System

Abstract

An object of a system according to an embodiment is to efficiently manage a large number of reminders and to provide notification at an appropriate timing.SOLUTION: A system according to an embodiment includes a reminder reception unit, an analysis unit, and a notification unit. The reminder receiving unit receives a reminder. The analysis unit analyzes the content of the reminder received by the reminder reception unit. The notification unit performs notification at an appropriate timing based on the content of the reminder analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has faced the challenge of efficiently managing a large number of reminders and sending them at the appropriate time.

[0005] The system according to the embodiment aims to efficiently manage a huge number of reminders and notify them at the appropriate time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reminder receiving unit, an analysis unit, and a notification unit. The reminder receiving unit receives a reminder. The analysis unit analyzes the content of the reminder received by the reminder receiving unit. The notification unit issues a notification at an appropriate time based on the content of the reminder analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage a large number of reminders and notify them at the appropriate time. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The reminder processing system according to the embodiment of the present invention is a system that uses AI to initially handle the huge number of reminders that occur daily, thereby reducing the manual reminder work. As a result, the reminder processing system can improve the efficiency of reminder work and reduce the burden.

[0029] A reminder processing system according to an embodiment includes a reminder receiving unit, an analysis unit, and a notification unit. The reminder receiving unit receives a reminder. For example, the reminder receiving unit can receive a reminder via email or a messaging app. The reminder receiving unit can also receive a reminder using voice input or image input. The analysis unit analyzes the content of the reminder received by the reminder receiving unit. For example, the analysis unit can analyze the content of the reminder using natural language processing technology and evaluate the importance and urgency of the reminder. The analysis unit can also automatically search for related past reminders based on the content of the reminder and present them as reference information. The notification unit provides a notification at an appropriate time based on the content of the reminder analyzed by the analysis unit. For example, the notification unit can optimize the timing of the notification based on the user's schedule and activity patterns. The notification unit can also evaluate the user's emotional state using an emotion estimation function and select a notification method to reduce stress. As a result, the reminder processing system according to an embodiment can improve the efficiency of reminder tasks and reduce the burden on the user. For example, the reminder processing system can manage reminder history and analyze past reminder content to optimize future reminders. The reminder processing system can also prioritize reminders and adjust schedules so that important tasks are prioritized.

[0030] The analysis unit can automatically evaluate the importance and urgency of reminders and set priorities. For example, when the generation AI receives a reminder, the analysis unit analyzes the content of the reminder and uses an algorithm to evaluate the importance and urgency. For example, it calculates an importance score based on keywords and deadlines contained in the reminder content and sets priorities. The analysis unit can also automatically search for related past reminders based on the reminder content and present them as reference information. In this way, by evaluating the importance and urgency of reminders and setting priorities, important tasks are given priority.

[0031] The analysis unit can automatically search for related past reminders based on the content of the reminder and present them as reference information. For example, when the generation AI receives a reminder, the analysis unit analyzes the content of the reminder and searches for related reminders from a database of past reminders. For example, past reminders related to the same project can be automatically presented. The analysis unit can also automatically search for and provide related documents and materials based on the content of the reminder. This allows for more appropriate responses by referring to past reminders.

[0032] The reminder receiving unit can receive reminder content in a variety of formats using voice input or image input. For example, the reminder receiving unit builds a system in which, when the generation AI receives a reminder, it receives the reminder content using voice input. For example, a user inputs a reminder by voice, and the generation AI analyzes the voice to understand the reminder content. The reminder receiving unit can also receive reminder content using image input. For example, a user uploads an image, and the generation AI analyzes the image to understand the reminder content. This makes it possible to receive reminder content in a variety of formats by using voice input or image input.

[0033] The reminder receiving unit can automatically synchronize reminder information between different devices. For example, when the generation AI receives a reminder, the reminder receiving unit builds a system that automatically synchronizes reminder information between different devices. For example, reminder information is synchronized in real time between smartphones, tablets, and PCs. The reminder receiving unit can also synchronize reminder information using a cloud service. This automatically synchronizes reminder information between different devices, maintaining consistency of information.

[0034] The analysis unit uses natural language processing technology to deeply understand the context of the reminder and generate a more appropriate response. For example, when the generation AI analyzes the reminder content, the analysis unit uses natural language processing technology to understand the context of the reminder. For example, it analyzes keywords and phrases contained in the reminder content to understand the context. The analysis unit can also automatically suggest related tasks and resources based on the reminder content. In this way, using natural language processing technology makes it possible to deeply understand the context of the reminder and generate a more appropriate response.

