System

Abstract

To remind a user about items and things necessary for the schedule of the user and to warn by diagnosing an anonymity degree of social media post.SOLUTION: A system includes a collection part, an analysis part, a remind part, a diagnosis part, and a warning part. The collection part collects information about a schedule or a message application, mail in a smart phone. The analysis part analyzes the information collected by the collection part and specifies items or things necessary for the schedule of a user. The remind part reminds the user based on the necessary items or things specified by the analysis part. The diagnosis part analyses a post content to social media and diagnoses an anonymity degree of personal information. The warning part warns in the case where the anonymity degree is determined to be low by the diagnosis part.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately remind users of necessary items and things related to their schedules, nor do they adequately diagnose and warn about the anonymity of SNS posts, so there is room for improvement.

[0005] The system according to the embodiment aims to remind the user of necessary items and things related to their schedule, and to diagnose the degree of anonymity of SNS posts and provide a warning. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a reminder unit, a diagnosis unit, and a warning unit. The collection unit collects information from schedules, message apps, and emails in a smartphone. The analysis unit analyzes the information collected by the collection unit and identifies what is necessary or what is required for the user's schedule. The reminder unit reminds the user based on the what is necessary or what is identified by the analysis unit. The diagnosis unit analyzes the content of posts to SNS and diagnoses the degree of anonymity of personal information. The warning unit issues a warning when the diagnosis unit determines that the anonymity is low. Effect of the Invention

[0007] The system according to the embodiment can remind users of things they need to do in their schedule, and can diagnose the degree of anonymity of SNS posts and issue warnings. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A smartphone app according to an embodiment of the present invention is a system that notifies a user of necessary items and things for his / her schedule in advance. This system collects and analyzes information such as schedules, message apps, and emails in the smartphone, and identifies necessary items and things for the user's schedule and sends a reminder. In addition, the system analyzes the contents of posts to SNS, diagnoses the degree of anonymity of personal information, and issues a warning if the anonymity is low. For example, if a user enters "class reunion" in the calendar, the app obtains the information and sends a reminder "Please make a reservation at a beauty salon" two weeks before the reunion. Furthermore, a reminder "Please prepare your attire" is sent one week before the reunion, and a reminder "Please prepare cash" is sent the day before. This allows the user to make the necessary preparations without forgetting. In addition, when posting to SNS, the system diagnoses the degree of anonymity of personal information, and if the anonymity is low, displays a warning saying "This post contains personal information. Do you want to continue posting?" This allows the user to prevent personal information from being leaked. This allows smartphone apps to proactively notify users of necessary items and things related to their schedules, and when posting to social media, diagnose the anonymity of personal information and issue a warning, thereby improving user convenience and safety.

[0029] The smartphone application according to the embodiment includes a collection unit, an analysis unit, a reminder unit, a diagnosis unit, and a warning unit. The collection unit collects information such as schedules, message apps, and emails in the smartphone. For example, the collection unit can collect schedule information entered in a calendar. The collection unit can also detect messages containing a specific keyword in a message app or email. For example, the collection unit acquires information entered as "class reunion" in a calendar, and detects messages containing the keyword "class reunion" in a message app or email. The analysis unit analyzes the information collected by the collection unit and identifies what is necessary for the user's schedule. For example, the analysis unit identifies that if there is a class reunion planned, clothing, cash, a reservation for a beauty salon, etc. are necessary. This analysis is performed using a generation AI. The generation AI analyzes the collected information and identifies what is necessary using, for example, a text generation AI (for example, LLM). The reminder unit reminds the user based on what is necessary and what is identified by the analysis unit. For example, the reminder unit sends a reminder "Please make a reservation at a hair salon" two weeks before the class reunion, and a reminder "Please prepare your clothes" one week before the class reunion. Also, the reminder unit sends a reminder "Please prepare cash" the day before the class reunion. The diagnosis unit analyzes the content of the post to the SNS and diagnoses the degree of anonymity of the personal information. For example, when a user tries to post "I'm going to a class reunion" on the SNS, the diagnosis unit analyzes the content of the post, and if it determines that the anonymity is low, displays a warning saying "This post contains personal information. Do you want to continue posting?". The warning unit issues a warning when the diagnosis unit determines that the anonymity is low. As a result, the smartphone app according to the embodiment can proactively notify the user of necessary things and events for the user's schedule, and diagnose the anonymity of personal information and issue a warning when posting to the SNS, thereby improving the convenience and safety of the user.

[0030] The collection unit collects information such as schedules, message apps, and emails in the smartphone. For example, the collection unit can collect schedule information entered in a calendar. Specifically, the collection unit periodically acquires schedule information entered by the user using the API of the calendar app and stores it in a central database. The collection unit can also detect messages containing specific keywords in message apps and emails. For example, the notification content of the message app is analyzed and messages containing specific keywords are filtered. For this, a method is used in which the content of the message is analyzed using natural language processing technology and a highly relevant message is identified by considering the frequency of appearance of the keyword and the context. For example, the collection unit acquires information entered as "class reunion" in the calendar and detects messages containing the keyword "class reunion" in the message app or email. This allows the collection unit to comprehensively grasp the user's schedule and message content and provide information necessary for subsequent analysis and reminders. Furthermore, the collection unit has a mechanism for encrypting and safely storing collected data in order to protect the user's privacy. This allows the collection unit to efficiently collect necessary information while protecting the user's personal information.

[0031] The analysis unit analyzes the information collected by the collection unit and identifies the necessary items and things for the user's schedule. For example, the analysis unit identifies that if a class reunion is planned, clothing, cash, a reservation for a beauty salon, etc. are necessary. This analysis is performed using a generation AI. The generation AI analyzes the collected information and identifies the necessary items and things using, for example, a text generation AI (e.g., LLM). Specifically, the generation AI receives the user's schedule information and message content as input and analyzes the text using natural language processing technology. For example, if a class reunion is planned, the generation AI lists the preparations necessary for the class reunion based on data about similar events in the past and general knowledge. This includes selecting clothing, preparing cash, making a reservation for a beauty salon, etc. The generation AI can also make more personalized suggestions by taking into account the user's past behavioral patterns and preferences. For example, if a user has previously purchased a particular brand of clothing, it can suggest new product information for that brand. This allows the analysis unit to accurately identify the necessary items and things for the user's schedule and provide them to the reminder unit. Furthermore, the analysis unit can continuously learn from the collected data and improve the accuracy of the analysis, thereby enabling the analysis unit to realize flexible and highly accurate analysis according to the needs of the user.

[0032] The reminder unit reminds the user based on the necessary things and things identified by the analysis unit. For example, the reminder unit sends a reminder "Please make a reservation at a beauty salon" two weeks before the reunion, and a reminder "Please prepare your clothes" one week before the reunion. Also, the reminder unit sends a reminder "Please prepare cash" the day before the reunion. The reminder unit sends reminders at optimal timing, taking into account the user's schedule and past behavioral patterns. For example, if the user has tended to forget to make a reservation at a beauty salon in the past, the frequency of reminders can be increased. In addition, the reminder unit can customize the method of reminders according to the user's preferences. For example, the user can select the most effective way to receive the reminder, such as a notification sound, vibration, or pop-up message. Furthermore, the reminder unit can collect user feedback and continuously improve the content and timing of the reminder. For example, the reminder unit tracks the user's behavior after receiving the reminder and evaluates the effectiveness of the reminder. As a result, the reminder unit can provide flexible and effective reminders according to the user's needs, improving the user's convenience.

[0033] The diagnostic unit analyzes the contents of posts on the SNS and diagnoses the degree of anonymity of the personal information. For example, when a user tries to post "I'm going to a class reunion" on the SNS, the diagnostic unit analyzes the contents of the post, and if it determines that the anonymity is low, it displays a warning saying "This post contains personal information. Do you want to continue posting?" The diagnostic unit uses natural language processing technology to analyze the contents of the post and judges whether it contains personal information. Specifically, it analyzes the text of the post and detects whether it contains personal information such as a name, address, phone number, and email address. In addition, it displays a warning when the personal information may be guessed by considering the context of the post. For example, if the post "I'm going to a class reunion" is related to a specific date and time or place, the information is a clue to identify the individual's behavior, so a warning is displayed. Furthermore, the diagnostic unit can refer to the user's past posting history and evaluate the anonymity. For example, if a similar post was made in the past, it compares the post content with the post content and evaluates the anonymity. As a result, the diagnostic unit can perform a highly accurate anonymity diagnosis for the user's SNS posts and prevent the leakage of personal information.

[0034] The warning unit issues a warning when the diagnosis unit determines that the anonymity is low. Specifically, when the content that the user is about to post on the SNS contains personal information, the warning unit displays a warning message saying, "This post contains personal information. Do you want to continue posting?" The warning unit allows the user to check the warning message and select whether to continue posting. For example, if the user ignores the warning message and continues posting, the post content may be analyzed again and a more detailed warning may be displayed. In addition, the warning unit may track the user's behavior after receiving the warning message and evaluate the effectiveness of the warning. For example, if the user corrects the post content after receiving the warning, the correction content is analyzed and the effectiveness of the warning is evaluated. In this way, the warning unit can strengthen the protection of the user's personal information and support safe communication on the SNS. In addition, the warning unit may collect user feedback and continuously improve the content and display method of the warning message. For example, the user's reaction and behavior when receiving the warning message are analyzed, and data is collected to provide a more effective warning message. In this way, the warning unit can thoroughly protect the user's personal information and improve safety on the SNS.

