system

Abstract

The system according to the embodiment aims to identify important matters and notify the user of them. [Solution] A system according to an embodiment includes an identifying unit, a notifying unit, and a providing unit. The identifying unit identifies important cases. The notifying unit notifies the cases identified by the identifying unit. The providing unit provides related information related to the cases notified by the notifying 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] With conventional technology, there was a problem that important matters could get buried among the many emails and messages and be overlooked.

[0005] The system according to the embodiment aims to identify important matters and notify the user of them. [Means for solving the problem]

[0006] The system according to the embodiment includes an identifying unit, a notifying unit, and a providing unit. The identifying unit identifies an important case. The notifying unit notifies the case identified by the identifying unit. The providing unit provides related information related to the case notified by the notifying unit. [Effects of the Invention]

[0007] The system according to the embodiment can identify important cases and notify the user. [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) A system according to an embodiment of the present invention functions like a butler or secretary, picking out important matters buried in emails and messaging apps, displaying them on a smartphone or smartwatch, and engaging in voice conversations. This system prevents important notices, such as flight cancellations and delays, emergency calls from schools and daycare centers, and unauthorized account use, from getting lost in the clutter of emails and messages. After the user confirms the notice, the system suggests related website information and phone numbers. For example, the system scans the user's emails and messaging apps to identify important matters. For example, it extracts messages containing keywords such as flight cancellations and delays, emergency calls from schools and daycare centers, and unauthorized account use. AI then analyzes the message content using natural language processing to determine its importance. Next, the identified important matters are notified to the smartphone or smartwatch. Notifications are not only visually displayed, but also audibly. For example, a voice prompt such as, "You have received a flight cancellation notice. Would you like to check the details?" is heard. When the user confirms the notice, the system suggests related website information and phone numbers. For example, if a flight is canceled, the airline's website and customer support phone number will be displayed, allowing users to respond quickly. This system prevents important matters from being buried and enables users to respond quickly. For example, an emergency call from a school can be responded to immediately without being overlooked. Also, if a notification of account fraud is received, countermeasures can be taken quickly. This allows the system to quickly identify important matters, notify users, and provide relevant information.

[0029] The system according to the embodiment includes an identification unit, a notification unit, and a providing unit. The identification unit identifies important matters. For example, the identification unit scans a user's emails or messaging apps to identify important matters. The identification unit analyzes the content of the messages using natural language processing and determines their importance. For example, the identification unit extracts messages containing keywords such as flight cancellations or delays, emergency calls from schools or daycare centers, and unauthorized account use. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The notification unit notifies the user of the matters identified by the identification unit. The notification unit notifies the user of the matters identified by the identification unit. The notification unit notifies the user of the matters notified by the identification unit not only visually but also through audio guidance. For example, the notification unit may provide audio guidance such as, "You have received a flight cancellation notice. Would you like to check the details?" The audio guidance is realized using text-to-speech or speech synthesis technology. The providing unit provides related information related to the matters notified by the notification unit. For example, in the case of a flight cancellation notice, the providing unit displays the airline's website and customer support phone number. The providing unit provides site information and phone number guidance. The site information includes a URL and a site summary. The telephone number is provided in a format such as an international telephone number format or an area code. As a result, the system according to the embodiment identifies important matters, notifies the user, and provides related information, allowing the user to respond quickly. Some or all of the above-described processing in the identification unit, notification unit, and provision unit may be performed using AI, or may be performed without AI. For example, the identification unit may analyze the content of a message and determine its importance using an AI model that performs natural language processing. The notification unit may generate voice guidance using an AI model that provides voice guidance. The provision unit may suggest site information and telephone numbers using an AI model that provides related information. As a result, the system may quickly identify important matters, notify the user, and provide related information.

[0030] The identification unit may include an analysis unit that analyzes a message using natural language processing and determines its importance. The analysis unit analyzes the message using natural language processing and determines its importance. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit may use morphological analysis to divide the words in the message, grammatical analysis to analyze the structure of the sentence, and semantic analysis to understand the meaning of the sentence. The analysis unit analyzes the content of the message and determines its importance. The importance is determined using methods such as scoring or rule-based evaluation. For example, the analysis unit may assign a score based on the content of the message and determine messages with high scores as important. The analysis unit may also use rule-based evaluation to determine messages containing specific keywords as important. In this way, the analysis unit can determine the importance of the message with high accuracy by using natural language processing. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit may analyze the content of the message and determine its importance using an AI model that performs natural language processing. This allows the analysis unit to use natural language processing to determine the importance of a message with high accuracy.

[0031] The notification unit can include a voice guidance unit that provides voice guidance. The voice guidance unit provides voice guidance. The voice guidance is realized using text-to-speech or voice synthesis technology. For example, the voice guidance unit converts text into voice using text-to-speech technology. The voice guidance unit can also generate natural voice using voice synthesis technology. For example, the voice guidance unit converts text into natural voice using voice synthesis technology. This allows the voice guidance unit to provide voice guidance. Some or all of the above-mentioned processing in the voice guidance unit may be performed using AI, or may be performed without using AI. For example, the voice guidance unit can generate voice guidance using an AI model that provides voice guidance. This allows the voice guidance unit to provide voice guidance.

[0032] The providing unit may include a guidance unit that provides guidance on site information or telephone numbers. The guidance unit provides guidance on site information or telephone numbers. Site information includes a URL, a site summary, etc. For example, the guidance unit provides the URL of an airline's website. The guidance unit can also display a site summary. For example, the guidance unit displays a summary of the airline's website. Phone numbers are provided in a format such as an international telephone number format or an area code. For example, the guidance unit provides a customer support telephone number. The guidance unit can also adjust the format of the phone number. For example, the guidance unit provides a telephone number in international telephone number format. This allows the guidance unit to provide guidance on site information or telephone numbers. Some or all of the above-described processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit can suggest site information or telephone numbers using an AI model that provides related information. This allows the guidance unit to provide guidance on site information or telephone numbers.

[0033] The providing unit may include an operation support unit that supports user operations. The operation support unit supports user operations. The operation support is provided in the form of a guide, a help message, a tutorial, or the like. For example, the operation support unit provides a guide. The guide explains how to use the system and operation procedures. The operation support unit can also display a help message. The help message provides detailed explanations about specific operations. Furthermore, the operation support unit can also provide a tutorial. The tutorial explains basic operation methods of the system step by step. This allows the operation support unit to support user operations. Some or all of the above-described processing in the operation support unit may be performed using AI, or may be performed without using AI. For example, the operation support unit can provide a guide, a help message, or a tutorial using an AI model that provides operation support. This allows the operation support unit to support user operations.

[0034] The identification unit can analyze the user's past message history to improve the accuracy of the identified cases. The identification unit can analyze the user's past message history to improve the accuracy of the identified cases. The message history includes the content of past messages, the date and time of sending, etc. For example, the identification unit can learn patterns of messages that the user previously determined to be important and identify similar messages. The identification unit can also learn patterns of messages that the user previously ignored and filter out messages of low importance. Furthermore, the identification unit can extract specific keywords or phrases from the user's past message history to identify important cases. In this way, the identification unit can improve the accuracy of the identified cases by analyzing the past message history. Some or all of the above-described processing in the identification unit may be performed using AI, or may be performed without using AI. For example, the identification unit can analyze the past message history using an AI model that analyzes message history to improve the accuracy of the identified cases. In this way, the identification unit can improve the accuracy of the identified cases by analyzing the past message history.

[0035] The identification unit can dynamically change the type of case to be identified depending on the user's current situation. The identification unit dynamically changes the type of case to be identified depending on the user's current situation. The current situation includes the user's location information, activity status, etc. For example, when the user is traveling, the identification unit can prioritize identifying travel-related cases. Furthermore, when the user is at work, the identification unit can prioritize identifying work-related cases. Furthermore, when the user is at home, the identification unit can prioritize identifying home-related cases. This allows the identification unit to change the type of case depending on the user's current situation. Some or all of the above-mentioned processing in the identification unit may be performed using AI, or may be performed without using AI. For example, the identification unit can dynamically change the type of case to be identified depending on the user's current situation using an AI model that determines the current situation. This allows the identification unit to change the type of case depending on the user's current situation.

[0036] The identification unit can filter the content of the identified cases based on the user's geographical location information. The identification unit filters the content of the identified cases based on the user's geographical location information. The geographical location information includes GPS data, an IP address, etc. For example, when the user is in a specific area, the identification unit can prioritize identifying cases related to that area. Furthermore, when the user is traveling, the identification unit can prioritize identifying cases related to the user's destination. Furthermore, when the user is at home, the identification unit can prioritize identifying cases related to the user's home. In this way, the identification unit can filter cases based on the geographical location information. Some or all of the above-described processing in the identification unit may be performed using AI, or may be performed without using AI. For example, the identification unit can filter the content of the identified cases based on the user's geographical location information using an AI model that analyzes geographical location information. In this way, the identification unit can filter cases based on the geographical location information.

[0037] The identification unit can customize the content of the identified case based on the user's social media activity. The identification unit customizes the content of the identified case based on the user's social media activity. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, the identification unit prioritizes identifying cases related to content mentioned by the user on social media. The identification unit can also prioritize identifying cases related to accounts the user follows on social media. Furthermore, the identification unit can prioritize identifying cases related to events the user is participating in on social media. In this way, the identification unit can customize the case based on the social media activity. Some or all of the above-described processing in the identification unit may be performed using AI, or may be performed without using AI. For example, the identification unit can customize the content of the identified case based on the user's social media activity using an AI model that analyzes social media activity. In this way, the identification unit can customize the case based on the social media activity.

