropriate advice based on the user's current goals and situation. Some or all of the above processing in the understanding unit may be performed using AI or not. For example, the understanding unit can input the user's goals and situation into the AI, and the AI ​​can automatically provide optimal advice.

[0037] The generation unit can generate a video message from the future self based on past data and current goals. For example, the generation unit can generate a video message from the future self that includes encouraging messages and specific advice based on past data and current goals. The generation unit uses AI to generate a detailed video message from the user's future self. For example, the generation unit can use text generation AI (e.g., LLM) to generate a message from the user's future self. The generation unit can also use multimodal generation AI to generate a video message from the user's future self. The generation unit can also use natural language processing technology to analyze the user's past data and current goals and generate the optimal video message. This allows for the generation of a video message from the future self based on the user's past and present information, providing specific advice and encouragement. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input past data and current goals into AI, and the AI ​​can automatically generate a video message.

[0038] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection method based on the user's past data collection history. The data collection unit can also analyze the user's past data collection history and customize the collection method. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past data collection history into AI, which can then automatically select the optimal collection method.

[0039] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to areas of interest that the user is currently interested in. The data collection unit can also filter highly relevant data according to the user's lifestyle. The data collection unit can also exclude unnecessary data, taking into account the user's current lifestyle. This allows for the collection of highly relevant data by filtering data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current lifestyle and areas of interest into the AI, which can then automatically filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also filter highly relevant data based on the user's current location. The data collection unit can also select the optimal data collection method by considering the user's geographical location information. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into the AI, which can then automatically prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze a user's social media activity and collect data based on their interests. The data collection unit can also suggest the optimal data collection method based on the user's social media activity history. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into AI, which can then automatically collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a speech recognition algorithm to audio data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can automatically apply the appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. The analysis unit can also adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data submission date into the AI, and the AI ​​can automatically determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can automatically adjust the order of analysis.

[0046] The tracking unit can analyze the user's past goal achievement history to select the optimal tracking method during the tracking process. For example, the tracking unit can propose the optimal tracking method based on goals the user has achieved in the past. The tracking unit can also analyze the user's past goal achievement history to select an efficient tracking method. The tracking unit can also provide a customized tracking method based on the user's past goal achievement history. This allows the optimal tracking method to be selected by analyzing the user's past goal achievement history. Some or all of the above processes in the tracking unit may be performed using AI or not. For example, the tracking unit can input the user's past goal achievement history into AI, and the AI ​​can automatically select the optimal tracking method.

[0047] The information gathering unit can customize the means of gathering goals and situations based on the user's current living situation during the information gathering process. For example, the information gathering unit can suggest the optimal means of gathering goals and situations according to the user's current living situation. The information gathering unit can also provide customized means of gathering, taking into account the user's living situation. The information gathering unit can also select efficient means of gathering based on the user's current living situation. This allows for more appropriate gathering by customizing the means of gathering goals and situations based on the user's current living situation. Some or all of the above-described processes in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's current living situation into AI, and the AI ​​can automatically customize the means of gathering goals and situations.

[0048] The information gathering unit can select the optimal information gathering method by considering the user's geographical location information during the information gathering process. For example, if the user is in a specific region, the information gathering unit will prioritize identifying goals related to that region. The information gathering unit can also filter highly relevant goals based on the user's current location. The information gathering unit can also select the optimal information gathering method by considering the user's geographical location information. This allows for the identification of highly relevant goals and situations by considering the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's geographical location information into the AI, which can then automatically select the optimal information gathering method.

[0049] The understanding unit can analyze the user's social media activity during the understanding process and propose methods for understanding goals and situations. For example, the understanding unit can understand relevant goals based on information shared by the user on social media. The understanding unit can also analyze the user's social media activity and understand goals based on their interests. The understanding unit can also propose the most suitable method of understanding based on the user's social media activity history. This allows for the understanding of highly relevant goals and situations by analyzing the user's social media activity. Some or all of the above processing in the understanding unit may be performed using AI or not. For example, the understanding unit can input the user's social media activity into AI, which can then automatically propose methods for understanding goals and situations.

[0050] The generation unit can generate the optimal video message by analyzing the user's past behavior and experiences during the generation process. For example, the generation unit can generate an encouraging video message based on the user's past successes. The generation unit can also generate a video message that includes advice for improvement based on the user's past failures. The generation unit can also generate the optimal video message by comprehensively analyzing the user's past experiences. In this way, the optimal video message can be generated by analyzing the user's past behavior and experiences. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past behavior and experiences into AI, and the AI ​​can automatically generate the optimal video message.