[0035] The collection unit can collect schedule information input to the calendar. The schedule information input to the calendar includes, for example, events, tasks, reminders, etc., but is not limited to such examples. The collection unit can acquire, for example, information that "class reunion" is input to the calendar. In addition, the collection unit can periodically collect schedule information input to the calendar and provide it to the analysis unit. In this way, by collecting schedule information input to the calendar, the user's schedule can be accurately understood. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input schedule information input to the calendar to AI, and the AI ​​can analyze and collect the information.

[0036] The collection unit can detect messages containing a specific keyword in a message app or email. The specific keyword includes, but is not limited to, for example, a keyword related to an important schedule or a frequently occurring keyword. The collection unit can detect, for example, a message containing the keyword "class reunion" in a message app or email. The collection unit can also periodically detect messages containing a specific keyword and provide them to the analysis unit. This makes it possible to efficiently collect information related to the user's schedule by detecting messages containing a specific keyword in a message app or email. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input messages containing a specific keyword in a message app or email to AI, and the AI ​​can analyze and detect the information.

[0037] The analysis unit can analyze the collected information and identify what is necessary or what is needed for the user's schedule. The necessary or what includes, but is not limited to, items to bring, preparations, action items, etc. For example, the analysis unit can identify that if a class reunion is planned, clothing, cash, a reservation for a beauty salon, etc. are necessary. This analysis is performed using a generation AI. The generation AI can analyze the collected information and identify what is necessary or what is needed, for example, using a text generation AI (for example, LLM). For example, the generation AI analyzes the information "class reunion" entered in the calendar and identifies what is necessary or what is needed for the class reunion. The generation AI can also analyze messages containing the keyword "class reunion" in a messaging app or email and identify what is necessary or what is needed for the class reunion. In this way, the collected information can be analyzed to accurately identify what is necessary or what is needed for the user's schedule.

[0038] The reminder unit can send a reminder, such as "Please make a reservation at a beauty salon" two weeks before the reunion. Two weeks before the reunion includes, but is not limited to, two weeks before an event or a specific date. The reminder unit can send a reminder, such as "Please make a reservation at a beauty salon" two weeks before the reunion. In addition, the reminder unit can send a reminder two weeks before the reunion so that the user does not forget to make necessary preparations. Some or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI or may be performed without using AI. For example, the reminder unit can input the timing for sending the reminder two weeks before the reunion to the AI, and the AI ​​can send the reminder.

[0039] The reminder unit can send a reminder "prepare your outfit" one week before the reunion. One week before the reunion includes, but is not limited to, one week before the event or a specific date. The reminder unit can send a reminder "prepare your outfit" one week before the reunion. In addition, the reminder unit can send a reminder one week before the reunion so that the user does not forget to make the necessary preparations. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reminder unit can input the timing for sending the reminder one week before the reunion to the AI, and the AI ​​can send the reminder.

[0040] The reminder unit can send a reminder "Please prepare cash" on the day before the reunion. The day before the reunion includes, but is not limited to, the day before an event or a specific date. The reminder unit can send a reminder "Please prepare cash" on the day before the reunion. In addition, the reminder unit can send a reminder on the day before the reunion so that the user does not forget to make the necessary preparations. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reminder unit can input the timing for sending the reminder on the day before the reunion to the AI, and the AI ​​can send the reminder.

[0041] The diagnostic unit can analyze the contents of posts on the SNS and diagnose the degree of anonymity of the personal information. The degree of anonymity of the personal information includes, for example, the possibility of identifying the personal information, the use of anonymization technology, and the like, but is not limited to such examples. For example, when a user tries to post "I'm going to a class reunion" on the SNS, the diagnostic unit can analyze the contents of the post and, if it determines that the anonymity is low, display a warning saying "This post contains personal information. Do you want to continue posting?" This analysis is performed using a generation AI. The generation AI can analyze the contents of the post and diagnose the degree of anonymity of the personal information, for example, using a text generation AI (for example, LLM). For example, the generation AI can analyze the text posted on the SNS and determine whether it contains personal information. In this way, the leak of the user's personal information can be prevented by analyzing the contents of posts on the SNS and diagnosing the degree of anonymity of the personal information.

[0042] The warning unit can display a warning saying "This post contains personal information. Do you want to continue posting?" when the anonymity is determined to be low. Low anonymity includes, for example, personal information that is easily identified, insufficient anonymization, and the like, but is not limited to such examples. The warning unit can display a warning saying "This post contains personal information. Do you want to continue posting?" when the diagnosis unit determines that the anonymity is low. In this way, by displaying a warning for posts with low anonymity, it is possible to prevent leakage of personal information of users. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the content of the post determined to have low anonymity by the diagnosis unit to AI, and the AI ​​can display a warning.

[0043] The collection unit can analyze the user's past schedule history and select the optimal collection method. The optimal collection method includes, for example, collection efficiency, collection accuracy, etc., but is not limited to such examples. The collection unit can, for example, preferentially collect information from a schedule app that the user has frequently used in the past. In addition, the collection unit can preferentially collect information related to a specific event from the user's past schedule history. Furthermore, the collection unit can analyze the user's past schedule history and collect information at the optimal timing. As a result, the optimal information collection method can be selected by analyzing the user's past schedule history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past schedule history to AI, and the AI ​​can select the optimal collection method.

[0044] The collection unit can perform filtering based on the user's current activity status and field of interest when collecting information. The current activity status includes, for example, at work, during a break, moving, etc., but is not limited to such examples. The field of interest includes, for example, hobbies, fields of expertise, topics of interest, etc., but is not limited to such examples. For example, when the user is at work, the collection unit can preferentially collect information related to work. Furthermore, when the user is on vacation, the collection unit can preferentially collect information related to travel and leisure. Furthermore, the collection unit can filter and collect related information based on the user's field of interest. As a result, highly relevant information can be efficiently collected by filtering information based on the user's current activity status and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's current activity status and field of interest into AI, and the AI ​​can filter the information.

[0045] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user when collecting information. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, when the user is in a specific area, the collection unit can preferentially collect information related to the area. In addition, when the user is traveling, the collection unit can preferentially collect information related to the travel destination. Furthermore, the collection unit can filter and collect highly relevant information based on the user's current location. Thereby, by considering the geographical location information of the user, highly relevant information can be efficiently collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can filter the information.

[0046] The collection unit can analyze the user's social media activity at the time of collecting information and collect related information. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, and the like, but is not limited to such examples. The collection unit can collect related information, for example, based on information shared by the user on social media. The collection unit can also collect information related to events and topics of interest from the user's social media activity. Furthermore, the collection unit can analyze the content of the user's social media post and filter and collect related information. In this way, highly relevant information can be efficiently collected by analyzing the user's social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI, and the AI ​​can analyze and collect information.

[0047] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. The importance of the information includes, for example, the impact level, the relevance level, the urgency level, and the like, but is not limited to such examples. For example, the analysis unit can perform a detailed analysis on important information. In addition, the analysis unit can perform a simplified analysis on information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. As a result, by adjusting the level of detail of the analysis based on the importance of the information, it is possible to perform a detailed analysis on important information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the information to an AI, and the AI ​​can adjust the level of detail of the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The categories of information include, for example, business information, personal information, technical information, and the like, but are not limited to such examples. The analysis unit can apply a schedule analysis algorithm to information related to a schedule, for example. The analysis unit can also apply a text analysis algorithm to information of a message app. Furthermore, the analysis unit can apply a natural language processing algorithm to information of an email. In this way, by applying different analysis algorithms depending on the category of information, optimal analysis can be performed for each category. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of information to AI, and the AI ​​can apply an appropriate analysis algorithm.

[0049] The analysis unit can determine the priority of the analysis based on the time of collection of the information during the analysis. The time of collection of the information includes, for example, the latest information, past information, and the like, but is not limited to such examples. For example, the analysis unit can prioritize the analysis of the latest information. Also, the analysis unit can analyze old information later. Furthermore, the analysis unit can dynamically adjust the priority of the analysis according to the time of collection of the information. As a result, the latest information can be analyzed preferentially by determining the priority of the analysis based on the time of collection of the information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the time of collection of the information to the AI, and the AI ​​can determine the priority of the analysis.

[0050] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The relevance of information includes, for example, a common theme, related topics, and the like, but is not limited to such examples. The analysis unit can, for example, prioritize analysis of information related to a schedule. In addition, the analysis unit can analyze less relevant information later. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of information. As a result, by adjusting the order of analysis based on the relevance of information, highly relevant information can be analyzed preferentially. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of information to AI, and the AI ​​can adjust the order of analysis.

[0051] The reminding unit can adjust the level of detail of the reminder based on the importance of the schedule at the time of reminding. The importance of the schedule includes, for example, the impact, urgency, and relevance, but is not limited to such examples. For example, the reminding unit can provide a detailed reminder for an important schedule. Also, the reminding unit can provide a simplified reminder for a schedule with a low importance. Furthermore, the reminding unit can dynamically adjust the level of detail of the reminder according to the importance of the schedule. As a result, by adjusting the level of detail of the reminder based on the importance of the schedule, a detailed reminder can be given for an important schedule. A part or all of the above-mentioned processing in the reminding unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reminding unit can input the importance of the schedule to an AI, and the AI ​​can adjust the level of detail of the reminder.