[0038] When notifying, the notification unit can select the optimal notification method by referring to the user's past notification history. When notifying, the notification unit can select the optimal notification method by referring to the user's past notification history. The notification history includes past notification content, notification date and time, etc. For example, the notification unit prioritizes selecting a notification method that the user has previously preferred. The notification unit can also avoid notification methods that the user has previously ignored. Furthermore, the notification unit can select the optimal notification method for a specific time period from the user's past notification history. In this way, the notification unit can select the optimal notification method by referring to the past notification history. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can select the optimal notification method by referring to the user's past notification history using an AI model that analyzes the notification history. In this way, the notification unit can select the optimal notification method by referring to the past notification history.

[0039] The notification unit can customize the notification content according to the user's current activity status. The notification unit customizes the notification content according to the user's current activity status. The activity status includes the user's location information, activity log, etc. For example, when the user is at work, the notification unit can prioritize work-related notifications. Furthermore, when the user is on vacation, the notification unit can prioritize vacation-related notifications. Furthermore, when the user is exercising, the notification unit can prioritize exercise-related notifications. This allows the notification unit to customize the notification content according to the current activity status. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can customize the notification content according to the user's current activity status using an AI model that determines the activity status. This allows the notification unit to customize the notification content according to the current activity status.

[0040] When notifying, the notification unit can select the optimal notification method based on the user's device information. When notifying, the notification unit selects the optimal notification method based on the user's device information. The device information includes the device type, OS version, etc. For example, if the user is using a smartphone, the notification unit can select the optimal notification method for the smartphone. Furthermore, if the user is using a smartwatch, the notification unit can also select the optimal notification method for the smartwatch. Furthermore, if the user is using a tablet, the notification unit can also select the optimal notification method for the tablet. In this way, the notification unit can select the optimal notification method by taking the device information into consideration. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can select the optimal notification method based on the user's device information using an AI model that analyzes the device information. In this way, the notification unit can select the optimal notification method by taking the device information into consideration.

[0041] The notification unit can adjust the content of the notification based on the user's calendar information. The notification unit adjusts the content of the notification based on the user's calendar information. The calendar information includes the content, date, time, location, etc. of the schedule. For example, the notification unit adjusts the timing of the notification based on the schedule registered in the user's calendar. The notification unit can also prioritize notifications related to specific events based on the user's calendar information. Furthermore, the notification unit can select the optimal notification method based on the schedule based on the user's calendar information. This allows the notification unit to adjust the content of the notification based on the calendar information. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can adjust the content of the notification based on the user's calendar information using an AI model that analyzes the calendar information. This allows the notification unit to adjust the content of the notification based on the calendar information.

[0042] The providing unit can select optimal information to provide by referring to the user's past operation history. The providing unit selects optimal information to provide by referring to the user's past operation history. The operation history includes past operation content, operation date and time, etc. For example, the providing unit prioritizes providing information that the user has used favorably in the past. The providing unit can also avoid information that the user has ignored in the past. Furthermore, the providing unit can provide optimal information for a specific time period based on the user's past operation history. In this way, the providing unit can provide optimal information by referring to the past operation history. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can select optimal information by referring to the user's past operation history using an AI model that analyzes the operation history. In this way, the providing unit can provide optimal information by referring to the past operation history.

[0043] The providing unit can dynamically change the information to be provided depending on the user's current situation. The providing unit dynamically changes the information to be provided depending on the user's current situation. The current situation includes the user's location information, activity status, etc. For example, when the user is at work, the providing unit can prioritize providing information related to work. Furthermore, when the user is on vacation, the providing unit can prioritize providing information related to vacation. Furthermore, when the user is exercising, the providing unit can prioritize providing information related to exercise. In this way, the providing unit can change the information depending on the current situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can dynamically change the information to be provided depending on the user's current situation using an AI model that determines the current situation. In this way, the providing unit can change the information depending on the current situation.

[0044] The providing unit can customize the information to be provided based on the user's geographical location information. The providing unit customizes the information to be provided based on the user's geographical location information. The geographical location information includes GPS data, an IP address, etc. For example, when the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, when the user is traveling, the providing unit can prioritize providing information related to the user's destination. Furthermore, when the user is at home, the providing unit can prioritize providing information related to the user's home. This allows the providing unit to customize the information based on the geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can customize the information to be provided based on the user's geographical location information using an AI model that analyzes the geographical location information. This allows the providing unit to customize the information based on the geographical location information.

[0045] The providing unit can adjust the information to be provided based on the user's social media activity. The providing unit adjusts the information to be provided based on the user's social media activity. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, the providing unit can prioritize providing information related to content mentioned by the user on social media. The providing unit can also prioritize providing information related to accounts the user follows on social media. Furthermore, the providing unit can prioritize providing information related to events the user is participating in on social media. In this way, the providing unit can adjust the information based on the social media activity. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can adjust the information to be provided based on the user's social media activity using an AI model that analyzes social media activity. In this way, the providing unit can adjust the information based on the social media activity.

[0046] When analyzing, the analysis unit can optimize the analysis algorithm by referring to the user's past message history. When analyzing, the analysis unit can optimize the analysis algorithm by referring to the user's past message history. The message history includes the content of past messages, the date and time of sending, etc. For example, the analysis unit learns patterns of messages that the user previously deemed important and prioritizes analyzing similar messages. The analysis unit can also learn patterns of messages that the user previously ignored and filter out messages of low importance. Furthermore, the analysis unit can extract specific keywords or phrases from the user's past message history and prioritize analyzing important messages. In this way, the analysis unit can optimize the analysis algorithm by referring to the past message history. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can optimize the analysis algorithm by referring to the user's past message history using an AI model that analyzes message history. In this way, the analysis unit can optimize the analysis algorithm by referring to the past message history.

[0047] The analysis unit can dynamically change the content to be analyzed depending on the user's current situation. The analysis unit dynamically changes the content to be analyzed depending on the user's current situation. The current situation includes the user's location information, activity status, etc. For example, if the user is traveling, the analysis unit can prioritize analyzing travel-related messages. Furthermore, if the user is at work, the analysis unit can prioritize analyzing work-related messages. Furthermore, if the user is at home, the analysis unit can prioritize analyzing home-related messages. This allows the analysis unit to change the analysis content depending on the user's current situation. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can dynamically change the content to be analyzed depending on the user's current situation using an AI model that determines the current situation. This allows the analysis unit to change the analysis content depending on the user's current situation.

[0048] The analysis unit can perform the analysis taking into account the user's geographical location information. The analysis unit can perform the analysis taking into account the user's geographical location information. Geographical location information includes GPS data, IP address, etc. For example, if the user is in a specific area, the analysis unit can prioritize analyzing messages related to that area. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing messages related to the user's destination. Furthermore, if the user is at home, the analysis unit can prioritize analyzing messages related to the user's home. This allows the analysis unit to perform the analysis taking into account the geographical location information. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis based on the user's geographical location information using an AI model that analyzes geographical location information. This allows the analysis unit to perform the analysis taking into account the geographical location information.

[0049] The analysis unit can customize the content to be analyzed based on the user's social media activity. The analysis unit customizes the content to be analyzed based on the user's social media activity. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, the analysis unit prioritizes analyzing messages related to content mentioned by the user on social media. The analysis unit can also prioritize analyzing messages related to accounts the user follows on social media. Furthermore, the analysis unit can prioritize analyzing messages related to events the user is participating in on social media. This allows the analysis unit to customize the analysis content based on the social media activity. Some or all of the above-described processing in the analysis unit may be performed using AI or may be performed without using AI. For example, the analysis unit can customize the analysis content based on the user's social media activity using an AI model that analyzes social media activity. This allows the analysis unit to customize the analysis content based on the social media activity.

[0050] When providing guidance, the voice guidance unit can select the optimal guidance method by referring to the user's past voice guidance history. When providing guidance, the voice guidance unit selects the optimal guidance method by referring to the user's past voice guidance history. The voice guidance history includes past guidance content, guidance date and time, etc. For example, the voice guidance unit prioritizes selecting a voice guidance tone that the user has previously preferred. The voice guidance unit can also avoid a voice guidance tone that the user has previously ignored. Furthermore, the voice guidance unit can select the optimal guidance method for a specific time period from the user's past voice guidance history. In this way, the voice guidance unit can select the optimal guidance method by referring to the past voice guidance history. Some or all of the above-described processing in the voice guidance unit may be performed using AI, or may be performed without using AI. For example, the voice guidance unit can select the optimal guidance method by referring to the user's past voice guidance history using an AI model that analyzes the voice guidance history. In this way, the voice guidance unit can select the optimal guidance method by referring to the past voice guidance history.

[0051] The voice guidance unit can dynamically change the content of the guidance provided depending on the user's current situation. The voice guidance unit dynamically changes the content of the guidance provided depending on the user's current situation. The current situation includes the user's location information, activity status, etc. For example, when the user is at work, the voice guidance unit can prioritize work-related guidance. Furthermore, when the user is on vacation, the voice guidance unit can prioritize vacation-related guidance. Furthermore, when the user is exercising, the voice guidance unit can prioritize exercise-related guidance. This allows the voice guidance unit to change the content of the guidance provided depending on the current situation. Some or all of the above-mentioned processing in the voice guidance unit may be performed using AI, or may be performed without using AI. For example, the voice guidance unit can dynamically change the content of the guidance provided depending on the user's current situation using an AI model that determines the current situation. This allows the voice guidance unit to change the content of the guidance provided depending on the current situation.