[0051] The generation unit can customize the content of the video message based on the user's current goals and circumstances during generation. For example, the generation unit can generate a video message containing specific advice based on the user's current career goals. The generation unit can also generate a video message containing suggestions for learning methods based on the user's current academic goals. The generation unit can also generate a video message containing health management advice based on the user's current health goals. This allows for more appropriate advice to be provided by customizing the content of the video message based on the user's current goals and circumstances. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's current goals and circumstances into the AI, which can then automatically customize the content of the video message.

[0052] The generation unit can generate the most suitable video message by considering the user's geographical location information during generation. For example, if the user is in a specific region, the generation unit will generate a video message relevant to that region. The generation unit can also generate a highly relevant video message based on the user's current location. The generation unit can also generate the most suitable video message by considering the user's geographical location information. This allows for the generation of highly relevant video messages by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's geographical location information into the AI, which can then automatically generate the most suitable video message.

[0053] The generation unit can analyze the user's social media activity during generation and suggest video message content. For example, the generation unit can generate relevant video messages based on information shared by the user on social media. The generation unit can also analyze the user's social media activity and suggest video message content based on their interests. The generation unit can also generate the most relevant video message by referring to the user's social media activity history. This allows for the generation of highly relevant video messages by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media activity into AI, which can then automatically suggest video message content.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The LifeMap Journey system can further include a hobby identification unit that identifies the user's hobbies and interests based on the user's past data. For example, the hobby identification unit can identify hobbies and interests from the user's past social media posts and emails. It can also identify hobbies and interests from the user's past voice memos and diary entries. Based on the user's hobbies and interests, the hobby identification unit can provide relevant information. This allows for the provision of more personalized information based on the user's hobbies and interests. Some or all of the above-described processes in the hobby identification unit may be performed using AI or not. For example, the hobby identification unit can input the user's past data into an AI, which can then automatically identify hobbies and interests.

[0056] The LifeMap Journey system may further include a learning style identification unit that identifies the user's learning style based on the user's past data. The learning style identification unit can, for example, identify the learning style from the user's past academic performance and learning history. It can also identify the learning style from the user's past learning notes and memos. Based on the user's learning style, the learning style identification unit can also suggest the most suitable learning method. This allows for the provision of more effective learning methods based on the user's learning style. Some or all of the above-described processes in the learning style identification unit may be performed using AI, or not. For example, the learning style identification unit can input the user's past data into an AI, which can then automatically identify the learning style.

[0057] The LifeMap Journey system may further include a career path identification unit that identifies the user's career path based on the user's past data. For example, the career path identification unit can identify a career path from the user's past work history and skill set. It can also identify a career path from the user's past projects and achievements. Based on the user's career path, the career path identification unit can provide optimal career advice. This allows for more specific career advice based on the user's career path. Some or all of the above-described processes in the career path identification unit may be performed using AI or not. For example, the career path identification unit can input the user's past data into an AI, which can then automatically identify the career path.

[0058] The LifeMap Journey system may further include a life event identification unit that identifies the user's life events based on the user's past data. For example, the life event identification unit can identify life events from the user's past social media posts or emails. It can also identify life events from the user's past voice memos or diary entries. The life event identification unit can also provide relevant information based on the user's life events. This allows for the provision of more personalized information based on the user's life events. Some or all of the above-described processes in the life event identification unit may be performed using AI or not. For example, the life event identification unit can input the user's past data into an AI, which can then automatically identify life events.

[0059] The LifeMap Journey system may further include a travel history identification unit that identifies the user's travel history based on the user's past data. The travel history identification unit can identify travel history from, for example, the user's past social media posts or emails. The travel history identification unit can also identify travel history from the user's past voice memos or diaries. The travel history identification unit can also provide relevant information based on the user's travel history. This allows for the provision of more personalized information based on the user's travel history. Some or all of the above processing in the travel history identification unit may be performed using AI or not. For example, the travel history identification unit can input the user's past data into an AI, which can then automatically identify the travel history.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects the user's past data. For example, the data collection unit collects data such as the user's emails, social media posts, voice memos, and diary entries. The data collection unit manages this data securely and respects privacy. Step 2: The analysis unit analyzes the data collected by the data collection unit to identify the user's strengths and weaknesses. For example, the analysis unit analyzes past successes and failures to identify the user's strengths and weaknesses. The analysis unit uses AI to analyze the collected data in detail. Step 3: The understanding unit grasps the user's current goals and situation. For example, the understanding unit analyzes the user's entered career goals, academic goals, health goals, etc., and provides optimal advice. The understanding unit uses AI to grasp the user's current goals and situation in detail. Step 4: The generation unit generates a video message from the future self based on the information obtained by the analysis unit and the understanding unit. For example, the generation unit generates a video message from the future self that includes encouraging messages and specific advice based on past data and current goals. The generation unit uses AI to generate a detailed video message from the user's future self.