[0035] The analysis unit can automatically suggest related tasks and resources based on the content of the reminder. For example, when the generation AI analyzes the content of the reminder, the analysis unit automatically suggests related tasks based on the content of the reminder. For example, for a reminder to "prepare for a meeting," the analysis unit suggests creating meeting materials and confirming participants. The analysis unit can also automatically search for and provide related documents and materials based on the content of the reminder. This enables efficient task management by suggesting related tasks and resources based on the content of the reminder.

[0036] The analysis unit can automatically translate reminder content in different languages, enabling multilingual support. For example, when the generation AI analyzes the reminder content, the analysis unit builds a system that automatically translates the reminder content in different languages. For example, it supports multiple languages ​​such as English, Japanese, and Chinese. In addition, when analyzing the reminder content, the analysis unit can use natural language processing technology to understand the context of the reminder and generate a more appropriate response. This makes it possible to automatically translate reminder content in different languages, enabling multilingual support.

[0037] The analysis unit can automatically search for and provide related documents and materials based on the reminder content. For example, when the generation AI analyzes the reminder content, the analysis unit builds a system that automatically searches for related documents and materials based on the reminder content. For example, it can present related materials based on the reminder keywords. The analysis unit can also automatically search for related past reminders based on the reminder content and present them as reference information. This enables efficient information management by providing related documents and materials based on the reminder content.

[0038] The notification unit can optimize the timing of a reminder notification based on the user's schedule and activity patterns. For example, when the generation AI sends a reminder notification, the notification unit analyzes the user's schedule and sets the optimal notification timing. For example, the notification is sent during a time when the user is not in a meeting or on a break. The notification unit can also analyze the user's activity patterns and select the optimal timing for sending a reminder notification. This makes it possible to reduce the burden on the user by optimizing the notification timing.

[0039] When sending a reminder notification, the notification unit can customize the notification content based on the user's past reactions and behavioral history. For example, when the generation AI sends a reminder notification, the notification unit analyzes the user's past reactions and behavioral history and customizes the notification content. For example, the notification unit may give priority to using notification formats that the user has preferred in the past. The notification unit can also adjust the reminder content based on the user's past behavioral history. This allows customizing the notification content to enable notifications according to the user's preferences.

[0040] The notification unit can synchronize notifications between different devices and platforms to ensure consistency when sending reminder notifications. For example, when the generation AI sends a reminder notification, the notification unit builds a system that synchronizes notifications between different devices and platforms. For example, notifications are synchronized in real time between smartphones, tablets, and PCs. The notification unit can also synchronize notifications using a cloud service. This allows for consistent notifications by synchronizing notifications between different devices and platforms.

[0041] When sending a reminder notification, the notification unit can use infographics and visual elements to make the notification content visually easy to understand. For example, when the generation AI sends a reminder notification, the notification unit uses infographics to make the notification content visually easy to understand. For example, the reminder content can be displayed in a graph or chart. The notification unit can also use visual elements to emphasize the notification content. For example, important reminders can be visually emphasized using icons or colors. This makes the notification content visually easy to understand, which can promote user understanding.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The reminder processing system can further include a health management unit that monitors the user's health condition. The health management unit, for example, acquires heart rate and sleep data from the wearable device and evaluates the user's health condition. This makes it possible to adjust reminders according to the user's health condition, such as reducing the frequency of reminders if the user is tired. The health management unit can also suggest appropriate break times based on the user's health data. This allows for reminder processing that takes the user's health into consideration.

[0044] The reminder processing system may further include a location information acquisition unit that acquires the user's location information. The location information acquisition unit may identify the user's current location using, for example, GPS, and adjust the content and timing of the reminder. For example, if the user is moving, the reminder may be delayed until the user has completed their movement. The location information acquisition unit may also send a reminder when the user arrives at a specific location. This allows for optimization of reminders based on the user's location information.

[0045] The reminder processing system may further include a voice recognition unit that accepts voice commands from the user. The voice recognition unit allows the user to, for example, set reminders or check the contents of reminders by voice. This allows the user to operate reminders without using their hands. The voice recognition unit may also change the priority of reminders based on the user's voice commands. This allows for flexible management of reminders through voice operations.