[0052] When providing guidance, the voice guidance unit can select the optimal guidance method by taking into account the user's device information. When providing guidance, the voice guidance unit selects the optimal guidance method by taking into account the user's device information. The device information includes the device type, OS version, etc. For example, if the user is using a smartphone, the voice guidance unit can select the optimal guidance method for the smartphone. Furthermore, if the user is using a smartwatch, the voice guidance unit can also select the optimal guidance method for the smartwatch. Furthermore, if the user is using a tablet, the voice guidance unit can also select the optimal guidance method for the tablet. In this way, the voice guidance unit can select the optimal guidance method by taking into account the device information. Some or all of the above-mentioned processing in the voice guidance unit may be performed using AI, or may be performed without using AI. For example, the voice guidance unit can select the optimal guidance method based on the user's device information using an AI model that analyzes the device information. In this way, the voice guidance unit can select the optimal guidance method by taking into account the device information.

[0053] The voice guidance unit can adjust the content of the guidance based on the user's calendar information. The voice guidance unit adjusts the content of the guidance based on the user's calendar information. The calendar information includes the content, date, time, location, etc. of the schedule. For example, the voice guidance unit adjusts the timing of the guidance based on the schedule registered in the user's calendar. The voice guidance unit can also prioritize guidance related to specific events based on the user's calendar information. Furthermore, the voice guidance unit can select the optimal guidance method based on the schedule based on the user's calendar information. This allows the voice guidance unit to adjust the content of the guidance based on the calendar information. Some or all of the above-described processing in the voice guidance unit may be performed using AI, or may be performed without using AI. For example, the voice guidance unit can adjust the content of the guidance based on the user's calendar information using an AI model that analyzes the calendar information. This allows the voice guidance unit to adjust the content of the guidance based on the calendar information.

[0054] When providing guidance, the guidance unit can select optimal information by referring to the user's past guidance history. When providing guidance, the guidance unit selects optimal information by referring to the user's past guidance history. The guidance history includes past guidance content, guidance date and time, etc. For example, the guidance unit prioritizes information that the user has used favorably in the past. The guidance unit can also avoid information that the user has ignored in the past. Furthermore, the guidance unit can also provide optimal information for a specific time period based on the user's past guidance history. In this way, the guidance unit can provide optimal information by referring to the past guidance history. Some or all of the above-described processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit can use an AI model that analyzes the guidance history to refer to the user's past guidance history and select optimal information. In this way, the guidance unit can provide optimal information by referring to the past guidance history.

[0055] The guidance unit can dynamically change the content of the guidance provided in accordance with the user's current situation. The guidance unit dynamically changes the content of the guidance provided in accordance with the user's current situation. The current situation includes the user's location information, activity status, etc. For example, when the user is at work, the guidance unit can prioritize information related to work. Furthermore, when the user is on vacation, the guidance unit can prioritize information related to vacation. Furthermore, when the user is exercising, the guidance unit can prioritize information related to exercise. This allows the guidance unit to change the content of the guidance provided in accordance with the current situation. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit can dynamically change the content of the guidance provided in accordance with the user's current situation using an AI model that determines the current situation. This allows the guidance unit to change the content of the guidance provided in accordance with the current situation.

[0056] The guidance unit can customize the information to be provided based on the user's geographical location information. The guidance unit customizes the information to be provided based on the user's geographical location information. The geographical location information includes GPS data, an IP address, etc. For example, when the user is in a specific area, the guidance unit can prioritize information related to that area. Furthermore, when the user is traveling, the guidance unit can prioritize information related to the user's destination. Furthermore, when the user is at home, the guidance unit can prioritize information related to the user's home. This allows the guidance unit to customize the information based on the geographical location information. Some or all of the above-described processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit can customize the information to be provided based on the user's geographical location information using an AI model that analyzes the geographical location information. This allows the guidance unit to customize the information based on the geographical location information.

[0057] The guidance unit can adjust the information to be presented based on the user's social media activity. The guidance unit adjusts the information to be presented based on the user's social media activity. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, the guidance unit can prioritize information related to content mentioned by the user on social media. The guidance unit can also prioritize information related to accounts the user follows on social media. Furthermore, the guidance unit can prioritize information related to events the user is participating in on social media. This allows the guidance unit to adjust the information based on the social media activity. Some or all of the above-described processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit can adjust the information to be presented based on the user's social media activity using an AI model that analyzes social media activity. This allows the guidance unit to adjust the information based on the social media activity.

[0058] When providing assistance, the operation assistance unit can select an optimal assistance method by referring to the user's past operation history. When providing assistance, the operation assistance unit selects an optimal assistance method by referring to the user's past operation history. The operation history includes past operation content, operation date and time, etc. For example, the operation assistance unit preferentially selects an operation assistance method that the user has used favorably in the past. The operation assistance unit can also avoid operation assistance methods that the user has ignored in the past. Furthermore, the operation assistance unit can select an optimal assistance method for a specific time period from the user's past operation history. In this way, the operation assistance unit can select an optimal assistance method by referring to the past operation history. Some or all of the above-described processing in the operation assistance unit may be performed using AI or without AI. For example, the operation assistance unit can select an optimal assistance method by referring to the user's past operation history using an AI model that analyzes the operation history. In this way, the operation assistance unit can select an optimal assistance method by referring to the past operation history.

[0059] The operation assistance unit can dynamically change the assistance content according to the user's current situation. The operation assistance unit dynamically changes the assistance content according to the user's current situation. The current situation includes the user's location information, activity status, etc. For example, when the user is at work, the operation assistance unit can prioritize work-related operation assistance. Furthermore, when the user is on vacation, the operation assistance unit can prioritize vacation-related operation assistance. Furthermore, when the user is exercising, the operation assistance unit can prioritize exercise-related operation assistance. This allows the operation assistance unit to change the assistance content according to the current situation. Some or all of the above-mentioned processing in the operation assistance unit may be performed using AI, or may be performed without using AI. For example, the operation assistance unit can dynamically change the assistance content according to the user's current situation using an AI model that determines the current situation. This allows the operation assistance unit to change the assistance content according to the current situation.

[0060] When providing assistance, the operation assistance unit can select the optimal assistance method by taking into account the user's device information. When providing assistance, the operation assistance unit selects the optimal assistance method by taking into account the user's device information. The device information includes the device type, OS version, etc. For example, if the user is using a smartphone, the operation assistance unit selects the optimal assistance method for the smartphone. Furthermore, if the user is using a smartwatch, the operation assistance unit can select the optimal assistance method for the smartwatch. Furthermore, if the user is using a tablet, the operation assistance unit can select the optimal assistance method for the tablet. In this way, the operation assistance unit can select the optimal assistance method by taking into account the device information. Some or all of the above-mentioned processing in the operation assistance unit may be performed using AI, or may be performed without using AI. For example, the operation assistance unit can select the optimal assistance method based on the user's device information by using an AI model that analyzes device information. In this way, the operation assistance unit can select the optimal assistance method by taking into account the device information.

[0061] The operation assistance unit can adjust the assistance content based on the user's calendar information. The operation assistance unit adjusts the assistance content based on the user's calendar information. The calendar information includes the content, date, time, location, etc. of the schedule. For example, the operation assistance unit adjusts the timing of the assistance based on the schedule registered in the user's calendar. The operation assistance unit can also prioritize assistance related to a specific event based on the user's calendar information. Furthermore, the operation assistance unit can select an optimal assistance method based on the schedule based on the user's calendar information. This allows the operation assistance unit to adjust the assistance content based on the calendar information. Some or all of the above-described processing in the operation assistance unit may be performed using AI or may be performed without using AI. For example, the operation assistance unit can adjust the assistance content based on the user's calendar information using an AI model that analyzes the calendar information. This allows the operation assistance unit to adjust the assistance content based on the calendar information.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The identification unit can learn the user's past behavioral patterns and improve the accuracy of the identified cases. For example, the identification unit can learn what messages the user has judged to be important in the past and prioritize identifying similar messages. The identification unit can also learn the patterns of messages the user has ignored in the past and filter out messages with low importance. Furthermore, the identification unit can extract specific keywords or phrases from the user's past behavioral patterns and identify important cases. In this way, the identification unit can improve the accuracy of the identified cases by learning past behavioral patterns.

[0064] The notification unit can adjust the notification method taking into account the remaining battery level of the user's device. For example, the notification unit can reduce the frequency of notifications when the remaining battery level is low. The notification unit can also increase the frequency of notifications when the remaining battery level is sufficient. Furthermore, the notification unit can prioritize only important notifications when the remaining battery level is very low. In this way, the notification unit can adjust the notification method according to the remaining battery level of the device.

[0065] The providing unit can determine the priority of information to be provided based on the user's current activity status. For example, when the user is at work, the providing unit can provide work-related information with priority. When the user is on vacation, the providing unit can also provide vacation-related information with priority. Furthermore, when the user is exercising, the providing unit can also provide exercise-related information with priority. In this way, the providing unit can determine the priority of information according to the user's current activity status.

[0066] The identification unit can analyze the user's past message history and improve the accuracy of the identified cases. For example, the identification unit learns patterns of messages that the user has previously judged to be important and identifies similar messages. The identification unit can also learn patterns of messages that the user has previously ignored and filter out messages with low importance. Furthermore, the identification unit can extract specific keywords or phrases from the user's past message history and identify important cases. In this way, the identification unit can improve the accuracy of the identified cases by analyzing the past message history.