[0052] The reminding unit can apply different reminding algorithms depending on the category of the plan at the time of reminding. The categories of plans include, but are not limited to, business plans, personal plans, and event plans. For example, the reminding unit can apply a business-oriented reminding algorithm to a work plan. Furthermore, the reminding unit can apply a personal-oriented reminding algorithm to a private plan. Furthermore, the reminding unit can apply an event-oriented reminding algorithm to an event plan. In this way, by applying different reminding algorithms depending on the category of the plan, optimal reminding can be performed for each category. Part or all of the above-mentioned processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input the category of the plan to AI, and the AI ​​can apply an appropriate reminding algorithm.

[0053] The reminding unit can determine the priority of the reminder based on the submission time of the schedule at the time of reminding. The submission time of the schedule includes, for example, the most recent schedule, past schedule, etc., but is not limited to such examples. The reminding unit can, for example, give priority to the most recent schedule. Also, the reminding unit can postpone the reminder of a schedule whose submission time is far away. Furthermore, the reminding unit can dynamically adjust the priority of the reminder according to the submission time of the schedule. As a result, by determining the priority of the reminder based on the submission time of the schedule, the most recent schedule can be given priority. Part or all of the above-mentioned processing in the reminding unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminding unit can input the submission time of the schedule to AI, and the AI ​​can determine the priority of the reminder.

[0054] The reminding unit can adjust the order of reminding based on the relevance of the schedule at the time of reminding. The relevance of the schedule includes, for example, a common theme, related topics, and the like, but is not limited to such examples. For example, the reminding unit can give priority to reminding of important schedules. In addition, the reminding unit can postpone reminding of less relevant schedules. Furthermore, the reminding unit can dynamically adjust the order of reminding according to the relevance of the schedule. As a result, by adjusting the order of reminding based on the relevance of the schedule, it is possible to give priority to reminding of highly relevant schedules. Part or all of the above-mentioned processing in the reminding unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminding unit can input the relevance of the schedule to AI, and the AI ​​can adjust the order of reminding.

[0055] The diagnostic unit can adjust the level of detail of the diagnosis based on the importance of the posted content at the time of diagnosis. The importance of the posted content includes, for example, the impact level, the relevance level, the urgency level, and the like, but is not limited to such examples. For example, the diagnostic unit can perform a detailed diagnosis on important posted content. In addition, the diagnostic unit can perform a simplified diagnosis on posted content with low importance. Furthermore, the diagnostic unit can dynamically adjust the level of detail of the diagnosis according to the importance of the posted content. As a result, by adjusting the level of detail of the diagnosis based on the importance of the posted content, a detailed diagnosis can be performed on important posted content. A part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnostic unit can input the importance of the posted content to an AI, and the AI ​​can adjust the level of detail of the diagnosis.

[0056] The diagnostic unit can apply different diagnostic algorithms depending on the category of the posted content at the time of diagnosis. The categories of the posted content include, for example, business posts, personal posts, technical posts, etc., but are not limited to such examples. For example, the diagnostic unit can apply a diagnostic algorithm specialized for personal information protection to private posted content. Furthermore, the diagnostic unit can apply a diagnostic algorithm specialized for business information protection to business-related posted content. Furthermore, the diagnostic unit can apply a diagnostic algorithm specialized for public information protection to public posted content. In this way, by applying different diagnostic algorithms depending on the category of the posted content, an optimal diagnosis can be made for each category. Part or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnostic unit can input the category of the posted content to AI, and the AI ​​can apply an appropriate diagnostic algorithm.

[0057] The diagnosis unit can determine the priority of diagnosis based on the submission time of the posted content at the time of diagnosis. The submission time of the posted content includes, for example, the most recent post, past posts, etc., but is not limited to such examples. The diagnosis unit can, for example, give priority to diagnosing the most recent posted content. In addition, the diagnosis unit can diagnose the posted content that is submitted a long time ago later. Furthermore, the diagnosis unit can dynamically adjust the priority of diagnosis according to the submission time of the posted content. As a result, the most recent posted content can be diagnosed preferentially by determining the priority of diagnosis based on the submission time of the posted content. A part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input the submission time of the posted content to AI, and the AI ​​can determine the priority of diagnosis.

[0058] The diagnostic unit can adjust the order of diagnosis based on the relevance of the posted contents at the time of diagnosis. The relevance of the posted contents includes, for example, a common theme, related topics, etc., but is not limited to such examples. For example, the diagnostic unit can preferentially diagnose important posted contents. In addition, the diagnostic unit can diagnose less relevant posted contents later. Furthermore, the diagnostic unit can dynamically adjust the order of diagnosis according to the relevance of the posted contents. As a result, by adjusting the order of diagnosis based on the relevance of the posted contents, it is possible to preferentially diagnose highly relevant posted contents. Part or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnostic unit can input the relevance of the posted contents to AI, and the AI ​​can adjust the order of diagnosis.

[0059] The warning unit can adjust the level of detail of the warning based on the importance of the posted content at the time of warning. The importance of the posted content includes, for example, impact, relevance, urgency, etc., but is not limited to such examples. The warning unit can provide a detailed warning, for example, for important posted content. Furthermore, the warning unit can provide a simplified warning for posted content with low importance. Furthermore, the warning unit can dynamically adjust the level of detail of the warning according to the importance of the posted content. As a result, a detailed warning can be issued for important posted content by adjusting the level of detail of the warning based on the importance of the posted content. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the importance of the posted content to AI, and the AI ​​can adjust the level of detail of the warning.

[0060] The warning unit can apply different warning algorithms depending on the category of the posted content when issuing a warning. The categories of the posted content include, for example, business posts, personal posts, and technical posts, but are not limited to such examples. For example, the warning unit can apply a warning algorithm specialized for personal information protection to private posted content. Furthermore, the warning unit can apply a warning algorithm specialized for business information protection to business-related posted content. Furthermore, the warning unit can apply a warning algorithm specialized for public information protection to public posted content. In this way, by applying different warning algorithms depending on the category of the posted content, it is possible to issue an optimal warning for each category. Part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the category of the posted content to AI, and the AI ​​can apply an appropriate warning algorithm.

[0061] The warning unit can determine the priority of the warning based on the submission time of the posted content at the time of warning. The submission time of the posted content includes, for example, the most recent post, past posts, etc., but is not limited to such examples. The warning unit can, for example, give priority to the most recent posted content. Also, the warning unit can give a warning later to posted content that is submitted in the distant future. Furthermore, the warning unit can dynamically adjust the priority of the warning according to the submission time of the posted content. As a result, the most recent posted content can be given priority by determining the priority of the warning based on the submission time of the posted content. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the submission time of the posted content to AI, and the AI ​​can determine the priority of the warning.

[0062] The warning unit can adjust the order of warnings based on the relevance of the posted contents at the time of warning. The relevance of the posted contents includes, for example, a common theme, related topics, and the like, but is not limited to such examples. For example, the warning unit can give a priority to warning important posted contents. Also, the warning unit can give a priority to warning less relevant posted contents. Furthermore, the warning unit can dynamically adjust the order of warnings according to the relevance of the posted contents. As a result, by adjusting the order of warnings based on the relevance of the posted contents, it is possible to give a priority to warning highly relevant posted contents. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the relevance of the posted contents to AI, and the AI ​​can adjust the order of warnings.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The collection unit can learn the user's past behavioral patterns and make predictions about future plans. For example, it can collect behavioral data from when the user participated in a specific event in the past and predict the preparations required for the next similar event. The collection unit can also provide information related to future plans based on places the user frequently visited in the past and services used by the user. Furthermore, the collection unit can analyze the user's past behavioral patterns and build a feedback loop to improve prediction accuracy. This makes it possible to more efficiently prepare for future plans by utilizing the user's past behavioral patterns.

[0065] The analysis unit can analyze the user's health data and evaluate the impact on the schedule. For example, the analysis unit can analyze the user's sleep data and exercise data and evaluate the impact of the user's physical condition on the schedule. The analysis unit can also analyze the user's biological data, such as stress level and heart rate, and determine the need to prepare for the schedule. Furthermore, the analysis unit can perform a risk assessment for the schedule based on the user's health data and suggest necessary measures. This allows the user to make preparations for the schedule while taking into account the user's health condition.

[0066] The reminding unit can provide reminders related to plans based on the user's location information. For example, when the user is in a specific location, the reminding unit can provide reminders about plans related to that location. Also, when the user is traveling, the reminding unit can provide reminders about plans related to the destination. Furthermore, the reminding unit can suggest optimal means of transportation and routes for plans based on the user's location information. This makes it possible to provide more effective reminders by utilizing the user's location information.

[0067] The diagnostic unit can analyze the user's social network and evaluate the impact on the schedule. For example, the schedules of the user's friends and family can be analyzed and the impact on the user's schedule can be evaluated. The diagnostic unit can also analyze events and activities in the user's social network and evaluate the relevance to the user's schedule. Furthermore, the diagnostic unit can perform a risk assessment for the schedule based on the user's social network and suggest necessary measures. This allows preparations for the schedule to be made taking into account the user's social network.

