System

Abstract

An object of a system according to an embodiment is to achieve both automation and privacy protection in toilet assistance.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a protection unit. The collection unit detects a motion of the user. The analysis unit analyzes the information collected by the collection unit. The providing unit provides assistance based on the analysis result obtained by the analyzing unit. The protection unit protects the privacy of the user.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 technologies have not sufficiently achieved both automation and privacy protection in toilet assistance, and there is room for improvement.

[0005] The system according to the embodiment aims to achieve both automation and privacy protection in toilet assistance. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a protection unit. The collection unit detects the user's movements. The analysis unit analyzes the information collected by the collection unit. The provision unit provides assistance based on the analysis results obtained by the analysis unit. The protection unit protects the user's privacy. [Effects of the Invention]

[0007] The system according to the embodiment can achieve both automation and privacy protection in toilet assistance. [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 smart toilet assistance system according to an embodiment of the present invention detects and analyzes a user's movements, provides assistance, and protects privacy. In the smart toilet assistance system, sensors detect a user's movements when the user enters a toilet, and AI determines whether assistance is needed. The AI ​​then provides the necessary assistance while protecting the user's privacy. Examples of such assistance include automatic toilet paper dispenser, automatic toilet seat raising and lowering, and even voice guidance. This allows the user to use the toilet with peace of mind and reduces the burden on the caregiver. For example, in the smart toilet assistance system, sensors capture the user's movements in detail when the user enters the toilet and transmit the information to the AI. For example, the system detects the user's movements, such as sitting down on the toilet seat or standing up. The AI ​​then analyzes the information from the sensors and determines whether assistance is needed. For example, if the user is about to lose balance while sitting on the toilet seat, the AI ​​automatically issues instructions to stabilize the seat. Furthermore, if the user has difficulty grabbing the toilet paper, the AI ​​automatically dispenses the toilet paper. Furthermore, the smart toilet assistance system provides the necessary assistance while protecting the user's privacy. For example, voice guidance can be used to provide appropriate instructions to the user. This allows the user to use the toilet by themselves without assistance from others. This allows the smart toilet assistance system to allow users to use the toilet with peace of mind and reduce the burden on caregivers. For example, when elderly people or people with physical disabilities use the toilet, AI can provide appropriate assistance, reducing the burden on caregivers. In addition, the user's privacy is protected, allowing them to use the toilet with peace of mind. This allows the smart toilet assistance system to detect and analyze the user's movements, provide assistance, and protect privacy. For example, when a user enters the toilet, a sensor detects their movement, and the AI ​​determines whether assistance is needed. The AI ​​then provides the necessary assistance while protecting the user's privacy. Examples include automatic toilet paper supply, automatic raising and lowering of the toilet seat, and even assistance via voice guidance. This allows users to use the toilet with peace of mind and reduces the burden on caregivers.

[0029] A smart toilet assistance system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a protection unit. The collection unit detects a user's movements. Examples of the user's movements include, but are not limited to, sitting on a toilet seat and standing up. The collection unit captures the user's movements in detail using, for example, a sensor. The collection unit can also detect the user's movements using a camera. For example, the collection unit can detect the user's movements using an infrared sensor. The collection unit can also detect the user's movements using a pressure sensor. The analysis unit analyzes the information collected by the collection unit. The analysis is performed using, for example, AI, but is not limited to, examples. For example, the analysis unit analyzes the user's movements using AI and determines the need for assistance. The analysis unit can also analyze the information using a machine learning algorithm. The analysis unit can also analyze the information using deep learning. For example, the analysis unit inputs the collected information into an AI model and outputs the need for assistance. The provision unit provides assistance based on the analysis results obtained by the analysis unit. Examples of assistance include, but are not limited to, automatic toilet paper supply, automatic raising and lowering of the toilet seat, and instructions via audio guidance. For example, the supply unit automatically supplies toilet paper. The supply unit can also automatically raise and lower the toilet seat. The supply unit can also give instructions to the user using audio guidance. For example, the supply unit controls the toilet paper supply mechanism to supply the required amount of toilet paper. The toilet seat is raised and lowered, for example, using a motor. The audio guidance gives instructions to the user, for example, using a speaker. The protection unit protects the user's privacy. Examples of privacy protection include, but are not limited to, data encryption, access control, anonymization technology, and the like. For example, the protection unit encrypts collected information to prevent third parties from accessing it. The protection unit can also perform access control to ensure that only specific users can access the information. The protection unit can also protect the user's personal information using anonymization technology. For example, the protection unit deletes personally identifiable information from the collected information and anonymizes it.As a result, the smart toilet assistance system according to the embodiment can detect and analyze the user's movements, provide assistance, and protect privacy. For example, the collection unit detects the user's movements using sensors, the analysis unit analyzes the information using AI, the provision unit automatically supplies toilet paper, automatically raises and lowers the toilet seat, and provides instructions via voice guidance, and the protection unit protects the user's privacy using data encryption, access control, and anonymization technology.