[0067] The providing unit can improve the accuracy of the information to be provided by referring to the user's past operation history. For example, the providing unit can provide information that the user has used favorably in the past with priority. The providing unit can also avoid information that the user has ignored in the past. Furthermore, the providing unit can provide optimal information for a specific time period based on the user's past operation history. In this way, the providing unit can improve the accuracy of the information to be provided by referring to the past operation history.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The identification unit identifies important matters. The identification unit scans the user's emails and messaging apps, analyzes the content of the messages using natural language processing, and determines their importance. For example, it extracts messages containing keywords such as flight cancellations or delays, emergency calls from schools or daycare centers, and unauthorized account use. Natural language processing is carried out using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Step 2: The notification unit notifies the user of the case identified by the identification unit. The notification unit not only displays the case visually, but also provides audio guidance. For example, it may provide audio guidance such as, "You have received a notice of a flight cancellation. Would you like to check the details?" The audio guidance is achieved using text-to-speech and speech synthesis technology. Step 3: The provision unit provides relevant information about the case notified by the notification unit. For example, in the case of a flight cancellation, the provision unit displays the airline's website and customer support phone number. The provision unit provides site information and phone number information. Site information includes the URL and a summary of the site, and phone numbers are provided in formats such as international phone number format and area code.

[0070] (Example 2) A system according to an embodiment of the present invention functions like a butler or secretary, picking out important matters buried in emails and messaging apps, displaying them on a smartphone or smartwatch, and engaging in voice conversations. This system prevents important notices, such as flight cancellations and delays, emergency calls from schools and daycare centers, and unauthorized account use, from getting lost in the clutter of emails and messages. After the user confirms the notice, the system suggests related website information and phone numbers. For example, the system scans the user's emails and messaging apps to identify important matters. For example, it extracts messages containing keywords such as flight cancellations and delays, emergency calls from schools and daycare centers, and unauthorized account use. AI then analyzes the message content using natural language processing to determine its importance. Next, the identified important matters are notified to the smartphone or smartwatch. Notifications are not only visually displayed, but also audibly. For example, a voice prompt such as, "You have received a flight cancellation notice. Would you like to check the details?" is heard. When the user confirms the notice, the system suggests related website information and phone numbers. For example, if a flight is canceled, the airline's website and customer support phone number will be displayed, allowing users to respond quickly. This system prevents important matters from being buried and enables users to respond quickly. For example, an emergency call from a school can be responded to immediately without being overlooked. Also, if a notification of account fraud is received, countermeasures can be taken quickly. This allows the system to quickly identify important matters, notify users, and provide relevant information.

[0071] The system according to the embodiment includes an identification unit, a notification unit, and a providing unit. The identification unit identifies important matters. For example, the identification unit scans a user's emails or messaging apps to identify important matters. The identification unit analyzes the content of the messages using natural language processing and determines their importance. For example, the identification unit extracts messages containing keywords such as flight cancellations or delays, emergency calls from schools or daycare centers, and unauthorized account use. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. The notification unit notifies the user of the matters identified by the identification unit. The notification unit notifies the user of the matters identified by the identification unit. The notification unit notifies the user of the matters notified by the identification unit not only visually but also through audio guidance. For example, the notification unit may provide audio guidance such as, "You have received a flight cancellation notice. Would you like to check the details?" The audio guidance is realized using text-to-speech or speech synthesis technology. The providing unit provides related information related to the matters notified by the notification unit. For example, in the case of a flight cancellation notice, the providing unit displays the airline's website and customer support phone number. The providing unit provides site information and phone number guidance. The site information includes a URL and a site summary. The telephone number is provided in a format such as an international telephone number format or an area code. As a result, the system according to the embodiment identifies important matters, notifies the user, and provides related information, allowing the user to respond quickly. Some or all of the above-described processing in the identification unit, notification unit, and provision unit may be performed using AI, or may be performed without AI. For example, the identification unit may analyze the content of a message and determine its importance using an AI model that performs natural language processing. The notification unit may generate voice guidance using an AI model that provides voice guidance. The provision unit may suggest site information and telephone numbers using an AI model that provides related information. As a result, the system may quickly identify important matters, notify the user, and provide related information.

[0072] The identification unit may include an analysis unit that analyzes a message using natural language processing and determines its importance. The analysis unit analyzes the message using natural language processing and determines its importance. Natural language processing is performed using techniques such as morphological analysis, grammatical analysis, and semantic analysis. For example, the analysis unit may use morphological analysis to divide the words in the message, grammatical analysis to analyze the structure of the sentence, and semantic analysis to understand the meaning of the sentence. The analysis unit analyzes the content of the message and determines its importance. The importance is determined using methods such as scoring or rule-based evaluation. For example, the analysis unit may assign a score based on the content of the message and determine messages with high scores as important. The analysis unit may also use rule-based evaluation to determine messages containing specific keywords as important. In this way, the analysis unit can determine the importance of the message with high accuracy by using natural language processing. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit may analyze the content of the message and determine its importance using an AI model that performs natural language processing. This allows the analysis unit to use natural language processing to determine the importance of a message with high accuracy.

[0073] The notification unit can include a voice guidance unit that provides voice guidance. The voice guidance unit provides voice guidance. The voice guidance is realized using text-to-speech or voice synthesis technology. For example, the voice guidance unit converts text into voice using text-to-speech technology. The voice guidance unit can also generate natural voice using voice synthesis technology. For example, the voice guidance unit converts text into natural voice using voice synthesis technology. This allows the voice guidance unit to provide voice guidance. Some or all of the above-mentioned processing in the voice guidance unit may be performed using AI, or may be performed without using AI. For example, the voice guidance unit can generate voice guidance using an AI model that provides voice guidance. This allows the voice guidance unit to provide voice guidance.

[0074] The providing unit may include a guidance unit that provides guidance on site information or telephone numbers. The guidance unit provides guidance on site information or telephone numbers. Site information includes a URL, a site summary, etc. For example, the guidance unit provides the URL of an airline's website. The guidance unit can also display a site summary. For example, the guidance unit displays a summary of the airline's website. Phone numbers are provided in a format such as an international telephone number format or an area code. For example, the guidance unit provides a customer support telephone number. The guidance unit can also adjust the format of the phone number. For example, the guidance unit provides a telephone number in international telephone number format. This allows the guidance unit to provide guidance on site information or telephone numbers. Some or all of the above-described processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit can suggest site information or telephone numbers using an AI model that provides related information. This allows the guidance unit to provide guidance on site information or telephone numbers.

[0075] The providing unit may include an operation support unit that supports user operations. The operation support unit supports user operations. The operation support is provided in the form of a guide, a help message, a tutorial, or the like. For example, the operation support unit provides a guide. The guide explains how to use the system and operation procedures. The operation support unit can also display a help message. The help message provides detailed explanations about specific operations. Furthermore, the operation support unit can also provide a tutorial. The tutorial explains basic operation methods of the system step by step. This allows the operation support unit to support user operations. Some or all of the above-described processing in the operation support unit may be performed using AI, or may be performed without using AI. For example, the operation support unit can provide a guide, a help message, or a tutorial using an AI model that provides operation support. This allows the operation support unit to support user operations.

[0076] The identification unit can estimate the user's emotion and determine the priority of the identified cases based on the estimated user's emotion. The identification unit can estimate the user's emotion and determine the priority of the identified cases based on the estimated user's emotion. The emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the identification unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The identification unit can also analyze the user's voice using voice analysis technology to estimate the emotion. The identification unit can also analyze the user's text message using text analysis technology to estimate the emotion. The identification unit determines the priority of the identified cases based on the estimated emotion. The priority is determined using methods such as scoring and rule-based evaluation. For example, if the user is feeling stressed, the identification unit can prioritize urgent cases. If the user is relaxed, the identification unit can prioritize important cases. If the user is in a hurry, the identification unit can prioritize time-related cases. This allows the identification unit to adjust the priority of cases according to the user's emotion. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit may use an AI model for emotion estimation to estimate the user's emotion and determine the priority of the case. This allows the determination unit to adjust the priority of the case according to the user's emotion.

[0077] The identification unit can analyze the user's past message history to improve the accuracy of the identified cases. The identification unit can analyze the user's past message history to improve the accuracy of the identified cases. The message history includes the content of past messages, the date and time of sending, etc. For example, the identification unit can learn patterns of messages that the user previously determined to be important and identify similar messages. The identification unit can also learn patterns of messages that the user previously ignored and filter out messages of low importance. Furthermore, the identification unit can extract specific keywords or phrases from the user's past message history to identify important cases. In this way, the identification unit can improve the accuracy of the identified cases by analyzing the past message history. Some or all of the above-described processing in the identification unit may be performed using AI, or may be performed without using AI. For example, the identification unit can analyze the past message history using an AI model that analyzes message history to improve the accuracy of the identified cases. In this way, the identification unit can improve the accuracy of the identified cases by analyzing the past message history.

[0078] The identification unit can dynamically change the type of case to be identified depending on the user's current situation. The identification unit dynamically changes the type of case to be identified depending on the user's current situation. The current situation includes the user's location information, activity status, etc. For example, when the user is traveling, the identification unit can prioritize identifying travel-related cases. Furthermore, when the user is at work, the identification unit can prioritize identifying work-related cases. Furthermore, when the user is at home, the identification unit can prioritize identifying home-related cases. This allows the identification unit to change the type of case depending on the user's current situation. Some or all of the above-mentioned processing in the identification unit may be performed using AI, or may be performed without using AI. For example, the identification unit can dynamically change the type of case to be identified depending on the user's current situation using an AI model that determines the current situation. This allows the identification unit to change the type of case depending on the user's current situation.