[0062] (Example of form 2) The LifeMap Journey system according to an embodiment of the present invention is a system that visualizes a user's past, present, and future self as a series of narratives. The LifeMap Journey system learns from the user's past data, reflects current goals and circumstances, and generates guidance and video messages for the future. This provides a consistent life map for long-term self-growth, in addition to self-improvement and increased motivation. For example, the LifeMap Journey system collects the user's past data. This includes emails, social media posts, voice memos, diaries, etc. Next, the AI ​​analyzes this data to learn from the user's past actions and experiences. For example, it analyzes past successes and failures to identify the user's strengths and weaknesses. Next, it grasps the current goals and circumstances. The user inputs their current goals and circumstances, which the AI ​​analyzes. For example, this may include career goals, academic goals, health goals, etc. Based on this information, the AI ​​provides advice to optimize the current situation. Furthermore, it generates guidance and video messages for the future. Based on past data and current goals, the AI ​​generates video messages from the future self. For example, these may include encouraging messages and specific advice from the future self. This allows users to have concrete plans for the future, which can be used for self-improvement and increased motivation. The LifeMap Journey system targets young and middle-aged people interested in self-improvement, students, career-oriented individuals, and those who want to clarify their life direction. It is particularly expected to be used by many digital natives interested in self-improvement. The LifeMap Journey system analyzes the user's past data to understand their current situation and goals, and generates a video message from their future self. This allows them to learn from the past, optimize the present, and present a consistent growth plan for the future. All data is managed securely and with respect for privacy. In this way, the LifeMap Journey system visualizes the user's past, present, and future self as a continuous narrative, which can be used for self-improvement and increased motivation.

[0063] The LifeMap Journey system according to this embodiment comprises a collection unit, an analysis unit, a comprehension unit, and a generation unit. The collection unit collects the user's past data. For example, the collection unit collects data such as the user's emails, social media posts, voice memos, and diaries. The collection unit manages this data securely and respects privacy. The analysis unit analyzes the data collected by the collection unit to identify the user's strengths and weaknesses. For example, the analysis unit analyzes past successes and failures to identify the user's strengths and weaknesses. The analysis unit uses AI to analyze the collected data in detail. The comprehension unit grasps the user's current goals and situation. For example, the comprehension unit analyzes career goals, academic goals, health goals, etc., entered by the user, and provides optimal advice. The comprehension unit uses AI to grasp the user's current goals and situation in detail. The generation unit generates a video message from the user's future self based on the information obtained by the analysis unit and the comprehension unit. The generation unit generates a video message containing encouraging messages and specific advice from the user's future self, based on past data and current goals, for example. The generation unit uses AI to generate a detailed video message from the user's future self. As a result, the LifeMap Journey system according to this embodiment visualizes the user's past, present, and future self as a series of stories, which can be used for self-improvement and motivation enhancement.

[0064] The data collection unit collects users' past data. For example, it collects data such as users' emails, social media posts, voice memos, and diaries. Specifically, it extracts users' communication patterns and important events from the content of emails, and analyzes users' interests, concerns, and emotional fluctuations from social media posts. Voice memos collect thoughts and ideas that users record on a daily basis, and diaries help understand users' long-term goals and emotional changes. This data is managed by the data collection unit in a secure and privacy-respecting manner. Specifically, the data is encrypted, and access rights are strictly controlled. Furthermore, the data collection unit collects data only with the user's consent and handles it in accordance with the privacy policy. In addition, the data collection unit allows users to set the frequency and scope of data collection, protecting user privacy to the fullest extent. This enables the data collection unit to efficiently collect diverse user data and improve the accuracy and reliability of the entire system.