[0030] The provision unit can automatically supply toilet paper. Automatic provision of toilet paper includes, but is not limited to, a supply mechanism, a supply timing, and a supply amount. For example, the provision unit includes a mechanism for automatically supplying toilet paper. For example, the provision unit controls the toilet paper supply mechanism and supplies the required amount of toilet paper. The provision unit can also control the timing of supplying toilet paper. For example, the provision unit automatically supplies toilet paper when the user needs it. The provision unit can also control the amount of toilet paper supplied. For example, the provision unit supplies the amount of toilet paper required by the user. This improves user convenience through the automatic supply of toilet paper. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the toilet paper supply mechanism into the generation AI and have the generation AI control the supply timing and supply amount.

[0031] The providing unit can automatically raise and lower the toilet seat. The automatic raising and lowering of the toilet seat includes, but is not limited to, a lifting mechanism, a lifting speed, and a lifting range. For example, the providing unit includes a mechanism for automatically raising and lowering the toilet seat. For example, the providing unit controls the lifting mechanism of the toilet seat to raise or lower it to a required height. The providing unit can also control the lifting speed of the toilet seat. For example, the providing unit raises or lowers the toilet seat at an appropriate speed when a user sits on the toilet seat. The providing unit can also control the lifting range of the toilet seat. For example, the providing unit raises or lowers the toilet seat to an appropriate height when a user sits on the toilet seat. This improves user convenience through the automatic raising and lowering of the toilet seat. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the lifting mechanism of the toilet seat to the generating AI and cause the generating AI to control the lifting speed and the lifting range.

[0032] The providing unit can give instructions to the user using an audio guide. The audio guide includes, for example, but is not limited to, the type of audio, the content of the guide, and the timing of providing the guide. For example, the providing unit has a mechanism for giving instructions to the user using the audio guide. For example, the providing unit controls the content of the audio guide and gives appropriate instructions to the user. The providing unit can also control the timing of providing the audio guide. For example, the providing unit provides the audio guide at a timing required by the user. The providing unit can also control the type of audio guide. For example, the providing unit provides guidance in an audio that is easy for the user to understand. This allows the user to use the toilet by themselves using the audio guide. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the content of the audio guide to a generating AI and have the generating AI control the content of the guide and the timing of providing it.

[0033] The protection unit can provide a means for protecting the user's privacy. Examples of privacy protection include, but are not limited to, data encryption, access control, and anonymization technology. For example, the protection unit can encrypt the collected information to prevent third parties from accessing it. The protection unit can also perform access control to ensure that only specific users can access the information. For example, the protection unit can use an access control list to limit the users who can access the information. The protection unit can also protect the user's personal information using anonymization technology. For example, the protection unit can delete personally identifiable information from the collected information and anonymize it. This protects the user's privacy, allowing them to use the restroom with peace of mind. Some or all of the above-described processing in the protection unit may be performed using, for example, AI, or may be performed without AI. For example, the protection unit can input the collected information into a generation AI and have the generation AI perform encryption and anonymization processing.

[0034] The collection unit can analyze the user's past behavior history and select an appropriate detection method. For example, the collection unit selects the optimal detection method based on behaviors frequently performed by the user in the past. The collection unit can also select a method for detecting a specific behavior pattern from the user's past behavior history. The collection unit can also analyze the user's past behavior history and adjust the detection method based on the frequency and timing of the behavior. This improves the accuracy of behavior detection by selecting the optimal detection method based on the past behavior history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavior history data into a generation AI and have the generation AI select the optimal detection method.