[0079] The identification unit can estimate the user's emotion and adjust the notification method for the identified event based on the estimated user emotion. The identification unit can estimate the user's emotion and adjust the notification method for the identified event based on the estimated user emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the identification unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The identification unit can also analyze the user's voice using voice analysis technology to estimate the emotion. Furthermore, the identification unit can analyze the user's text message using text analysis technology to estimate the emotion. The identification unit adjusts the notification method for the identified event based on the estimated emotion. The notification method is performed via email, push notification, SMS, or other methods. For example, the identification unit can select a simple notification method if the user is feeling stressed. The identification unit can also select a detailed notification method if the user is relaxed. Furthermore, the identification unit can prioritize voice notification if the user is in a hurry. This allows the identification unit to adjust the notification method according to the user's emotion. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using AI, or may be performed without using AI. For example, the determination unit can estimate the user's emotion using an AI model that estimates emotion and adjust the notification method. This allows the determination unit to adjust the notification method according to the user's emotion.

[0080] The identification unit can filter the content of the identified cases based on the user's geographical location information. The identification unit filters the content of the identified cases based on the user's geographical location information. The geographical location information includes GPS data, an IP address, etc. For example, when the user is in a specific area, the identification unit can prioritize identifying cases related to that area. Furthermore, when the user is traveling, the identification unit can prioritize identifying cases related to the user's destination. Furthermore, when the user is at home, the identification unit can prioritize identifying cases related to the user's home. In this way, the identification unit can filter cases based on the geographical location information. Some or all of the above-described processing in the identification unit may be performed using AI, or may be performed without using AI. For example, the identification unit can filter the content of the identified cases based on the user's geographical location information using an AI model that analyzes geographical location information. In this way, the identification unit can filter cases based on the geographical location information.

[0081] The identification unit can customize the content of the identified case based on the user's social media activity. The identification unit customizes the content of the identified case based on the user's social media activity. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, the identification unit prioritizes identifying cases related to content mentioned by the user on social media. The identification unit can also prioritize identifying cases related to accounts the user follows on social media. Furthermore, the identification unit can prioritize identifying cases related to events the user is participating in on social media. In this way, the identification unit can customize the case based on the social media activity. Some or all of the above-described processing in the identification unit may be performed using AI, or may be performed without using AI. For example, the identification unit can customize the content of the identified case based on the user's social media activity using an AI model that analyzes social media activity. In this way, the identification unit can customize the case based on the social media activity.

[0082] The notification unit can estimate the user's emotion and adjust the timing of notifications based on the estimated user's emotion. The notification unit can estimate the user's emotion and adjust the timing of notifications based on the estimated user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the notification unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The notification unit can also analyze the user's voice using voice analysis technology to estimate the emotion. Furthermore, the notification unit can analyze the user's text message using text analysis technology to estimate the emotion. The notification unit adjusts the timing of notifications based on the estimated emotion. The timing of notifications is adjusted based on criteria such as the user's activity status and time period. For example, the notification unit can reduce the frequency of notifications if the user is feeling stressed. The notification unit can also increase the frequency of notifications if the user is relaxed. Furthermore, the notification unit can provide immediate notifications if the user is in a hurry. This allows the notification unit to adjust the timing of notifications according to the user's emotion. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit may estimate the user's emotion using an AI model that estimates emotion and adjust the timing of notification. This allows the notification unit to adjust the timing of notification according to the user's emotion.

[0083] When notifying, the notification unit can select the optimal notification method by referring to the user's past notification history. When notifying, the notification unit can select the optimal notification method by referring to the user's past notification history. The notification history includes past notification content, notification date and time, etc. For example, the notification unit prioritizes selecting a notification method that the user has previously preferred. The notification unit can also avoid notification methods that the user has previously ignored. Furthermore, the notification unit can select the optimal notification method for a specific time period from the user's past notification history. In this way, the notification unit can select the optimal notification method by referring to the past notification history. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can select the optimal notification method by referring to the user's past notification history using an AI model that analyzes the notification history. In this way, the notification unit can select the optimal notification method by referring to the past notification history.

[0084] The notification unit can customize the notification content according to the user's current activity status. The notification unit customizes the notification content according to the user's current activity status. The activity status includes the user's location information, activity log, etc. For example, when the user is at work, the notification unit can prioritize work-related notifications. Furthermore, when the user is on vacation, the notification unit can prioritize vacation-related notifications. Furthermore, when the user is exercising, the notification unit can prioritize exercise-related notifications. This allows the notification unit to customize the notification content according to the current activity status. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can customize the notification content according to the user's current activity status using an AI model that determines the activity status. This allows the notification unit to customize the notification content according to the current activity status.

[0085] The notification unit can estimate a user's emotion and determine the priority of notifications based on the estimated user's emotion. The notification unit can estimate a user's emotion and determine the priority of notifications based on the estimated user's emotion. The emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the notification unit can analyze a user's facial expression using facial expression recognition technology to estimate the emotion. The notification unit can also analyze a user's voice using voice analysis technology to estimate the emotion. The notification unit can also analyze a user's text message using text analysis technology to estimate the emotion. The notification unit determines the priority of notifications based on the estimated emotion. The priority is determined using methods such as scoring and rule-based evaluation. For example, if the user is feeling stressed, the notification unit can prioritize notifications with a high level of urgency. Also, if the user is relaxed, the notification unit can prioritize notifications with a high level of importance. Furthermore, if the user is in a hurry, the notification unit can prioritize notifications related to time. This allows the notification unit to determine the priority of notifications according to the user's emotion. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit may estimate the user's emotion using an AI model that estimates emotion and determine the priority of notifications. This allows the notification unit to determine the priority of notifications according to the user's emotion.

[0086] When notifying, the notification unit can select the optimal notification method based on the user's device information. When notifying, the notification unit selects the optimal notification method based on the user's device information. The device information includes the device type, OS version, etc. For example, if the user is using a smartphone, the notification unit can select the optimal notification method for the smartphone. Furthermore, if the user is using a smartwatch, the notification unit can also select the optimal notification method for the smartwatch. Furthermore, if the user is using a tablet, the notification unit can also select the optimal notification method for the tablet. In this way, the notification unit can select the optimal notification method by taking the device information into consideration. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can select the optimal notification method based on the user's device information using an AI model that analyzes the device information. In this way, the notification unit can select the optimal notification method by taking the device information into consideration.

[0087] The notification unit can adjust the content of the notification based on the user's calendar information. The notification unit adjusts the content of the notification based on the user's calendar information. The calendar information includes the content, date, time, location, etc. of the schedule. For example, the notification unit adjusts the timing of the notification based on the schedule registered in the user's calendar. The notification unit can also prioritize notifications related to specific events based on the user's calendar information. Furthermore, the notification unit can select the optimal notification method based on the schedule based on the user's calendar information. This allows the notification unit to adjust the content of the notification based on the calendar information. Some or all of the above-mentioned processing in the notification unit may be performed using AI, or may be performed without using AI. For example, the notification unit can adjust the content of the notification based on the user's calendar information using an AI model that analyzes the calendar information. This allows the notification unit to adjust the content of the notification based on the calendar information.

[0088] The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion and determine the priority of information to be provided based on the estimated user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the providing unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The providing unit can also analyze the user's voice using voice analysis technology to estimate the emotion. Furthermore, the providing unit can analyze the user's text message using text analysis technology to estimate the emotion. The providing unit determines the priority of information to be provided based on the estimated emotion. The priority is determined by methods such as scoring and rule-based evaluation. For example, if the user is feeling stressed, the providing unit can prioritize providing information with a high level of urgency. Furthermore, if the user is relaxed, the providing unit can prioritize providing information with a high level of importance. Furthermore, if the user is in a hurry, the providing unit can prioritize providing information related to time. This allows the providing unit to prioritize information according to the user's emotion. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may use an AI model for estimating emotions to estimate the user's emotions and determine the priority of information. This allows the providing unit to determine the priority of information according to the user's emotions.

[0089] The providing unit can select optimal information to provide by referring to the user's past operation history. The providing unit selects optimal information to provide by referring to the user's past operation history. The operation history includes past operation content, operation date and time, etc. For example, the providing unit prioritizes providing information that the user has used favorably in the past. The providing unit can also avoid information that the user has ignored in the past. Furthermore, the providing unit can provide optimal information for a specific time period based on the user's past operation history. In this way, the providing unit can provide optimal information by referring to the past operation history. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can select optimal information by referring to the user's past operation history using an AI model that analyzes the operation history. In this way, the providing unit can provide optimal information by referring to the past operation history.

[0090] The providing unit can dynamically change the information to be provided depending on the user's current situation. The providing unit dynamically changes the information to be provided depending on the user's current situation. The current situation includes the user's location information, activity status, etc. For example, when the user is at work, the providing unit can prioritize providing information related to work. Furthermore, when the user is on vacation, the providing unit can prioritize providing information related to vacation. Furthermore, when the user is exercising, the providing unit can prioritize providing information related to exercise. In this way, the providing unit can change the information depending on the current situation. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can dynamically change the information to be provided depending on the user's current situation using an AI model that determines the current situation. In this way, the providing unit can change the information depending on the current situation.

[0091] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the providing unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The providing unit can also analyze the user's voice using voice analysis technology to estimate the emotion. Furthermore, the providing unit can analyze the user's text message using text analysis technology to estimate the emotion. The providing unit adjusts the display method of the information to be provided based on the estimated emotion. The display method can be a text display, a graphical display, or the like. For example, the providing unit can select a simple display method when the user is stressed. The providing unit can also select a detailed display method when the user is relaxed. Furthermore, the providing unit can prioritize voice guidance when the user is in a hurry. This allows the providing unit to adjust the display method of the information according to the user's emotion. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can estimate the user's emotion using an AI model that estimates emotion and adjust the method of displaying information. This allows the providing unit to adjust the method of displaying information according to the user's emotion.