[0065] The analysis unit analyzes the data collected by the data collection unit to identify the user's strengths and weaknesses. For example, the analysis unit analyzes past successes and failures to identify the user's strengths and weaknesses. Specifically, it uses AI to analyze the content of emails and social media posts using natural language processing technology to extract the user's emotions and behavioral patterns. The content of voice memos and diaries is similarly analyzed to reveal the changes in the user's thoughts and emotions. The AI ​​integrates this data to identify the user's strengths and weaknesses in detail. For example, past successes can identify the situations in which the user performs best, and failures can reveal the situations in which challenges arise. Furthermore, the analysis unit can analyze the user's behavioral patterns and emotional fluctuations over time to grasp long-term trends and tendencies. This allows the analysis unit to identify the user's strengths and weaknesses in detail, improving the accuracy and reliability of the entire system.

[0066] The understanding unit grasps the user's current goals and situation. For example, it analyzes career goals, academic goals, and health goals entered by the user and provides optimal advice. Specifically, the AI ​​analyzes the goals and situation entered by the user to understand the user's current state and progress toward achieving those goals. For example, regarding career goals, it analyzes the user's work history and skill set to suggest the optimal career path and methods for skill development. Regarding academic goals, it analyzes the user's learning history and grades to provide effective learning methods and resources. Regarding health goals, it analyzes the user's health data and lifestyle habits to provide appropriate exercise and dietary advice. Furthermore, the understanding unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. As a result, the understanding unit can grasp the user's current goals and situation in detail and provide optimal advice.

[0067] The generation unit generates a video message from the future self based on the information obtained by the analysis and understanding units. For example, the generation unit generates a video message from the future self that includes encouraging messages and specific advice based on past data and current goals. Specifically, it uses AI to analyze the user's past data and current goals and generates a message from the future self. The AI ​​uses natural language generation technology to create the most effective message for the user. For example, it generates an encouraging message from the future self based on past successes to improve the user's motivation. It also generates a message that includes specific advice for current goals, providing the user with concrete steps to achieve those goals. Furthermore, the generation unit can collect user feedback and continuously improve the content and format of the messages. This allows the generation unit to visualize the user's past, present, and future selves as a series of narratives, which can be used for self-improvement and motivation enhancement.

[0068] The data collection unit can collect data such as user emails, social media posts, voice memos, and diary entries. For example, the data collection unit can automatically collect user emails and store them for analysis. The data collection unit can also periodically collect social media posts and use them to analyze user behavior patterns. The data collection unit can also analyze voice memos to understand user emotions and intentions. The data collection unit can also analyze diary entries to learn about the user's past behavior and experiences. This allows for more accurate analysis by collecting information from diverse user data sources. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emails into an AI, which can then automatically extract important information.

[0069] The analysis unit can analyze collected data, learn from the user's past behavior and experiences, and identify their strengths and weaknesses. For example, the analysis unit can analyze past successes and failures to identify the user's strengths and weaknesses. The analysis unit uses AI to analyze the collected data in detail. For example, the analysis unit can use natural language processing technology to analyze the user's past behavior and experiences as text data. The analysis unit can also use machine learning algorithms to analyze the user's behavior patterns and identify their strengths and weaknesses. The analysis unit can also use data mining technology to extract useful information from the user's past data. This allows for the identification of individual strengths and weaknesses by analyzing the user's past behavior and experiences. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected data into AI, which can then automatically identify strengths and weaknesses.

[0070] The understanding unit can analyze the user's current goals and situation and provide optimal advice. For example, the understanding unit can analyze the user's career goals, academic goals, health goals, etc., and provide optimal advice. The understanding unit uses AI to understand the user's current goals and situation in detail. For example, the understanding unit can use natural language processing technology to analyze the user's goals and situation as text data. The understanding unit can also use machine learning algorithms to provide optimal advice for the user to achieve their goals. The understanding unit can also use data mining technology to extract information to optimize the user's current situation. This allows the understanding unit to provide appropriate advice based on the user's current goals and situation. Some or all of the above processing in the understanding unit may be performed using AI or not. For example, the understanding unit can input the user's goals and situation into the AI, and the AI ​​can automatically provide optimal advice.

[0071] The generation unit can generate a video message from the future self based on past data and current goals. For example, the generation unit can generate a video message from the future self that includes encouraging messages and specific advice based on past data and current goals. The generation unit uses AI to generate a detailed video message from the user's future self. For example, the generation unit can use text generation AI (e.g., LLM) to generate a message from the user's future self. The generation unit can also use multimodal generation AI to generate a video message from the user's future self. The generation unit can also use natural language processing technology to analyze the user's past data and current goals and generate the optimal video message. This allows for the generation of a video message from the future self based on the user's past and present information, providing specific advice and encouragement. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input past data and current goals into AI, and the AI ​​can automatically generate a video message.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect data during relaxed periods. The data collection unit can also collect data when the user is focused. If the user is tired, the data collection unit can collect data after they have rested. By adjusting the timing of data collection according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then automatically adjust the timing of data collection.

