system

Abstract

The system according to the embodiment aims to effectively utilize information obtained through smart glasses to provide appropriate suggestions and reminders to users. [Solution] A system according to an embodiment includes a conversion unit, an analysis unit, a suggestion unit, and a notification unit. The conversion unit converts an image captured by a camera in the smart glasses into text using OCR technology. The analysis unit analyzes the text converted by the conversion unit using NLP technology. The suggestion unit makes suggestions appropriate to the user based on the information analyzed by the analysis unit. The notification unit notifies the user of the information suggested by the suggestion unit.

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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of not being able to effectively utilize the information obtained through smart glasses to provide appropriate suggestions and reminders to users.

[0005] The system according to the embodiment aims to effectively utilize information obtained through smart glasses to provide appropriate suggestions and reminders to users. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversion unit, an analysis unit, a suggestion unit, and a notification unit. The conversion unit converts an image captured by a camera in the smart glasses into text using OCR technology. The analysis unit analyzes the text converted by the conversion unit using NLP technology. The suggestion unit makes suggestions suitable for the user based on the information analyzed by the analysis unit. The notification unit notifies the user of the information suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize information obtained through smart glasses to provide appropriate suggestions and reminders to users. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded 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, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An information analysis system according to an embodiment of the present invention stores information acquired through smart glasses as text, analyzes the text, and sends personalized suggestions and reminders to the user. This information analysis system converts images captured by the smart glasses' camera into text using OCR technology and analyzes the text using natural language processing (NLP) technology. Based on the analyzed information, the system provides personalized suggestions to the user. It also acquires information such as periodic activities and expiration dates from the stored text and sends reminders at appropriate locations and times. It also acquires location information and text from smartphone usage history, analyzes the information, and sends suggestions and reminders. Finally, it also includes a mechanism for rewarding notifications that the user highly rates and app developers who provide the information. For example, by converting images captured by the smart glasses' camera into text using OCR technology, the information viewed by the user can be saved as digital data. Next, the text data is analyzed using NLP technology to understand the user's behavioral patterns and interests. For example, by analyzing information such as the user's frequently visited locations and purchased products, personalized suggestions can be made to the user. It also automatically acquires periodic activities and expiration dates from the stored text data and sends reminders at appropriate times. For example, it can remind users of their weekly exercise routine or product expiration dates. Furthermore, it can obtain location information and text from smartphone usage history and analyze that information to provide suggestions and reminders. For example, if a user is in a specific location, it can notify them of information related to that location. Finally, it also includes a mechanism to reward notifications that users rate highly and the app developers that provide that information. For example, if a user rates a specific notification highly, the app developer that provided that notification can be given points or monetary rewards. This can increase app developers' motivation and provide better service. This allows the information analysis system to efficiently analyze the information obtained through smart glasses and provide suggestions and notifications that are appropriate for the user.

[0029] An information analysis system according to an embodiment includes a conversion unit, an analysis unit, a suggestion unit, and a notification unit. The conversion unit converts an image captured by a camera in the smart glasses into text using OCR technology. For example, the conversion unit can extract character information from the image using OCR technology such as Tesseract or ABBYY FineReader. The conversion unit can also recognize both handwritten and printed characters. For example, the conversion unit can convert handwritten notes and notebooks into digital text using a handwritten character recognition algorithm. The conversion unit can also convert printed documents and books into digital text using a printed character recognition algorithm. The analysis unit analyzes the text converted by the conversion unit using NLP technology. For example, the analysis unit can understand the content of the text using NLP technology such as morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can also extract important information from the text using text mining technology. For example, the analysis unit can extract keywords and phrases from the text and, based on these, identify the user's interests. The suggestion unit makes suggestions appropriate to the user based on the information analyzed by the analysis unit. For example, the suggestion unit can suggest products and services suitable for the user based on the user's past behavior history and current situation. The suggestion unit can also make suggestions at appropriate times based on the user's schedule and location information. The notification unit notifies the user of the information suggested by the suggestion unit. For example, the notification unit can provide the user with information by methods such as push notification, email notification, or alert. The notification unit can also select an optimal notification method depending on the user's device. For example, the notification unit can select an appropriate notification method for devices such as smartphones, tablets, and smartwatches. This allows the information analysis system according to the embodiment to efficiently analyze information obtained through smart glasses and provide suggestions and notifications suitable for the user.

[0030] The information analysis system includes an acquisition unit that acquires periodic actions, expiration dates, etc. from the stored text. The acquisition unit acquires periodic actions, expiration dates, etc. from the stored text. For example, the acquisition unit can extract periodic actions, such as daily exercise or weekly meetings, from the text. The acquisition unit can also acquire information, such as product expiration dates or contract expiration dates, from the text. For example, the acquisition unit can analyze descriptions related to date and time information or expiration dates in the text and extract such information. This allows the acquisition unit to automatically acquire the user's periodic actions and expiration dates and send reminders at appropriate times.

[0031] The reminding unit can issue reminders based on the information acquired by the acquisition unit. The reminding unit issues reminders based on the information acquired by the acquisition unit. For example, the reminding unit can remind the user of the amount of exercise they should do each day. The reminding unit can also issue reminders when the expiration date of a product is approaching. For example, the reminding unit can issue reminders at appropriate times based on the user's schedule. This allows the reminding unit to issue reminders at appropriate times based on the acquired information.

[0032] The information analysis system includes a location information acquisition unit that acquires location information from the smartphone usage history. The location information acquisition unit acquires location information from the smartphone usage history. For example, the location information acquisition unit can acquire the user's current location and past movement history using GPS data, Wi-Fi location information, etc. The location information acquisition unit can also acquire location information from the smartphone's app usage history and browser history. For example, the location information acquisition unit can acquire information on places the user has visited and checked in. As a result, the location information acquisition unit can acquire location information from the smartphone usage history and provide suggestions and reminders appropriate for the user.

[0033] The information analysis system includes a text acquisition unit that acquires text from the smartphone usage history. The text acquisition unit acquires text from the smartphone usage history. For example, the text acquisition unit can acquire text from message history or browser history. The text acquisition unit can also acquire text from the smartphone app usage history. For example, the text acquisition unit can acquire text information from web pages or apps viewed by the user. This allows the text acquisition unit to acquire text from the smartphone usage history and provide suggestions and reminders appropriate for the user.

[0034] The analysis unit can analyze the information acquired by the location information acquisition unit and the text acquisition unit. The analysis unit analyzes the information acquired by the location information acquisition unit and the text acquisition unit. For example, the analysis unit can analyze the acquired text information using text mining technology. The analysis unit can also analyze the acquired location information using data analysis technology. For example, the analysis unit can analyze the user's movement patterns and behavior history and make suggestions and reminders based on the results. This allows the analysis unit to analyze the acquired information and make suggestions and reminders appropriate for the user.

[0035] The notification unit can provide suggestions and reminders based on the information analyzed by the analysis unit. The notification unit provides suggestions and reminders based on the information analyzed by the analysis unit. For example, the notification unit can provide suggestions at appropriate times based on the user's behavioral history. The notification unit can also provide reminders based on the user's schedule. For example, the notification unit can provide notifications at optimal times by referring to the user's calendar information. This allows the notification unit to provide suggestions and reminders at appropriate times based on the analyzed information.

[0036] The information analysis system includes a reward unit that rewards notifications that a user has given a high rating and app developers that provide the information. The reward unit rewards notifications that a user has given a high rating and app developers that provide the information. For example, when a user gives a high rating to a specific notification, the reward unit can reward the app developer that provided the notification with points or a monetary reward. The reward unit can also adjust the content of the reward based on user feedback. For example, when a user gives a high rating to a specific notification, the reward unit can provide an interesting reward when the user is relaxed. In this way, the reward unit can improve the motivation of app developers by giving rewards based on the user's high rating.

[0037] The conversion unit can apply different OCR algorithms depending on the type of image. The conversion unit applies different OCR algorithms depending on the type of image. For example, the conversion unit can apply an OCR algorithm specialized for handwritten character recognition to an image of handwritten characters. The conversion unit can also apply a highly accurate printed character recognition algorithm to an image of printed text. Furthermore, the conversion unit can apply a hybrid algorithm that can recognize both handwritten and printed characters to an image that contains a mixture of handwritten and printed characters. This allows the conversion unit to improve recognition accuracy by applying the optimal OCR algorithm depending on the type of image.