[0046] The reminder processing system may further include a learning unit that learns the user's past behavioral patterns. The learning unit may, for example, record how the user responded to past reminders and reflect this in the next reminder. This allows for optimization of reminders based on the user's behavioral patterns. The learning unit may also learn the user's preferences and habits and customize the content and timing of reminders. This allows for more appropriate reminders to be provided to the user.

[0047] The reminder processing system may further include a hobby learning unit that learns the user's hobbies and interests. The hobby learning unit may, for example, record the user's past hobbies and interests and reflect them in the next reminder. This allows for optimization of reminders based on the user's hobbies and interests. The hobby learning unit may also learn the user's preferences and habits and customize the content and timing of reminders. This allows for more appropriate reminders to be provided to the user.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The reminder receiving unit receives a reminder. For example, the reminder receiving unit can receive a reminder via email or a messaging app. The reminder receiving unit can also receive a reminder using voice input or image input. Step 2: The analysis unit analyzes the content of the reminder received by the reminder receiving unit. For example, the analysis unit may use natural language processing technology to analyze the content of the reminder and evaluate its importance and urgency. The analysis unit may also automatically search for related past reminders based on the content of the reminder and present them as reference information. Step 3: The notification unit notifies the user at an appropriate time based on the content of the reminder analyzed by the analysis unit. For example, the notification unit optimizes the timing of notifications based on the user's schedule and activity patterns. The notification unit can also use an emotion estimation function to evaluate the user's emotional state and select a notification method to reduce stress.

[0050] (Example 2) The reminder processing system according to the embodiment of the present invention is a system that uses AI to initially handle the huge number of reminders that occur daily, thereby reducing the manual reminder work. As a result, the reminder processing system can improve the efficiency of reminder work and reduce the burden.

[0051] A reminder processing system according to an embodiment includes a reminder receiving unit, an analysis unit, and a notification unit. The reminder receiving unit receives a reminder. For example, the reminder receiving unit can receive a reminder via email or a messaging app. The reminder receiving unit can also receive a reminder using voice input or image input. The analysis unit analyzes the content of the reminder received by the reminder receiving unit. For example, the analysis unit can analyze the content of the reminder using natural language processing technology and evaluate the importance and urgency of the reminder. The analysis unit can also automatically search for related past reminders based on the content of the reminder and present them as reference information. The notification unit provides a notification at an appropriate time based on the content of the reminder analyzed by the analysis unit. For example, the notification unit can optimize the timing of the notification based on the user's schedule and activity patterns. The notification unit can also evaluate the user's emotional state using an emotion estimation function and select a notification method to reduce stress. As a result, the reminder processing system according to an embodiment can improve the efficiency of reminder tasks and reduce the burden on the user. For example, the reminder processing system can manage reminder history and analyze past reminder content to optimize future reminders. The reminder processing system can also prioritize reminders and adjust schedules so that important tasks are prioritized.

[0052] The analysis unit can automatically evaluate the importance and urgency of reminders and set priorities. For example, when the generation AI receives a reminder, the analysis unit analyzes the content of the reminder and uses an algorithm to evaluate the importance and urgency. For example, it calculates an importance score based on keywords and deadlines contained in the reminder content and sets priorities. The analysis unit can also automatically search for related past reminders based on the reminder content and present them as reference information. In this way, by evaluating the importance and urgency of reminders and setting priorities, important tasks are given priority.

[0053] The analysis unit can automatically search for related past reminders based on the content of the reminder and present them as reference information. For example, when the generation AI receives a reminder, the analysis unit analyzes the content of the reminder and searches for related reminders from a database of past reminders. For example, past reminders related to the same project can be automatically presented. The analysis unit can also automatically search for and provide related documents and materials based on the content of the reminder. This allows for more appropriate responses by referring to past reminders.

[0054] The analysis unit can use the emotion estimation function to evaluate the user's emotional state when receiving a reminder and adjust the reminder content to reduce stress. For example, when the generation AI receives a reminder, the analysis unit analyzes the user's emotional state in real time and adjusts the reminder content to reduce stress. For example, if the user is feeling stressed, the reminder wording can be softened. The analysis unit can also use the emotion estimation function to take the user's emotional state into account when analyzing the reminder content and generate an emotionally positive response. This makes it possible to adjust the reminder content to reduce stress by taking the user's emotional state into account.

