system

Abstract

The system according to this embodiment aims to efficiently predict and create monthly activity plans and to visually confirm them. [Solution] The system according to the embodiment comprises a reading unit, an analysis unit, a generation unit, and a display unit. The reading unit reads calendar information. The analysis unit analyzes the information read by the reading unit. The generation unit generates an activity schedule for the following month based on the information analyzed by the analysis unit. The display unit displays the activity schedule generated by the generation unit via smart glasses.

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, there is a problem that it is difficult to efficiently predict and create a monthly action plan and visually confirm it.

[0005] The system according to the embodiment aims to efficiently predict and create a monthly action plan and visually confirm it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reading unit, an analysis unit, a generation unit, and a display unit. The reading unit reads calendar information. The analysis unit analyzes the information read by the reading unit. The generation unit generates an activity schedule for the following month based on the information analyzed by the analysis unit. The display unit displays the activity schedule generated by the generation unit via smart glasses. [Effects of the Invention]

[0007] The system according to this embodiment allows for the efficient prediction and creation of monthly activity plans, which can then be visually confirmed. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An embodiment of the present invention provides an activity schedule prediction system that uses a generating AI to predict and create a monthly activity schedule and projects it onto a virtual calendar via smart glasses. The activity schedule prediction system works by having the user read calendar information on their smartphone. Next, the generating AI analyzes the input calendar information and automatically generates the activity schedule for the following month. The generated activity schedule is then projected as a virtual calendar via smart glasses. This system helps prevent forgetfulness and makes it easier for the elderly to create schedules. In particular, for elderly people who have difficulty operating smartphones, being able to check their schedules via smart glasses offers significant convenience. Furthermore, using smart glasses eliminates the need to look at handwritten calendars or small-print smartphone screens, reducing visual strain. This system is also useful for elderly people living alone, allowing them to manage their schedules even without family or caregivers. This improves the quality of life for the elderly and allows them to live with peace of mind. Thus, the activity schedule prediction system helps prevent forgetfulness and makes it easier for the elderly to create schedules.

[0029] The behavioral schedule prediction system according to this embodiment comprises a reading unit, an analysis unit, a generation unit, and a display unit. The reading unit reads calendar information. Calendar information includes, but is not limited to, digital calendars, paper calendars, and calendar information from specific applications. The reading unit can, for example, digitize paper calendar information using OCR technology. The reading unit can also acquire digital calendar information through an API. Furthermore, the reading unit can read paper calendar information using a smartphone camera. For example, the reading unit takes a picture of a paper calendar with a smartphone camera and converts it into text information using OCR technology. Digital calendar information acquired through an API is directly transmitted to the analysis unit. The analysis unit analyzes the calendar information read by the reading unit using a generation AI. The analysis is performed by, for example, text analysis, pattern recognition, and machine learning algorithms, but is not limited to these methods. For example, the analysis unit analyzes the content of the calendar information using text analysis technology. The analysis unit can also analyze past behavioral patterns using pattern recognition technology. The analysis unit can also perform analysis of calendar information using machine learning algorithms. For example, the analysis unit uses text analysis technology to analyze the content of calendar information and extract important events and tasks. Pattern recognition technology is used to predict the next month's schedule based on past behavioral patterns. Machine learning algorithms learn from a large amount of calendar information to provide more accurate analysis results. The generation unit generates the next month's schedule based on the information analyzed by the analysis unit. Generation is performed using, for example, a generation AI, but is not limited to such examples. For example, the generation unit automatically generates the next month's schedule using a generation AI. The generation unit can also generate a schedule based on past behavioral patterns. Furthermore, the generation unit can generate a schedule based on the user's current living situation. For example, the generation unit automatically generates the next month's schedule using a generation AI and displays it on the user's smart glasses. By generating a schedule based on past behavioral patterns, a more accurate schedule can be provided.By generating a schedule based on the user's current lifestyle, the system can provide a schedule tailored to the user's needs. The display unit displays the schedule generated by the generation unit via smart glasses. The display is, for example, as a virtual calendar, but is not limited to this example. For example, the display unit projects the schedule as a virtual calendar via smart glasses. The display unit may also include a visual aid unit to reduce visual strain. For example, the display unit projects the schedule as a virtual calendar via smart glasses and adjusts the font size to reduce visual strain. The visual aid unit can adjust the brightness and contrast of the screen to reduce the user's visual strain. As a result, the schedule prediction system according to this embodiment can automatically generate a schedule for the user and display it via smart glasses. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can display the schedule using an AI model that takes the schedule generated by the generation unit as input and displays it as a virtual calendar.

[0030] The reading unit reads calendar information. Calendar information includes, but is not limited to, digital calendars, paper calendars, and calendar information from specific applications. For example, the reading unit can digitize paper calendar information using OCR technology. OCR technology uses optical character recognition to convert characters and numbers written on a paper calendar into digital data. Specifically, a paper calendar is photographed with a smartphone camera, and the image is input into OCR software. The OCR software analyzes the characters in the image and extracts them as digital text. This digital text is then used by the subsequent analysis unit. The reading unit can also acquire digital calendar information via an API. An API is an interface for exchanging data between different software, allowing for direct acquisition of calendar information from digital calendar applications. Furthermore, the reading unit can also read paper calendar information using a smartphone camera. For example, the reading unit photographs a paper calendar with a smartphone camera and converts it into text information using OCR technology. The digital calendar information acquired via the API is sent directly to the analysis unit. This allows the reading unit to efficiently collect and digitize calendar information in various formats.

[0031] The analysis unit uses a generative AI to analyze the calendar information read by the reading unit. Analysis is performed using methods such as text analysis, pattern recognition, and machine learning algorithms, but is not limited to these examples. Specifically, text analysis technology is used to analyze the content of the calendar information and extract important events and tasks. Text analysis technology uses natural language processing (NLP) to analyze the text within the calendar information and extract important information such as event names, dates, and locations. The analysis unit can also analyze past behavioral patterns using pattern recognition technology. Pattern recognition technology is used to identify user behavioral patterns based on past calendar information and predict the next month's schedule. Furthermore, the analysis unit can also perform analysis of calendar information using machine learning algorithms. Machine learning algorithms learn from large amounts of calendar information and provide more accurate analysis results. For example, the analysis unit uses text analysis technology to analyze the content of the calendar information and extract important events and tasks. Pattern recognition technology is used to predict the next month's schedule based on past behavioral patterns. Machine learning algorithms learn from large amounts of calendar information and provide more accurate analysis results. This allows the analysis unit to quickly and accurately analyze the collected calendar information and predict the user's planned activities.

[0032] The generation unit generates a schedule for the following month based on the information analyzed by the analysis unit. Generation is performed, for example, using a generation AI, but is not limited to such an example. The generation AI uses natural language generation (NLG) technology to automatically generate a schedule based on the analyzed information. Specifically, the generation AI creates a schedule for the following month based on information on important events and tasks provided by the analysis unit. The generation AI can generate an optimal schedule by considering past behavioral patterns and the user's current living situation. For example, the generation unit automatically generates a schedule for the following month using the generation AI and displays it on the user's smart glasses. By generating a schedule based on past behavioral patterns, a more accurate schedule can be provided. By generating a schedule based on the user's current living situation, a schedule tailored to the user's needs can be provided. The generation unit can also revise the schedule based on user feedback to provide a more accurate schedule. In this way, the generation unit can efficiently generate schedules for users and support their lives.