[0038] The conversion unit can automatically adjust the light conditions and angle when capturing an image to obtain an optimal image. The conversion unit automatically adjusts the light conditions and angle when capturing an image to obtain an optimal image. For example, if there is insufficient light, the conversion unit can automatically adjust the exposure of the camera to ensure brightness. In addition, if there is backlight, the conversion unit can automatically adjust the angle of the camera to ensure optimal shooting conditions. Furthermore, if the image is blurry, the conversion unit can automatically adjust the focus of the camera to obtain a clear image. As a result, the conversion unit can automatically adjust the light conditions and angle to obtain an optimal image and improve the accuracy of OCR.

[0039] The conversion unit can preferentially convert highly relevant images by taking into account the user's geographical location information when capturing an image. The conversion unit can preferentially convert highly relevant images by taking into account the user's geographical location information when capturing an image. For example, if the user is in a specific location, the conversion unit can preferentially convert text related to the location. Furthermore, if the user is traveling, the conversion unit can preferentially convert text related to tourist spots. Furthermore, if the user is at work, the conversion unit can preferentially convert work-related text. In this way, the conversion unit can preferentially convert highly relevant information by taking into account the user's geographical location information.

[0040] The conversion unit can analyze the user's social media activity when capturing an image and prioritize converting related images. The conversion unit can analyze the user's social media activity when capturing an image and prioritize converting related images. For example, the conversion unit can prioritize converting images that the user has shared on social media. The conversion unit can also prioritize converting images that the user has tagged on social media. Furthermore, the conversion unit can prioritize converting images that the user has commented on on social media. In this way, the conversion unit can prioritize converting highly relevant information by analyzing the user's social media activity.

[0041] The analysis unit can apply different NLP algorithms depending on the content of the text. The analysis unit applies different NLP algorithms depending on the content of the text. For example, the analysis unit can apply a sentiment analysis algorithm to text that requires sentiment analysis. The analysis unit can also apply a semantic analysis algorithm to text that requires semantic analysis. Furthermore, the analysis unit can apply a keyword extraction algorithm to text that requires keyword extraction. In this way, the analysis unit can improve analysis accuracy by applying the optimal NLP algorithm depending on the content of the text.

[0042] The analysis unit can adjust the level of analysis detail depending on the length and complexity of the text. The analysis unit adjusts the level of analysis detail depending on the length and complexity of the text. For example, the analysis unit can adjust the level of analysis detail by applying a summarization algorithm to long text. The analysis unit can also perform detailed analysis on short text. Furthermore, the analysis unit can perform detailed analysis on complex text by combining multiple analysis algorithms. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of analysis detail depending on the length and complexity of the text.

[0043] When analyzing text, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral history. When analyzing text, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral history. For example, the analysis unit can improve the accuracy of the analysis based on keywords that the user frequently used in the past. The analysis unit can also improve the accuracy of the analysis by extracting related information from the user's past behavioral history. Furthermore, the analysis unit can analyze the user's past behavioral patterns and improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral history.

[0044] The analysis unit can improve the accuracy of the analysis by referring to related external data when analyzing text. The analysis unit can improve the accuracy of the analysis by referring to related external data when analyzing text. For example, the analysis unit can improve the accuracy of the text analysis by referring to external news data. The analysis unit can also improve the accuracy of the text analysis by referring to external social media data. Furthermore, the analysis unit can improve the accuracy of the text analysis by referring to an external specialized database. In this way, the analysis unit can improve the accuracy of the analysis by referring to related external data.

[0045] The suggestion unit can apply different suggestion algorithms depending on the content of the suggestion. The suggestion unit can apply different suggestion algorithms depending on the content of the suggestion. For example, the suggestion unit can apply a shopping algorithm to a suggestion related to shopping. The suggestion unit can also apply a restaurant algorithm to a suggestion related to restaurants. Furthermore, the suggestion unit can apply a travel algorithm to a suggestion related to travel. In this way, the suggestion unit can improve the accuracy of the suggestion by applying the optimal suggestion algorithm depending on the content of the suggestion.

[0046] The suggestion unit can optimize the timing of the suggestion based on the user's schedule. The suggestion unit optimizes the timing of the suggestion based on the user's schedule. For example, the suggestion unit can make the suggestion at the optimal timing by referring to the user's calendar information. The suggestion unit can also adjust the timing of the suggestion to match the user's schedule. Furthermore, the suggestion unit can optimize the timing of the suggestion based on the user's schedule. In this way, the suggestion unit can make more effective suggestions by optimizing the timing of the suggestion based on the user's schedule.

[0047] The suggestion unit can customize the content of the suggestion based on the geographical location information of the user. The suggestion unit customizes the content of the suggestion based on the geographical location information of the user. For example, when the user is in a specific location, the suggestion unit can make suggestions related to the location. Furthermore, when the user is traveling, the suggestion unit can make suggestions related to tourist spots. Furthermore, when the user is at work, the suggestion unit can make suggestions related to work. In this way, the suggestion unit can make more relevant suggestions by customizing the content of the suggestion based on the geographical location information of the user.

[0048] The suggestion unit can customize the content of the suggestions based on the user's social media activity. The suggestion unit customizes the content of the suggestions based on the user's social media activity. For example, the suggestion unit can make suggestions based on information shared by the user on social media. The suggestion unit can also make suggestions based on information tagged by the user on social media. Furthermore, the suggestion unit can make suggestions based on information commented on by the user on social media. In this way, the suggestion unit can make more relevant suggestions by customizing the content of the suggestions based on the user's social media activity.

[0049] The notification unit can apply different notification algorithms depending on the content of the notification. The notification unit applies different notification algorithms depending on the content of the notification. For example, the notification unit can apply a notification algorithm that performs highlighting to important notifications. The notification unit can also apply a standard notification algorithm to normal notifications. Furthermore, the notification unit can apply a notification algorithm that performs immediate notification to urgent notifications. In this way, the notification unit can improve the accuracy of notifications by applying the optimal notification algorithm depending on the content of the notification.

[0050] The notification unit can optimize the timing of notifications based on the user's schedule. The notification unit optimizes the timing of notifications based on the user's schedule. For example, the notification unit can refer to the user's calendar information and provide notifications at the optimal timing. The notification unit can also adjust the timing of notifications to match the user's schedule. Furthermore, the notification unit can optimize the timing of notifications based on the user's plans. As a result, the notification unit can provide more effective notifications by optimizing the timing of notifications based on the user's schedule.

[0051] The notification unit can customize the content of the notification based on the user's geographical location information. The notification unit customizes the content of the notification based on the user's geographical location information. For example, when the user is in a specific location, the notification unit can provide a notification related to the location. Furthermore, when the user is traveling, the notification unit can provide a notification related to tourist spots. Furthermore, when the user is at work, the notification unit can provide a work-related notification. In this way, the notification unit can provide more relevant notifications by customizing the content of the notification based on the user's geographical location information.

[0052] The notification unit can customize the content of the notification based on the user's social media activity. The notification unit customizes the content of the notification based on the user's social media activity. For example, the notification unit can provide a notification based on information shared by the user on social media. The notification unit can also provide a notification based on information tagged by the user on social media. Furthermore, the notification unit can provide a notification based on information commented on by the user on social media. In this way, the notification unit can provide more relevant notifications by customizing the content of the notification based on the user's social media activity.

[0053] The acquisition unit can apply different acquisition algorithms depending on the content of the text. The acquisition unit applies different acquisition algorithms depending on the content of the text. For example, the acquisition unit can apply an algorithm that can be quickly acquired to important text. The acquisition unit can also apply a standard acquisition algorithm to ordinary text. Furthermore, the acquisition unit can apply an acquisition algorithm that performs detailed analysis to complex text. In this way, the acquisition unit can improve acquisition accuracy by applying the optimal acquisition algorithm depending on the content of the text.

[0054] The acquisition unit can adjust the level of detail of acquisition according to the length and complexity of the text. The acquisition unit adjusts the level of detail of acquisition according to the length and complexity of the text. For example, the acquisition unit can adjust the level of detail of acquisition by applying a summarization algorithm to long text. The acquisition unit can also perform detailed acquisition for short text. Furthermore, the acquisition unit can perform detailed acquisition by combining multiple acquisition algorithms for complex text. In this way, the acquisition unit can acquire appropriate information by adjusting the level of detail of acquisition according to the length and complexity of the text.

[0055] The acquisition unit can improve the accuracy of acquisition by referring to the user's past behavioral history when acquiring text. The acquisition unit can improve the accuracy of acquisition by referring to the user's past behavioral history when acquiring text. For example, the acquisition unit can improve the accuracy of acquisition based on keywords that the user frequently used in the past. The acquisition unit can also improve the accuracy of acquisition by extracting related information from the user's past behavioral history. Furthermore, the acquisition unit can improve the accuracy of acquisition by analyzing the user's past behavioral patterns. As a result, the acquisition unit can improve the accuracy of acquisition by referring to the user's past behavioral history.