[0068] The warning unit can analyze the user's past posting history and evaluate the risk of anonymity. For example, the warning unit can analyze the content posted by the user in the past and identify posts that have a high risk of anonymity. The warning unit can also evaluate the risk of future posts based on the user's past posting history and suggest necessary measures. Furthermore, the warning unit can analyze the user's past posting history and provide guidelines for reducing the risk of anonymity. In this way, the user's past posting history can be utilized to evaluate the risk of anonymity and take appropriate measures.

[0069] The process flow of the first embodiment will be briefly described below.

[0070] Step 1: The collection unit collects information from the smartphone, such as schedules, messaging apps, and emails. For example, it detects schedule information entered in a calendar, or messages containing specific keywords in messaging apps or emails. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies what is needed for the user's plans. For example, if a class reunion is planned, it identifies what is needed, such as clothing, cash, and a hair salon reservation. This analysis is performed using a generative AI. Step 3: The reminder unit reminds the user based on the needs and things identified by the analysis unit. For example, a reminder to "make a reservation at a hair salon" is sent two weeks before the reunion, a reminder to "prepare your outfit" is sent one week before the reunion, and a reminder to "prepare cash" is sent the day before the reunion. Step 4: The diagnostic unit analyzes the content of the post on the SNS and diagnoses the degree of anonymity of the personal information. For example, if a user tries to post on the SNS "I'm going to a class reunion," the content of the post is analyzed, and if it is determined that the anonymity is low, a warning message is displayed saying "This post contains personal information. Do you want to continue posting?" Step 5: The warning unit issues a warning when the diagnosis unit determines that the anonymity is low, thereby enabling the user to prevent the leakage of personal information.

[0071] (Example 2) A smartphone app according to an embodiment of the present invention is a system that notifies a user of necessary items and things for his / her schedule in advance. This system collects and analyzes information such as schedules, message apps, and emails in the smartphone, and identifies necessary items and things for the user's schedule and sends a reminder. In addition, the system analyzes the contents of posts to SNS, diagnoses the degree of anonymity of personal information, and issues a warning if the anonymity is low. For example, if a user enters "class reunion" in the calendar, the app obtains the information and sends a reminder "Please make a reservation at a beauty salon" two weeks before the reunion. Furthermore, a reminder "Please prepare your attire" is sent one week before the reunion, and a reminder "Please prepare cash" is sent the day before. This allows the user to make the necessary preparations without forgetting. In addition, when posting to SNS, the system diagnoses the degree of anonymity of personal information, and if the anonymity is low, displays a warning saying "This post contains personal information. Do you want to continue posting?" This allows the user to prevent personal information from being leaked. This allows smartphone apps to proactively notify users of necessary items and things related to their schedules, and when posting to social media, diagnose the anonymity of personal information and issue a warning, thereby improving user convenience and safety.

[0072] The smartphone application according to the embodiment includes a collection unit, an analysis unit, a reminder unit, a diagnosis unit, and a warning unit. The collection unit collects information such as schedules, message apps, and emails in the smartphone. For example, the collection unit can collect schedule information entered in a calendar. The collection unit can also detect messages containing a specific keyword in a message app or email. For example, the collection unit acquires information entered as "class reunion" in a calendar, and detects messages containing the keyword "class reunion" in a message app or email. The analysis unit analyzes the information collected by the collection unit and identifies what is necessary for the user's schedule. For example, the analysis unit identifies that if there is a class reunion planned, clothing, cash, a reservation for a beauty salon, etc. are necessary. This analysis is performed using a generation AI. The generation AI analyzes the collected information and identifies what is necessary using, for example, a text generation AI (for example, LLM). The reminder unit reminds the user based on what is necessary and what is identified by the analysis unit. For example, the reminder unit sends a reminder "Please make a reservation at a hair salon" two weeks before the class reunion, and a reminder "Please prepare your clothes" one week before the class reunion. Also, the reminder unit sends a reminder "Please prepare cash" the day before the class reunion. The diagnosis unit analyzes the content of the post to the SNS and diagnoses the degree of anonymity of the personal information. For example, when a user tries to post "I'm going to a class reunion" on the SNS, the diagnosis unit analyzes the content of the post, and if it determines that the anonymity is low, displays a warning saying "This post contains personal information. Do you want to continue posting?". The warning unit issues a warning when the diagnosis unit determines that the anonymity is low. As a result, the smartphone app according to the embodiment can proactively notify the user of necessary things and events for the user's schedule, and diagnose the anonymity of personal information and issue a warning when posting to the SNS, thereby improving the convenience and safety of the user.

[0073] The collection unit collects information such as schedules, message apps, and emails in the smartphone. For example, the collection unit can collect schedule information entered in a calendar. Specifically, the collection unit periodically acquires schedule information entered by the user using the API of the calendar app and stores it in a central database. The collection unit can also detect messages containing specific keywords in message apps and emails. For example, the notification content of the message app is analyzed and messages containing specific keywords are filtered. For this, a method is used in which the content of the message is analyzed using natural language processing technology and a highly relevant message is identified by considering the frequency of appearance of the keyword and the context. For example, the collection unit acquires information entered as "class reunion" in the calendar and detects messages containing the keyword "class reunion" in the message app or email. This allows the collection unit to comprehensively grasp the user's schedule and message content and provide information necessary for subsequent analysis and reminders. Furthermore, the collection unit has a mechanism for encrypting and safely storing collected data in order to protect the user's privacy. This allows the collection unit to efficiently collect necessary information while protecting the user's personal information.

[0074] The analysis unit analyzes the information collected by the collection unit and identifies the necessary items and things for the user's schedule. For example, the analysis unit identifies that if a class reunion is planned, clothing, cash, a reservation for a beauty salon, etc. are necessary. This analysis is performed using a generation AI. The generation AI analyzes the collected information and identifies the necessary items and things using, for example, a text generation AI (e.g., LLM). Specifically, the generation AI receives the user's schedule information and message content as input and analyzes the text using natural language processing technology. For example, if a class reunion is planned, the generation AI lists the preparations necessary for the class reunion based on data about similar events in the past and general knowledge. This includes selecting clothing, preparing cash, making a reservation for a beauty salon, etc. The generation AI can also make more personalized suggestions by taking into account the user's past behavioral patterns and preferences. For example, if a user has previously purchased a particular brand of clothing, it can suggest new product information for that brand. This allows the analysis unit to accurately identify the necessary items and things for the user's schedule and provide them to the reminder unit. Furthermore, the analysis unit can continuously learn from the collected data and improve the accuracy of the analysis, thereby enabling the analysis unit to realize flexible and highly accurate analysis according to the needs of the user.

[0075] The reminder unit reminds the user based on the necessary things and things identified by the analysis unit. For example, the reminder unit sends a reminder "Please make a reservation at a beauty salon" two weeks before the class reunion, and a reminder "Please prepare your clothes" one week before the class reunion. Also, the reminder unit sends a reminder "Please prepare cash" the day before the class reunion. The reminder unit sends reminders at optimal timing taking into consideration the user's schedule and past behavioral patterns. For example, if the user has tended to forget to make a reservation at a beauty salon in the past, the frequency of reminders can be increased. In addition, the reminder unit can customize the method of reminders according to the user's preferences. For example, the user can select the most effective way to receive the reminder, such as a notification sound, vibration, or pop-up message. Furthermore, the reminder unit can collect user feedback and continuously improve the content and timing of the reminder. For example, the user's behavior after receiving the reminder is tracked to evaluate the effectiveness of the reminder. As a result, the reminder unit can provide flexible and effective reminders according to the user's needs and improve the user's convenience.

[0076] The diagnostic unit analyzes the contents of posts on the SNS and diagnoses the degree of anonymity of the personal information. For example, when a user tries to post "I'm going to a class reunion" on the SNS, the diagnostic unit analyzes the contents of the post, and if it determines that the anonymity is low, it displays a warning saying "This post contains personal information. Do you want to continue posting?" The diagnostic unit uses natural language processing technology to analyze the contents of the post and judges whether it contains personal information. Specifically, it analyzes the text of the post and detects whether it contains personal information such as a name, address, phone number, and email address. In addition, it displays a warning when the personal information may be guessed by considering the context of the post. For example, if the post "I'm going to a class reunion" is related to a specific date and time or place, the information is a clue to identify the individual's behavior, so a warning is displayed. Furthermore, the diagnostic unit can refer to the user's past posting history and evaluate the anonymity. For example, if a similar post was made in the past, it compares the post content with the post content and evaluates the anonymity. As a result, the diagnostic unit can perform a highly accurate anonymity diagnosis for the user's SNS posts and prevent the leakage of personal information.

[0077] The warning unit issues a warning when the diagnosis unit determines that the anonymity is low. Specifically, when the content that the user is about to post on the SNS contains personal information, the warning unit displays a warning message saying, "This post contains personal information. Do you want to continue posting?" The warning unit allows the user to check the warning message and select whether to continue posting. For example, if the user ignores the warning message and continues posting, the post content may be analyzed again and a more detailed warning may be displayed. In addition, the warning unit may track the user's behavior after receiving the warning message and evaluate the effectiveness of the warning. For example, if the user corrects the post content after receiving the warning, the correction content is analyzed and the effectiveness of the warning is evaluated. In this way, the warning unit can strengthen the protection of the user's personal information and support safe communication on the SNS. In addition, the warning unit may collect user feedback and continuously improve the content and display method of the warning message. For example, the user's reaction and behavior when receiving the warning message are analyzed, and data is collected to provide a more effective warning message. In this way, the warning unit can thoroughly protect the user's personal information and improve safety on the SNS.