[0055] The reminder receiving unit can receive reminder content in a variety of formats using voice input or image input. For example, the reminder receiving unit builds a system in which, when the generation AI receives a reminder, it receives the reminder content using voice input. For example, a user inputs a reminder by voice, and the generation AI analyzes the voice to understand the reminder content. The reminder receiving unit can also receive reminder content using image input. For example, a user uploads an image, and the generation AI analyzes the image to understand the reminder content. This makes it possible to receive reminder content in a variety of formats by using voice input or image input.

[0056] The reminder receiving unit can automatically synchronize reminder information between different devices. For example, when the generation AI receives a reminder, the reminder receiving unit builds a system that automatically synchronizes reminder information between different devices. For example, reminder information is synchronized in real time between smartphones, tablets, and PCs. The reminder receiving unit can also synchronize reminder information using a cloud service. This automatically synchronizes reminder information between different devices, maintaining consistency of information.

[0057] The analysis unit can use the emotion estimation function to adjust the timing and method of reminders according to the user's emotions when receiving a reminder. For example, the analysis unit can use the emotion estimation function to analyze the user's emotional state in real time and adjust the timing and method of reminders. For example, the analysis unit can send reminders during times when the user is relaxed. The analysis unit can also use the emotion estimation function to take the user's emotional state into consideration when analyzing the reminder content and generate an emotionally positive response. This makes it possible to reduce stress by adjusting the timing and method of reminders according to the user's emotions.

[0058] The analysis unit uses natural language processing technology to deeply understand the context of the reminder and generate a more appropriate response. For example, when the generation AI analyzes the reminder content, the analysis unit uses natural language processing technology to understand the context of the reminder. For example, it analyzes keywords and phrases contained in the reminder content to understand the context. The analysis unit can also automatically suggest related tasks and resources based on the reminder content. In this way, using natural language processing technology makes it possible to deeply understand the context of the reminder and generate a more appropriate response.

[0059] The analysis unit can automatically suggest related tasks and resources based on the content of the reminder. For example, when the generation AI analyzes the content of the reminder, the analysis unit automatically suggests related tasks based on the content of the reminder. For example, for a reminder to "prepare for a meeting," the analysis unit suggests creating meeting materials and confirming participants. The analysis unit can also automatically search for and provide related documents and materials based on the content of the reminder. This enables efficient task management by suggesting related tasks and resources based on the content of the reminder.

[0060] The analysis unit can take the user's emotional state into consideration when analyzing the reminder content using the emotion estimation function, and generate an emotionally positive response. For example, the analysis unit can use the emotion estimation function to take the user's emotional state into consideration when the generation AI analyzes the reminder content. For example, if the user is feeling stressed, the reminder content can be made softer. The analysis unit can also use the emotion estimation function to evaluate the user's emotional state when analyzing the reminder content and adjust the priority of the response. This makes it possible to generate an emotionally positive response by taking the user's emotional state into consideration.

[0061] The analysis unit can automatically translate reminder content in different languages, enabling multilingual support. For example, when the generation AI analyzes the reminder content, the analysis unit builds a system that automatically translates the reminder content in different languages. For example, it supports multiple languages ​​such as English, Japanese, and Chinese. In addition, when analyzing the reminder content, the analysis unit can use natural language processing technology to understand the context of the reminder and generate a more appropriate response. This makes it possible to automatically translate reminder content in different languages, enabling multilingual support.

[0062] The analysis unit can automatically search for and provide related documents and materials based on the reminder content. For example, when the generation AI analyzes the reminder content, the analysis unit builds a system that automatically searches for related documents and materials based on the reminder content. For example, it can present related materials based on the reminder keywords. The analysis unit can also automatically search for related past reminders based on the reminder content and present them as reference information. This enables efficient information management by providing related documents and materials based on the reminder content.

[0063] The notification unit can optimize the timing of a reminder notification based on the user's schedule and activity patterns. For example, when the generation AI sends a reminder notification, the notification unit analyzes the user's schedule and sets the optimal notification timing. For example, the notification is sent during a time when the user is not in a meeting or on a break. The notification unit can also analyze the user's activity patterns and select the optimal timing for sending a reminder notification. This makes it possible to reduce the burden on the user by optimizing the notification timing.