[0056] The reminding unit can apply different reminding algorithms depending on the content of the reminder. The reminding unit applies different reminding algorithms depending on the content of the reminder. For example, the reminding unit can apply a reminding algorithm that highlights important reminders. The reminding unit can also apply a standard reminding algorithm to normal reminders. Furthermore, the reminding unit can apply a reminding algorithm that provides immediate reminders to urgent reminders. In this way, the reminding unit can improve the accuracy of reminders by applying the optimal reminding algorithm depending on the content of the reminder.

[0057] The reminding unit can optimize the timing of reminders based on the user's schedule. The reminding unit optimizes the timing of reminders based on the user's schedule. For example, the reminding unit can refer to the user's calendar information to remind at the optimal timing. The reminding unit can also adjust the timing of reminders to match the user's schedule. Furthermore, the reminding unit can optimize the timing of reminders based on the user's plans. As a result, the reminding unit can provide more effective reminders by optimizing the timing of reminders based on the user's schedule.

[0058] The reminding unit can customize the content of the reminder based on the geographical location information of the user. The reminding unit customizes the content of the reminder based on the geographical location information of the user. For example, when the user is in a specific location, the reminding unit can provide a reminder related to the location. Furthermore, when the user is traveling, the reminding unit can provide a reminder related to tourist spots. Furthermore, when the user is at work, the reminding unit can provide a work-related reminder. In this way, the reminding unit can provide more relevant reminders by customizing the content of the reminder based on the geographical location information of the user.

[0059] When acquiring location information, the location information acquisition unit can select the optimal acquisition method by referring to the user's past movement history. When acquiring location information, the location information acquisition unit can select the optimal acquisition method by referring to the user's past movement history. For example, the location information acquisition unit can acquire location information based on places that the user has frequently visited in the past. The location information acquisition unit can also suggest routes that avoid congestion based on the user's past movement history. Furthermore, the location information acquisition unit can analyze the user's past movement patterns and select the most efficient location information acquisition method. As a result, the location information acquisition unit can select the optimal location information acquisition method by referring to the user's past movement history.

[0060] The location information acquisition unit can analyze the user's social media activity and acquire related location information when acquiring location information. The location information acquisition unit can analyze the user's social media activity and acquire related location information when acquiring location information. For example, the location information acquisition unit can acquire location information of places the user has shared on social media. The location information acquisition unit can also acquire location information of places the user has tagged on social media. Furthermore, the location information acquisition unit can acquire location information of places the user has commented on on social media. In this way, the location information acquisition unit can acquire highly relevant location information by analyzing the user's social media activity.

[0061] When acquiring text, the text acquisition unit can select the optimal acquisition method by referring to the user's past text history. When acquiring text, the text acquisition unit selects the optimal acquisition method by referring to the user's past text history. For example, the text acquisition unit can acquire text based on keywords that the user has frequently used in the past. The text acquisition unit can also acquire text by extracting related information from the user's past text history. Furthermore, the text acquisition unit can analyze the user's past text patterns and select the most efficient text acquisition method. As a result, the text acquisition unit can select the optimal text acquisition method by referring to the user's past text history.

[0062] The text acquisition unit can analyze the user's social media activity and acquire related text when acquiring text. The text acquisition unit can analyze the user's social media activity and acquire related text when acquiring text. For example, the text acquisition unit can acquire text of information shared by the user on social media. The text acquisition unit can also acquire text of information tagged by the user on social media. Furthermore, the text acquisition unit can acquire text of information commented on by the user on social media. In this way, the text acquisition unit can acquire highly relevant text by analyzing the user's social media activity.

[0063] The reward unit can apply different reward algorithms depending on the content of the reward. The reward unit applies different reward algorithms depending on the content of the reward. For example, the reward unit can apply a reward algorithm that performs highlighting to important rewards. The reward unit can also apply a standard reward algorithm to ordinary rewards. Furthermore, the reward unit can apply a reward algorithm that adds special effects to special rewards. In this way, the reward unit can improve the accuracy of rewards by applying the optimal reward algorithm depending on the content of the reward.

[0064] The reward unit can customize the content of the reward based on the user's geographic location information. The reward unit customizes the content of the reward based on the user's geographic location information. For example, when the user is in a specific location, the reward unit can provide a reward related to the location. Also, when the user is traveling, the reward unit can provide a reward related to a tourist spot. Furthermore, when the user is at work, the reward unit can provide a reward related to work. In this way, the reward unit can provide more relevant rewards by customizing the content of the reward based on the user's geographic location information.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The information analysis system may also include a voice analysis unit that analyzes the user's voice data. The voice analysis unit can analyze the user's conversations and voice memos and convert them into text data. For example, the voice analysis unit can convert voice recorded by the user during a meeting into text and save it as meeting minutes. The voice analysis unit can also convert notes entered by the user by voice while driving into text so that they can be checked later. Furthermore, the voice analysis unit can analyze the user's voice commands and operate the system by voice. In this way, the information analysis system can improve user convenience by analyzing voice data.

[0067] The information analysis system may also include a health data acquisition unit that acquires the user's health data. The health data acquisition unit can acquire the user's heart rate, step count, sleep data, and the like from devices such as smartwatches and fitness trackers. For example, the health data acquisition unit can monitor the user's heart rate and notify the user if an abnormality is detected. The health data acquisition unit can also count the user's steps and remind the user to achieve their goals. Furthermore, the health data acquisition unit can analyze the user's sleep data and make suggestions to improve the quality of their sleep. In this way, the information analysis system can support health management by acquiring the user's health data.

[0068] The information analysis system may also include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit analyzes data on products and services purchased by the user in the past, and can grasp the user's purchasing trends. For example, the purchase history analysis unit can identify products that the user frequently purchases and notify the user that those products are on sale. The purchase history analysis unit can also remind the user of the expiration dates of products purchased by the user in the past. Furthermore, the purchase history analysis unit can suggest new products and services based on the user's purchasing trends. This allows the information analysis system to make more personalized suggestions by analyzing the user's purchase history.

[0069] The information analysis system may also include a social media analysis unit that analyzes a user's social media activities. The social media analysis unit analyzes information, comments, and tagged information shared by the user on social media to understand the user's interests. For example, the social media analysis unit may analyze articles and posts shared by the user to suggest related news and articles. The social media analysis unit may also analyze comments made by the user to suggest related events and activities. Furthermore, the social media analysis unit may analyze tagged information by the user to suggest related products and services. This allows the information analysis system to make more personalized suggestions by analyzing the user's social media activities.

[0070] The information analysis system may also include a location information analysis unit that analyzes the user's location information. The location information analysis unit analyzes the user's current location and past movement history to understand the user's behavioral patterns. For example, the location information analysis unit may identify places the user frequently visits and suggest information related to those places. The location information analysis unit may also analyze the user's movement patterns and suggest optimal routes and means of transportation. Furthermore, when the user is in a specific location, the location information analysis unit may suggest events and activities related to that location. This allows the information analysis system to make more personalized suggestions by analyzing the user's location information.

[0071] The information analysis system may also include a learning history analysis unit that analyzes the user's learning history. The learning history analysis unit can analyze the content the user has learned in the past and their progress, and make suggestions to improve the effectiveness of their learning. For example, the learning history analysis unit can suggest what content the user should learn next based on the content the user has learned in the past. The learning history analysis unit can also monitor the user's learning progress and remind them to achieve their goals. Furthermore, the learning history analysis unit can analyze the user's learning patterns and suggest effective learning methods. In this way, the information analysis system can improve the effectiveness of learning by analyzing the user's learning history.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The converter converts images captured by the smart glasses' camera into text using OCR technology. For example, the converter can extract text information from images using OCR technologies such as Tesseract or ABBYY FineReader. The converter can also recognize both handwritten and printed text. Handwritten notes and notebooks can be converted into digital text using a handwritten text recognition algorithm, and printed documents and books can be converted into digital text using a printed text recognition algorithm. Step 2: The analysis unit uses NLP technology to analyze the text converted by the conversion unit. For example, the analysis unit can understand the content of the text using NLP techniques such as morphological analysis, grammatical analysis, and semantic analysis. It can also extract important information from the text using text mining technology. The analysis unit can extract keywords and phrases from the text and use them to understand the user's interests and concerns. Step 3: The suggestion unit makes suggestions suitable for the user based on the information analyzed by the analysis unit. For example, the suggestion unit can suggest products and services suitable for the user based on the user's past behavior history and current situation. It can also make suggestions at appropriate times based on the user's schedule and location information. Step 4: The notification unit notifies the user of the information suggested by the suggestion unit. For example, the notification unit can provide the user with information by push notification, email notification, alert, or other methods. The notification unit can also select the optimal notification method depending on the user's device. An appropriate notification method can be selected for devices such as smartphones, tablets, and smartwatches.