[0078] The collection unit can collect schedule information input to the calendar. The schedule information input to the calendar includes, for example, events, tasks, reminders, etc., but is not limited to such examples. The collection unit can acquire, for example, information that "class reunion" is input to the calendar. In addition, the collection unit can periodically collect schedule information input to the calendar and provide it to the analysis unit. In this way, by collecting schedule information input to the calendar, the user's schedule can be accurately understood. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input schedule information input to the calendar to AI, and the AI ​​can analyze and collect the information.

[0079] The collection unit can detect messages containing a specific keyword in a message app or email. The specific keyword includes, but is not limited to, for example, a keyword related to an important schedule or a frequently occurring keyword. The collection unit can detect, for example, a message containing the keyword "class reunion" in a message app or email. The collection unit can also periodically detect messages containing a specific keyword and provide them to the analysis unit. This makes it possible to efficiently collect information related to the user's schedule by detecting messages containing a specific keyword in a message app or email. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input messages containing a specific keyword in a message app or email to AI, and the AI ​​can analyze and detect the information.

[0080] The analysis unit can analyze the collected information and identify what is necessary or what is needed for the user's schedule. The necessary or what includes, but is not limited to, items to bring, preparations, action items, etc. For example, the analysis unit can identify that if a class reunion is planned, clothing, cash, a reservation for a beauty salon, etc. are necessary. This analysis is performed using a generation AI. The generation AI can analyze the collected information and identify what is necessary or what is needed, for example, using a text generation AI (for example, LLM). For example, the generation AI analyzes the information "class reunion" entered in the calendar and identifies what is necessary or what is needed for the class reunion. The generation AI can also analyze messages containing the keyword "class reunion" in a messaging app or email and identify what is necessary or what is needed for the class reunion. In this way, the collected information can be analyzed to accurately identify what is necessary or what is needed for the user's schedule.

[0081] The reminder unit can send a reminder, such as "Please make a reservation at a beauty salon" two weeks before the reunion. Two weeks before the reunion includes, but is not limited to, two weeks before an event or a specific date. The reminder unit can send a reminder, such as "Please make a reservation at a beauty salon" two weeks before the reunion. In addition, the reminder unit can send a reminder two weeks before the reunion so that the user does not forget to make necessary preparations. Some or all of the above-mentioned processing in the reminder unit may be performed, for example, using AI or may be performed without using AI. For example, the reminder unit can input the timing for sending the reminder two weeks before the reunion to the AI, and the AI ​​can send the reminder.

[0082] The reminder unit can send a reminder "prepare your outfit" one week before the reunion. One week before the reunion includes, but is not limited to, one week before the event or a specific date. The reminder unit can send a reminder "prepare your outfit" one week before the reunion. In addition, the reminder unit can send a reminder one week before the reunion so that the user does not forget to make the necessary preparations. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reminder unit can input the timing for sending the reminder one week before the reunion to the AI, and the AI ​​can send the reminder.

[0083] The reminder unit can send a reminder "Please prepare cash" on the day before the reunion. The day before the reunion includes, but is not limited to, the day before an event or a specific date. The reminder unit can send a reminder "Please prepare cash" on the day before the reunion. In addition, the reminder unit can send a reminder on the day before the reunion so that the user does not forget to make the necessary preparations. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reminder unit can input the timing for sending the reminder on the day before the reunion to the AI, and the AI ​​can send the reminder.

[0084] The diagnostic unit can analyze the contents of posts on the SNS and diagnose the degree of anonymity of the personal information. The degree of anonymity of the personal information includes, for example, the possibility of identifying the personal information, the use of anonymization technology, and the like, but is not limited to such examples. For example, when a user tries to post "I'm going to a class reunion" on the SNS, the diagnostic unit can analyze the contents of the post and, if it determines that the anonymity is low, display a warning saying "This post contains personal information. Do you want to continue posting?" This analysis is performed using a generation AI. The generation AI can analyze the contents of the post and diagnose the degree of anonymity of the personal information, for example, using a text generation AI (for example, LLM). For example, the generation AI can analyze the text posted on the SNS and determine whether it contains personal information. In this way, the leak of the user's personal information can be prevented by analyzing the contents of posts on the SNS and diagnosing the degree of anonymity of the personal information.

[0085] The warning unit can display a warning saying "This post contains personal information. Do you want to continue posting?" when the anonymity is determined to be low. Low anonymity includes, for example, personal information that is easily identified, insufficient anonymization, and the like, but is not limited to such examples. The warning unit can display a warning saying "This post contains personal information. Do you want to continue posting?" when the diagnosis unit determines that the anonymity is low. In this way, by displaying a warning for posts with low anonymity, it is possible to prevent leakage of personal information of users. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the content of the post determined to have low anonymity by the diagnosis unit to AI, and the AI ​​can display a warning.

[0086] The collection unit can estimate the user's emotion and adjust the timing of information collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit can reduce the frequency of information collection and collect only important information. In addition, when the user is relaxed, the collection unit can collect detailed information and provide it to the user. Furthermore, when the user is in a hurry, the collection unit can quickly collect necessary information and notify the user immediately. This reduces the user's stress by adjusting the timing of information collection based on the user's emotion, and enables efficient information collection. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI, and the generation AI can adjust the timing of information collection.

[0087] The collection unit can analyze the user's past schedule history and select the optimal collection method. The optimal collection method includes, for example, collection efficiency, collection accuracy, etc., but is not limited to such examples. The collection unit can, for example, preferentially collect information from a schedule app that the user has frequently used in the past. In addition, the collection unit can preferentially collect information related to a specific event from the user's past schedule history. Furthermore, the collection unit can analyze the user's past schedule history and collect information at the optimal timing. As a result, the optimal information collection method can be selected by analyzing the user's past schedule history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past schedule history to AI, and the AI ​​can select the optimal collection method.

[0088] The collection unit can perform filtering based on the user's current activity status and field of interest when collecting information. The current activity status includes, for example, at work, during a break, moving, etc., but is not limited to such examples. The field of interest includes, for example, hobbies, fields of expertise, topics of interest, etc., but is not limited to such examples. For example, when the user is at work, the collection unit can preferentially collect information related to work. Furthermore, when the user is on vacation, the collection unit can preferentially collect information related to travel and leisure. Furthermore, the collection unit can filter and collect related information based on the user's field of interest. As a result, highly relevant information can be efficiently collected by filtering information based on the user's current activity status and field of interest. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's current activity status and field of interest into AI, and the AI ​​can filter the information.

[0089] The collection unit can estimate the user's emotion and determine the priority of information to be collected based on the estimated user's emotion. The priority of information includes, for example, importance, urgency, relevance, etc., but is not limited to such examples. For example, when the user is stressed, the collection unit can preferentially collect only important information. Furthermore, when the user is relaxed, the collection unit can preferentially collect detailed information. Furthermore, when the user is in a hurry, the collection unit can preferentially collect information that is quickly required. Thereby, by determining the priority of information based on the user's emotion, it is possible to preferentially collect information that is important to the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of information.

[0090] The collection unit can preferentially collect highly relevant information in consideration of the geographical location information of the user when collecting information. The geographical location information includes, for example, GPS data, location information services, and the like, but is not limited to such examples. For example, when the user is in a specific area, the collection unit can preferentially collect information related to the area. In addition, when the user is traveling, the collection unit can preferentially collect information related to the travel destination. Furthermore, the collection unit can filter and collect highly relevant information based on the user's current location. Thereby, by considering the geographical location information of the user, highly relevant information can be efficiently collected. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI, and the AI ​​can filter the information.

[0091] The collection unit can analyze the user's social media activity at the time of collecting information and collect related information. The social media activity includes, for example, the content of the post, the number of likes, the number of followers, and the like, but is not limited to such examples. The collection unit can collect related information, for example, based on information shared by the user on social media. The collection unit can also collect information related to events and topics of interest from the user's social media activity. Furthermore, the collection unit can analyze the content of the user's social media post and filter and collect related information. In this way, highly relevant information can be efficiently collected by analyzing the user's social media activity. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity into AI, and the AI ​​can analyze and collect information.

[0092] The analysis unit can estimate the user's emotion and adjust the method of expressing the analysis based on the estimated user's emotion. The method of expressing the analysis includes, for example, a graph display, a text display, an interactive display, and the like, but is not limited to such examples. For example, when the user is nervous, the analysis unit can provide a simple and highly visible analysis result. Furthermore, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can provide an analysis result that covers the main points. In this way, by adjusting the method of expressing the analysis based on the user's emotion, it is possible to provide an analysis result that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI, and the generation AI can adjust the method of expressing the analysis.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. The importance of the information includes, for example, the impact level, the relevance level, the urgency level, and the like, but is not limited to such examples. For example, the analysis unit can perform a detailed analysis on important information. In addition, the analysis unit can perform a simplified analysis on information with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the information. As a result, by adjusting the level of detail of the analysis based on the importance of the information, it is possible to perform a detailed analysis on important information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the importance of the information to an AI, and the AI ​​can adjust the level of detail of the analysis.