[0064] When sending a reminder notification, the notification unit can customize the notification content based on the user's past reactions and behavioral history. For example, when the generation AI sends a reminder notification, the notification unit analyzes the user's past reactions and behavioral history and customizes the notification content. For example, the notification unit may give priority to using notification formats that the user has preferred in the past. The notification unit can also adjust the reminder content based on the user's past behavioral history. This allows customizing the notification content to enable notifications according to the user's preferences.

[0065] The notification unit can use the emotion estimation function to consider the user's emotional state when sending a reminder and select a notification method that reduces stress. For example, the notification unit uses the emotion estimation function to evaluate the user's emotional state when the generation AI sends a reminder and selects a notification method that reduces stress. For example, if the user is feeling stressed, the notification can be changed to softer wording. The notification unit can also adjust the frequency and timing of notifications by considering the user's emotional state. This makes it possible to select a notification method that reduces stress by considering the user's emotional state.

[0066] The notification unit can synchronize notifications between different devices and platforms to ensure consistency when sending reminder notifications. For example, when the generation AI sends a reminder notification, the notification unit builds a system that synchronizes notifications between different devices and platforms. For example, notifications are synchronized in real time between smartphones, tablets, and PCs. The notification unit can also synchronize notifications using a cloud service. This allows for consistent notifications by synchronizing notifications between different devices and platforms.

[0067] When sending a reminder notification, the notification unit can use infographics and visual elements to make the notification content visually easy to understand. For example, when the generation AI sends a reminder notification, the notification unit uses infographics to make the notification content visually easy to understand. For example, the reminder content can be displayed in a graph or chart. The notification unit can also use visual elements to emphasize the notification content. For example, important reminders can be visually emphasized using icons or colors. This makes the notification content visually easy to understand, which can promote user understanding.

[0068] The notification unit can adjust the frequency and method of notifications according to the user's emotions when sending reminder notifications using the emotion estimation function. For example, the notification unit uses the emotion estimation function to evaluate the user's emotional state when the generation AI sends a reminder notification, and adjusts the frequency and method of notifications. For example, if the user is feeling stressed, the notification unit can reduce the frequency of notifications. The notification unit can also adjust the timing and content of notifications taking the user's emotional state into consideration. This makes it possible to reduce stress by adjusting the frequency and method of notifications according to the user's emotions.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The reminder processing system can further include a health management unit that monitors the user's health condition. The health management unit, for example, acquires heart rate and sleep data from the wearable device and evaluates the user's health condition. This makes it possible to adjust reminders according to the user's health condition, such as reducing the frequency of reminders if the user is tired. The health management unit can also suggest appropriate break times based on the user's health data. This allows for reminder processing that takes the user's health into consideration.

[0071] The reminder processing system may further include a location information acquisition unit that acquires the user's location information. The location information acquisition unit may identify the user's current location using, for example, GPS, and adjust the content and timing of the reminder. For example, if the user is moving, the reminder may be delayed until the user has completed their movement. The location information acquisition unit may also send a reminder when the user arrives at a specific location. This allows for optimization of reminders based on the user's location information.

[0072] The reminder processing system may further include a voice recognition unit that accepts voice commands from the user. The voice recognition unit allows the user to, for example, set reminders or check the contents of reminders by voice. This allows the user to operate reminders without using their hands. The voice recognition unit may also change the priority of reminders based on the user's voice commands. This allows for flexible management of reminders through voice operations.

[0073] The reminder processing system may further include a learning unit that learns the user's past behavioral patterns. The learning unit may, for example, record how the user responded to past reminders and reflect this in the next reminder. This allows for optimization of reminders based on the user's behavioral patterns. The learning unit may also learn the user's preferences and habits and customize the content and timing of reminders. This allows for more appropriate reminders to be provided to the user.

[0074] The reminder processing system may further include an emotion adjustment unit that estimates the user's emotion and adjusts the content of the reminder based on the estimated emotion. For example, if the user is feeling stressed, the emotion adjustment unit softens the wording of the reminder. The emotion adjustment unit may also send reminders during times when the user is relaxed. This makes it possible to adjust reminders according to the user's emotional state. The emotion adjustment unit may also adjust the frequency of reminders taking the user's emotional state into consideration. This allows for reminder processing that reduces the user's stress.