[0074] (Example 2) An information analysis system according to an embodiment of the present invention stores information acquired through smart glasses as text, analyzes the text, and sends personalized suggestions and reminders to the user. This information analysis system converts images captured by the smart glasses' camera into text using OCR technology and analyzes the text using natural language processing (NLP) technology. Based on the analyzed information, the system provides personalized suggestions to the user. It also acquires information such as periodic activities and expiration dates from the stored text and sends reminders at appropriate locations and times. It also acquires location information and text from smartphone usage history, analyzes the information, and sends suggestions and reminders. Finally, it also includes a mechanism for rewarding notifications that the user highly rates and app developers who provide the information. For example, by converting images captured by the smart glasses' camera into text using OCR technology, the information viewed by the user can be saved as digital data. Next, the text data is analyzed using NLP technology to understand the user's behavioral patterns and interests. For example, by analyzing information such as the user's frequently visited locations and purchased products, personalized suggestions can be made to the user. It also automatically acquires periodic activities and expiration dates from the stored text data and sends reminders at appropriate times. For example, it can remind users of their weekly exercise routine or product expiration dates. Furthermore, it can obtain location information and text from smartphone usage history and analyze that information to provide suggestions and reminders. For example, if a user is in a specific location, it can notify them of information related to that location. Finally, it also includes a mechanism to reward notifications that users rate highly and the app developers that provide that information. For example, if a user rates a specific notification highly, the app developer that provided that notification can be given points or monetary rewards. This can increase app developers' motivation and provide better service. This allows the information analysis system to efficiently analyze the information obtained through smart glasses and provide suggestions and notifications that are appropriate for the user.

[0075] An information analysis system according to an embodiment includes a conversion unit, an analysis unit, a suggestion unit, and a notification unit. The conversion unit converts an image captured by a camera in the smart glasses into text using OCR technology. For example, the conversion unit can extract character information from the image using OCR technology such as Tesseract or ABBYY FineReader. The conversion unit can also recognize both handwritten and printed characters. For example, the conversion unit can convert handwritten notes and notebooks into digital text using a handwritten character recognition algorithm. The conversion unit can also convert printed documents and books into digital text using a printed character recognition algorithm. The analysis unit analyzes the text converted by the conversion unit using NLP technology. For example, the analysis unit can understand the content of the text using NLP technology such as morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can also extract important information from the text using text mining technology. For example, the analysis unit can extract keywords and phrases from the text and, based on these, identify the user's interests. The suggestion unit makes suggestions appropriate to the user based on the information analyzed by the analysis unit. For example, the suggestion unit can suggest products and services suitable for the user based on the user's past behavior history and current situation. The suggestion unit can also make suggestions at appropriate times based on the user's schedule and location information. The notification unit notifies the user of the information suggested by the suggestion unit. For example, the notification unit can provide the user with information by methods such as push notification, email notification, or alert. The notification unit can also select an optimal notification method depending on the user's device. For example, the notification unit can select an appropriate notification method for devices such as smartphones, tablets, and smartwatches. This allows the information analysis system according to the embodiment to efficiently analyze information obtained through smart glasses and provide suggestions and notifications suitable for the user.

[0076] The information analysis system includes an acquisition unit that acquires periodic actions, expiration dates, etc. from the stored text. The acquisition unit acquires periodic actions, expiration dates, etc. from the stored text. For example, the acquisition unit can extract periodic actions, such as daily exercise or weekly meetings, from the text. The acquisition unit can also acquire information, such as product expiration dates or contract expiration dates, from the text. For example, the acquisition unit can analyze descriptions related to date and time information or expiration dates in the text and extract such information. This allows the acquisition unit to automatically acquire the user's periodic actions and expiration dates and send reminders at appropriate times.

[0077] The reminding unit can issue reminders based on the information acquired by the acquisition unit. The reminding unit issues reminders based on the information acquired by the acquisition unit. For example, the reminding unit can remind the user of the amount of exercise they should do each day. The reminding unit can also issue reminders when the expiration date of a product is approaching. For example, the reminding unit can issue reminders at appropriate times based on the user's schedule. This allows the reminding unit to issue reminders at appropriate times based on the acquired information.

[0078] The information analysis system includes a location information acquisition unit that acquires location information from the smartphone usage history. The location information acquisition unit acquires location information from the smartphone usage history. For example, the location information acquisition unit can acquire the user's current location and past movement history using GPS data, Wi-Fi location information, etc. The location information acquisition unit can also acquire location information from the smartphone's app usage history and browser history. For example, the location information acquisition unit can acquire information on places the user has visited and checked in. As a result, the location information acquisition unit can acquire location information from the smartphone usage history and provide suggestions and reminders appropriate for the user.

[0079] The information analysis system includes a text acquisition unit that acquires text from the smartphone usage history. The text acquisition unit acquires text from the smartphone usage history. For example, the text acquisition unit can acquire text from message history or browser history. The text acquisition unit can also acquire text from the smartphone app usage history. For example, the text acquisition unit can acquire text information from web pages or apps viewed by the user. This allows the text acquisition unit to acquire text from the smartphone usage history and provide suggestions and reminders appropriate for the user.

[0080] The analysis unit can analyze the information acquired by the location information acquisition unit and the text acquisition unit. The analysis unit analyzes the information acquired by the location information acquisition unit and the text acquisition unit. For example, the analysis unit can analyze the acquired text information using text mining technology. The analysis unit can also analyze the acquired location information using data analysis technology. For example, the analysis unit can analyze the user's movement patterns and behavior history and make suggestions and reminders based on the results. This allows the analysis unit to analyze the acquired information and make suggestions and reminders appropriate for the user.

[0081] The notification unit can provide suggestions and reminders based on the information analyzed by the analysis unit. The notification unit provides suggestions and reminders based on the information analyzed by the analysis unit. For example, the notification unit can provide suggestions at appropriate times based on the user's behavioral history. The notification unit can also provide reminders based on the user's schedule. For example, the notification unit can provide notifications at optimal times by referring to the user's calendar information. This allows the notification unit to provide suggestions and reminders at appropriate times based on the analyzed information.

[0082] The information analysis system includes a reward unit that rewards notifications that a user has given a high rating and app developers that provide the information. The reward unit rewards notifications that a user has given a high rating and app developers that provide the information. For example, when a user gives a high rating to a specific notification, the reward unit can reward the app developer that provided the notification with points or a monetary reward. The reward unit can also adjust the content of the reward based on user feedback. For example, when a user gives a high rating to a specific notification, the reward unit can provide an interesting reward when the user is relaxed. In this way, the reward unit can improve the motivation of app developers by giving rewards based on the user's high rating.

[0083] The conversion unit can estimate the user's emotions and adjust the OCR accuracy based on the estimated user's emotions. The conversion unit can estimate the user's emotions and adjust the OCR accuracy based on the estimated user's emotions. For example, if the user is feeling stressed, the conversion unit can increase the OCR accuracy to reduce false recognition. Also, if the user is relaxed, the conversion unit can set the OCR accuracy to normal and prioritize processing speed. Furthermore, if the user is in a hurry, the conversion unit can lower the OCR accuracy to maximize processing speed. In this way, the conversion unit can reduce false recognition by adjusting the OCR accuracy according to the user's emotions.

[0084] The conversion unit can apply different OCR algorithms depending on the type of image. The conversion unit applies different OCR algorithms depending on the type of image. For example, the conversion unit can apply an OCR algorithm specialized for handwritten character recognition to an image of handwritten characters. The conversion unit can also apply a highly accurate printed character recognition algorithm to an image of printed text. Furthermore, the conversion unit can apply a hybrid algorithm that can recognize both handwritten and printed characters to an image that contains a mixture of handwritten and printed characters. This allows the conversion unit to improve recognition accuracy by applying the optimal OCR algorithm depending on the type of image.