[0033] The display unit displays the schedule generated by the generation unit via smart glasses. The display is, for example, as a virtual calendar, but is not limited to this example. Specifically, the display unit projects the schedule as a virtual calendar via smart glasses. Smart glasses are devices that display information directly in the user's field of view, allowing the user to check their schedule hands-free. The display unit may also include a visual aid unit to reduce visual strain. For example, the display unit projects the schedule as a virtual calendar via smart glasses and adjusts the font size to reduce visual strain. The visual aid unit can adjust the brightness and contrast of the screen to reduce the user's visual strain. Furthermore, the display unit can improve the displayed content based on user feedback and provide a more user-friendly interface. For example, a function can be added to highlight important events so that the user does not miss certain events. This allows the display unit to efficiently display the user's schedule and support the user's life. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model that takes the activity schedule generated by the generation unit as input and displays it as a virtual calendar to show the activity schedule.

[0034] The reading unit can read calendar information using a smartphone. For example, the reading unit can read paper calendar information using a smartphone's camera. For example, the reading unit can take a picture of a paper calendar with a smartphone's camera and convert it into text information using OCR technology. The reading unit can also acquire digital calendar information using a smartphone application. For example, the reading unit can acquire calendar information from a smartphone's calendar application via an API. The reading unit can also read handwritten calendar information using a smartphone's camera. For example, the reading unit can take a picture of a handwritten calendar with a smartphone's camera and convert it into text information using OCR technology. This allows users to easily input information by reading calendar information with their smartphone. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input image data acquired by a smartphone's camera into a generation AI and have the generation AI generate text data from the image data.

[0035] The analysis unit can predict and create a schedule for the following month based on past behavioral patterns. For example, the analysis unit can analyze past calendar information and extract behavioral patterns. For example, the analysis unit can analyze past calendar information using text analysis technology and extract behavioral patterns. The analysis unit can also analyze past behavioral history data and extract behavioral patterns. For example, the analysis unit can analyze past behavioral history data using pattern recognition technology and extract behavioral patterns. The analysis unit can also analyze past behavioral patterns using machine learning algorithms. For example, the analysis unit can analyze past behavioral patterns using machine learning algorithms and predict a schedule for the following month. This allows for the generation of more accurate schedules by predicting and creating schedules based on past behavioral patterns. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past calendar information into a generative AI and have the generative AI perform the extraction of behavioral patterns.

[0036] The display unit can project the schedule as a virtual calendar via smart glasses. For example, the display unit can display the schedule as a virtual calendar using smart glasses. For example, the display unit projects the schedule onto the smart glasses' display so that the user can visually confirm it. The display unit can also display the virtual calendar in 3D. For example, the display unit can display the schedule in 3D using smart glasses so that the user can interact with it. The display unit can also add interactive functions to the virtual calendar. For example, the display unit can interactively display the schedule using smart glasses so that the user can operate it with touch operations or voice commands. This reduces the visual burden and makes it easier to check the schedule by projecting the schedule via smart glasses. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can display the schedule using an AI model that takes the schedule generated by the generation unit as input and displays it as a virtual calendar.

[0037] The display unit may include a visual aid to reduce visual strain. For example, the display unit may adjust the font size to reduce visual strain. For example, the display unit may increase the size of the characters displayed on the smart glasses' screen. The display unit may also adjust the brightness of the screen. For example, the display unit may adjust the brightness of the smart glasses' screen to reduce visual strain. The display unit may also adjust the contrast. For example, the display unit may adjust the contrast of the smart glasses' screen to reduce visual strain. This reduces visual strain, allowing the user to check their schedule more comfortably. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may provide visual assistance using an AI model that automatically adjusts the display settings to reduce the user's visual strain.

[0038] The reading unit can analyze the user's past calendar usage history and select the optimal reading method. For example, the reading unit may prioritize reading data from calendar apps that the user has frequently used in the past. For example, the reading unit may analyze past calendar usage history and select the optimal reading method. The reading unit may also select the optimal reading method based on the format of the calendar the user has used in the past (digital, handwritten, etc.). For example, the reading unit may select a reading method for a specific time period based on past calendar usage history. In this way, the optimal reading method can be selected by analyzing past calendar usage history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit may input past calendar usage history into a generating AI and have the generating AI select the optimal reading method.

[0039] The reading unit can filter calendar information based on the user's current lifestyle and areas of interest. For example, the reading unit can prioritize reading calendar information related to events or activities the user is currently interested in. For example, the reading unit can filter relevant calendar information based on the user's lifestyle (work, family, hobbies, etc.). The reading unit can also select the most relevant calendar information based on the user's current health status and lifestyle. For example, the reading unit can prioritize reading relevant calendar information based on the user's health status. This allows for the acquisition of highly relevant information by filtering information based on the user's lifestyle and areas of interest. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input data about the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The reading unit can prioritize reading highly relevant information by considering the user's geographical location when reading calendar information. For example, the reading unit can prioritize reading events and appointments related to the user's current location. For example, the reading unit can prioritize reading nearby appointments based on the user's geographical location. Furthermore, if the user is traveling, the reading unit can prioritize reading appointments at their travel destination. For example, the reading unit can prioritize reading appointments at their travel destination based on the user's geographical location. This allows for the priority acquisition of highly relevant information by considering geographical location. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant information.

[0041] The reading unit can analyze the user's social media activity and extract relevant information when reading calendar information. For example, the reading unit can prioritize reading events the user plans to attend on social media. For example, the reading unit can extract relevant calendar information based on the user's social media activity. The reading unit can also extract relevant calendar information based on the user's interests on social media. For example, the reading unit can extract relevant information by referring to the schedules of the user's friends on social media. This allows for the efficient acquisition of relevant information by analyzing social media activity. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input data on the user's social media activity into a generating AI and have the generating AI select relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the calendar information during the analysis. For example, the analysis unit performs a detailed analysis for important appointments. For example, the analysis unit performs a detailed analysis based on the importance of the calendar information. The analysis unit can also perform a simplified analysis for general appointments. For example, the analysis unit performs a simplified analysis based on the importance of the calendar information. The analysis unit can also adjust the level of detail of the analysis based on the user's level of interest. For example, the analysis unit performs a detailed analysis based on the user's level of interest. In this way, by adjusting the level of detail of the analysis based on the importance of the calendar information, important information can be analyzed in detail. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the importance of the calendar information into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the calendar information during analysis. For example, the analysis unit can apply a business-oriented analysis algorithm to work-related appointments. For example, the analysis unit applies a business-oriented analysis algorithm based on the category of the calendar information. The analysis unit can also apply a personal-oriented analysis algorithm to private appointments. For example, the analysis unit applies a personal-oriented analysis algorithm based on the category of the calendar information. The analysis unit can also apply a health management-oriented analysis algorithm to health-related appointments. For example, the analysis unit applies a health management-oriented analysis algorithm based on the category of the calendar information. By applying analysis algorithms according to the category of the calendar information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the categories of the calendar information into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the input timing of calendar information during analysis. For example, the analysis unit may prioritize analyzing the most recent appointments. For example, the analysis unit may prioritize analyzing the most recent appointments based on the input timing of calendar information. The analysis unit can also postpone the analysis of long-term appointments. For example, the analysis unit may postpone the analysis of long-term appointments based on the input timing of calendar information. The analysis unit can also determine the priority of analysis based on the user's level of interest. For example, the analysis unit may prioritize the analysis of the most recent appointments based on the user's level of interest. In this way, by determining the priority of analysis based on the input timing of calendar information, the analysis unit can prioritize the analysis of the most recent appointments. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit may input the input timing of calendar information to the generative AI and have the generative AI perform the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of calendar information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant appointments. For example, the analysis unit can prioritize the analysis of highly relevant appointments based on the relevance of calendar information. The analysis unit can also postpone the analysis of less relevant appointments. For example, the analysis unit can postpone the analysis of less relevant appointments based on the relevance of calendar information. The analysis unit can also adjust the order of analysis based on the user's level of interest. For example, the analysis unit can prioritize the analysis of highly relevant appointments based on the user's level of interest. In this way, by adjusting the order of analysis based on the relevance of calendar information, highly relevant information can be prioritized for analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the relevance of calendar information into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0046] The generation unit can analyze the user's past behavior patterns and select the optimal generation method when generating an action plan. For example, the generation unit can generate an action plan based on the user's frequently performed past behavior patterns. For example, the generation unit can generate an optimal action plan based on past behavior patterns. The generation unit can also set the action plan to the optimal time based on the user's past behavior patterns. For example, the generation unit can set the action plan to the optimal time based on past behavior patterns. The generation unit can also analyze the user's past behavior patterns and generate an efficient action plan. For example, the generation unit can generate an efficient action plan based on past behavior patterns. In this way, an optimal action plan can be generated by analyzing past behavior patterns. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input past behavior patterns into a generation AI and have the generation AI execute the generation of an optimal action plan.