[0094] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The categories of information include, for example, business information, personal information, technical information, and the like, but are not limited to such examples. The analysis unit can apply a schedule analysis algorithm to information related to a schedule, for example. The analysis unit can also apply a text analysis algorithm to information of a message app. Furthermore, the analysis unit can apply a natural language processing algorithm to information of an email. In this way, by applying different analysis algorithms depending on the category of information, optimal analysis can be performed for each category. Part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of information to AI, and the AI ​​can apply an appropriate analysis algorithm.

[0095] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The length of the analysis includes, for example, a detailed analysis, a simple analysis, and the like, but is not limited to such examples. For example, when the user is in a hurry, the analysis unit can provide a short and to the point analysis result. Furthermore, when the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, when the user is excited, the analysis unit can provide a visually stimulating analysis result. In this way, by adjusting the length of the analysis based on the user's emotion, an analysis result of an appropriate length for the user can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI, and the generation AI can adjust the length of the analysis.

[0096] The analysis unit can determine the priority of the analysis based on the time of collection of the information during the analysis. The time of collection of the information includes, for example, the latest information, past information, and the like, but is not limited to such examples. For example, the analysis unit can prioritize the analysis of the latest information. Also, the analysis unit can analyze old information later. Furthermore, the analysis unit can dynamically adjust the priority of the analysis according to the time of collection of the information. As a result, the latest information can be analyzed preferentially by determining the priority of the analysis based on the time of collection of the information. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the time of collection of the information to the AI, and the AI ​​can determine the priority of the analysis.

[0097] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The relevance of information includes, for example, a common theme, related topics, and the like, but is not limited to such examples. The analysis unit can, for example, prioritize analysis of information related to a schedule. In addition, the analysis unit can analyze less relevant information later. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of information. As a result, by adjusting the order of analysis based on the relevance of information, highly relevant information can be analyzed preferentially. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of information to AI, and the AI ​​can adjust the order of analysis.

[0098] The reminder unit can estimate the user's emotion and adjust the way of expressing the reminder based on the estimated user's emotion. The way of expressing the reminder includes, but is not limited to, for example, text notification, voice notification, visual notification, etc. The reminder unit can provide a simple and highly visible reminder when the user is nervous, for example. The reminder unit can provide a detailed reminder when the user is relaxed. Furthermore, the reminder unit can provide a reminder that covers the main points when the user is in a hurry. This allows the reminder to be provided in an easy-to-understand manner by adjusting the way of expressing the reminder based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reminder unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reminder unit can input the user's emotion data into the generation AI, and the generation AI can adjust the way of expressing the reminder.

[0099] The reminding unit can adjust the level of detail of the reminder based on the importance of the schedule at the time of reminding. The importance of the schedule includes, for example, the impact, urgency, and relevance, but is not limited to such examples. For example, the reminding unit can provide a detailed reminder for an important schedule. Also, the reminding unit can provide a simplified reminder for a schedule with a low importance. Furthermore, the reminding unit can dynamically adjust the level of detail of the reminder according to the importance of the schedule. As a result, by adjusting the level of detail of the reminder based on the importance of the schedule, a detailed reminder can be given for an important schedule. A part or all of the above-mentioned processing in the reminding unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reminding unit can input the importance of the schedule to an AI, and the AI ​​can adjust the level of detail of the reminder.

[0100] The reminding unit can apply different reminding algorithms depending on the category of the plan at the time of reminding. The categories of plans include, but are not limited to, business plans, personal plans, and event plans. For example, the reminding unit can apply a business-oriented reminding algorithm to a work plan. Furthermore, the reminding unit can apply a personal-oriented reminding algorithm to a private plan. Furthermore, the reminding unit can apply an event-oriented reminding algorithm to an event plan. In this way, by applying different reminding algorithms depending on the category of the plan, optimal reminding can be performed for each category. Part or all of the above-mentioned processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input the category of the plan to AI, and the AI ​​can apply an appropriate reminding algorithm.

[0101] The reminder unit can estimate the user's emotion and adjust the timing of the reminder based on the estimated user's emotion. The timing of the reminder includes, for example, the day before an event, a specific time period, and the like, but is not limited to such examples. For example, the reminder unit can perform the reminder early when the user is nervous. Furthermore, the reminder unit can perform the reminder at an appropriate timing when the user is relaxed. Furthermore, the reminder unit can perform the reminder just before when the user is in a hurry. In this way, the reminder can be performed at an appropriate timing for the user by adjusting the timing of the reminder based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reminder unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reminder unit can input the user's emotion data into the generation AI, and the generation AI can adjust the timing of the reminder.

[0102] The reminding unit can determine the priority of the reminder based on the submission time of the schedule at the time of reminding. The submission time of the schedule includes, for example, the most recent schedule, past schedule, etc., but is not limited to such examples. The reminding unit can, for example, give priority to the most recent schedule. Also, the reminding unit can postpone the reminder of a schedule whose submission time is far away. Furthermore, the reminding unit can dynamically adjust the priority of the reminder according to the submission time of the schedule. As a result, by determining the priority of the reminder based on the submission time of the schedule, the most recent schedule can be given priority. Part or all of the above-mentioned processing in the reminding unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminding unit can input the submission time of the schedule to AI, and the AI ​​can determine the priority of the reminder.

[0103] The reminding unit can adjust the order of reminding based on the relevance of the schedule at the time of reminding. The relevance of the schedule includes, for example, a common theme, related topics, and the like, but is not limited to such examples. For example, the reminding unit can give priority to reminding of important schedules. In addition, the reminding unit can postpone reminding of less relevant schedules. Furthermore, the reminding unit can dynamically adjust the order of reminding according to the relevance of the schedule. As a result, by adjusting the order of reminding based on the relevance of the schedule, it is possible to give priority to reminding of highly relevant schedules. Part or all of the above-mentioned processing in the reminding unit may be performed, for example, using AI, or may be performed without using AI. For example, the reminding unit can input the relevance of the schedule to AI, and the AI ​​can adjust the order of reminding.

[0104] The diagnostic unit can estimate the user's emotion and adjust the way of expressing the diagnosis based on the estimated user's emotion. The way of expressing the diagnosis includes, for example, text display, graph display, interactive display, etc., but is not limited to such examples. For example, when the user is nervous, the diagnostic unit can provide a simple and highly visible diagnostic result. Furthermore, when the user is relaxed, the diagnostic unit can provide a detailed diagnostic result. Furthermore, when the user is in a hurry, the diagnostic unit can provide a diagnostic result that covers the main points. In this way, by adjusting the way of expressing the diagnosis based on the user's emotion, it is possible to provide a diagnostic result that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnostic unit can input the user's emotion data to the generation AI, and the generation AI can adjust the way of expressing the diagnosis.

[0105] The diagnostic unit can adjust the level of detail of the diagnosis based on the importance of the posted content at the time of diagnosis. The importance of the posted content includes, for example, the impact level, the relevance level, the urgency level, and the like, but is not limited to such examples. For example, the diagnostic unit can perform a detailed diagnosis on important posted content. In addition, the diagnostic unit can perform a simplified diagnosis on posted content with low importance. Furthermore, the diagnostic unit can dynamically adjust the level of detail of the diagnosis according to the importance of the posted content. As a result, by adjusting the level of detail of the diagnosis based on the importance of the posted content, a detailed diagnosis can be performed on important posted content. A part or all of the above-mentioned processing in the diagnostic unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnostic unit can input the importance of the posted content to an AI, and the AI ​​can adjust the level of detail of the diagnosis.

[0106] The diagnostic unit can apply different diagnostic algorithms depending on the category of the posted content at the time of diagnosis. The categories of the posted content include, for example, business posts, personal posts, technical posts, etc., but are not limited to such examples. For example, the diagnostic unit can apply a diagnostic algorithm specialized for personal information protection to private posted content. Furthermore, the diagnostic unit can apply a diagnostic algorithm specialized for business information protection to business-related posted content. Furthermore, the diagnostic unit can apply a diagnostic algorithm specialized for public information protection to public posted content. In this way, by applying different diagnostic algorithms depending on the category of the posted content, an optimal diagnosis can be made for each category. Part or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnostic unit can input the category of the posted content to AI, and the AI ​​can apply an appropriate diagnostic algorithm.

[0107] The diagnosis unit can estimate the user's emotion and adjust the timing of diagnosis based on the estimated user's emotion. The timing of diagnosis includes, for example, immediately after posting, a specific time period, and the like, but is not limited to such examples. For example, the diagnosis unit can perform diagnosis early when the user is nervous. Furthermore, the diagnosis unit can perform diagnosis at an appropriate timing when the user is relaxed. Furthermore, the diagnosis unit can perform diagnosis immediately before when the user is in a hurry. Thereby, by adjusting the timing of diagnosis based on the user's emotion, diagnosis can be performed at an appropriate timing for the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the diagnosis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the diagnosis unit can input the user's emotion data to the generation AI, and the generation AI can adjust the timing of diagnosis.

[0108] The diagnosis unit can determine the priority of diagnosis based on the submission time of the posted content at the time of diagnosis. The submission time of the posted content includes, for example, the most recent post, past posts, etc., but is not limited to such examples. The diagnosis unit can, for example, give priority to diagnosing the most recent posted content. In addition, the diagnosis unit can diagnose the posted content that is submitted a long time ago later. Furthermore, the diagnosis unit can dynamically adjust the priority of diagnosis according to the submission time of the posted content. As a result, the most recent posted content can be diagnosed preferentially by determining the priority of diagnosis based on the submission time of the posted content. A part or all of the above-mentioned processing in the diagnosis unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnosis unit can input the submission time of the posted content to AI, and the AI ​​can determine the priority of diagnosis.