[0085] The conversion unit can automatically adjust the light conditions and angle when capturing an image to obtain an optimal image. The conversion unit automatically adjusts the light conditions and angle when capturing an image to obtain an optimal image. For example, if there is insufficient light, the conversion unit can automatically adjust the exposure of the camera to ensure brightness. In addition, if there is backlight, the conversion unit can automatically adjust the angle of the camera to ensure optimal shooting conditions. Furthermore, if the image is blurry, the conversion unit can automatically adjust the focus of the camera to obtain a clear image. As a result, the conversion unit can automatically adjust the light conditions and angle to obtain an optimal image and improve the accuracy of OCR.

[0086] The conversion unit can estimate the user's emotion and determine the priority of text to be converted based on the estimated user's emotion. The conversion unit can estimate the user's emotion and determine the priority of text to be converted based on the estimated user's emotion. For example, when the user is feeling stressed, the conversion unit can preferentially convert important text. Also, when the user is relaxed, the conversion unit can convert all text equally. Furthermore, when the user is in a hurry, the conversion unit can preferentially convert short text. In this way, the conversion unit can preferentially convert important information by determining the priority of text according to the user's emotion.

[0087] The conversion unit can preferentially convert highly relevant images by taking into account the user's geographical location information when capturing an image. The conversion unit can preferentially convert highly relevant images by taking into account the user's geographical location information when capturing an image. For example, if the user is in a specific location, the conversion unit can preferentially convert text related to the location. Furthermore, if the user is traveling, the conversion unit can preferentially convert text related to tourist spots. Furthermore, if the user is at work, the conversion unit can preferentially convert work-related text. In this way, the conversion unit can preferentially convert highly relevant information by taking into account the user's geographical location information.

[0088] The conversion unit can analyze the user's social media activity when capturing an image and prioritize converting related images. The conversion unit can analyze the user's social media activity when capturing an image and prioritize converting related images. For example, the conversion unit can prioritize converting images that the user has shared on social media. The conversion unit can also prioritize converting images that the user has tagged on social media. Furthermore, the conversion unit can prioritize converting images that the user has commented on on social media. In this way, the conversion unit can prioritize converting highly relevant information by analyzing the user's social media activity.

[0089] The analysis unit can estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the analysis method based on the estimated user's emotion. For example, the analysis unit can provide a concise analysis result when the user is feeling stressed. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a quick analysis result when the user is in a hurry. In this way, the analysis unit can provide an appropriate analysis result by adjusting the analysis method according to the user's emotion.

[0090] The analysis unit can apply different NLP algorithms depending on the content of the text. The analysis unit applies different NLP algorithms depending on the content of the text. For example, the analysis unit can apply a sentiment analysis algorithm to text that requires sentiment analysis. The analysis unit can also apply a semantic analysis algorithm to text that requires semantic analysis. Furthermore, the analysis unit can apply a keyword extraction algorithm to text that requires keyword extraction. In this way, the analysis unit can improve analysis accuracy by applying the optimal NLP algorithm depending on the content of the text.

[0091] The analysis unit can adjust the level of analysis detail depending on the length and complexity of the text. The analysis unit adjusts the level of analysis detail depending on the length and complexity of the text. For example, the analysis unit can adjust the level of analysis detail by applying a summarization algorithm to long text. The analysis unit can also perform detailed analysis on short text. Furthermore, the analysis unit can perform detailed analysis on complex text by combining multiple analysis algorithms. In this way, the analysis unit can provide appropriate analysis results by adjusting the level of analysis detail depending on the length and complexity of the text.

[0092] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user's emotions. The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing important text. Also, if the user is relaxed, the analysis unit can analyze all texts equally. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing short texts. In this way, the analysis unit can prioritize analyzing important information by determining the analysis priorities according to the user's emotions.

[0093] When analyzing text, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral history. When analyzing text, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral history. For example, the analysis unit can improve the accuracy of the analysis based on keywords that the user frequently used in the past. The analysis unit can also improve the accuracy of the analysis by extracting related information from the user's past behavioral history. Furthermore, the analysis unit can analyze the user's past behavioral patterns and improve the accuracy of the analysis. In this way, the analysis unit can improve the accuracy of the analysis by referring to the user's past behavioral history.

[0094] The analysis unit can improve the accuracy of the analysis by referring to related external data when analyzing text. The analysis unit can improve the accuracy of the analysis by referring to related external data when analyzing text. For example, the analysis unit can improve the accuracy of the text analysis by referring to external news data. The analysis unit can also improve the accuracy of the text analysis by referring to external social media data. Furthermore, the analysis unit can improve the accuracy of the text analysis by referring to an external specialized database. In this way, the analysis unit can improve the accuracy of the analysis by referring to related external data.

[0095] The suggestion unit can estimate the user's emotions and adjust the content of the suggestion based on the estimated user's emotions. The suggestion unit can estimate the user's emotions and adjust the content of the suggestion based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can make a suggestion that will help the user relax. Also, if the user is relaxed, the suggestion unit can make an interesting suggestion. Furthermore, if the user is in a hurry, the suggestion unit can make a suggestion that can be quickly implemented. In this way, the suggestion unit can make more appropriate suggestions by adjusting the content of the suggestion according to the user's emotions.

[0096] The suggestion unit can apply different suggestion algorithms depending on the content of the suggestion. The suggestion unit can apply different suggestion algorithms depending on the content of the suggestion. For example, the suggestion unit can apply a shopping algorithm to a suggestion related to shopping. The suggestion unit can also apply a restaurant algorithm to a suggestion related to restaurants. Furthermore, the suggestion unit can apply a travel algorithm to a suggestion related to travel. In this way, the suggestion unit can improve the accuracy of the suggestion by applying the optimal suggestion algorithm depending on the content of the suggestion.

[0097] The suggestion unit can optimize the timing of the suggestion based on the user's schedule. The suggestion unit optimizes the timing of the suggestion based on the user's schedule. For example, the suggestion unit can make the suggestion at the optimal timing by referring to the user's calendar information. The suggestion unit can also adjust the timing of the suggestion to match the user's schedule. Furthermore, the suggestion unit can optimize the timing of the suggestion based on the user's schedule. In this way, the suggestion unit can make more effective suggestions by optimizing the timing of the suggestion based on the user's schedule.

[0098] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. For example, when the user is feeling stressed, the suggestion unit can prioritize important suggestions. Furthermore, when the user is relaxed, the suggestion unit can equally prioritize all suggestions. Furthermore, when the user is in a hurry, the suggestion unit can prioritize suggestions that can be implemented quickly. In this way, the suggestion unit can prioritize important suggestions by determining the priority of suggestions according to the user's emotions.

[0099] The suggestion unit can customize the content of the suggestion based on the geographical location information of the user. The suggestion unit customizes the content of the suggestion based on the geographical location information of the user. For example, when the user is in a specific location, the suggestion unit can make suggestions related to the location. Furthermore, when the user is traveling, the suggestion unit can make suggestions related to tourist spots. Furthermore, when the user is at work, the suggestion unit can make suggestions related to work. In this way, the suggestion unit can make more relevant suggestions by customizing the content of the suggestion based on the geographical location information of the user.

[0100] The suggestion unit can customize the content of the suggestions based on the user's social media activity. The suggestion unit customizes the content of the suggestions based on the user's social media activity. For example, the suggestion unit can make suggestions based on information shared by the user on social media. The suggestion unit can also make suggestions based on information tagged by the user on social media. Furthermore, the suggestion unit can make suggestions based on information commented on by the user on social media. In this way, the suggestion unit can make more relevant suggestions by customizing the content of the suggestions based on the user's social media activity.

[0101] The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. The notification unit can estimate the user's emotion and adjust the notification method based on the estimated user's emotion. For example, the notification unit can use a quiet notification sound when the user is feeling stressed. The notification unit can also use a normal notification sound when the user is relaxed. Furthermore, the notification unit can use a noticeable notification sound when the user is in a hurry. In this way, the notification unit can adjust the notification method according to the user's emotion, thereby providing more appropriate notifications.

[0102] The notification unit can apply different notification algorithms depending on the content of the notification. The notification unit applies different notification algorithms depending on the content of the notification. For example, the notification unit can apply a notification algorithm that performs highlighting to important notifications. The notification unit can also apply a standard notification algorithm to normal notifications. Furthermore, the notification unit can apply a notification algorithm that performs immediate notification to urgent notifications. In this way, the notification unit can improve the accuracy of notifications by applying the optimal notification algorithm depending on the content of the notification.

[0103] The notification unit can optimize the timing of notifications based on the user's schedule. The notification unit optimizes the timing of notifications based on the user's schedule. For example, the notification unit can refer to the user's calendar information and provide notifications at the optimal timing. The notification unit can also adjust the timing of notifications to match the user's schedule. Furthermore, the notification unit can optimize the timing of notifications based on the user's plans. As a result, the notification unit can provide more effective notifications by optimizing the timing of notifications based on the user's schedule.