[0073] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize data collection methods that the user has frequently used in the past. The data collection unit can also suggest the most efficient collection method based on the user's past data collection history. The data collection unit can also analyze the user's past data collection history and customize the collection method. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past data collection history into AI, which can then automatically select the optimal collection method.

[0074] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to areas of interest that the user is currently interested in. The data collection unit can also filter highly relevant data according to the user's lifestyle. The data collection unit can also exclude unnecessary data, taking into account the user's current lifestyle. This allows for the collection of highly relevant data by filtering data based on the user's current lifestyle and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's current lifestyle and areas of interest into the AI, which can then automatically filter the data.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting positive data. If the user is depressed, the data collection unit may also prioritize collecting encouraging data. If the user is relaxed, the data collection unit may also prioritize collecting relaxation-related data. This allows for more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then automatically determine the data priority.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also filter highly relevant data based on the user's current location. The data collection unit can also select the optimal data collection method by considering the user's geographical location information. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into the AI, which can then automatically prioritize the collection of highly relevant data.

[0077] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze a user's social media activity and collect data based on their interests. The data collection unit can also suggest the optimal data collection method based on the user's social media activity history. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media activity into AI, which can then automatically collect relevant data.

[0078] 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. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. If the user is excited, the analysis unit can also provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into AI, and the AI ​​can automatically adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can automatically adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a speech recognition algorithm to audio data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can automatically apply the appropriate analysis algorithm.

[0081] 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 result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI, and the AI ​​can automatically adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also postpone the analysis of older data. The analysis unit can also adjust the analysis schedule based on the submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data submission date into the AI, and the AI ​​can automatically determine the analysis priority.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can automatically adjust the order of analysis.

[0084] The understanding unit can estimate the user's emotions and adjust how goals and situations are understood based on the estimated emotions. For example, if the user is relaxed, the understanding unit can provide a detailed understanding of goals and situations. If the user is in a hurry, the understanding unit can provide a concise understanding of goals and situations. If the user is excited, the understanding unit can provide a visually stimulating understanding of goals and situations. This allows for more appropriate understanding by adjusting how goals and situations are understood according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the understanding unit may be performed using AI or not. For example, the understanding unit can input user emotion data into an AI, which can then automatically adjust how goals and situations are understood.

[0085] The tracking unit can analyze the user's past goal achievement history to select the optimal tracking method during the tracking process. For example, the tracking unit can propose the optimal tracking method based on goals the user has achieved in the past. The tracking unit can also analyze the user's past goal achievement history to select an efficient tracking method. The tracking unit can also provide a customized tracking method based on the user's past goal achievement history. This allows the optimal tracking method to be selected by analyzing the user's past goal achievement history. Some or all of the above processes in the tracking unit may be performed using AI or not. For example, the tracking unit can input the user's past goal achievement history into AI, and the AI ​​can automatically select the optimal tracking method.

[0086] The information gathering unit can customize the means of gathering goals and situations based on the user's current living situation during the information gathering process. For example, the information gathering unit can suggest the optimal means of gathering goals and situations according to the user's current living situation. The information gathering unit can also provide customized means of gathering, taking into account the user's living situation. The information gathering unit can also select efficient means of gathering based on the user's current living situation. This allows for more appropriate gathering by customizing the means of gathering goals and situations based on the user's current living situation. Some or all of the above-described processes in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's current living situation into AI, and the AI ​​can automatically customize the means of gathering goals and situations.

[0087] The understanding unit can estimate the user's emotions and determine the priority of goals and situations based on the estimated emotions. For example, if the user is excited, the understanding unit will prioritize positive goals. If the user is depressed, the understanding unit can also prioritize encouraging goals. If the user is relaxed, the understanding unit can also prioritize relaxation-related goals. This allows for more appropriate goal setting by determining the priority of goals and situations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the understanding unit may be performed using AI or not. For example, the understanding unit can input user emotion data into an AI, which can then automatically determine the priority of goals and situations.

[0088] The information gathering unit can select the optimal information gathering method by considering the user's geographical location information during the information gathering process. For example, if the user is in a specific region, the information gathering unit will prioritize identifying goals related to that region. The information gathering unit can also filter highly relevant goals based on the user's current location. The information gathering unit can also select the optimal information gathering method by considering the user's geographical location information. This allows for the identification of highly relevant goals and situations by considering the user's geographical location information. Some or all of the above processing in the information gathering unit may be performed using AI or not. For example, the information gathering unit can input the user's geographical location information into the AI, which can then automatically select the optimal information gathering method.