[0092] The providing unit can customize the information to be provided based on the user's geographical location information. The providing unit customizes the information to be provided based on the user's geographical location information. The geographical location information includes GPS data, an IP address, etc. For example, when the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, when the user is traveling, the providing unit can prioritize providing information related to the user's destination. Furthermore, when the user is at home, the providing unit can prioritize providing information related to the user's home. This allows the providing unit to customize the information based on the geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can customize the information to be provided based on the user's geographical location information using an AI model that analyzes the geographical location information. This allows the providing unit to customize the information based on the geographical location information.

[0093] The providing unit can adjust the information to be provided based on the user's social media activity. The providing unit adjusts the information to be provided based on the user's social media activity. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, the providing unit can prioritize providing information related to content mentioned by the user on social media. The providing unit can also prioritize providing information related to accounts the user follows on social media. Furthermore, the providing unit can prioritize providing information related to events the user is participating in on social media. In this way, the providing unit can adjust the information based on the social media activity. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can adjust the information to be provided based on the user's social media activity using an AI model that analyzes social media activity. In this way, the providing unit can adjust the information based on the social media activity.

[0094] The analysis unit can estimate the user's emotions and improve the accuracy of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions and improve the accuracy of the analysis based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The analysis unit can also analyze the user's voice using voice analysis technology to estimate emotions. The analysis unit can also analyze the user's text messages using text analysis technology to estimate emotions. The analysis unit improves the accuracy of the analysis based on the estimated emotions. The accuracy of the analysis can be improved by improving the algorithm, increasing the accuracy of the data, and other methods. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing messages with a high level of urgency. If the user is relaxed, the analysis unit can prioritize analyzing messages with a high level of importance. If the user is in a hurry, the analysis unit can prioritize analyzing messages related to time. This allows the analysis unit to improve the accuracy of the analysis according to the user's emotions. Emotion estimation is realized using an emotion estimation function using an emotion engine or generative AI. Generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit may estimate the user's emotion using an AI model that estimates emotion, thereby improving the accuracy of the analysis. This allows the analysis unit to improve the accuracy of the analysis according to the user's emotion.

[0095] When analyzing, the analysis unit can optimize the analysis algorithm by referring to the user's past message history. When analyzing, the analysis unit can optimize the analysis algorithm by referring to the user's past message history. The message history includes the content of past messages, the date and time of sending, etc. For example, the analysis unit learns patterns of messages that the user previously deemed important and prioritizes analyzing similar messages. The analysis unit can also learn patterns of messages that the user previously ignored and filter out messages of low importance. Furthermore, the analysis unit can extract specific keywords or phrases from the user's past message history and prioritize analyzing important messages. In this way, the analysis unit can optimize the analysis algorithm by referring to the past message history. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can optimize the analysis algorithm by referring to the user's past message history using an AI model that analyzes message history. In this way, the analysis unit can optimize the analysis algorithm by referring to the past message history.

[0096] The analysis unit can dynamically change the content to be analyzed depending on the user's current situation. The analysis unit dynamically changes the content to be analyzed depending on the user's current situation. The current situation includes the user's location information, activity status, etc. For example, if the user is traveling, the analysis unit can prioritize analyzing travel-related messages. Furthermore, if the user is at work, the analysis unit can prioritize analyzing work-related messages. Furthermore, if the user is at home, the analysis unit can prioritize analyzing home-related messages. This allows the analysis unit to change the analysis content depending on the user's current situation. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can dynamically change the content to be analyzed depending on the user's current situation using an AI model that determines the current situation. This allows the analysis unit to change the analysis content depending on the user's current situation.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can analyze the user's facial expressions using facial expression recognition technology to estimate emotions. The analysis unit can also analyze the user's voice using voice analysis technology to estimate emotions. The analysis unit can also analyze the user's text messages using text analysis technology to estimate emotions. The analysis unit adjusts the display method of the analysis results based on the estimated emotions. The display method can be a text display, a graphical display, or other methods. For example, the analysis unit can select a simple display method if the user is feeling stressed. The analysis unit can select a detailed display method if the user is relaxed. The analysis unit can also select a display method that focuses on the main points if the user is in a hurry. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, or can be performed without AI. For example, the analysis unit can estimate the user's emotion using an AI model that estimates emotion and adjust the display method of the analysis results. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotion.

[0098] The analysis unit can perform the analysis taking into account the user's geographical location information. The analysis unit can perform the analysis taking into account the user's geographical location information. Geographical location information includes GPS data, IP address, etc. For example, if the user is in a specific area, the analysis unit can prioritize analyzing messages related to that area. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing messages related to the user's destination. Furthermore, if the user is at home, the analysis unit can prioritize analyzing messages related to the user's home. This allows the analysis unit to perform the analysis taking into account the geographical location information. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit can perform the analysis based on the user's geographical location information using an AI model that analyzes geographical location information. This allows the analysis unit to perform the analysis taking into account the geographical location information.

[0099] The analysis unit can customize the content to be analyzed based on the user's social media activity. The analysis unit customizes the content to be analyzed based on the user's social media activity. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, the analysis unit prioritizes analyzing messages related to content mentioned by the user on social media. The analysis unit can also prioritize analyzing messages related to accounts the user follows on social media. Furthermore, the analysis unit can prioritize analyzing messages related to events the user is participating in on social media. This allows the analysis unit to customize the analysis content based on the social media activity. Some or all of the above-described processing in the analysis unit may be performed using AI or may be performed without using AI. For example, the analysis unit can customize the analysis content based on the user's social media activity using an AI model that analyzes social media activity. This allows the analysis unit to customize the analysis content based on the social media activity.

[0100] The voice guidance unit can estimate a user's emotion and adjust the tone of the voice guidance based on the estimated user's emotion. The voice guidance unit can estimate a user's emotion and adjust the tone of the voice guidance based on the estimated user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the voice guidance unit can analyze a user's facial expression using face recognition technology to estimate the emotion. The voice guidance unit can also analyze a user's voice using voice analysis technology to estimate the emotion. Furthermore, the voice guidance unit can analyze a user's text message using text analysis technology to estimate the emotion. The voice guidance unit adjusts the tone of the voice guidance based on the estimated emotion. The tone is adjusted based on criteria such as voice pitch, speed, and emotional expression. For example, if the user is nervous, the voice guidance unit can provide guidance in a calm tone. If the user is relaxed, the voice guidance unit can provide guidance in a bright tone. Furthermore, if the user is in a hurry, the voice guidance unit can provide guidance in a quick and concise tone. This allows the voice guidance unit to adjust the tone of the voice guidance according to the user's emotions. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the voice guidance unit may be performed using AI, or may be performed without using AI. For example, the voice guidance unit can estimate the user's emotions using an AI model that estimates emotions and adjust the tone of the voice guidance. This allows the voice guidance unit to adjust the tone of the voice guidance according to the user's emotions.

[0101] When providing guidance, the voice guidance unit can select the optimal guidance method by referring to the user's past voice guidance history. When providing guidance, the voice guidance unit selects the optimal guidance method by referring to the user's past voice guidance history. The voice guidance history includes past guidance content, guidance date and time, etc. For example, the voice guidance unit prioritizes selecting a voice guidance tone that the user has previously preferred. The voice guidance unit can also avoid a voice guidance tone that the user has previously ignored. Furthermore, the voice guidance unit can select the optimal guidance method for a specific time period from the user's past voice guidance history. In this way, the voice guidance unit can select the optimal guidance method by referring to the past voice guidance history. Some or all of the above-described processing in the voice guidance unit may be performed using AI, or may be performed without using AI. For example, the voice guidance unit can select the optimal guidance method by referring to the user's past voice guidance history using an AI model that analyzes the voice guidance history. In this way, the voice guidance unit can select the optimal guidance method by referring to the past voice guidance history.

[0102] The voice guidance unit can dynamically change the content of the guidance provided depending on the user's current situation. The voice guidance unit dynamically changes the content of the guidance provided depending on the user's current situation. The current situation includes the user's location information, activity status, etc. For example, when the user is at work, the voice guidance unit can prioritize work-related guidance. Furthermore, when the user is on vacation, the voice guidance unit can prioritize vacation-related guidance. Furthermore, when the user is exercising, the voice guidance unit can prioritize exercise-related guidance. This allows the voice guidance unit to change the content of the guidance provided depending on the current situation. Some or all of the above-mentioned processing in the voice guidance unit may be performed using AI, or may be performed without using AI. For example, the voice guidance unit can dynamically change the content of the guidance provided depending on the user's current situation using an AI model that determines the current situation. This allows the voice guidance unit to change the content of the guidance provided depending on the current situation.

[0103] The voice guidance unit can estimate the user's emotion and determine the priority of the voice guidance based on the estimated user's emotion. The voice guidance unit can estimate the user's emotion and determine the priority of the voice guidance based on the estimated user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the voice guidance unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The voice guidance unit can also analyze the user's voice using voice analysis technology to estimate the emotion. Furthermore, the voice guidance unit can analyze the user's text message using text analysis technology to estimate the emotion. The voice guidance unit determines the priority of the voice guidance based on the estimated emotion. The priority is determined using methods such as scoring and rule-based evaluation. For example, if the user is feeling stressed, the voice guidance unit can prioritize guidance with a high level of urgency. Furthermore, if the user is relaxed, the voice guidance unit can prioritize guidance with a high level of importance. Furthermore, if the user is in a hurry, the voice guidance unit can prioritize guidance related to time. This allows the voice guidance unit to determine the priority of voice guidance according to the user's emotions. Emotion estimation is realized using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the voice guidance unit may be performed using AI, or may be performed without using AI. For example, the voice guidance unit can estimate the user's emotions using an AI model that estimates emotions and determine the priority of voice guidance. This allows the voice guidance unit to determine the priority of voice guidance according to the user's emotions.