[0104] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. For example, when the user is feeling stressed, the notification unit can prioritize important notifications. Furthermore, when the user is relaxed, the notification unit can also distribute all notifications equally. Furthermore, when the user is in a hurry, the notification unit can prioritize notifications that require a quick response. In this way, the notification unit can prioritize important notifications by determining the priority of notifications according to the user's emotion.

[0105] The notification unit can customize the content of the notification based on the user's geographical location information. The notification unit customizes the content of the notification based on the user's geographical location information. For example, when the user is in a specific location, the notification unit can provide a notification related to the location. Furthermore, when the user is traveling, the notification unit can provide a notification related to tourist spots. Furthermore, when the user is at work, the notification unit can provide a work-related notification. In this way, the notification unit can provide more relevant notifications by customizing the content of the notification based on the user's geographical location information.

[0106] The notification unit can customize the content of the notification based on the user's social media activity. The notification unit customizes the content of the notification based on the user's social media activity. For example, the notification unit can provide a notification based on information shared by the user on social media. The notification unit can also provide a notification based on information tagged by the user on social media. Furthermore, the notification unit can provide a notification based on information commented on by the user on social media. In this way, the notification unit can provide more relevant notifications by customizing the content of the notification based on the user's social media activity.

[0107] The acquisition unit can estimate the user's emotion and determine the priority of information to be acquired based on the estimated user's emotion. The acquisition unit can estimate the user's emotion and determine the priority of information to be acquired based on the estimated user's emotion. For example, when the user is feeling stressed, the acquisition unit can prioritize acquiring important information. Furthermore, when the user is relaxed, the acquisition unit can also acquire all information equally. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring information that can be acquired quickly. In this way, the acquisition unit can prioritize acquiring important information by determining the priority of information according to the user's emotion.

[0108] The acquisition unit can apply different acquisition algorithms depending on the content of the text. The acquisition unit applies different acquisition algorithms depending on the content of the text. For example, the acquisition unit can apply an algorithm that can be quickly acquired to important text. The acquisition unit can also apply a standard acquisition algorithm to ordinary text. Furthermore, the acquisition unit can apply an acquisition algorithm that performs detailed analysis to complex text. In this way, the acquisition unit can improve acquisition accuracy by applying the optimal acquisition algorithm depending on the content of the text.

[0109] The acquisition unit can adjust the level of detail of acquisition according to the length and complexity of the text. The acquisition unit adjusts the level of detail of acquisition according to the length and complexity of the text. For example, the acquisition unit can adjust the level of detail of acquisition by applying a summarization algorithm to long text. The acquisition unit can also perform detailed acquisition for short text. Furthermore, the acquisition unit can perform detailed acquisition by combining multiple acquisition algorithms for complex text. In this way, the acquisition unit can acquire appropriate information by adjusting the level of detail of acquisition according to the length and complexity of the text.

[0110] The acquisition unit can estimate the user's emotion and adjust the display method of the acquired information based on the estimated user's emotion. The acquisition unit can estimate the user's emotion and adjust the display method of the acquired information based on the estimated user's emotion. For example, the acquisition unit can provide a simple display method when the user is feeling stressed. Furthermore, the acquisition unit can provide a detailed display method when the user is relaxed. Furthermore, the acquisition unit can provide a display method that allows the user to quickly check information when the user is in a hurry. In this way, the acquisition unit can provide more appropriate information by adjusting the display method of information according to the user's emotion.

[0111] The acquisition unit can improve the accuracy of acquisition by referring to the user's past behavioral history when acquiring text. The acquisition unit can improve the accuracy of acquisition by referring to the user's past behavioral history when acquiring text. For example, the acquisition unit can improve the accuracy of acquisition based on keywords that the user frequently used in the past. The acquisition unit can also improve the accuracy of acquisition by extracting related information from the user's past behavioral history. Furthermore, the acquisition unit can improve the accuracy of acquisition by analyzing the user's past behavioral patterns. As a result, the acquisition unit can improve the accuracy of acquisition by referring to the user's past behavioral history.

[0112] The reminding unit can estimate the user's emotions and adjust the content of the reminder based on the estimated user's emotions. The reminding unit can estimate the user's emotions and adjust the content of the reminder based on the estimated user's emotions. For example, if the user is feeling stressed, the reminding unit can prioritize important reminders. Also, if the user is relaxed, the reminding unit can distribute all reminders equally. Furthermore, if the user is in a hurry, the reminding unit can prioritize reminders that require a quick response. In this way, the reminding unit can adjust the content of the reminder according to the user's emotions, thereby providing more appropriate reminders.

[0113] The reminding unit can apply different reminding algorithms depending on the content of the reminder. The reminding unit applies different reminding algorithms depending on the content of the reminder. For example, the reminding unit can apply a reminding algorithm that highlights important reminders. The reminding unit can also apply a standard reminding algorithm to normal reminders. Furthermore, the reminding unit can apply a reminding algorithm that provides immediate reminders to urgent reminders. In this way, the reminding unit can improve the accuracy of reminders by applying the optimal reminding algorithm depending on the content of the reminder.

[0114] The reminding unit can optimize the timing of reminders based on the user's schedule. The reminding unit optimizes the timing of reminders based on the user's schedule. For example, the reminding unit can refer to the user's calendar information to remind at the optimal timing. The reminding unit can also adjust the timing of reminders to match the user's schedule. Furthermore, the reminding unit can optimize the timing of reminders based on the user's plans. As a result, the reminding unit can provide more effective reminders by optimizing the timing of reminders based on the user's schedule.

[0115] The reminding unit can estimate the user's emotions and determine the priority of reminders based on the estimated user's emotions. The reminding unit can estimate the user's emotions and determine the priority of reminders based on the estimated user's emotions. For example, if the user is feeling stressed, the reminding unit can prioritize important reminders. Also, if the user is relaxed, the reminding unit can distribute all reminders equally. Furthermore, if the user is in a hurry, the reminding unit can prioritize reminders that require a quick response. In this way, the reminding unit can prioritize important reminders by determining the priority of reminders according to the user's emotions.

[0116] The reminding unit can customize the content of the reminder based on the geographical location information of the user. The reminding unit customizes the content of the reminder based on the geographical location information of the user. For example, when the user is in a specific location, the reminding unit can provide a reminder related to the location. Furthermore, when the user is traveling, the reminding unit can provide a reminder related to tourist spots. Furthermore, when the user is at work, the reminding unit can provide a work-related reminder. In this way, the reminding unit can provide more relevant reminders by customizing the content of the reminder based on the geographical location information of the user.

[0117] The location information acquisition unit can estimate the user's emotion and adjust the timing of acquiring location information based on the estimated user's emotion. The location information acquisition unit can estimate the user's emotion and adjust the timing of acquiring location information based on the estimated user's emotion. For example, if the user is feeling stressed, the location information acquisition unit can frequently acquire location information to provide a sense of security. Also, if the user is relaxed, the location information acquisition unit can acquire location information at a normal frequency. Furthermore, if the user is in a hurry, the location information acquisition unit can quickly acquire location information to provide an optimal route. In this way, the location information acquisition unit can provide more appropriate location information by adjusting the timing of acquiring location information according to the user's emotion.

[0118] When acquiring location information, the location information acquisition unit can select the optimal acquisition method by referring to the user's past movement history. When acquiring location information, the location information acquisition unit can select the optimal acquisition method by referring to the user's past movement history. For example, the location information acquisition unit can acquire location information based on places that the user has frequently visited in the past. The location information acquisition unit can also suggest routes that avoid congestion based on the user's past movement history. Furthermore, the location information acquisition unit can analyze the user's past movement patterns and select the most efficient location information acquisition method. As a result, the location information acquisition unit can select the optimal location information acquisition method by referring to the user's past movement history.

[0119] The location information acquisition unit can estimate the user's emotion and determine the priority of location information to be acquired based on the estimated user's emotion. The location information acquisition unit can estimate the user's emotion and determine the priority of location information to be acquired based on the estimated user's emotion. For example, when the user is feeling stressed, the location information acquisition unit can prioritize acquiring important location information. Furthermore, when the user is relaxed, the location information acquisition unit can also acquire all location information equally. Furthermore, when the user is in a hurry, the location information acquisition unit can prioritize acquiring location information that can be acquired quickly. In this way, the location information acquisition unit can prioritize acquiring important location information by determining the priority of location information according to the user's emotion.