[0047] The generation unit can customize the generation method based on the user's current living situation when generating an action plan. For example, the generation unit can generate an optimal action plan based on the user's current living situation. For example, the generation unit can customize the action plan to match the user's daily rhythm. The generation unit can also generate an action plan based on the user's current health condition. For example, the generation unit can generate an optimal action plan based on the user's health condition. The generation unit can also generate an action plan based on the user's current occupation and family environment. For example, the generation unit can generate an optimal action plan based on the user's occupation and family environment. By customizing the action plan based on the current living situation, a more appropriate action plan can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data about the user's living situation into a generation AI and have the generation AI perform the action plan customization.

[0048] The generation unit can select the optimal generation method when generating an action plan, taking into account the user's geographical location information. For example, the generation unit can generate an action plan related to the user's current location. For example, the generation unit can generate an optimal action plan based on the user's geographical location information. The generation unit can also generate nearby action plans based on the user's geographical location information. For example, the generation unit can generate nearby action plans based on the user's geographical location information. Furthermore, if the user is traveling, the generation unit can generate an action plan for their travel destination. For example, the generation unit can generate an action plan for their travel destination based on the user's geographical location information. This allows for the generation of highly relevant action plans by considering geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI execute the generation of an optimal action plan.

[0049] The generation unit can analyze the user's social media activity and propose a method for generating an action plan. For example, the generation unit can generate an action plan based on events the user plans to attend on social media. For example, the generation unit can generate an optimal action plan based on the user's social media activity. The generation unit can also generate an action plan based on the user's interests on social media. For example, the generation unit can generate an optimal action plan based on the user's interests on social media. The generation unit can also generate an action plan by referring to the schedules of the user's friends on social media. For example, the generation unit can generate an optimal action plan based on the schedules of the user's friends on social media. In this way, by analyzing social media activity, it is possible to generate an action plan that is highly relevant. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI and have the generation AI perform the generation of an optimal action plan.

[0050] The display unit can select the optimal display method by referring to the user's past operation history when displaying the virtual calendar. For example, the display unit may prioritize display methods previously used by the user. For example, the display unit may select the optimal display method based on past operation history. The display unit can also select the optimal display method from the user's past operation history. For example, the display unit may provide a display method that is less visually burdensome based on past operation history. The display unit can also analyze the user's past operation history and provide the optimal display method. For example, the display unit may provide the optimal display method based on past operation history. This allows the optimal display method to be selected by referring to past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input past operation history into a generating AI and have the generating AI select the optimal display method.

[0051] The display unit can customize display means to reduce the user's visual burden when displaying a virtual calendar. For example, the display unit can increase the font size to reduce the user's visual burden. For example, the display unit can increase the font size of the virtual calendar. The display unit can also adjust the background color. For example, the display unit can adjust the background color of the virtual calendar to reduce the visual burden. The display unit can also adjust the contrast. For example, the display unit can adjust the contrast of the virtual calendar to reduce the visual burden. By reducing the visual burden, the user can check their schedule more comfortably. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can provide visual assistance using an AI model that automatically adjusts the display settings to reduce the user's visual burden.

[0052] The display unit can select the optimal display method when displaying a virtual calendar, taking into account the user's device information. For example, if the user is using smart glasses, the display unit can provide a display method that is highly visible. For example, the display unit can provide a display method optimized for the smart glasses display. Also, if the user is using a smartphone, the display unit can provide a display method that is adapted to the screen size. For example, the display unit can provide a display method optimized for the smartphone screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. For example, the display unit can provide a display method optimized for the tablet screen size. In this way, the optimal display method can be provided by taking device information into consideration. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0053] The display unit can analyze the user's social media activity and adjust the displayed content when displaying a virtual calendar. For example, the display unit may prioritize displaying events the user plans to attend on social media. For example, the display unit may display relevant events based on the user's social media activity. The display unit can also display relevant events based on the user's interests on social media. For example, the display unit may display relevant events based on the user's interests on social media. The display unit can also adjust the displayed content by referring to the schedules of the user's friends on social media. For example, the display unit may display relevant events based on the schedules of the user's friends on social media. In this way, by analyzing social media activity, highly relevant information can be displayed. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input data on the user's social media activity into a generating AI and have the generating AI perform the adjustment of the displayed content.

[0054] The visual assistance unit can select the optimal assistance method by referring to the user's past visual assistance history when providing visual assistance. For example, the visual assistance unit may prioritize providing visual assistance methods that the user has used in the past. For example, the visual assistance unit may select the optimal assistance method based on the user's past visual assistance history. The visual assistance unit can also select the optimal assistance method from the user's past visual assistance history. For example, the visual assistance unit may provide an assistance method that is less visually burdensome based on the user's past visual assistance history. The visual assistance unit may also analyze the user's past visual assistance history and provide the optimal assistance method. For example, the visual assistance unit may provide the optimal assistance method based on the user's past visual assistance history. This allows the system to select the optimal assistance method by referring to the user's past visual assistance history. Some or all of the above processing in the visual assistance unit may be performed using AI, for example, or without AI. For example, the visual assistance unit may input the user's past visual assistance history into a generating AI and have the generating AI select the optimal assistance method.

[0055] The visual assistance unit can select the optimal assistance method by considering the user's device information when providing visual assistance. For example, if the user is using smart glasses, the visual assistance unit can provide an assistance method that is highly visible. For example, the visual assistance unit can provide an assistance method optimized for the smart glasses' display. Also, if the user is using a smartphone, the visual assistance unit can provide an assistance method that is adapted to the screen size. For example, the visual assistance unit can provide an assistance method optimized for the smartphone's screen size. Also, if the user is using a tablet, the visual assistance unit can provide an assistance method optimized for a larger screen. For example, the visual assistance unit can provide an assistance method optimized for the tablet's screen size. This allows for the provision of optimal visual assistance by considering device information. Some or all of the above processing in the visual assistance unit may be performed using AI, for example, or without AI. For example, the visual assistance unit can input the user's device information into a generating AI and have the generating AI select the optimal assistance method.

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

[0057] The activity plan prediction system can acquire user health data and adjust activity plans based on their health status. For example, it can acquire the user's heart rate and sleep data and generate a plan that prioritizes rest if the user is fatigued. It can also add activity plans to encourage exercise if the user is not getting enough exercise. Furthermore, it can acquire the user's dietary data and suggest meal plans that take nutritional balance into consideration. In this way, it can support health management by providing activity plans tailored to the user's health status.

[0058] The activity plan prediction system can analyze a user's past behavioral data and optimize their activity plan based on their behavioral patterns. For example, it can generate a plan that prioritizes activities the user has frequently performed in the past. It can also suggest a plan that eliminates activities the user has avoided in the past. Furthermore, it can set activity plans for the optimal time slots based on the user's past behavioral data. In this way, by providing activity plans based on the user's behavioral patterns, it can support efficient schedule management.