[0035] The collection unit can perform filtering based on the user's physical condition when detecting movement. For example, if the user is tired, the collection unit can increase the movement detection accuracy to accurately capture even small movements. Furthermore, if the user is healthy, the collection unit can return the movement detection accuracy to normal to capture natural movements. Furthermore, if the user is in poor health, the collection unit can increase the movement detection accuracy to detect abnormal movements early. This improves the accuracy of movement detection through filtering according to the user's physical condition. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's physical condition data into the generation AI and have the generation AI adjust the filtering.

[0036] When detecting a movement, the collection unit can select an appropriate detection means according to the user's input method. For example, if the user uses voice input, the collection unit detects the movement using voice recognition technology. Furthermore, if the user uses gesture input, the collection unit can also detect the movement using gesture recognition technology. Furthermore, if the user uses gaze input, the collection unit can also detect the movement using gaze tracking technology. This improves the accuracy of movement detection by selecting a detection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method data into a generation AI and have the generation AI select the optimal detection means.

[0037] When detecting a movement, the collection unit can prioritize detecting highly relevant actions based on the user's geographical location information. For example, if the user is in a specific location, the collection unit prioritizes detecting actions at that location. Furthermore, if the user is moving, the collection unit can prioritize detecting actions related to movement. Furthermore, if the user is in a specific area, the collection unit can prioritize detecting actions in that area. This allows highly relevant actions to be detected preferentially by detecting actions based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to a generation AI and cause the generation AI to determine the priority of highly relevant actions.

[0038] When detecting a movement, the collection unit can analyze the user's social media activity and detect related movements. For example, the collection unit can detect movements related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts to detect related movements. The collection unit can also detect related movements by referring to the activities of the user's friends on social media. This allows for accurate detection of related movements through movement detection based on social media activity. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to detect related movements.

[0039] When detecting a movement, the collection unit can customize the detection method by reflecting the user's past feedback. The collection unit customizes the detection method, for example, based on feedback provided by the user in the past. The collection unit can also select a method to preferentially detect a specific movement based on the user's past feedback. The collection unit can also analyze the user's past feedback and optimize the detection method. This improves the accuracy of movement detection by customizing the detection method based on the past feedback. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the detection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the action. For example, the analysis unit performs a detailed analysis on important actions. The analysis unit can also perform a standard analysis on normal actions. The analysis unit can also perform a simplified analysis on actions with low importance. In this way, by adjusting the level of detail of the analysis according to the importance of the action, important actions can be analyzed in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input action importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the movement. For example, the analysis unit can apply an analysis algorithm that emphasizes stability to the movement of sitting down. The analysis unit can also apply an analysis algorithm that emphasizes balance to the movement of standing up. The analysis unit can also apply an analysis algorithm that emphasizes hand movement to the movement of picking up toilet paper. In this way, application of an analysis algorithm depending on the movement category improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input movement category data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of detecting specific movement patterns from the user's past analysis results. The analysis unit can also analyze the user's past analysis results and optimize the analysis accuracy. This improves the analysis accuracy by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of occurrence of the motion. For example, the analysis unit prioritizes analysis of motions that occurred recently. The analysis unit can also prioritize analysis of motions that occurred frequently in the past. The analysis unit can also prioritize analysis of motions that occurred during a specific time period. In this way, by determining the analysis priority based on the time of occurrence of the motion, important motions are analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of occurrence of the motion to a generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of actions. For example, the analysis unit prioritizes analysis of highly relevant actions. The analysis unit can also postpone analysis of less relevant actions. The analysis unit can also optimize the order of analysis based on the relevance of actions. As a result, highly relevant actions are analyzed with priority by adjusting the order of analysis based on the relevance of actions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of actions to a generation AI and cause the generation AI to adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This makes it easier to understand the analysis results by providing analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI to adjust the use of technical terminology.

[0046] The providing unit can adjust the level of detail of the assistance based on the importance of the action when providing assistance. For example, the providing unit provides detailed assistance for an important action. The providing unit can also provide standard assistance for a normal action. The providing unit can also provide simplified assistance for an action with low importance. In this way, detailed assistance is provided for an important action by adjusting the level of detail of the assistance according to the importance of the action. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input action importance data to the generating AI and cause the generating AI to adjust the level of detail of the assistance.

[0047] When providing assistance, the providing unit can apply different assistance algorithms depending on the category of the action. For example, the providing unit can apply an assistance algorithm that emphasizes stability to the action of sitting down. The providing unit can also apply an assistance algorithm that emphasizes balance to the action of standing up. The providing unit can also apply an assistance algorithm that emphasizes hand movement to the action of picking up toilet paper. This improves the accuracy of assistance by applying an assistance algorithm according to the category of the action. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input action category data into the generating AI and cause the generating AI to apply an appropriate assistance algorithm.