[0120] The location information acquisition unit can analyze the user's social media activity and acquire related location information when acquiring location information. The location information acquisition unit can analyze the user's social media activity and acquire related location information when acquiring location information. For example, the location information acquisition unit can acquire location information of places the user has shared on social media. The location information acquisition unit can also acquire location information of places the user has tagged on social media. Furthermore, the location information acquisition unit can acquire location information of places the user has commented on on social media. In this way, the location information acquisition unit can acquire highly relevant location information by analyzing the user's social media activity.

[0121] The text acquisition unit can estimate the user's emotion and adjust the timing of text acquisition based on the estimated user's emotion. The text acquisition unit can estimate the user's emotion and adjust the timing of text acquisition based on the estimated user's emotion. For example, when the user is feeling stressed, the text acquisition unit can acquire text frequently to provide a sense of security. When the user is relaxed, the text acquisition unit can also acquire text at a normal frequency. Furthermore, when the user is in a hurry, the text acquisition unit can quickly acquire text to provide optimal information. As a result, the text acquisition unit can provide more appropriate information by adjusting the timing of text acquisition according to the user's emotion.

[0122] When acquiring text, the text acquisition unit can select the optimal acquisition method by referring to the user's past text history. When acquiring text, the text acquisition unit selects the optimal acquisition method by referring to the user's past text history. For example, the text acquisition unit can acquire text based on keywords that the user has frequently used in the past. The text acquisition unit can also acquire text by extracting related information from the user's past text history. Furthermore, the text acquisition unit can analyze the user's past text patterns and select the most efficient text acquisition method. As a result, the text acquisition unit can select the optimal text acquisition method by referring to the user's past text history.

[0123] The text acquisition unit can estimate the user's emotion and determine the priority of text to be acquired based on the estimated user's emotion. The text acquisition unit can estimate the user's emotion and determine the priority of text to be acquired based on the estimated user's emotion. For example, when the user is feeling stressed, the text acquisition unit can prioritize acquiring important text. Furthermore, when the user is relaxed, the text acquisition unit can also acquire all text equally. Furthermore, when the user is in a hurry, the text acquisition unit can prioritize acquiring text that can be acquired quickly. In this way, the text acquisition unit can prioritize acquiring important text by determining the priority of text according to the user's emotion.

[0124] The text acquisition unit can analyze the user's social media activity and acquire related text when acquiring text. The text acquisition unit can analyze the user's social media activity and acquire related text when acquiring text. For example, the text acquisition unit can acquire text of information shared by the user on social media. The text acquisition unit can also acquire text of information tagged by the user on social media. Furthermore, the text acquisition unit can acquire text of information commented on by the user on social media. In this way, the text acquisition unit can acquire highly relevant text by analyzing the user's social media activity.

[0125] The reward unit can estimate the user's emotion and adjust the content of the reward based on the estimated user's emotion. The reward unit can estimate the user's emotion and adjust the content of the reward based on the estimated user's emotion. For example, if the user is feeling stressed, the reward unit can provide a reward that helps the user relax. Also, if the user is relaxed, the reward unit can provide an interesting reward. Furthermore, if the user is in a hurry, the reward unit can provide a reward that can be received quickly. In this way, the reward unit can provide a more appropriate reward by adjusting the content of the reward according to the user's emotion.

[0126] The reward unit can apply different reward algorithms depending on the content of the reward. The reward unit applies different reward algorithms depending on the content of the reward. For example, the reward unit can apply a reward algorithm that performs highlighting to important rewards. The reward unit can also apply a standard reward algorithm to ordinary rewards. Furthermore, the reward unit can apply a reward algorithm that adds special effects to special rewards. In this way, the reward unit can improve the accuracy of rewards by applying the optimal reward algorithm depending on the content of the reward.

[0127] The reward unit can estimate the user's emotions and determine the priority of rewards based on the estimated user's emotions. The reward unit can estimate the user's emotions and determine the priority of rewards based on the estimated user's emotions. For example, when the user is feeling stressed, the reward unit can prioritize providing important rewards. Also, when the user is relaxed, the reward unit can provide all rewards equally. Furthermore, when the user is in a hurry, the reward unit can prioritize providing rewards that can be received quickly. In this way, the reward unit can prioritize providing important rewards by determining the priority of rewards according to the user's emotions.

[0128] The reward unit can customize the content of the reward based on the user's geographic location information. The reward unit customizes the content of the reward based on the user's geographic location information. For example, when the user is in a specific location, the reward unit can provide a reward related to the location. Also, when the user is traveling, the reward unit can provide a reward related to a tourist spot. Furthermore, when the user is at work, the reward unit can provide a reward related to work. In this way, the reward unit can provide more relevant rewards by customizing the content of the reward based on the user's geographic location information. === Hard Collateral 1-1 === Each of the multiple elements, including the conversion unit, analysis unit, suggestion unit, notification unit, acquisition unit, location information acquisition unit, text acquisition unit, reward unit, and remind unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the conversion unit acquires an image using the camera 42 of the smart device 14, and the specific processing unit 290 of the data processing device 12 converts the image into text using OCR technology. The analysis unit analyzes the text using NLP technology by the specific processing unit 290 of the data processing device 12. The suggestion unit makes suggestions suitable for the user based on the analyzed information, and the notification unit notifies the user of the suggested information via the output device 40 of the smart device 14. The acquisition unit acquires periodic actions and expiration dates from the stored text, and the location information acquisition unit acquires location information using GPS data of the smart device 14. The text acquisition unit acquires text from the usage history of the smart device 14, and the reward unit provides a reward based on the user's high rating. The reminder unit provides reminders at appropriate times based on the acquired information. === Hard Collateral 1-2 === Each of the multiple elements, including the conversion unit, analysis unit, suggestion unit, notification unit, acquisition unit, location information acquisition unit, text acquisition unit, reward unit, and remind 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 conversion unit acquires an image using the camera 42 of the smart glasses 214, and the specific processing unit 290 of the data processing device 12 converts the image into text using OCR technology. The analysis unit analyzes the text using NLP technology by the specific processing unit 290 of the data processing device 12. The suggestion unit makes suggestions suitable for the user based on the analyzed information, and the notification unit notifies the user of the suggested information through the speaker 240 of the smart glasses 214. The acquisition unit acquires periodic actions and expiration dates from the stored text, and the location information acquisition unit acquires location information using GPS data of the smart glasses 214. The text acquisition unit acquires text from the usage history of the smart glasses 214, and the reward unit provides a reward based on the user's high rating. The reminder unit provides reminders at appropriate times based on the acquired information. === Hard Collateral 1-3 === Each of the multiple elements, including the conversion unit, analysis unit, suggestion unit, notification unit, acquisition unit, location information acquisition unit, text acquisition unit, reward unit, and remind unit, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the conversion unit acquires an image using the camera 42 of the headset type terminal 314, and the specific processing unit 290 of the data processing device 12 converts the image into text using OCR technology. The analysis unit analyzes the text using NLP technology by the specific processing unit 290 of the data processing device 12. The suggestion unit makes suggestions suitable for the user based on the analyzed information, and the notification unit notifies the user of the suggested information through the speaker 240 of the headset type terminal 314. The acquisition unit acquires periodic actions and expiration dates from the stored text, and the location information acquisition unit acquires location information using GPS data of the headset type terminal 314. The text acquisition unit acquires text from the usage history of the headset type terminal 314, and the reward unit provides a reward based on the user's high rating. The reminder unit provides reminders at appropriate times based on the acquired information. === Hard Collateral 1-4 === Each of the multiple elements, including the conversion unit, analysis unit, suggestion unit, notification unit, acquisition unit, location information acquisition unit, text acquisition unit, reward unit, and remind unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the conversion unit acquires an image using the camera 42 of the robot 414, and the specific processing unit 290 of the data processing device 12 converts the image into text using OCR technology. The analysis unit analyzes the text using NLP technology by the specific processing unit 290 of the data processing device 12. The suggestion unit makes suggestions suitable for the user based on the analyzed information, and the notification unit notifies the user of the suggested information through the speaker 240 of the robot 414. The acquisition unit acquires periodic actions and expiration dates from the stored text, and the location information acquisition unit acquires location information using GPS data of the robot 414. The text acquisition unit acquires text from the usage history of the robot 414, and the reward unit provides a reward based on the user's high rating. The reminder unit provides reminders at appropriate times based on the acquired information.

[0129] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0130] The information analysis system may also include a voice analysis unit that analyzes the user's voice data. The voice analysis unit can analyze the user's conversations and voice memos and convert them into text data. For example, the voice analysis unit can convert voice recorded by the user during a meeting into text and save it as meeting minutes. The voice analysis unit can also convert notes entered by the user by voice while driving into text so that they can be checked later. Furthermore, the voice analysis unit can analyze the user's voice commands and operate the system by voice. In this way, the information analysis system can improve user convenience by analyzing voice data.