[0104] When providing guidance, the voice guidance unit can select the optimal guidance method by taking into account the user's device information. When providing guidance, the voice guidance unit selects the optimal guidance method by taking into account the user's device information. The device information includes the device type, OS version, etc. For example, if the user is using a smartphone, the voice guidance unit can select the optimal guidance method for the smartphone. Furthermore, if the user is using a smartwatch, the voice guidance unit can also select the optimal guidance method for the smartwatch. Furthermore, if the user is using a tablet, the voice guidance unit can also select the optimal guidance method for the tablet. In this way, the voice guidance unit can select the optimal guidance method by taking into account the device information. Some or all of the above-mentioned processing in the voice guidance unit may be performed using AI, or may be performed without using AI. For example, the voice guidance unit can select the optimal guidance method based on the user's device information using an AI model that analyzes the device information. In this way, the voice guidance unit can select the optimal guidance method by taking into account the device information.

[0105] The voice guidance unit can adjust the content of the guidance based on the user's calendar information. The voice guidance unit adjusts the content of the guidance based on the user's calendar information. The calendar information includes the content, date, time, location, etc. of the schedule. For example, the voice guidance unit adjusts the timing of the guidance based on the schedule registered in the user's calendar. The voice guidance unit can also prioritize guidance related to specific events based on the user's calendar information. Furthermore, the voice guidance unit can select the optimal guidance method based on the schedule based on the user's calendar information. This allows the voice guidance unit to adjust the content of the guidance based on the calendar information. Some or all of the above-described processing in the voice guidance unit may be performed using AI, or may be performed without using AI. For example, the voice guidance unit can adjust the content of the guidance based on the user's calendar information using an AI model that analyzes the calendar information. This allows the voice guidance unit to adjust the content of the guidance based on the calendar information.

[0106] The guidance unit can estimate the user's emotion and determine the priority of information to be guided based on the estimated user's emotion. The guidance unit can estimate the user's emotion and determine the priority of information to be guided based on the estimated user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the guidance unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The guidance unit can also analyze the user's voice using voice analysis technology to estimate the emotion. Furthermore, the guidance unit can analyze the user's text message using text analysis technology to estimate the emotion. The guidance unit determines the priority of information to be guided based on the estimated emotion. The priority is determined using methods such as scoring and rule-based evaluation. For example, if the user is feeling stressed, the guidance unit can prioritize information with a high level of urgency. Furthermore, if the user is relaxed, the guidance unit can prioritize information with a high level of importance. Furthermore, if the user is in a hurry, the guidance unit can prioritize information related to time. This allows the guidance unit to prioritize information according to the user's emotion. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit may estimate the user's emotion using an AI model that estimates emotion and determine the priority of information. This allows the guidance unit to determine the priority of information according to the user's emotion.

[0107] When providing guidance, the guidance unit can select optimal information by referring to the user's past guidance history. When providing guidance, the guidance unit selects optimal information by referring to the user's past guidance history. The guidance history includes past guidance content, guidance date and time, etc. For example, the guidance unit prioritizes information that the user has used favorably in the past. The guidance unit can also avoid information that the user has ignored in the past. Furthermore, the guidance unit can also provide optimal information for a specific time period based on the user's past guidance history. In this way, the guidance unit can provide optimal information by referring to the past guidance history. Some or all of the above-described processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit can use an AI model that analyzes the guidance history to refer to the user's past guidance history and select optimal information. In this way, the guidance unit can provide optimal information by referring to the past guidance history.

[0108] The guidance unit can dynamically change the content of the guidance provided in accordance with the user's current situation. The guidance unit dynamically changes the content of the guidance provided in accordance with the user's current situation. The current situation includes the user's location information, activity status, etc. For example, when the user is at work, the guidance unit can prioritize information related to work. Furthermore, when the user is on vacation, the guidance unit can prioritize information related to vacation. Furthermore, when the user is exercising, the guidance unit can prioritize information related to exercise. This allows the guidance unit to change the content of the guidance provided in accordance with the current situation. Some or all of the above-mentioned processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit can dynamically change the content of the guidance provided in accordance with the user's current situation using an AI model that determines the current situation. This allows the guidance unit to change the content of the guidance provided in accordance with the current situation.

[0109] The guidance unit can estimate a user's emotion and adjust the display method of the guidance information based on the estimated user's emotion. The guidance unit can estimate a user's emotion and adjust the display method of the guidance information based on the estimated user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the guidance unit can analyze a user's facial expression using facial expression recognition technology to estimate the emotion. The guidance unit can also analyze a user's voice using voice analysis technology to estimate the emotion. Furthermore, the guidance unit can analyze a user's text message using text analysis technology to estimate the emotion. The guidance unit adjusts the display method of the guidance information based on the estimated emotion. The display method can be a text display, a graphical display, or other methods. For example, the guidance unit can select a simple display method when the user is stressed. The guidance unit can also select a detailed display method when the user is relaxed. Furthermore, the guidance unit can prioritize voice guidance when the user is in a hurry. This allows the guidance unit to adjust the display method of the guidance information according to the user's emotion. Emotion estimation is realized using an emotion estimation function that uses an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit may estimate the user's emotion using an AI model that estimates emotion and adjust the display method of the information to be guided. This allows the guidance unit to adjust the display method of the information to be guided according to the user's emotion.

[0110] The guidance unit can customize the information to be provided based on the user's geographical location information. The guidance unit customizes the information to be provided based on the user's geographical location information. The geographical location information includes GPS data, an IP address, etc. For example, when the user is in a specific area, the guidance unit can prioritize information related to that area. Furthermore, when the user is traveling, the guidance unit can prioritize information related to the user's destination. Furthermore, when the user is at home, the guidance unit can prioritize information related to the user's home. This allows the guidance unit to customize the information based on the geographical location information. Some or all of the above-described processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit can customize the information to be provided based on the user's geographical location information using an AI model that analyzes the geographical location information. This allows the guidance unit to customize the information based on the geographical location information.

[0111] The guidance unit can adjust the information to be presented based on the user's social media activity. The guidance unit adjusts the information to be presented based on the user's social media activity. Social media activity includes the content of posts, the number of likes, the number of followers, etc. For example, the guidance unit can prioritize information related to content mentioned by the user on social media. The guidance unit can also prioritize information related to accounts the user follows on social media. Furthermore, the guidance unit can prioritize information related to events the user is participating in on social media. This allows the guidance unit to adjust the information based on the social media activity. Some or all of the above-described processing in the guidance unit may be performed using AI, or may be performed without using AI. For example, the guidance unit can adjust the information to be presented based on the user's social media activity using an AI model that analyzes social media activity. This allows the guidance unit to adjust the information based on the social media activity.

[0112] The operation assistance unit can estimate the user's emotion and adjust the operation assistance method based on the estimated user's emotion. The operation assistance unit can estimate the user's emotion and adjust the operation assistance method based on the estimated user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the operation assistance unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The operation assistance unit can also analyze the user's voice using voice analysis technology to estimate the emotion. Furthermore, the operation assistance unit can analyze the user's text message using text analysis technology to estimate the emotion. The operation assistance unit adjusts the operation assistance method based on the estimated emotion. The operation assistance method is performed using methods such as guides, help messages, and tutorials. For example, the operation assistance unit can select a simple operation assistance method when the user is stressed. The operation assistance unit can also select a detailed operation assistance method when the user is relaxed. Furthermore, the operation assistance unit can prioritize voice operation assistance when the user is in a hurry. This allows the operation assistance unit to adjust the operation assistance method according to the user's emotion. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the operation assistance unit may be performed using AI, or may be performed without using AI. For example, the operation assistance unit can estimate the user's emotion using an AI model that estimates emotion and adjust the operation assistance method. This allows the operation assistance unit to adjust the operation assistance method according to the user's emotion.

[0113] When providing assistance, the operation assistance unit can select an optimal assistance method by referring to the user's past operation history. When providing assistance, the operation assistance unit selects an optimal assistance method by referring to the user's past operation history. The operation history includes past operation content, operation date and time, etc. For example, the operation assistance unit preferentially selects an operation assistance method that the user has used favorably in the past. The operation assistance unit can also avoid operation assistance methods that the user has ignored in the past. Furthermore, the operation assistance unit can select an optimal assistance method for a specific time period from the user's past operation history. In this way, the operation assistance unit can select an optimal assistance method by referring to the past operation history. Some or all of the above-described processing in the operation assistance unit may be performed using AI or without AI. For example, the operation assistance unit can select an optimal assistance method by referring to the user's past operation history using an AI model that analyzes the operation history. In this way, the operation assistance unit can select an optimal assistance method by referring to the past operation history.

[0114] The operation assistance unit can dynamically change the assistance content according to the user's current situation. The operation assistance unit dynamically changes the assistance content according to the user's current situation. The current situation includes the user's location information, activity status, etc. For example, when the user is at work, the operation assistance unit can prioritize work-related operation assistance. Furthermore, when the user is on vacation, the operation assistance unit can prioritize vacation-related operation assistance. Furthermore, when the user is exercising, the operation assistance unit can prioritize exercise-related operation assistance. This allows the operation assistance unit to change the assistance content according to the current situation. Some or all of the above-mentioned processing in the operation assistance unit may be performed using AI, or may be performed without using AI. For example, the operation assistance unit can dynamically change the assistance content according to the user's current situation using an AI model that determines the current situation. This allows the operation assistance unit to change the assistance content according to the current situation.