[0059] The activity plan prediction system can acquire a user's geographical location information and adjust their activity plan based on that location. For example, if a user is in a specific location, it can generate a plan that prioritizes events and activities related to that location. If a user is traveling, it can also suggest tourist attractions and events at their destination. Furthermore, it can create an activity plan that takes commute time into account based on the user's commute route. This allows for improved travel efficiency by providing activity plans based on the user's location information.

[0060] The activity plan prediction system analyzes a user's social media activity and can adjust their schedule based on events and friends' plans on social media. For example, it can generate a schedule that prioritizes events the user plans to attend on social media. It can also suggest events that the user's friends will be attending. Furthermore, it can add relevant activities based on the user's interests on social media. In this way, it can support social activities by providing an activity plan based on the user's social media activity.

[0061] The activity plan prediction system can acquire user device information and provide an activity plan optimized for that device. For example, if a user is using smart glasses, it can provide a display method that is easy to see. If a user is using a smartphone, it can display an activity plan that is adapted to the screen size. Furthermore, if a user is using a tablet, it can provide an activity plan optimized for the larger screen. By providing an activity plan based on the user's device information, the system can reduce visual strain.

[0062] The activity plan prediction system can analyze a user's past activity history and propose the optimal way to generate an activity plan. For example, it can prioritize providing functions that the user has frequently used in the past. It can also propose a plan that excludes functions the user has avoided in the past. Furthermore, it can set activity plans at the optimal time based on the user's past activity history. In this way, by providing activity plans based on the user's activity history, it can support efficient schedule management.

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

[0064] Step 1: The reading unit reads calendar information. This information includes digital calendars, paper calendars, and calendar information from specific applications. The reading unit uses OCR technology to digitize paper calendar information. It can also obtain digital calendar information via an API. Furthermore, it can read paper calendar information using a smartphone camera. Step 2: The analysis unit analyzes the calendar information read by the reading unit. The analysis is performed using methods such as text analysis, pattern recognition, and machine learning algorithms. For example, text analysis techniques are used to analyze the content of the calendar information and extract important events and tasks. Pattern recognition techniques are used to predict the next month's schedule based on past behavioral patterns. Machine learning algorithms learn from large amounts of calendar information to provide more accurate analysis results. Step 3: The generation unit generates the next month's activity schedule based on the information analyzed by the analysis unit. The generation is performed using a generation AI. For example, the generation AI is used to automatically generate the next month's activity schedule, and the schedule is generated based on past activity patterns and the user's current living situation. Step 4: The display unit displays the activity schedule generated by the generation unit via smart glasses. The display is done as a virtual calendar. For example, the activity schedule is projected as a virtual calendar via smart glasses, and the font size, screen brightness, and contrast are adjusted to reduce visual strain.

[0065] (Example of form 2) An embodiment of the present invention provides an activity schedule prediction system that uses a generating AI to predict and create a monthly activity schedule and projects it onto a virtual calendar via smart glasses. The activity schedule prediction system works by having the user read calendar information on their smartphone. Next, the generating AI analyzes the input calendar information and automatically generates the activity schedule for the following month. The generated activity schedule is then projected as a virtual calendar via smart glasses. This system helps prevent forgetfulness and makes it easier for the elderly to create schedules. In particular, for elderly people who have difficulty operating smartphones, being able to check their schedules via smart glasses offers significant convenience. Furthermore, using smart glasses eliminates the need to look at handwritten calendars or small-print smartphone screens, reducing visual strain. This system is also useful for elderly people living alone, allowing them to manage their schedules even without family or caregivers. This improves the quality of life for the elderly and allows them to live with peace of mind. Thus, the activity schedule prediction system helps prevent forgetfulness and makes it easier for the elderly to create schedules.

[0066] The behavioral schedule prediction system according to this embodiment comprises a reading unit, an analysis unit, a generation unit, and a display unit. The reading unit reads calendar information. Calendar information includes, but is not limited to, digital calendars, paper calendars, and calendar information from specific applications. The reading unit can, for example, digitize paper calendar information using OCR technology. The reading unit can also acquire digital calendar information through an API. Furthermore, the reading unit can read paper calendar information using a smartphone camera. For example, the reading unit takes a picture of a paper calendar with a smartphone camera and converts it into text information using OCR technology. Digital calendar information acquired through an API is directly transmitted to the analysis unit. The analysis unit analyzes the calendar information read by the reading unit using a generation AI. The analysis is performed by, for example, text analysis, pattern recognition, and machine learning algorithms, but is not limited to these methods. For example, the analysis unit analyzes the content of the calendar information using text analysis technology. The analysis unit can also analyze past behavioral patterns using pattern recognition technology. The analysis unit can also perform analysis of calendar information using machine learning algorithms. For example, the analysis unit uses text analysis technology to analyze the content of calendar information and extract important events and tasks. Pattern recognition technology is used to predict the next month's schedule based on past behavioral patterns. Machine learning algorithms learn from a large amount of calendar information to provide more accurate analysis results. The generation unit generates the next month's schedule based on the information analyzed by the analysis unit. Generation is performed using, for example, a generation AI, but is not limited to such examples. For example, the generation unit automatically generates the next month's schedule using a generation AI. The generation unit can also generate a schedule based on past behavioral patterns. Furthermore, the generation unit can generate a schedule based on the user's current living situation. For example, the generation unit automatically generates the next month's schedule using a generation AI and displays it on the user's smart glasses. By generating a schedule based on past behavioral patterns, a more accurate schedule can be provided.By generating a schedule based on the user's current lifestyle, the system can provide a schedule tailored to the user's needs. The display unit displays the schedule generated by the generation unit via smart glasses. The display is, for example, as a virtual calendar, but is not limited to this example. For example, the display unit projects the schedule as a virtual calendar via smart glasses. The display unit may also include a visual aid unit to reduce visual strain. For example, the display unit projects the schedule as a virtual calendar via smart glasses and adjusts the font size to reduce visual strain. The visual aid unit can adjust the brightness and contrast of the screen to reduce the user's visual strain. As a result, the schedule prediction system according to this embodiment can automatically generate a schedule for the user and display it via smart glasses. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can display the schedule using an AI model that takes the schedule generated by the generation unit as input and displays it as a virtual calendar.

[0067] The reading unit reads calendar information. Calendar information includes, but is not limited to, digital calendars, paper calendars, and calendar information from specific applications. For example, the reading unit can digitize paper calendar information using OCR technology. OCR technology uses optical character recognition to convert characters and numbers written on a paper calendar into digital data. Specifically, a paper calendar is photographed with a smartphone camera, and the image is input into OCR software. The OCR software analyzes the characters in the image and extracts them as digital text. This digital text is then used by the subsequent analysis unit. The reading unit can also acquire digital calendar information via an API. An API is an interface for exchanging data between different software, allowing for direct acquisition of calendar information from digital calendar applications. Furthermore, the reading unit can also read paper calendar information using a smartphone camera. For example, the reading unit photographs a paper calendar with a smartphone camera and converts it into text information using OCR technology. The digital calendar information acquired via the API is sent directly to the analysis unit. This allows the reading unit to efficiently collect and digitize calendar information in various formats.

[0068] The analysis unit uses a generative AI to analyze the calendar information read by the reading unit. Analysis is performed using methods such as text analysis, pattern recognition, and machine learning algorithms, but is not limited to these examples. Specifically, text analysis technology is used to analyze the content of the calendar information and extract important events and tasks. Text analysis technology uses natural language processing (NLP) to analyze the text within the calendar information and extract important information such as event names, dates, and locations. The analysis unit can also analyze past behavioral patterns using pattern recognition technology. Pattern recognition technology is used to identify user behavioral patterns based on past calendar information and predict the next month's schedule. Furthermore, the analysis unit can also perform analysis of calendar information using machine learning algorithms. Machine learning algorithms learn from large amounts of calendar information and provide more accurate analysis results. For example, the analysis unit uses text analysis technology to analyze the content of the calendar information and extract important events and tasks. Pattern recognition technology is used to predict the next month's schedule based on past behavioral patterns. Machine learning algorithms learn from large amounts of calendar information and provide more accurate analysis results. This allows the analysis unit to quickly and accurately analyze the collected calendar information and predict the user's planned activities.