[0089] The understanding unit can analyze the user's social media activity during the understanding process and propose methods for understanding goals and situations. For example, the understanding unit can understand relevant goals based on information shared by the user on social media. The understanding unit can also analyze the user's social media activity and understand goals based on their interests. The understanding unit can also propose the most suitable method of understanding based on the user's social media activity history. This allows for the understanding of highly relevant goals and situations by analyzing the user's social media activity. Some or all of the above processing in the understanding unit may be performed using AI or not. For example, the understanding unit can input the user's social media activity into AI, which can then automatically propose methods for understanding goals and situations.

[0090] The generation unit can estimate the user's emotions and adjust the video message generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a video message that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can also generate a video message that emphasizes the shortest route. If the user is excited, the generation unit can also generate a video message with visually stimulating effects. This allows for the provision of more appropriate video messages by adjusting the video message generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI, and the AI ​​can automatically adjust the video message generation method.

[0091] The generation unit can generate the optimal video message by analyzing the user's past behavior and experiences during the generation process. For example, the generation unit can generate an encouraging video message based on the user's past successes. The generation unit can also generate a video message that includes advice for improvement based on the user's past failures. The generation unit can also generate the optimal video message by comprehensively analyzing the user's past experiences. In this way, the optimal video message can be generated by analyzing the user's past behavior and experiences. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's past behavior and experiences into AI, and the AI ​​can automatically generate the optimal video message.

[0092] The generation unit can customize the content of the video message based on the user's current goals and circumstances during generation. For example, the generation unit can generate a video message containing specific advice based on the user's current career goals. The generation unit can also generate a video message containing suggestions for learning methods based on the user's current academic goals. The generation unit can also generate a video message containing health management advice based on the user's current health goals. This allows for more appropriate advice to be provided by customizing the content of the video message based on the user's current goals and circumstances. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's current goals and circumstances into the AI, which can then automatically customize the content of the video message.

[0093] The generation unit can estimate the user's emotions and prioritize video messages based on the estimated emotions. For example, if the user is excited, the generation unit will prioritize generating positive video messages. If the user is depressed, the generation unit can also prioritize generating encouraging video messages. If the user is relaxed, the generation unit can also prioritize generating relaxation-related video messages. This allows for the provision of more appropriate video messages by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI, which can then automatically determine the priority of video messages.

[0094] The generation unit can generate the most suitable video message by considering the user's geographical location information during generation. For example, if the user is in a specific region, the generation unit will generate a video message relevant to that region. The generation unit can also generate a highly relevant video message based on the user's current location. The generation unit can also generate the most suitable video message by considering the user's geographical location information. This allows for the generation of highly relevant video messages by considering the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the user's geographical location information into the AI, which can then automatically generate the most suitable video message.

[0095] The generation unit can analyze the user's social media activity during generation and suggest video message content. For example, the generation unit can generate relevant video messages based on information shared by the user on social media. The generation unit can also analyze the user's social media activity and suggest video message content based on their interests. The generation unit can also generate the most relevant video message by referring to the user's social media activity history. This allows for the generation of highly relevant video messages by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's social media activity into AI, which can then automatically suggest video message content.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The LifeMap Journey system may further include a prediction unit that estimates the user's emotions and predicts the user's behavior based on the estimated emotions. For example, if the user is feeling stressed, the prediction unit may predict actions to relieve stress. If the user is excited, the prediction unit may also predict actions to maintain excitement. If the user is relaxed, the prediction unit may also predict actions to maintain relaxation. This allows for more appropriate advice to be provided by predicting behavior based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input the user's emotion data into an AI, which can then automatically predict behavior.

[0098] The LifeMap Journey system can further include a hobby identification unit that identifies the user's hobbies and interests based on the user's past data. For example, the hobby identification unit can identify hobbies and interests from the user's past social media posts and emails. It can also identify hobbies and interests from the user's past voice memos and diary entries. Based on the user's hobbies and interests, the hobby identification unit can provide relevant information. This allows for the provision of more personalized information based on the user's hobbies and interests. Some or all of the above-described processes in the hobby identification unit may be performed using AI or not. For example, the hobby identification unit can input the user's past data into an AI, which can then automatically identify hobbies and interests.