[0131] The information analysis system may also include a health data acquisition unit that acquires the user's health data. The health data acquisition unit can acquire the user's heart rate, step count, sleep data, and the like from devices such as smartwatches and fitness trackers. For example, the health data acquisition unit can monitor the user's heart rate and notify the user if an abnormality is detected. The health data acquisition unit can also count the user's steps and remind the user to achieve their goals. Furthermore, the health data acquisition unit can analyze the user's sleep data and make suggestions to improve the quality of their sleep. In this way, the information analysis system can support health management by acquiring the user's health data.

[0132] The information analysis system can estimate the user's emotions and adjust the content of notifications based on the estimated user's emotions. For example, if the user is feeling stressed, the notification unit can notify the user of relaxing music or meditation suggestions. If the user is feeling relaxed, the notification unit can also notify the user of interesting news or articles. Furthermore, if the user is in a hurry, the notification unit can prioritize notifications of important tasks or schedules. In this way, the notification unit can provide more appropriate information by adjusting the content of notifications according to the user's emotions.

[0133] The information analysis system may also include a purchase history analysis unit that analyzes a user's purchase history. The purchase history analysis unit analyzes data on products and services purchased by the user in the past, and can grasp the user's purchasing trends. For example, the purchase history analysis unit can identify products that the user frequently purchases and notify the user that those products are on sale. The purchase history analysis unit can also remind the user of the expiration dates of products purchased by the user in the past. Furthermore, the purchase history analysis unit can suggest new products and services based on the user's purchasing trends. This allows the information analysis system to make more personalized suggestions by analyzing the user's purchase history.

[0134] The information analysis system can estimate the user's emotions and adjust the timing of reminders based on the estimated user emotions. For example, if the user is feeling stressed, the reminder unit can reduce the frequency of reminders to reduce the burden on the user. Also, if the user is relaxed, the reminder unit can provide reminders at a normal frequency. Furthermore, if the user is in a hurry, the reminder unit can prioritize important reminders. This allows the reminder unit to provide more effective reminders by adjusting the timing of reminders according to the user's emotions.

[0135] The information analysis system may also include a social media analysis unit that analyzes a user's social media activities. The social media analysis unit analyzes information, comments, and tagged information shared by the user on social media to understand the user's interests. For example, the social media analysis unit may analyze articles and posts shared by the user to suggest related news and articles. The social media analysis unit may also analyze comments made by the user to suggest related events and activities. Furthermore, the social media analysis unit may analyze tagged information by the user to suggest related products and services. This allows the information analysis system to make more personalized suggestions by analyzing the user's social media activities.

[0136] The information analysis system can estimate the user's emotions and adjust the content of suggestions based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest activities or places where the user can relax. Also, if the user is relaxed, the suggestion unit can suggest events or activities that will pique the user's interest. Furthermore, if the user is in a hurry, the suggestion unit can suggest tasks or activities that can be performed quickly. In this way, the suggestion unit can make more appropriate suggestions by adjusting the content of suggestions according to the user's emotions.

[0137] The information analysis system may also include a location information analysis unit that analyzes the user's location information. The location information analysis unit analyzes the user's current location and past movement history to understand the user's behavioral patterns. For example, the location information analysis unit may identify places the user frequently visits and suggest information related to those places. The location information analysis unit may also analyze the user's movement patterns and suggest optimal routes and means of transportation. Furthermore, when the user is in a specific location, the location information analysis unit may suggest events and activities related to that location. This allows the information analysis system to make more personalized suggestions by analyzing the user's location information.

[0138] The information analysis system can estimate the user's emotions and adjust the content of the reward based on the estimated user's emotions. For example, if the user is feeling stressed, the reward unit can provide a reward that helps the user relax. Also, if the user is relaxed, the reward unit can provide an interesting reward. Furthermore, if the user is in a hurry, the reward unit can provide a reward that can be received quickly. In this way, the reward unit can provide more appropriate rewards by adjusting the content of the reward according to the user's emotions.

[0139] The information analysis system may also include a learning history analysis unit that analyzes the user's learning history. The learning history analysis unit can analyze the content the user has learned in the past and their progress, and make suggestions to improve the effectiveness of their learning. For example, the learning history analysis unit can suggest what content the user should learn next based on the content the user has learned in the past. The learning history analysis unit can also monitor the user's learning progress and remind them to achieve their goals. Furthermore, the learning history analysis unit can analyze the user's learning patterns and suggest effective learning methods. In this way, the information analysis system can improve the effectiveness of learning by analyzing the user's learning history.

[0140] The processing flow of the second embodiment will be briefly explained below.

[0141] Step 1: The converter converts images captured by the smart glasses' camera into text using OCR technology. For example, the converter can extract text information from images using OCR technologies such as Tesseract or ABBYY FineReader. The converter can also recognize both handwritten and printed text. Handwritten notes and notebooks can be converted into digital text using a handwritten text recognition algorithm, and printed documents and books can be converted into digital text using a printed text recognition algorithm. Step 2: The analysis unit uses NLP technology to analyze the text converted by the conversion unit. For example, the analysis unit can understand the content of the text using NLP techniques such as morphological analysis, grammatical analysis, and semantic analysis. It can also extract important information from the text using text mining technology. The analysis unit can extract keywords and phrases from the text and use them to understand the user's interests and concerns. Step 3: The suggestion unit makes suggestions suitable for the user based on the information analyzed by the analysis unit. For example, the suggestion unit can suggest products and services suitable for the user based on the user's past behavior history and current situation. It can also make suggestions at appropriate times based on the user's schedule and location information. Step 4: The notification unit notifies the user of the information suggested by the suggestion unit. For example, the notification unit can provide the user with information by push notification, email notification, alert, or other methods. The notification unit can also select the optimal notification method depending on the user's device. An appropriate notification method can be selected for devices such as smartphones, tablets, and smartwatches.

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0143] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0144] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0147] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0148] 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, RAM 30, and 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.

[0149] 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.

[0150] 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 audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] 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 carried out in a secure state.

[0153] Fig. 4 shows an example of the 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.

[0154] 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.

[0155] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0157] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0159] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0160] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0163] 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.

[0164] 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, RAM 30, and 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.

[0165] 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.

[0166] 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 audio according to instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0168] 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 carried out in a secure state.

[0169] 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.

[0170] 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.

[0171] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0172] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0173] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0174] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0175] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0176] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0179] 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.

[0180] 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, RAM 30, and 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.

[0181] 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.

[0182] 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 audio according to instructions from the processor 46.

[0183] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0184] 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 carried out in a secure state.

[0185] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0186] 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.

[0187] 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.

[0188] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0189] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0190] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0191] 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 voice indicating a user input regarding 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.

[0192] 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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0193] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0194] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0195] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0196] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0197] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0198] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0199] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0200] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0201] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0202] 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.

[0203] 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 USB (Universal Serial Bus) 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.

[0204] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0205] 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.

[0206] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0207] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0208] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0209] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0210] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0211] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0212] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0213] [Explanation of symbols]

[0214] 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 conversion unit converts images captured by the smart glasses camera into text using OCR technology, and an analysis unit that analyzes the text converted by the conversion unit using NLP technology; a suggestion unit that makes a suggestion suitable for the user based on the information analyzed by the analysis unit; a notification unit that notifies a user of the information proposed by the proposal unit. A system characterized by:

2. Equipped with an acquisition unit that acquires periodic actions, expiration dates, etc. from the saved text 2. The system of claim 1.

3. a reminding unit that notifies a reminder based on the information acquired by the acquisition unit; 3. The system of claim 2.

4. Equipped with a location information acquisition unit that acquires location information from smartphone usage history 2. The system of claim 1.

5. Equipped with a text acquisition unit that acquires text from smartphone usage history 2. The system of claim 1.

6. an analysis unit that analyzes the information acquired by the location information acquisition unit and the text acquisition unit; 5. The system of claim 4.

7. A notification unit that notifies suggestions and reminders based on the information analyzed by the analysis unit. The system of claim 6 .

8. It has a reward section that rewards notifications that users have rated highly and the app developers who provided that information.

2. The system of claim 1.

9. The conversion unit Estimate user emotions and adjust OCR accuracy based on the estimated user emotions 2. The system of claim 1.

10. The conversion unit Apply different OCR algorithms depending on the type of image 2. The system of claim 1.

Images