[0069] The generation unit generates a schedule for the following month based on the information analyzed by the analysis unit. Generation is performed, for example, using a generation AI, but is not limited to such an example. The generation AI uses natural language generation (NLG) technology to automatically generate a schedule based on the analyzed information. Specifically, the generation AI creates a schedule for the following month based on information on important events and tasks provided by the analysis unit. The generation AI can generate an optimal schedule by considering past behavioral patterns and the user's current living situation. For example, the generation unit automatically generates a schedule for the following month using the generation AI and displays it on the user's smart glasses. By generating a schedule based on past behavioral patterns, a more accurate schedule can be provided. By generating a schedule based on the user's current living situation, a schedule tailored to the user's needs can be provided. The generation unit can also revise the schedule based on user feedback to provide a more accurate schedule. In this way, the generation unit can efficiently generate schedules for users and support their lives.

[0070] The display unit displays the schedule generated by the generation unit via smart glasses. The display is, for example, as a virtual calendar, but is not limited to this example. Specifically, the display unit projects the schedule as a virtual calendar via smart glasses. Smart glasses are devices that display information directly in the user's field of view, allowing the user to check their schedule hands-free. The display unit may also include a visual aid unit to reduce visual strain. For example, the display unit projects the schedule as a virtual calendar via smart glasses and adjusts the font size to reduce visual strain. The visual aid unit can adjust the brightness and contrast of the screen to reduce the user's visual strain. Furthermore, the display unit can improve the displayed content based on user feedback and provide a more user-friendly interface. For example, a function can be added to highlight important events so that the user does not miss certain events. This allows the display unit to efficiently display the user's schedule and support the user's life. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model that takes the activity schedule generated by the generation unit as input and displays it as a virtual calendar to show the activity schedule.

[0071] The reading unit can read calendar information using a smartphone. For example, the reading unit can read paper calendar information using a smartphone's camera. For example, the reading unit can take a picture of a paper calendar with a smartphone's camera and convert it into text information using OCR technology. The reading unit can also acquire digital calendar information using a smartphone application. For example, the reading unit can acquire calendar information from a smartphone's calendar application via an API. The reading unit can also read handwritten calendar information using a smartphone's camera. For example, the reading unit can take a picture of a handwritten calendar with a smartphone's camera and convert it into text information using OCR technology. This allows users to easily input information by reading calendar information with their smartphone. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input image data acquired by a smartphone's camera into a generation AI and have the generation AI generate text data from the image data.

[0072] The analysis unit can predict and create a schedule for the following month based on past behavioral patterns. For example, the analysis unit can analyze past calendar information and extract behavioral patterns. For example, the analysis unit can analyze past calendar information using text analysis technology and extract behavioral patterns. The analysis unit can also analyze past behavioral history data and extract behavioral patterns. For example, the analysis unit can analyze past behavioral history data using pattern recognition technology and extract behavioral patterns. The analysis unit can also analyze past behavioral patterns using machine learning algorithms. For example, the analysis unit can analyze past behavioral patterns using machine learning algorithms and predict a schedule for the following month. This allows for the generation of more accurate schedules by predicting and creating schedules based on past behavioral patterns. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past calendar information into a generative AI and have the generative AI perform the extraction of behavioral patterns.

[0073] The display unit can project the schedule as a virtual calendar via smart glasses. For example, the display unit can display the schedule as a virtual calendar using smart glasses. For example, the display unit projects the schedule onto the smart glasses' display so that the user can visually confirm it. The display unit can also display the virtual calendar in 3D. For example, the display unit can display the schedule in 3D using smart glasses so that the user can interact with it. The display unit can also add interactive functions to the virtual calendar. For example, the display unit can interactively display the schedule using smart glasses so that the user can operate it with touch operations or voice commands. This reduces the visual burden and makes it easier to check the schedule by projecting the schedule via smart glasses. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can display the schedule using an AI model that takes the schedule generated by the generation unit as input and displays it as a virtual calendar.

[0074] The display unit may include a visual aid to reduce visual strain. For example, the display unit may adjust the font size to reduce visual strain. For example, the display unit may increase the size of the characters displayed on the smart glasses' screen. The display unit may also adjust the brightness of the screen. For example, the display unit may adjust the brightness of the smart glasses' screen to reduce visual strain. The display unit may also adjust the contrast. For example, the display unit may adjust the contrast of the smart glasses' screen to reduce visual strain. This reduces visual strain, allowing the user to check their schedule more comfortably. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may provide visual assistance using an AI model that automatically adjusts the display settings to reduce the user's visual strain.

[0075] The reading unit can estimate the user's emotions and adjust the timing of reading calendar information based on the estimated emotions. For example, if the user is stressed, the reading unit can adjust the timing to read calendar information during a time when the user is relaxed. For example, the reading unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The reading unit can also select a time to read calendar information quickly and efficiently if the user is busy. For example, the reading unit can record the user's voice and estimate the emotion using voice analysis technology. The reading unit can also select a time to read detailed calendar information if the user is relaxed. For example, the reading unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. This allows for information to be acquired at a more appropriate time by adjusting the timing of reading calendar information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input user image data captured by the camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0076] The reading unit can analyze the user's past calendar usage history and select the optimal reading method. For example, the reading unit may prioritize reading data from calendar apps that the user has frequently used in the past. For example, the reading unit may analyze past calendar usage history and select the optimal reading method. The reading unit may also select the optimal reading method based on the format of the calendar the user has used in the past (digital, handwritten, etc.). For example, the reading unit may select a reading method for a specific time period based on past calendar usage history. In this way, the optimal reading method can be selected by analyzing past calendar usage history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit may input past calendar usage history into a generating AI and have the generating AI select the optimal reading method.

[0077] The reading unit can filter calendar information based on the user's current lifestyle and areas of interest. For example, the reading unit can prioritize reading calendar information related to events or activities the user is currently interested in. For example, the reading unit can filter relevant calendar information based on the user's lifestyle (work, family, hobbies, etc.). The reading unit can also select the most relevant calendar information based on the user's current health status and lifestyle. For example, the reading unit can prioritize reading relevant calendar information based on the user's health status. This allows for the acquisition of highly relevant information by filtering information based on the user's lifestyle and areas of interest. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input data about the user's lifestyle and areas of interest into a generating AI and have the generating AI perform the filtering.

[0078] The reading unit can estimate the user's emotions and determine the priority of calendar information to read based on the estimated emotions. For example, if the user is stressed, the reading unit will prioritize reading important appointments. For example, the reading unit may capture the user's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. The reading unit can also prioritize reading detailed appointments if the user is relaxed. For example, the reading unit may record the user's voice and estimate their emotions using voice analysis technology. The reading unit can also prioritize appointments that can be checked quickly if the user is busy. For example, the reading unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to prioritize important information by determining the priority of calendar information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input user image data captured by the camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0079] The reading unit can prioritize reading highly relevant information by considering the user's geographical location when reading calendar information. For example, the reading unit can prioritize reading events and appointments related to the user's current location. For example, the reading unit can prioritize reading nearby appointments based on the user's geographical location. Furthermore, if the user is traveling, the reading unit can prioritize reading appointments at their travel destination. For example, the reading unit can prioritize reading appointments at their travel destination based on the user's geographical location. This allows for the priority acquisition of highly relevant information by considering geographical location. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's geographical location information into a generating AI and have the generating AI select highly relevant information.