[0099] The LifeMap Journey system may further include a health assessment unit that estimates the user's emotions and evaluates the user's health status based on the estimated emotions. For example, the health assessment unit can assess the stress level if the user is feeling stressed. It can also assess the degree of fatigue if the user is tired. It can also assess the degree of relaxation if the user is relaxed. This allows for more appropriate health management advice to be provided by evaluating the health status based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above-described processes in the health assessment unit may be performed using AI or not. For example, the health assessment unit can input the user's emotion data into an AI, which can then automatically evaluate the health status.

[0100] The LifeMap Journey system may further include a learning style identification unit that identifies the user's learning style based on the user's past data. The learning style identification unit can, for example, identify the learning style from the user's past academic performance and learning history. It can also identify the learning style from the user's past learning notes and memos. Based on the user's learning style, the learning style identification unit can also suggest the most suitable learning method. This allows for the provision of more effective learning methods based on the user's learning style. Some or all of the above-described processes in the learning style identification unit may be performed using AI, or not. For example, the learning style identification unit can input the user's past data into an AI, which can then automatically identify the learning style.

[0101] The LifeMap Journey system may further include a communication evaluation unit that estimates the user's emotions and evaluates the user's communication style based on the estimated emotions. For example, if the user is excited, the communication evaluation unit may evaluate an active communication style. If the user is depressed, the communication evaluation unit may also evaluate a passive communication style. If the user is relaxed, the communication evaluation unit may also evaluate a relaxed communication style. This allows for more appropriate communication advice to be provided by evaluating the communication style based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above processing in the communication evaluation unit may be performed using AI or not. For example, the communication evaluation unit can input the user's emotion data into an AI, which can then automatically evaluate the communication style.

[0102] The LifeMap Journey system may further include a career path identification unit that identifies the user's career path based on the user's past data. For example, the career path identification unit can identify a career path from the user's past work history and skill set. It can also identify a career path from the user's past projects and achievements. Based on the user's career path, the career path identification unit can provide optimal career advice. This allows for more specific career advice based on the user's career path. Some or all of the above-described processes in the career path identification unit may be performed using AI or not. For example, the career path identification unit can input the user's past data into an AI, which can then automatically identify the career path.

[0103] The LifeMap Journey system may further include a stress assessment unit that estimates the user's emotions and evaluates the user's stress level based on the estimated emotions. For example, if the user is feeling stressed, the stress assessment unit will evaluate the stress level in detail. If the user is relaxed, the stress assessment unit may also evaluate the stress level as low. If the user is excited, the stress assessment unit may also evaluate the stress level as moderate. This allows for more appropriate stress management advice to be provided by evaluating the stress level based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above processing in the stress assessment unit may be performed using AI or not. For example, the stress assessment unit can input the user's emotion data into an AI, which can then automatically evaluate the stress level.

[0104] The LifeMap Journey system may further include a life event identification unit that identifies the user's life events based on the user's past data. For example, the life event identification unit can identify life events from the user's past social media posts or emails. It can also identify life events from the user's past voice memos or diary entries. The life event identification unit can also provide relevant information based on the user's life events. This allows for the provision of more personalized information based on the user's life events. Some or all of the above-described processes in the life event identification unit may be performed using AI or not. For example, the life event identification unit can input the user's past data into an AI, which can then automatically identify life events.

[0105] The LifeMap Journey system may further include a motivation assessment unit that estimates the user's emotions and evaluates the user's motivation level based on the estimated emotions. For example, the motivation assessment unit may evaluate the user's motivation level as high if the user is excited. The motivation assessment unit may also evaluate the user's motivation level as low if the user is depressed. The motivation assessment unit may also evaluate the user's motivation level as moderate if the user is relaxed. This allows for more appropriate motivation improvement advice to be provided by evaluating the motivation level based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Some or all of the above processing in the motivation assessment unit may be performed using AI or not. For example, the motivation assessment unit can input the user's emotion data into an AI, which can then automatically evaluate the motivation level.