[0048] When providing assistance, the providing unit can improve the accuracy of the assistance by referring to the user's past assistance results. The providing unit, for example, adjusts the assistance algorithm based on the user's past assistance results. The providing unit can also improve the accuracy of detecting a specific movement pattern from the user's past assistance results. The providing unit can also analyze the user's past assistance results and optimize the accuracy of the assistance. As a result, the accuracy of the assistance is improved by referring to the past assistance results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past assistance result data into the generating AI and cause the generating AI to improve the accuracy of the assistance.

[0049] When providing assistance, the providing unit can determine the priority of assistance based on the time of occurrence of the action. For example, the providing unit can provide assistance preferentially to an action that occurred recently. The providing unit can also provide standard assistance to an action that occurred frequently in the past. The providing unit can also provide assistance preferentially to an action that occurred during a specific time period. In this way, by determining the priority of assistance based on the time of occurrence of the action, important actions are given priority in assistance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of occurrence of the action into the generating AI and cause the generating AI to determine the priority of assistance.

[0050] The providing unit can adjust the order of assistance based on the relevance of the actions when providing assistance. For example, the providing unit can provide assistance preferentially to actions with high relevance. The providing unit can also provide assistance later to actions with low relevance. The providing unit can also optimize the order of assistance based on the relevance of the actions. As a result, assistance is given priority to actions with high relevance by adjusting the order of assistance based on the relevance of the actions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input action relevance data to a generating AI and cause the generating AI to adjust the order of assistance.

[0051] When providing assistance, the providing unit can adjust the use of technical terminology for the assistance depending on the user's level of expertise. For example, if the user has technical expertise, the providing unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide assistance in simple language. Furthermore, the providing unit can adjust the way the assistance is presented depending on the user's level of expertise. This makes it easier to understand the assistance result by providing an assistance result that is appropriate for the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generating AI to adjust the use of technical terminology.

[0052] During privacy protection, the protection unit can select an appropriate protection method by referring to the user's past privacy settings. For example, the protection unit selects the optimal protection method based on the privacy protection level previously set by the user. The protection unit can also select a specific protection method from the user's past privacy settings. The protection unit can also analyze the user's past privacy settings and select the optimal protection method. In this way, the user's privacy is appropriately protected by selecting a protection method based on the past privacy settings. Some or all of the above-described processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input the user's past privacy setting data into a generation AI and have the generation AI select the optimal protection method.

[0053] During privacy protection, the protection unit can customize protection measures based on the user's current situation. For example, the protection unit can increase the level of privacy protection when the user is in a public place. The protection unit can also maintain a normal level of privacy protection when the user is at home. The protection unit can also provide privacy protection measures according to the situation when the user is in a specific situation. This allows the user's privacy to be appropriately protected by customizing the privacy protection measures according to the current situation. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the user's current situation data to the generation AI and have the generation AI customize the protection measures.

[0054] The protection unit can improve the protection method by reflecting user feedback during privacy protection. For example, the protection unit improves the privacy protection method based on feedback provided by the user. The protection unit can also improve a specific privacy protection method based on the user's past feedback. The protection unit can also analyze the user's feedback and optimize the privacy protection method. This allows the user's privacy to be appropriately protected by improving the protection method based on the feedback. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input user feedback data into a generation AI and have the generation AI improve the protection method.

[0055] The protection unit can select the optimal protection method during privacy protection, taking into account the user's geographical location information. For example, the protection unit can increase the level of privacy protection when the user is in a public place. The protection unit can also maintain a normal level of privacy protection when the user is at home. The protection unit can also provide privacy protection measures appropriate for a specific area when the user is in that area. This allows the user's privacy to be appropriately protected by selecting a protection method based on the geographical location information. Some or all of the above-described processing in the protection unit can be performed using, for example, AI, or can be performed without using AI. For example, the protection unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal protection method.