[0080] The reading unit can analyze the user's social media activity and extract relevant information when reading calendar information. For example, the reading unit can prioritize reading events the user plans to attend on social media. For example, the reading unit can extract relevant calendar information based on the user's social media activity. The reading unit can also extract relevant calendar information based on the user's interests on social media. For example, the reading unit can extract relevant information by referring to the schedules of the user's friends on social media. This allows for the efficient acquisition of relevant information by analyzing social media activity. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input data on the user's social media activity into a generating AI and have the generating AI select relevant information.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also provide concise and to-the-point analysis results if the user is stressed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. The analysis unit can also provide analysis results that can be understood in a short time if the user is busy. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more easily understandable analysis results by adjusting the presentation of the analysis 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the calendar information during the analysis. For example, the analysis unit performs a detailed analysis for important appointments. For example, the analysis unit performs a detailed analysis based on the importance of the calendar information. The analysis unit can also perform a simplified analysis for general appointments. For example, the analysis unit performs a simplified analysis based on the importance of the calendar information. The analysis unit can also adjust the level of detail of the analysis based on the user's level of interest. For example, the analysis unit performs a detailed analysis based on the user's level of interest. In this way, by adjusting the level of detail of the analysis based on the importance of the calendar information, important information can be analyzed in detail. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the importance of the calendar information into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the calendar information during analysis. For example, the analysis unit can apply a business-oriented analysis algorithm to work-related appointments. For example, the analysis unit applies a business-oriented analysis algorithm based on the category of the calendar information. The analysis unit can also apply a personal-oriented analysis algorithm to private appointments. For example, the analysis unit applies a personal-oriented analysis algorithm based on the category of the calendar information. The analysis unit can also apply a health management-oriented analysis algorithm to health-related appointments. For example, the analysis unit applies a health management-oriented analysis algorithm based on the category of the calendar information. By applying analysis algorithms according to the category of the calendar information, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the categories of the calendar information into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0084] 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 relaxed, the analysis unit can perform a detailed analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also perform a concise analysis if the user is stressed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. The analysis unit can also perform an analysis that can be understood in a short time if the user is busy. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user image data captured by a camera into a generating AI, allowing the AI ​​to estimate the user's emotions.

[0085] The analysis unit can determine the priority of analysis based on the input timing of calendar information during analysis. For example, the analysis unit may prioritize analyzing the most recent appointments. For example, the analysis unit may prioritize analyzing the most recent appointments based on the input timing of calendar information. The analysis unit can also postpone the analysis of long-term appointments. For example, the analysis unit may postpone the analysis of long-term appointments based on the input timing of calendar information. The analysis unit can also determine the priority of analysis based on the user's level of interest. For example, the analysis unit may prioritize the analysis of the most recent appointments based on the user's level of interest. In this way, by determining the priority of analysis based on the input timing of calendar information, the analysis unit can prioritize the analysis of the most recent appointments. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit may input the input timing of calendar information to the generative AI and have the generative AI perform the determination of the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of calendar information during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant appointments. For example, the analysis unit can prioritize the analysis of highly relevant appointments based on the relevance of calendar information. The analysis unit can also postpone the analysis of less relevant appointments. For example, the analysis unit can postpone the analysis of less relevant appointments based on the relevance of calendar information. The analysis unit can also adjust the order of analysis based on the user's level of interest. For example, the analysis unit can prioritize the analysis of highly relevant appointments based on the user's level of interest. In this way, by adjusting the order of analysis based on the relevance of calendar information, highly relevant information can be prioritized for analysis. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the relevance of calendar information into a generative AI and have the generative AI perform the adjustment of the analysis order.

[0087] The generation unit can estimate the user's emotions and adjust the method of generating the action plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a detailed action plan. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The generation unit can also generate a concise action plan if the user is stressed. For example, the generation unit can record the user's voice and estimate the emotions using voice analysis technology. The generation unit can also generate an action plan that can be understood in a short time if the user is busy. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for the generation of a more appropriate action plan by adjusting the method of generating the action plan 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0088] The generation unit can analyze the user's past behavior patterns and select the optimal generation method when generating an action plan. For example, the generation unit can generate an action plan based on the user's frequently performed past behavior patterns. For example, the generation unit can generate an optimal action plan based on past behavior patterns. The generation unit can also set the action plan to the optimal time based on the user's past behavior patterns. For example, the generation unit can set the action plan to the optimal time based on past behavior patterns. The generation unit can also analyze the user's past behavior patterns and generate an efficient action plan. For example, the generation unit can generate an efficient action plan based on past behavior patterns. In this way, an optimal action plan can be generated by analyzing past behavior patterns. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input past behavior patterns into a generation AI and have the generation AI execute the generation of an optimal action plan.

[0089] The generation unit can customize the generation method based on the user's current living situation when generating an action plan. For example, the generation unit can generate an optimal action plan based on the user's current living situation. For example, the generation unit can customize the action plan to match the user's daily rhythm. The generation unit can also generate an action plan based on the user's current health condition. For example, the generation unit can generate an optimal action plan based on the user's health condition. The generation unit can also generate an action plan based on the user's current occupation and family environment. For example, the generation unit can generate an optimal action plan based on the user's occupation and family environment. By customizing the action plan based on the current living situation, a more appropriate action plan can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data about the user's living situation into a generation AI and have the generation AI perform the action plan customization.

[0090] The generation unit can estimate the user's emotions and determine the priority of planned activities based on those emotions. For example, if the user is relaxed, the generation unit will prioritize generating detailed activity plans. For instance, the generation unit might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can also prioritize generating important activity plans if the user is stressed. For example, the generation unit might record the user's voice and estimate their emotions using voice analysis technology. The generation unit can also prioritize generating activity plans that can be reviewed quickly if the user is busy. For example, the generation unit might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the generation unit to prioritize important activities based on 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user image data captured by a camera into a generation AI and have the generation AI perform the estimation of the user's emotions.

[0091] The generation unit can select the optimal generation method when generating an action plan, taking into account the user's geographical location information. For example, the generation unit can generate an action plan related to the user's current location. For example, the generation unit can generate an optimal action plan based on the user's geographical location information. The generation unit can also generate nearby action plans based on the user's geographical location information. For example, the generation unit can generate nearby action plans based on the user's geographical location information. Furthermore, if the user is traveling, the generation unit can generate an action plan for their travel destination. For example, the generation unit can generate an action plan for their travel destination based on the user's geographical location information. This allows for the generation of highly relevant action plans by considering geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI and have the generation AI execute the generation of an optimal action plan.

[0092] The generation unit can analyze the user's social media activity and propose a method for generating an action plan. For example, the generation unit can generate an action plan based on events the user plans to attend on social media. For example, the generation unit can generate an optimal action plan based on the user's social media activity. The generation unit can also generate an action plan based on the user's interests on social media. For example, the generation unit can generate an optimal action plan based on the user's interests on social media. The generation unit can also generate an action plan by referring to the schedules of the user's friends on social media. For example, the generation unit can generate an optimal action plan based on the schedules of the user's friends on social media. In this way, by analyzing social media activity, it is possible to generate an action plan that is highly relevant. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input data on the user's social media activity into a generation AI and have the generation AI perform the generation of an optimal action plan.