[0106] The LifeMap Journey system may further include a travel history identification unit that identifies the user's travel history based on the user's past data. The travel history identification unit can identify travel history from, for example, the user's past social media posts or emails. The travel history identification unit can also identify travel history from the user's past voice memos or diaries. The travel history identification unit can also provide relevant information based on the user's travel history. This allows for the provision of more personalized information based on the user's travel history. Some or all of the above processing in the travel history identification unit may be performed using AI or not. For example, the travel history identification unit can input the user's past data into an AI, which can then automatically identify the travel history.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit collects the user's past data. For example, the data collection unit collects data such as the user's emails, social media posts, voice memos, and diary entries. The data collection unit manages this data securely and respects privacy. Step 2: The analysis unit analyzes the data collected by the data collection unit to identify the user's strengths and weaknesses. For example, the analysis unit analyzes past successes and failures to identify the user's strengths and weaknesses. The analysis unit uses AI to analyze the collected data in detail. Step 3: The understanding unit grasps the user's current goals and situation. For example, the understanding unit analyzes the user's entered career goals, academic goals, health goals, etc., and provides optimal advice. The understanding unit uses AI to grasp the user's current goals and situation in detail. Step 4: The generation unit generates a video message from the future self based on the information obtained by the analysis unit and the understanding unit. For example, the generation unit generates a video message from the future self that includes encouraging messages and specific advice based on past data and current goals. The generation unit uses AI to generate a detailed video message from the user's future self.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, understanding unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 38B of the smart device 14 and manages it with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The understanding unit grasps the user's current goals and situation using the control unit 46A of the smart device 14. The generation unit generates a future video message from the user using the specific processing unit 290 of the data processing unit 12. The collection unit can also be implemented in the specific processing unit 290 of the data processing unit 12 and adjusts the timing of data collection based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, understanding unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the smart glasses 214 and manages it with the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The understanding unit grasps the user's current goals and situation, for example, by the control unit 46A of the smart glasses 214. The generation unit generates a future video message from the user, for example, by the identification processing unit 290 of the data processing unit 12. The collection unit can also be implemented, for example, by the identification processing unit 290 of the data processing unit 12 and adjusts the timing of data collection based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[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 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.

[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 (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).

[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] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, understanding unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the headset terminal 314 and manages it with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The understanding unit grasps the user's current goals and situation, for example, by the control unit 46A of the headset terminal 314. The generation unit generates a future video message from the user, for example, by the specific processing unit 290 of the data processing unit 12. The collection unit can also be implemented, for example, by the specific processing unit 290 of the data processing unit 12 and adjusts the timing of data collection based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, grasping unit, and generation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the robot 414 and manages it with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The grasping unit grasps the user's current goals and situation, for example, by the control unit 46A of the robot 414. The generation unit generates a future video message from oneself, for example, by the specific processing unit 290 of the data processing unit 12. The collection unit is also implemented, for example, by the specific processing unit 290 of the data processing unit 12 and adjusts the timing of data collection based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects the user's past data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the user's strengths and weaknesses, A unit that grasps the user's current goals and situation, The system includes a generation unit that generates a future video message from itself based on the information obtained by the analysis unit and the understanding unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data such as users' emails, social media posts, voice memos, and diary entries. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By analyzing the collected data, we learn from the user's past behavior and experiences, and identify their strengths and weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The gripping part is, It analyzes the user's current goals and situation and provides optimal advice. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on past data and current goals, generate a video message from your future self. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The gripping part is, It estimates the user's emotions and adjusts the way goals and situations are understood based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The gripping part is, When assessing user performance, the system analyzes the user's past goal achievement history to select the most appropriate assessment method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The gripping part is, When assessing a user's situation, the means of assessing their goals and circumstances are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The gripping part is, It estimates the user's emotions and determines the priority of goals and situations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The gripping part is, When gathering data, the optimal gathering method is selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The gripping part is, During the assessment process, we analyze users' social media activity and propose methods for understanding their goals and current situation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is It estimates the user's emotions and adjusts how video messages are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the system analyzes the user's past behavior and experiences to generate the most suitable video message. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the video message content is customized based on the user's current goals and circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is It estimates the user's emotions and prioritizes video messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is During generation, the system takes the user's geographical location into consideration to create the most suitable video message. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the system analyzes the user's social media activity to suggest video message content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects the user's past data, An analysis unit analyzes the data collected by the aforementioned collection unit to identify the user's strengths and weaknesses, A unit that grasps the user's current goals and situation, The system includes a generation unit that generates a future video message from itself based on the information obtained by the analysis unit and the understanding unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects data such as users' emails, social media posts, voice memos, and diary entries. The system according to feature 1.

3. The aforementioned analysis unit, By analyzing the collected data, we learn from the user's past behavior and experiences, and identify their strengths and weaknesses. The system according to feature 1.

4. The gripping part is, It analyzes the user's current goals and situation and provides optimal advice. The system according to feature 1.

5. The generating unit is Based on past data and current goals, generate a video message from your future self. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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