[0109] The diagnostic unit can adjust the order of diagnosis based on the relevance of the posted contents at the time of diagnosis. The relevance of the posted contents includes, for example, a common theme, related topics, etc., but is not limited to such examples. For example, the diagnostic unit can preferentially diagnose important posted contents. In addition, the diagnostic unit can diagnose less relevant posted contents later. Furthermore, the diagnostic unit can dynamically adjust the order of diagnosis according to the relevance of the posted contents. As a result, by adjusting the order of diagnosis based on the relevance of the posted contents, it is possible to preferentially diagnose highly relevant posted contents. Part or all of the above-mentioned processing in the diagnostic unit may be performed, for example, using AI, or may be performed without using AI. For example, the diagnostic unit can input the relevance of the posted contents to AI, and the AI ​​can adjust the order of diagnosis.

[0110] The warning unit can estimate the user's emotion and adjust the expression method of the warning based on the estimated user's emotion. The expression method of the warning includes, for example, text display, audio notification, visual notification, etc., but is not limited to such examples. For example, when the user is nervous, the warning unit can provide a simple and highly visible warning. Furthermore, when the user is relaxed, the warning unit can provide a detailed warning. Furthermore, when the user is in a hurry, the warning unit can provide a warning that covers the main points. In this way, by adjusting the expression method of the warning based on the user's emotion, a warning that is easy for the user to understand can be provided. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the warning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the warning unit can input the user's emotion data to the generation AI, and the generation AI can adjust the expression method of the warning.

[0111] The warning unit can adjust the level of detail of the warning based on the importance of the posted content at the time of warning. The importance of the posted content includes, for example, impact, relevance, urgency, etc., but is not limited to such examples. The warning unit can provide a detailed warning, for example, for important posted content. Furthermore, the warning unit can provide a simplified warning for posted content with low importance. Furthermore, the warning unit can dynamically adjust the level of detail of the warning according to the importance of the posted content. As a result, a detailed warning can be issued for important posted content by adjusting the level of detail of the warning based on the importance of the posted content. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the importance of the posted content to AI, and the AI ​​can adjust the level of detail of the warning.

[0112] The warning unit can apply different warning algorithms depending on the category of the posted content when issuing a warning. The categories of the posted content include, for example, business posts, personal posts, and technical posts, but are not limited to such examples. For example, the warning unit can apply a warning algorithm specialized for personal information protection to private posted content. Furthermore, the warning unit can apply a warning algorithm specialized for business information protection to business-related posted content. Furthermore, the warning unit can apply a warning algorithm specialized for public information protection to public posted content. In this way, by applying different warning algorithms depending on the category of the posted content, it is possible to issue an optimal warning for each category. Part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the category of the posted content to AI, and the AI ​​can apply an appropriate warning algorithm.

[0113] The warning unit can estimate the user's emotion and adjust the timing of the warning based on the estimated user's emotion. The timing of the warning includes, for example, immediately after posting, a specific time period, and the like, but is not limited to such examples. For example, the warning unit can issue a warning early when the user is nervous. Furthermore, the warning unit can issue a warning at an appropriate timing when the user is relaxed. Furthermore, the warning unit can issue a warning just before when the user is in a hurry. In this way, the warning can be issued at an appropriate timing for the user by adjusting the timing of the warning based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the warning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the warning unit can input the user's emotion data to the generation AI, and the generation AI can adjust the timing of the warning.

[0114] The warning unit can determine the priority of the warning based on the submission time of the posted content at the time of warning. The submission time of the posted content includes, for example, the most recent post, past posts, etc., but is not limited to such examples. The warning unit can, for example, give priority to the most recent posted content. Also, the warning unit can give a warning later to posted content that is submitted in the distant future. Furthermore, the warning unit can dynamically adjust the priority of the warning according to the submission time of the posted content. As a result, the most recent posted content can be given priority by determining the priority of the warning based on the submission time of the posted content. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the submission time of the posted content to AI, and the AI ​​can determine the priority of the warning.

[0115] The warning unit can adjust the order of warnings based on the relevance of the posted contents at the time of warning. The relevance of the posted contents includes, for example, a common theme, related topics, and the like, but is not limited to such examples. For example, the warning unit can give a priority to warning important posted contents. Also, the warning unit can give a priority to warning less relevant posted contents. Furthermore, the warning unit can dynamically adjust the order of warnings according to the relevance of the posted contents. As a result, by adjusting the order of warnings based on the relevance of the posted contents, it is possible to give a priority to warning highly relevant posted contents. A part or all of the above-mentioned processing in the warning unit may be performed, for example, using AI, or may be performed without using AI. For example, the warning unit can input the relevance of the posted contents to AI, and the AI ​​can adjust the order of warnings.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The collection unit can learn the user's past behavioral patterns and make predictions about future plans. For example, it can collect behavioral data from when the user participated in a specific event in the past and predict the preparations required for the next similar event. The collection unit can also provide information related to future plans based on places the user frequently visited in the past and services used by the user. Furthermore, the collection unit can analyze the user's past behavioral patterns and build a feedback loop to improve prediction accuracy. This makes it possible to more efficiently prepare for future plans by utilizing the user's past behavioral patterns.

[0118] The analysis unit can analyze the user's health data and evaluate the impact on the schedule. For example, the analysis unit can analyze the user's sleep data and exercise data and evaluate the impact of the user's physical condition on the schedule. The analysis unit can also analyze the user's biological data, such as stress level and heart rate, and determine the need to prepare for the schedule. Furthermore, the analysis unit can perform a risk assessment for the schedule based on the user's health data and suggest necessary measures. This allows the user to make preparations for the schedule while taking into account the user's health condition.

[0119] The reminding unit can provide reminders related to plans based on the user's location information. For example, when the user is in a specific location, the reminding unit can provide reminders about plans related to that location. Also, when the user is traveling, the reminding unit can provide reminders about plans related to the destination. Furthermore, the reminding unit can suggest optimal means of transportation and routes for plans based on the user's location information. This makes it possible to provide more effective reminders by utilizing the user's location information.

[0120] The diagnostic unit can analyze the user's social network and evaluate the impact on the schedule. For example, the schedules of the user's friends and family can be analyzed and the impact on the user's schedule can be evaluated. The diagnostic unit can also analyze events and activities in the user's social network and evaluate the relevance to the user's schedule. Furthermore, the diagnostic unit can perform a risk assessment for the schedule based on the user's social network and suggest necessary measures. This allows preparations for the schedule to be made taking into account the user's social network.

[0121] The warning unit can analyze the user's past posting history and evaluate the risk of anonymity. For example, the warning unit can analyze the content posted by the user in the past and identify posts that have a high risk of anonymity. The warning unit can also evaluate the risk of future posts based on the user's past posting history and suggest necessary measures. Furthermore, the warning unit can analyze the user's past posting history and provide guidelines for reducing the risk of anonymity. In this way, the user's past posting history can be utilized to evaluate the risk of anonymity and take appropriate measures.

[0122] The collection unit can estimate the user's emotions and determine the priority of information collection based on the estimated user's emotions. For example, when the user is feeling stressed, only important information can be collected preferentially. Furthermore, when the user is relaxed, the collection unit can collect detailed information preferentially. Furthermore, when the user is in a hurry, the collection unit can collect necessary information quickly preferentially. Thus, by determining the priority of information collection based on the user's emotions, it is possible to efficiently collect information important to the user.

[0123] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible analysis result can be provided. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide an analysis result that focuses on the main points. In this way, by adjusting the way in which the analysis results are presented based on the user's emotions, it is possible to provide an analysis result that is easy for the user to understand.

[0124] The reminding unit can estimate the user's emotions and adjust the timing of the reminder based on the estimated user's emotions. For example, if the user is nervous, the reminder unit can give an early reminder. Also, if the user is relaxed, the reminder unit can give a reminder at an appropriate time. Furthermore, if the user is in a hurry, the reminder unit can give a reminder just before the user needs to leave. In this way, by adjusting the timing of the reminder based on the user's emotions, the reminder can be given at an appropriate time for the user.

[0125] The diagnostic unit can estimate the user's emotions and adjust the way in which the diagnostic result is expressed based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible diagnostic result can be provided. Furthermore, if the user is relaxed, the diagnostic unit can provide a detailed diagnostic result. Furthermore, if the user is in a hurry, the diagnostic unit can provide a diagnostic result that focuses on the main points. In this way, by adjusting the way in which the diagnostic result is expressed based on the user's emotions, a diagnostic result that is easy for the user to understand can be provided.

[0126] The warning unit can estimate the user's emotion and adjust the way the warning is expressed based on the estimated user's emotion. For example, if the user is nervous, a simple and highly visible warning can be provided. If the user is relaxed, the warning unit can provide a detailed warning. If the user is in a hurry, the warning unit can provide a warning that focuses on the main points. In this way, by adjusting the way the warning is expressed based on the user's emotion, a warning that is easy for the user to understand can be provided.

[0127] The process flow of the second embodiment will be briefly described below.