[0093] The display unit can estimate the user's emotions and adjust the display method of the virtual calendar based on the estimated emotions. For example, if the user is relaxed, the display unit can display a detailed virtual calendar. For example, the display unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The display unit can also display a concise virtual calendar if the user is stressed. For example, the display unit can record the user's voice and estimate the emotion using voice analysis technology. The display unit can also display a virtual calendar that can be checked in a short time if the user is busy. For example, the display unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. This allows for a more appropriate display by adjusting the display method 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-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0094] The display unit can select the optimal display method by referring to the user's past operation history when displaying the virtual calendar. For example, the display unit may prioritize display methods previously used by the user. For example, the display unit may select the optimal display method based on past operation history. The display unit can also select the optimal display method from the user's past operation history. For example, the display unit may provide a display method that is less visually burdensome based on past operation history. The display unit can also analyze the user's past operation history and provide the optimal display method. For example, the display unit may provide the optimal display method based on past operation history. This allows the optimal display method to be selected by referring to past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input past operation history into a generating AI and have the generating AI select the optimal display method.

[0095] The display unit can customize display means to reduce the user's visual burden when displaying a virtual calendar. For example, the display unit can increase the font size to reduce the user's visual burden. For example, the display unit can increase the font size of the virtual calendar. The display unit can also adjust the background color. For example, the display unit can adjust the background color of the virtual calendar to reduce the visual burden. The display unit can also adjust the contrast. For example, the display unit can adjust the contrast of the virtual calendar to reduce the visual burden. By reducing the visual burden, the user can check their schedule more comfortably. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can provide visual assistance using an AI model that automatically adjusts the display settings to reduce the user's visual burden.

[0096] The display unit can estimate the user's emotions and adjust the display order of the virtual calendar based on the estimated emotions. For example, if the user is relaxed, the display unit will prioritize displaying detailed appointments. For example, the display unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The display unit can also prioritize displaying important appointments if the user is stressed. For example, the display unit may record the user's voice and estimate their emotions using voice analysis technology. The display unit can also prioritize displaying appointments that can be checked quickly if the user is busy. For example, the display unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the display order to be adjusted according to the user's emotions, prioritizing the display of important appointments. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0097] The display unit can select the optimal display method when displaying a virtual calendar, taking into account the user's device information. For example, if the user is using smart glasses, the display unit can provide a display method that is highly visible. For example, the display unit can provide a display method optimized for the smart glasses display. Also, if the user is using a smartphone, the display unit can provide a display method that is adapted to the screen size. For example, the display unit can provide a display method optimized for the smartphone screen size. Also, if the user is using a tablet, the display unit can provide a display method optimized for a larger screen. For example, the display unit can provide a display method optimized for the tablet screen size. In this way, the optimal display method can be provided by taking device information into consideration. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0098] The display unit can analyze the user's social media activity and adjust the displayed content when displaying a virtual calendar. For example, the display unit may prioritize displaying events the user plans to attend on social media. For example, the display unit may display relevant events based on the user's social media activity. The display unit can also display relevant events based on the user's interests on social media. For example, the display unit may display relevant events based on the user's interests on social media. The display unit can also adjust the displayed content by referring to the schedules of the user's friends on social media. For example, the display unit may display relevant events based on the schedules of the user's friends on social media. In this way, by analyzing social media activity, highly relevant information can be displayed. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input data on the user's social media activity into a generating AI and have the generating AI perform the adjustment of the displayed content.

[0099] The visual assistance unit can estimate the user's emotions and adjust the method of visual assistance based on the estimated emotions. For example, if the user is relaxed, the visual assistance unit can provide detailed visual assistance. For example, the visual assistance unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The visual assistance unit can also provide concise visual assistance if the user is stressed. For example, the visual assistance unit can record the user's voice and estimate the emotions using voice analysis technology. The visual assistance unit can also provide visual assistance that can be checked in a short time if the user is busy. For example, the visual assistance unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions using an emotion estimation algorithm. This allows for more appropriate visual assistance to be provided by adjusting the method of visual assistance 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-described processing in the visual assistance unit may be performed using AI, for example, or without AI. For example, the visual assistance unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0100] The visual assistance unit can select the optimal assistance method by referring to the user's past visual assistance history when providing visual assistance. For example, the visual assistance unit may prioritize providing visual assistance methods that the user has used in the past. For example, the visual assistance unit may select the optimal assistance method based on the user's past visual assistance history. The visual assistance unit can also select the optimal assistance method from the user's past visual assistance history. For example, the visual assistance unit may provide an assistance method that is less visually burdensome based on the user's past visual assistance history. The visual assistance unit may also analyze the user's past visual assistance history and provide the optimal assistance method. For example, the visual assistance unit may provide the optimal assistance method based on the user's past visual assistance history. This allows the system to select the optimal assistance method by referring to the user's past visual assistance history. Some or all of the above processing in the visual assistance unit may be performed using AI, for example, or without AI. For example, the visual assistance unit may input the user's past visual assistance history into a generating AI and have the generating AI select the optimal assistance method.

[0101] The visual assistance unit can estimate the user's emotions and prioritize visual assistance based on those emotions. For example, if the user is relaxed, the visual assistance unit will prioritize detailed visual assistance. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The visual assistance unit can also prioritize important visual assistance if the user is stressed. For example, it might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is busy, the visual assistance unit can prioritize visual assistance that can be viewed quickly. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the system to prioritize important assistance by determining the priority of visual assistance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the visual assistance unit may be performed using AI, for example, or without AI. For example, the visual assistance unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0102] The visual assistance unit can select the optimal assistance method by considering the user's device information when providing visual assistance. For example, if the user is using smart glasses, the visual assistance unit can provide an assistance method that is highly visible. For example, the visual assistance unit can provide an assistance method optimized for the smart glasses' display. Also, if the user is using a smartphone, the visual assistance unit can provide an assistance method that is adapted to the screen size. For example, the visual assistance unit can provide an assistance method optimized for the smartphone's screen size. Also, if the user is using a tablet, the visual assistance unit can provide an assistance method optimized for a larger screen. For example, the visual assistance unit can provide an assistance method optimized for the tablet's screen size. This allows for the provision of optimal visual assistance by considering device information. Some or all of the above processing in the visual assistance unit may be performed using AI, for example, or without AI. For example, the visual assistance unit can input the user's device information into a generating AI and have the generating AI select the optimal assistance method.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The activity plan prediction system can acquire user health data and adjust activity plans based on their health status. For example, it can acquire the user's heart rate and sleep data and generate a plan that prioritizes rest if the user is fatigued. It can also add activity plans to encourage exercise if the user is not getting enough exercise. Furthermore, it can acquire the user's dietary data and suggest meal plans that take nutritional balance into consideration. In this way, it can support health management by providing activity plans tailored to the user's health status.

[0105] The activity plan prediction system can estimate a user's emotions and adjust their activity plan based on those emotions. For example, if a user is feeling stressed, it can generate a plan that prioritizes relaxing activities. If a user is excited, it can suggest a plan that avoids tasks requiring concentration. Furthermore, if a user is sad, it can add activities that will help lift their spirits. In this way, by providing activity plans that are tailored to the user's emotions, it can support their mental health.

[0106] The activity plan prediction system can analyze a user's past behavioral data and optimize their activity plan based on their behavioral patterns. For example, it can generate a plan that prioritizes activities the user has frequently performed in the past. It can also suggest a plan that eliminates activities the user has avoided in the past. Furthermore, it can set activity plans for the optimal time slots based on the user's past behavioral data. In this way, by providing activity plans based on the user's behavioral patterns, it can support efficient schedule management.

[0107] The activity plan prediction system can acquire a user's geographical location information and adjust their activity plan based on that location. For example, if a user is in a specific location, it can generate a plan that prioritizes events and activities related to that location. If a user is traveling, it can also suggest tourist attractions and events at their destination. Furthermore, it can create an activity plan that takes commute time into account based on the user's commute route. This allows for improved travel efficiency by providing activity plans based on the user's location information.

[0108] The activity plan prediction system analyzes a user's social media activity and can adjust their schedule based on events and friends' plans on social media. For example, it can generate a schedule that prioritizes events the user plans to attend on social media. It can also suggest events that the user's friends will be attending. Furthermore, it can add relevant activities based on the user's interests on social media. In this way, it can support social activities by providing an activity plan based on the user's social media activity.