[0056] During privacy protection, the protection unit can analyze the user's social media activities and suggest protection measures. For example, the protection unit can suggest privacy protection measures related to places where the user has checked in on social media. The protection unit can also analyze the content of the user's social media posts and suggest related privacy protection measures. The protection unit can also suggest related privacy protection measures based on the activities of the user's friends on social media. In this way, the user's privacy is appropriately protected by suggesting protection measures based on social media activities. Some or all of the above-described processing in the protection unit can be performed using, for example, AI, or without AI. For example, the protection unit can input the user's social media activity data into a generation AI and have the generation AI execute the suggestion of protection measures.

[0057] The protection unit can customize the protection method by reflecting the user's past feedback during privacy protection. For example, the protection unit customizes the privacy protection method based on feedback provided by the user. The protection unit can also customize a specific privacy protection method based on the user's past feedback. The protection unit can also analyze the user's feedback and optimize the privacy protection method. This allows the user's privacy to be appropriately protected by customizing the protection method based on the past feedback. Some or all of the above-described processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the protection method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The collection unit can analyze the user's past behavior history and select an appropriate detection method. For example, the collection unit can select the optimal detection method based on behaviors frequently performed by the user in the past. The collection unit can also select a method for detecting a specific behavior pattern from the user's past behavior history. Furthermore, the collection unit can analyze the user's past behavior history and adjust the detection method based on the frequency and timing of the behavior. This improves the accuracy of behavior detection by selecting the optimal detection method based on the past behavior history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past behavior history data into the generation AI and have the generation AI select the optimal detection method.

[0060] When detecting a movement, the collection unit can perform filtering based on the user's physical condition. For example, if the user is tired, the movement detection accuracy can be increased to accurately capture even small movements. Also, if the user is in good health, the movement detection accuracy can be returned to normal to capture natural movements. Furthermore, if the user is in poor health, the movement detection accuracy can be increased to detect abnormal movements early. This improves the accuracy of movement detection through filtering according to the user's physical condition. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's physical condition data into the generation AI and have the generation AI adjust the filtering.

[0061] When detecting a movement, the collection unit can select an appropriate detection means according to the user's input method. For example, if the user uses voice input, the movement can be detected using voice recognition technology. Also, if the user uses gesture input, the movement can be detected using gesture recognition technology. Furthermore, if the user uses gaze input, the movement can be detected using gaze tracking technology. This improves the accuracy of movement detection by selecting a detection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input method data into the generation AI and have the generation AI select the optimal detection means.

[0062] When detecting a movement, the collection unit can prioritize detection of highly relevant movements based on the user's geographical location information. For example, if the user is in a specific location, it can prioritize detection of movements at that location. Also, if the user is moving, it can prioritize detection of movements related to the movement. Furthermore, if the user is in a specific area, it can prioritize detection of movements in that area. This allows highly relevant movements to be detected preferentially by detecting movements based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to determine the priority of highly relevant movements.

[0063] When detecting a movement, the collection unit can analyze the user's social media activity and detect related movements. For example, it can detect movements related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts to detect related movements. Furthermore, the collection unit can also detect related movements by referring to the activities of the user's friends on social media. This allows for accurate detection of related movements through movement detection based on social media activity. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to detect related movements.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit detects the user's movements. These movements include sitting on the toilet seat and standing up. The collection unit captures the user's movements in detail using sensors and cameras. For example, infrared sensors and pressure sensors are used to detect the user's movements. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using AI, machine learning algorithms, and deep learning. For example, the analysis unit inputs the collected information into an AI model to determine whether assistance is needed. Step 3: The provision unit provides assistance based on the analysis results obtained by the analysis unit. Assistance includes automatic toilet paper supply, automatic raising and lowering of the toilet seat, and instructions via audio guidance. For example, the provision unit controls the toilet paper supply mechanism, raises and lowers the toilet seat with a motor, and provides audio guidance using a speaker. Step 4: The protection unit protects the user's privacy. Privacy protection includes data encryption, access control, anonymization technology, etc. For example, the protection unit encrypts collected information, controls access, and uses anonymization technology to delete personally identifiable information.