[0128] Step 1: The collection unit collects information from the smartphone, such as schedules, messaging apps, and emails. For example, it detects schedule information entered in a calendar, or messages containing specific keywords in messaging apps or emails. Step 2: The analysis unit analyzes the information collected by the collection unit and identifies what is needed for the user's plans. For example, if a class reunion is planned, it identifies what is needed, such as clothing, cash, and a hair salon reservation. This analysis is performed using a generative AI. Step 3: The reminder unit reminds the user based on the needs and things identified by the analysis unit. For example, a reminder to "make a reservation at a hair salon" is sent two weeks before the reunion, a reminder to "prepare your outfit" is sent one week before the reunion, and a reminder to "prepare cash" is sent the day before the reunion. Step 4: The diagnostic unit analyzes the content of the post on the SNS and diagnoses the degree of anonymity of the personal information. For example, if a user tries to post on the SNS "I'm going to a class reunion," the content of the post is analyzed, and if it is determined that the anonymity is low, a warning message is displayed saying "This post contains personal information. Do you want to continue posting?" Step 5: The warning unit issues a warning when the diagnosis unit determines that the anonymity is low, thereby enabling the user to prevent the leakage of personal information.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0131] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the collection unit, analysis unit, remind unit, diagnosis unit, and warning unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart device 14, and collects information such as schedules, message apps, and emails in the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and identifies what is necessary and what is required for the user's schedule. The remind unit is realized, for example, by the control unit 46A of the smart device 14, and reminds the user based on what is necessary and what is identified by the analysis unit. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents of posts to SNS and diagnoses the degree of anonymity of personal information. The warning unit is realized, for example, by the control unit 46A of the smart device 14, and issues a warning when the diagnosis unit determines that the anonymity is low. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0135] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0140] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the collection unit, analysis unit, remind unit, diagnosis unit, and warning unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214, and collects information such as schedules, message apps, and emails in the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and identifies what is necessary and what is required for the user's schedule. The remind unit is realized, for example, by the control unit 46A of the smart glasses 214, and reminds the user based on what is necessary and what is identified by the analysis unit. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents of posts to SNS and diagnoses the degree of anonymity of personal information. The warning unit is realized, for example, by the control unit 46A of the smart glasses 214, and issues a warning when the diagnosis unit determines that the anonymity is low. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0151] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0157] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0158] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0162] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements including the above-mentioned collection unit, analysis unit, remind unit, diagnosis unit, and warning unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset type terminal 314, and collects information such as schedules, message apps, and emails in the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and identifies what is necessary and what is required for the user's schedule. The remind unit is realized, for example, by the control unit 46A of the headset type terminal 314, and reminds the user based on what is necessary and what is identified by the analysis unit. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents of posts to SNS and diagnoses the degree of anonymity of personal information. The warning unit is realized, for example, by the control unit 46A of the headset type terminal 314, and issues a warning when the diagnosis unit determines that the anonymity is low. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 7, a 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.

[0167] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0173] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0174] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0175] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0176] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0178] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0179] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0180] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] Each of the multiple elements including the collection unit, analysis unit, remind unit, diagnosis unit, and warning unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414, and collects information such as schedules, message apps, and emails in the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and identifies what is necessary and what is required for the user's schedule. The remind unit is realized, for example, by the control unit 46A of the robot 414, and reminds the user based on what is necessary and what is identified by the analysis unit. The diagnosis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the contents of posts to SNS and diagnoses the degree of anonymity of personal information. The warning unit is realized, for example, by the control unit 46A of the robot 414, and issues a warning when the diagnosis unit determines that the anonymity is low. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0183] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0185] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0186] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0187] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0189] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0190] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0191] In addition, 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 upon request from the data processing device 12.

[0192] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0193] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0194] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0195] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0196] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0198] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0200] (Appendix 1) A system comprising: a collection unit that collects information from schedules, message apps, and emails in a smartphone; an analysis unit that analyzes the information collected by the collection unit and identifies necessary items or things for the user's schedule; a reminder unit that issues reminders to the user based on the necessary items or things identified by the analysis unit; a diagnosis unit that analyzes content posted to SNS and diagnoses the degree of anonymity of personal information; and a warning unit that issues a warning if the diagnosis unit determines that the anonymity is low. (Appendix 2) The collecting unit includes: Collect event information entered in the calendar 2. The system of claim 1, (Appendix 3) The collecting unit includes: Detect messages in messaging apps and emails that contain specific keywords 2. The system of claim 1, (Appendix 4) The system described in Appendix 1, characterized in that the analysis unit analyzes the collected information and identifies what is needed or what needs to be done for the user's schedule. (Appendix 5) The reminding unit is Send a "make a salon appointment" reminder two weeks before the reunion 2. The system of claim 1, (Appendix 6) The reminding unit is Send a "please prepare your outfit" reminder one week before the reunion 2. The system of claim 1, (Appendix 7) The reminding unit is Send a reminder the day before the reunion to "have cash ready" 2. The system of claim 1, (Appendix 8) The diagnosis unit includes: Analyzing the content of posts on social media and assessing the degree of anonymity of personal information 2. The system of claim 1, (Appendix 9) The warning unit is If anonymity is deemed low, a warning message will be displayed saying "This post contains personal information. Do you want to continue posting?" 2. The system of claim 1, (Appendix 10) The collecting unit includes: To estimate a user's emotions and adjust the timing of information collection based on the estimated user's emotions. 2. The system of claim 1, (Appendix 11) The collecting unit includes: Analyze the user's past schedule history and select the optimal collection method 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting information, it is filtered based on the user's current activity and interests. 2. The system of claim 1, (Appendix 13) The collecting unit includes: Estimate user emotions and prioritize information to be collected based on the estimated user emotions. 2. The system of claim 1, (Appendix 14) The collecting unit includes: When collecting information, the system prioritizes collecting information that is highly relevant by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 15) The collecting unit includes: When collecting information, we analyze your social media activity and collect relevant information. 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the level of detail based on the importance of the information 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of information 2. The system of claim 1, (Appendix 19) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 20) The analysis unit is During analysis, prioritize analysis based on when the information was collected 2. The system of claim 1, (Appendix 21) The analysis unit is During analysis, adjust the order of analysis based on the relevance of information 2. The system of claim 1, (Appendix 22) The reminding unit is Infer user emotions and adjust the way reminders are presented based on the estimated user emotions 2. The system of claim 1, (Appendix 23) The reminding unit is When reminding, adjust the level of detail of the reminder based on the importance of the event 2. The system of claim 1, (Appendix 24) The reminding unit is When reminding, apply different reminder algorithms depending on the event category 2. The system of claim 1, (Appendix 25) The reminding unit is Estimate the user's emotions and adjust the timing of reminders based on the estimated user emotions. 2. The system of claim 1, (Appendix 26) The reminding unit is When reminding, prioritize reminders based on when the event was submitted 2. The system of claim 1, (Appendix 27) The reminding unit is When reminding, adjust the order of reminders based on event relevance 2. The system of claim 1, (Appendix 28) The diagnosis unit includes: Inferring user emotions and adjusting the way the diagnosis is presented based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The diagnosis unit includes: During diagnostics, adjust diagnostic detail based on the importance of the submitted content 2. The system of claim 1, (Appendix 30) The diagnosis unit includes: During diagnosis, different diagnosis algorithms are applied depending on the category of the post content. 2. The system of claim 1, (Appendix 31) The diagnosis unit includes: To estimate a user's emotion and adjust the timing of diagnosis based on the estimated user's emotion. 2. The system of claim 1, (Appendix 32) The diagnosis unit includes: During diagnostics, we prioritize diagnostics based on when the post was submitted. 2. The system of claim 1, (Appendix 33) The diagnosis unit includes: When diagnosing, adjust the order of diagnoses based on the relevance of the post 2. The system of claim 1, (Appendix 34) The warning unit is Infer user emotions and adjust warning wording based on the inferred user emotions 2. The system of claim 1, (Appendix 35) The warning unit is When alerting, adjust the verbosity of the alert based on the importance of the post 2. The system of claim 1, (Appendix 36) The warning unit is When warning, apply different warning algorithms depending on the post's content category 2. The system of claim 1, (Appendix 37) The warning unit is Infer user emotions and adjust the timing of warnings based on the estimated user emotions 2. The system of claim 1, (Appendix 38) The warning unit is When a warning is issued, it will be prioritized based on when the post was submitted 2. The system of claim 1, (Appendix 39) The warning unit is When alerting, adjust the order of alerts based on the relevance of the post content 2. The system of claim 1, [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects information on schedules, message apps, and emails in the smartphone; an analysis unit that analyzes the information collected by the collection unit and identifies what is needed or what is required for the user's schedule; A reminding unit that reminds a user based on the necessary items or matters identified by the analysis unit; A diagnostic section that analyzes the content posted on SNS and assesses the degree of anonymity of personal information; a warning unit that issues a warning when the diagnosis unit determines that the anonymity is low; A system characterized by:

2. The collecting unit includes: Collect event information entered in the calendar 2. The system of claim 1 .

3. The collecting unit includes: Detect messages in messaging apps and emails that contain specific keywords 2. The system of claim 1 .

4. The analysis unit is Analyzing the information collected by the collection unit to identify needs or things for the user's schedule.

2. The system of claim 1 .

5. The warning unit is When a warning is issued, it will be prioritized based on when the post was submitted 2. The system of claim 1 .

6. The warning unit is When alerting, adjust the order of alerts based on the relevance of the post content 2. The system of claim 1 .

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