[0115] The operation assistance unit can estimate the user's emotion and determine the priority of operation assistance based on the estimated user's emotion. The operation assistance unit can estimate the user's emotion and determine the priority of operation assistance based on the estimated user's emotion. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the operation assistance unit can analyze the user's facial expression using facial expression recognition technology to estimate the emotion. The operation assistance unit can also analyze the user's voice using voice analysis technology to estimate the emotion. Furthermore, the operation assistance unit can analyze the user's text message using text analysis technology to estimate the emotion. The operation assistance unit determines the priority of operation assistance based on the estimated emotion. The priority is determined using methods such as scoring and rule-based evaluation. For example, if the user is feeling stressed, the operation assistance unit can prioritize operation assistance with a high level of urgency. Furthermore, if the user is relaxed, the operation assistance unit can prioritize operation assistance with a high level of importance. Furthermore, if the user is in a hurry, the operation assistance unit can prioritize operation assistance related to time. This allows the operation assistance unit to determine the priority of operation assistance according to the user's emotions. Emotion estimation is realized using an emotion estimation function using an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the operation assistance unit may be performed using AI, or may be performed without using AI. For example, the operation assistance unit can estimate the user's emotions using an AI model that estimates emotions and determine the priority of operation assistance. This allows the operation assistance unit to determine the priority of operation assistance according to the user's emotions.

[0116] When providing assistance, the operation assistance unit can select the optimal assistance method by taking into account the user's device information. When providing assistance, the operation assistance unit selects the optimal assistance method by taking into account the user's device information. The device information includes the device type, OS version, etc. For example, if the user is using a smartphone, the operation assistance unit selects the optimal assistance method for the smartphone. Furthermore, if the user is using a smartwatch, the operation assistance unit can select the optimal assistance method for the smartwatch. Furthermore, if the user is using a tablet, the operation assistance unit can select the optimal assistance method for the tablet. In this way, the operation assistance unit can select the optimal assistance method by taking into account the device information. Some or all of the above-mentioned processing in the operation assistance unit may be performed using AI, or may be performed without using AI. For example, the operation assistance unit can select the optimal assistance method based on the user's device information by using an AI model that analyzes device information. In this way, the operation assistance unit can select the optimal assistance method by taking into account the device information.

[0117] The operation assistance unit can adjust the assistance content based on the user's calendar information. The operation assistance unit adjusts the assistance content based on the user's calendar information. The calendar information includes the content, date, time, location, etc. of the schedule. For example, the operation assistance unit adjusts the timing of the assistance based on the schedule registered in the user's calendar. The operation assistance unit can also prioritize assistance related to a specific event based on the user's calendar information. Furthermore, the operation assistance unit can select an optimal assistance method based on the schedule based on the user's calendar information. This allows the operation assistance unit to adjust the assistance content based on the calendar information. Some or all of the above-described processing in the operation assistance unit may be performed using AI or may be performed without using AI. For example, the operation assistance unit can adjust the assistance content based on the user's calendar information using an AI model that analyzes the calendar information. This allows the operation assistance unit to adjust the assistance content based on the calendar information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned identifying unit, notifying unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the identifying unit is realized by the processor 46 of the smart device 14 and scans the user's email or message app to identify important matters. The notifying unit is realized, for example, by the output device 40 of the smart device 14 and provides visual display and audio guidance. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides related information. The identifying unit, notifying unit, and providing unit may also be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned identifying unit, notifying unit, and providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the identifying unit is realized by the processor 46 of the smart glasses 214 and scans the user's email or messaging app to identify important matters. The notifying unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides visual display and audio guidance. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides related information. The identifying unit, notifying unit, and providing unit may also be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned identifying unit, notifying unit, and providing unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the identifying unit is realized by the processor 46 of the headset type terminal 314 and scans the user's email or message app to identify important matters. The notifying unit is realized, for example, by the display 343 and speaker 240 of the headset type terminal 314 and provides visual display and audio guidance. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides related information. The identifying unit, notifying unit, and providing unit may also be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned identifying unit, notifying unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the identifying unit is realized by the processor 46 of the robot 414 and scans the user's email or message app to identify important matters. The notifying unit is realized, for example, by the speaker 240 and LEDs in the eyes of the robot 414 and provides visual display and audio guidance. The providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides related information. The identifying unit, notifying unit, and providing unit may also be realized, for example, by the specific processing unit 290 of the data processing device 12.

[0118] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0119] The identification unit can learn the user's past behavioral patterns and improve the accuracy of the identified cases. For example, the identification unit can learn what messages the user has judged to be important in the past and prioritize identifying similar messages. The identification unit can also learn the patterns of messages the user has ignored in the past and filter out messages with low importance. Furthermore, the identification unit can extract specific keywords or phrases from the user's past behavioral patterns and identify important cases. In this way, the identification unit can improve the accuracy of the identified cases by learning past behavioral patterns.

[0120] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing messages with a high level of urgency. Also, if the user is relaxed, the analysis unit can prioritize analyzing messages with a high level of importance. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing messages related to time. This allows the analysis unit to determine the priority of analysis according to the user's emotions.

[0121] The notification unit can adjust the notification method taking into account the remaining battery level of the user's device. For example, the notification unit can reduce the frequency of notifications when the remaining battery level is low. The notification unit can also increase the frequency of notifications when the remaining battery level is sufficient. Furthermore, the notification unit can prioritize only important notifications when the remaining battery level is very low. In this way, the notification unit can adjust the notification method according to the remaining battery level of the device.

[0122] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide information in a simple format. If the user is relaxed, the providing unit can also provide information in a detailed format. Furthermore, if the user is in a hurry, the providing unit can also provide information in a format that focuses on the main points. In this way, the providing unit can adjust the format of the information according to the user's emotions.

[0123] The providing unit can determine the priority of information to be provided based on the user's current activity status. For example, when the user is at work, the providing unit can provide work-related information with priority. When the user is on vacation, the providing unit can also provide vacation-related information with priority. Furthermore, when the user is exercising, the providing unit can also provide exercise-related information with priority. In this way, the providing unit can determine the priority of information according to the user's current activity status.

[0124] The identification unit can estimate the user's emotions and adjust the notification method for the identified case based on the estimated user's emotions. For example, the identification unit can select a simple notification method when the user is feeling stressed. The identification unit can also select a detailed notification method when the user is relaxed. Furthermore, the identification unit can prioritize voice notification when the user is in a hurry. This allows the identification unit to adjust the notification method according to the user's emotions.

[0125] The identification unit can analyze the user's past message history and improve the accuracy of the identified cases. For example, the identification unit learns patterns of messages that the user has previously judged to be important and identifies similar messages. The identification unit can also learn patterns of messages that the user has previously ignored and filter out messages with low importance. Furthermore, the identification unit can extract specific keywords or phrases from the user's past message history and identify important cases. In this way, the identification unit can improve the accuracy of the identified cases by analyzing the past message history.

[0126] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated user emotions. For example, the notification unit can reduce the frequency of notifications when the user is feeling stressed. The notification unit can also increase the frequency of notifications when the user is relaxed. Furthermore, the notification unit can also provide immediate notifications when the user is in a hurry. This allows the notification unit to adjust the timing of notifications according to the user's emotions.

[0127] The providing unit can improve the accuracy of the information to be provided by referring to the user's past operation history. For example, the providing unit can provide information that the user has used favorably in the past with priority. The providing unit can also avoid information that the user has ignored in the past. Furthermore, the providing unit can provide optimal information for a specific time period based on the user's past operation history. In this way, the providing unit can improve the accuracy of the information to be provided by referring to the past operation history.

[0128] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can select a simple display method. Also, if the user is relaxed, the providing unit can select a detailed display method. Furthermore, if the user is in a hurry, the providing unit can prioritize voice guidance. In this way, the providing unit can adjust the display method of information according to the user's emotions.

[0129] The processing flow of the second embodiment will be briefly explained below.

[0130] Step 1: The identification unit identifies important matters. The identification unit scans the user's emails and messaging apps, analyzes the content of the messages using natural language processing, and determines their importance. For example, it extracts messages containing keywords such as flight cancellations or delays, emergency calls from schools or daycare centers, and unauthorized account use. Natural language processing is carried out using techniques such as morphological analysis, grammatical analysis, and semantic analysis. Step 2: The notification unit notifies the user of the case identified by the identification unit. The notification unit not only displays the case visually, but also provides audio guidance. For example, it may provide audio guidance such as, "You have received a notice of a flight cancellation. Would you like to check the details?" The audio guidance is achieved using text-to-speech and speech synthesis technology. Step 3: The provision unit provides relevant information about the case notified by the notification unit. For example, in the case of a flight cancellation, the provision unit displays the airline's website and customer support phone number. The provision unit provides site information and phone number information. Site information includes the URL and a summary of the site, and phone numbers are provided in formats such as international phone number format and area code.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] [Explanation of symbols]

[0203] 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. An identification department that identifies important cases; a notification unit that notifies the case identified by the identification unit; a providing unit that provides related information regarding the case notified by the notifying unit; Equipped with A system characterized by:

2. The identification unit Equipped with an analysis unit that uses natural language processing to analyze messages and determine their importance 2. The system of claim 1.

3. The notification unit Equipped with a voice guidance unit that provides voice guidance 2. The system of claim 1.

4. The providing unit Equipped with a guide section that provides site information or telephone number information 2. The system of claim 1.

5. The providing unit Equipped with an operation support unit that supports user operations 2. The system of claim 1.

6. The identification unit Estimate user sentiment and prioritize cases to be identified based on the estimated user sentiment.

2. The system of claim 1.

7. The identification unit Analyze users' past message history to improve the accuracy of case identification 2. The system of claim 1.

8. The identification unit Dynamically change the type of issue to identify based on the user's current situation 2. The system of claim 1.

9. The identification unit Infer user sentiment and adjust notification methods for specific cases based on the estimated user sentiment 2. The system of claim 1.

Images