[0066] (Example 2) A smart toilet assistance system according to an embodiment of the present invention detects and analyzes a user's movements, provides assistance, and protects privacy. In the smart toilet assistance system, sensors detect a user's movements when the user enters a toilet, and AI determines whether assistance is needed. The AI ​​then provides the necessary assistance while protecting the user's privacy. Examples of such assistance include automatic toilet paper dispenser, automatic toilet seat raising and lowering, and even voice guidance. This allows the user to use the toilet with peace of mind and reduces the burden on the caregiver. For example, in the smart toilet assistance system, sensors capture the user's movements in detail when the user enters the toilet and transmit the information to the AI. For example, the system detects the user's movements, such as sitting down on the toilet seat or standing up. The AI ​​then analyzes the information from the sensors and determines whether assistance is needed. For example, if the user is about to lose balance while sitting on the toilet seat, the AI ​​automatically issues instructions to stabilize the seat. Furthermore, if the user has difficulty grabbing the toilet paper, the AI ​​automatically dispenses the toilet paper. Furthermore, the smart toilet assistance system provides the necessary assistance while protecting the user's privacy. For example, voice guidance can be used to provide appropriate instructions to the user. This allows the user to use the toilet by themselves without assistance from others. This allows the smart toilet assistance system to allow users to use the toilet with peace of mind and reduce the burden on caregivers. For example, when elderly people or people with physical disabilities use the toilet, AI can provide appropriate assistance, reducing the burden on caregivers. In addition, the user's privacy is protected, allowing them to use the toilet with peace of mind. This allows the smart toilet assistance system to detect and analyze the user's movements, provide assistance, and protect privacy. For example, when a user enters the toilet, a sensor detects their movement, and the AI ​​determines whether assistance is needed. The AI ​​then provides the necessary assistance while protecting the user's privacy. Examples include automatic toilet paper supply, automatic raising and lowering of the toilet seat, and even assistance via voice guidance. This allows users to use the toilet with peace of mind and reduces the burden on caregivers.

[0067] A smart toilet assistance system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a protection unit. The collection unit detects a user's movements. Examples of the user's movements include, but are not limited to, sitting on a toilet seat and standing up. The collection unit captures the user's movements in detail using, for example, a sensor. The collection unit can also detect the user's movements using a camera. For example, the collection unit can detect the user's movements using an infrared sensor. The collection unit can also detect the user's movements using a pressure sensor. The analysis unit analyzes the information collected by the collection unit. The analysis is performed using, for example, AI, but is not limited to, examples. For example, the analysis unit analyzes the user's movements using AI and determines the need for assistance. The analysis unit can also analyze the information using a machine learning algorithm. The analysis unit can also analyze the information using deep learning. For example, the analysis unit inputs the collected information into an AI model and outputs the need for assistance. The provision unit provides assistance based on the analysis results obtained by the analysis unit. Examples of assistance include, but are not limited to, automatic toilet paper supply, automatic raising and lowering of the toilet seat, and instructions via audio guidance. For example, the supply unit automatically supplies toilet paper. The supply unit can also automatically raise and lower the toilet seat. The supply unit can also give instructions to the user using audio guidance. For example, the supply unit controls the toilet paper supply mechanism to supply the required amount of toilet paper. The toilet seat is raised and lowered, for example, using a motor. The audio guidance gives instructions to the user, for example, using a speaker. The protection unit protects the user's privacy. Examples of privacy protection include, but are not limited to, data encryption, access control, anonymization technology, and the like. For example, the protection unit encrypts collected information to prevent third parties from accessing it. The protection unit can also perform access control to ensure that only specific users can access the information. The protection unit can also protect the user's personal information using anonymization technology. For example, the protection unit deletes personally identifiable information from the collected information and anonymizes it.As a result, the smart toilet assistance system according to the embodiment can detect and analyze the user's movements, provide assistance, and protect privacy. For example, the collection unit detects the user's movements using sensors, the analysis unit analyzes the information using AI, the provision unit automatically supplies toilet paper, automatically raises and lowers the toilet seat, and provides instructions via voice guidance, and the protection unit protects the user's privacy using data encryption, access control, and anonymization technology.

[0068] The provision unit can automatically supply toilet paper. Automatic provision of toilet paper includes, but is not limited to, a supply mechanism, a supply timing, and a supply amount. For example, the provision unit includes a mechanism for automatically supplying toilet paper. For example, the provision unit controls the toilet paper supply mechanism and supplies the required amount of toilet paper. The provision unit can also control the timing of supplying toilet paper. For example, the provision unit automatically supplies toilet paper when the user needs it. The provision unit can also control the amount of toilet paper supplied. For example, the provision unit supplies the amount of toilet paper required by the user. This improves user convenience through the automatic supply of toilet paper. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the toilet paper supply mechanism into the generation AI and have the generation AI control the supply timing and supply amount.