[0109] The activity plan prediction system can estimate a user's emotions and prioritize activities based on those emotions. For example, if a user is relaxed, it can prioritize generating detailed activity plans. If a user is stressed, it can prioritize generating important activity plans. Furthermore, if a user is busy, it can prioritize generating activity plans that can be reviewed in a short amount of time. By providing activity plans that are prioritized according to the user's emotions, it can help prevent users from missing important appointments.

[0110] The activity plan prediction system can acquire user device information and provide an activity plan optimized for that device. For example, if a user is using smart glasses, it can provide a display method that is easy to see. If a user is using a smartphone, it can display an activity plan that is adapted to the screen size. Furthermore, if a user is using a tablet, it can provide an activity plan optimized for the larger screen. By providing an activity plan based on the user's device information, the system can reduce visual strain.

[0111] The activity schedule prediction system can estimate the user's emotions and adjust how the activity schedule is displayed based on those emotions. For example, if the user is relaxed, a detailed virtual calendar can be displayed. If the user is stressed, a simplified virtual calendar can be displayed. Furthermore, if the user is busy, a virtual calendar that can be checked in a short time can be displayed. In this way, by providing a display method that suits the user's emotions, the visual burden can be reduced.

[0112] The activity plan prediction system can analyze a user's past activity history and propose the optimal way to generate an activity plan. For example, it can prioritize providing functions that the user has frequently used in the past. It can also propose a plan that excludes functions the user has avoided in the past. Furthermore, it can set activity plans at the optimal time based on the user's past activity history. In this way, by providing activity plans based on the user's activity history, it can support efficient schedule management.

[0113] The behavioral plan prediction system can estimate the user's emotions and adjust the method of generating the behavioral plan based on those emotions. For example, if the user is relaxed, it can generate a detailed behavioral plan. If the user is stressed, it can generate a concise behavioral plan. Furthermore, if the user is busy, it can generate a behavioral plan that can be understood in a short amount of time. In this way, by providing a method of generating behavioral plans that responds to the user's emotions, it can provide more appropriate behavioral plans.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The reading unit reads calendar information. This information includes digital calendars, paper calendars, and calendar information from specific applications. The reading unit uses OCR technology to digitize paper calendar information. It can also obtain digital calendar information via an API. Furthermore, it can read paper calendar information using a smartphone camera. Step 2: The analysis unit analyzes the calendar information read by the reading unit. The analysis is performed using methods such as text analysis, pattern recognition, and machine learning algorithms. For example, text analysis techniques are used to analyze the content of the calendar information and extract important events and tasks. Pattern recognition techniques are used to predict the next month's schedule based on past behavioral patterns. Machine learning algorithms learn from large amounts of calendar information to provide more accurate analysis results. Step 3: The generation unit generates the next month's activity schedule based on the information analyzed by the analysis unit. The generation is performed using a generation AI. For example, the generation AI is used to automatically generate the next month's activity schedule, and the schedule is generated based on past activity patterns and the user's current living situation. Step 4: The display unit displays the activity schedule generated by the generation unit via smart glasses. The display is done as a virtual calendar. For example, the activity schedule is projected as a virtual calendar via smart glasses, and the font size, screen brightness, and contrast are adjusted to reduce visual strain.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the reading unit, analysis unit, generation unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reading unit can read paper calendar information using the camera 42 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and similarly, the generation unit is also implemented by the specific processing unit 290 of the data processing unit 12. The display unit is implemented by the control unit 46A of the smart device 14 and projects the schedule as a virtual calendar. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the reading unit, analysis unit, generation unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reading unit can read paper calendar information using the camera 42 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and similarly, the generation unit is also implemented by the specific processing unit 290 of the data processing unit 12. The display unit is implemented by the control unit 46A of the smart glasses 214 and projects the schedule as a virtual calendar. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reading unit, analysis unit, generation unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reading unit can read paper calendar information using the camera 42 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and similarly, the generation unit is also implemented by the specific processing unit 290 of the data processing unit 12. The display unit is implemented by the control unit 46A of the headset terminal 314 and projects the schedule as a virtual calendar. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the reading unit, analysis unit, generation unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reading unit can read paper calendar information using the camera 42 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and similarly, the generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The display unit is implemented by the control unit 46A of the robot 414 and projects the activity schedule as a virtual calendar. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A reading unit that reads calendar information, An analysis unit analyzes the information read by the reading unit, A generation unit generates an action plan for the following month based on the information analyzed by the analysis unit, A display unit that displays the action schedule generated by the generation unit via smart glasses, Equipped with A system characterized by the following features. (Note 2) The reading unit is Read calendar information with your smartphone The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on past behavioral patterns, predict and create a schedule for the following month. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is Project your schedule as a virtual calendar via smart glasses. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is Equipped with a visual aid to reduce visual strain. The system described in Appendix 1, characterized by the features described herein. (Note 6) The reading unit is It estimates the user's emotions and adjusts the timing of reading calendar information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The reading unit is Analyze the user's past calendar usage history and select the optimal reading method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The reading unit is When reading calendar information, 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 reading unit is It estimates the user's emotions and determines the priority of calendar information to read based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The reading unit is When reading calendar information, the system prioritizes reading the most relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The reading unit is When reading calendar information, the system analyzes the user's social media activity and extracts relevant information. 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 calendar information. 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 category of calendar information. 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 analysis priority is determined based on the timing of calendar information input. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the calendar information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is We estimate the user's emotions and adjust the method of generating action plans based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating action plans, the system analyzes the user's past behavioral patterns to select the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating an action plan, the generation method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and determines the priority of planned actions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating an activity plan, the optimal generation method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating action plans, we analyze the user's social media activity and suggest methods for generating them. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is It estimates the user's emotions and adjusts how the virtual calendar is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is When displaying the virtual calendar, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is Customize the display method to reduce the user's visual burden when viewing the virtual calendar. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is It estimates the user's emotions and adjusts the display order of the virtual calendar based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying the virtual calendar, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying the virtual calendar, the system analyzes the user's social media activity and adjusts the displayed content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned visual aid unit is It estimates the user's emotions and adjusts the visual assistance methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned visual aid unit is When providing visual assistance, the system selects the most suitable assistance method by referring to the user's past visual assistance history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned visual aid unit is It estimates the user's emotions and prioritizes visual aids based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned visual aid unit is When providing visual assistance, the optimal assistance method is selected by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0188] 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 reading unit that reads calendar information, An analysis unit analyzes the information read by the reading unit, A generation unit generates an action plan for the following month based on the information analyzed by the analysis unit, A display unit that displays the action schedule generated by the generation unit via smart glasses, Equipped with A system characterized by the following features.

2. The reading unit is Read calendar information with your smartphone The system according to feature 1.

3. The aforementioned analysis unit, Based on past behavioral patterns, predict and create a schedule for the following month. The system according to feature 1.

4. The aforementioned display unit is Project your schedule as a virtual calendar via smart glasses. The system according to feature 1.

5. The aforementioned display unit is Equipped with a visual aid to reduce visual strain. The system according to feature 1.

6. The reading unit is It estimates the user's emotions and adjusts the timing of reading calendar information based on the estimated emotions. The system according to feature 1.

7. The reading unit is Analyze the user's past calendar usage history and select the optimal reading method. The system according to feature 1.

8. The reading unit is When reading calendar information, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The reading unit is It estimates the user's emotions and determines the priority of calendar information to read based on the estimated user emotions. The system according to feature 1.

10. The reading unit is When reading calendar information, the system prioritizes reading the most relevant information by considering the user's geographical location. The system according to feature 1.

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