[0075] The reminder processing system may further include a priority adjustment unit that estimates the user's emotions and adjusts the priority of reminders based on the estimated emotions. For example, if the user is feeling stressed, the priority adjustment unit postpones reminders with low importance. The priority adjustment unit may also send important reminders during times when the user is relaxed. This makes it possible to adjust the priority of reminders according to the user's emotional state. The priority adjustment unit may also adjust the content of reminders taking the user's emotional state into consideration. This allows for reminder processing that reduces the user's stress.

[0076] The reminder processing system may further include a notification adjustment unit that estimates the user's emotions and adjusts the reminder notification method based on the estimated emotions. For example, if the user is feeling stressed, the notification adjustment unit changes the notification to softer wording. The notification adjustment unit may also send notifications during times when the user is relaxed. This makes it possible to adjust the notification method according to the user's emotional state. The notification adjustment unit may also adjust the frequency of notifications taking the user's emotional state into consideration. This makes it possible to realize a notification method that reduces the user's stress.

[0077] The reminder processing system may further include a content customization unit that estimates the user's emotions and customizes the content of the reminder based on the estimated emotions. For example, if the user is feeling stressed, the content customization unit may soften the content of the reminder. The content customization unit may also send reminders during times when the user is relaxed. This makes it possible to customize the content of the reminder according to the user's emotional state. The content customization unit may also adjust the frequency of reminders taking the user's emotional state into consideration. This makes it possible to customize the content of the reminder to reduce the user's stress.

[0078] The reminder processing system may further include a timing adjustment unit that estimates the user's emotions and adjusts the timing of the reminder based on the estimated emotions. For example, the timing adjustment unit delays the timing of the reminder when the user is feeling stressed. The timing adjustment unit may also send a reminder during a time period when the user is relaxed. This makes it possible to adjust the timing of the reminder according to the user's emotional state. The timing adjustment unit may also adjust the content of the reminder taking the user's emotional state into consideration. This makes it possible to adjust the reminder timing to reduce the user's stress.

[0079] The reminder processing system may further include a hobby learning unit that learns the user's hobbies and interests. The hobby learning unit may, for example, record the user's past hobbies and interests and reflect them in the next reminder. This allows for optimization of reminders based on the user's hobbies and interests. The hobby learning unit may also learn the user's preferences and habits and customize the content and timing of reminders. This allows for more appropriate reminders to be provided to the user.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The reminder receiving unit receives a reminder. For example, the reminder receiving unit can receive a reminder via email or a messaging app. The reminder receiving unit can also receive a reminder using voice input or image input. Step 2: The analysis unit analyzes the content of the reminder received by the reminder receiving unit. For example, the analysis unit may use natural language processing technology to analyze the content of the reminder and evaluate its importance and urgency. The analysis unit may also automatically search for related past reminders based on the content of the reminder and present them as reference information. Step 3: The notification unit notifies the user at an appropriate time based on the content of the reminder analyzed by the analysis unit. For example, the notification unit optimizes the timing of notifications based on the user's schedule and activity patterns. The notification unit can also use an emotion estimation function to evaluate the user's emotional state and select a notification method to reduce stress.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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. [Explanation of symbols]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reminder receiving unit for receiving reminders; an analysis unit that analyzes the contents of the reminder received by the reminder receiving unit; a notification unit that notifies the user at an appropriate time based on the content of the reminder analyzed by the analysis unit. A system characterized by:

2. The reminder receiving unit Receive reminders in various formats using voice and image input 2. The system of claim 1.

3. The analysis unit Evaluating the user's emotional state when receiving the reminder and adjusting the reminder content to reduce stress 2. The system of claim 1.

4. The analysis unit Use natural language processing technology to deeply understand the context of the reminder and generate a more appropriate response 2. The system of claim 1.

5. The notification unit When sending the reminder, the timing of the notification is optimized based on the user's schedule and activity patterns.

2. The system of claim 1.

6. The notification unit When sending the reminder, the user's emotional state is taken into consideration and a notification method that reduces stress is selected.

2. The system of claim 1.

7. The analysis unit Considering the user's emotional state when analyzing reminder content, generating emotionally positive responses 2. The system of claim 1.

8. The notification unit When sending the reminder, adjust the frequency and method of notification according to the user's emotions.

2. The system of claim 1.

Images