[0069] The providing unit can automatically raise and lower the toilet seat. The automatic raising and lowering of the toilet seat includes, but is not limited to, a lifting mechanism, a lifting speed, and a lifting range. For example, the providing unit includes a mechanism for automatically raising and lowering the toilet seat. For example, the providing unit controls the lifting mechanism of the toilet seat to raise or lower it to a required height. The providing unit can also control the lifting speed of the toilet seat. For example, the providing unit raises or lowers the toilet seat at an appropriate speed when a user sits on the toilet seat. The providing unit can also control the lifting range of the toilet seat. For example, the providing unit raises or lowers the toilet seat to an appropriate height when a user sits on the toilet seat. This improves user convenience through the automatic raising and lowering of the toilet seat. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the lifting mechanism of the toilet seat to the generating AI and cause the generating AI to control the lifting speed and the lifting range.

[0070] The providing unit can give instructions to the user using an audio guide. The audio guide includes, for example, but is not limited to, the type of audio, the content of the guide, and the timing of providing the guide. For example, the providing unit has a mechanism for giving instructions to the user using the audio guide. For example, the providing unit controls the content of the audio guide and gives appropriate instructions to the user. The providing unit can also control the timing of providing the audio guide. For example, the providing unit provides the audio guide at a timing required by the user. The providing unit can also control the type of audio guide. For example, the providing unit provides guidance in an audio that is easy for the user to understand. This allows the user to use the toilet by themselves using the audio guide. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the content of the audio guide to a generating AI and have the generating AI control the content of the guide and the timing of providing it.

[0071] The protection unit can provide a means for protecting the user's privacy. Examples of privacy protection include, but are not limited to, data encryption, access control, and anonymization technology. For example, the protection unit can encrypt the collected information to prevent third parties from accessing it. The protection unit can also perform access control to ensure that only specific users can access the information. For example, the protection unit can use an access control list to limit the users who can access the information. The protection unit can also protect the user's personal information using anonymization technology. For example, the protection unit can delete personally identifiable information from the collected information and anonymize it. This protects the user's privacy, allowing them to use the restroom with peace of mind. Some or all of the above-described processing in the protection unit may be performed using, for example, AI, or may be performed without AI. For example, the protection unit can input the collected information into a generation AI and have the generation AI perform encryption and anonymization processing.

[0072] The collection unit can estimate the user's emotions and adjust the movement detection accuracy based on the estimated user's emotions. For example, when the user is nervous, the collection unit can increase the movement detection accuracy to accurately capture even small movements. Furthermore, when the user is relaxed, the collection unit can return the movement detection accuracy to normal to capture natural movements. Furthermore, when the user is in a hurry, the collection unit can increase the movement detection accuracy to accommodate quick movements. This enables more accurate movement detection by adjusting the movement detection accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the movement detection accuracy.

[0073] The collection unit can analyze the user's past behavior history and select an appropriate detection method. For example, the collection unit selects the optimal detection method based on behaviors frequently performed by the user in the past. The collection unit can also select a method for detecting a specific behavior pattern from the user's past behavior history. The collection unit can also analyze the user's past behavior history and adjust the detection method based on the frequency and timing of the behavior. This improves the accuracy of behavior detection by selecting the optimal detection method based on the past behavior history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavior history data into a generation AI and have the generation AI select the optimal detection method.

[0074] The collection unit can perform filtering based on the user's physical condition when detecting movement. For example, if the user is tired, the collection unit can increase the movement detection accuracy to accurately capture even small movements. Furthermore, if the user is healthy, the collection unit can return the movement detection accuracy to normal to capture natural movements. Furthermore, if the user is in poor health, the collection unit can increase the movement detection accuracy to detect abnormal movements early. This improves the accuracy of movement detection through filtering according to the user's physical condition. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's physical condition data into the generation AI and have the generation AI adjust the filtering.

[0075] When detecting a movement, the collection unit can select an appropriate detection means according to the user's input method. For example, if the user uses voice input, the collection unit detects the movement using voice recognition technology. Furthermore, if the user uses gesture input, the collection unit can also detect the movement using gesture recognition technology. Furthermore, if the user uses gaze input, the collection unit can also detect the movement using gaze tracking technology. This improves the accuracy of movement detection by selecting a detection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method data into a generation AI and have the generation AI select the optimal detection means.

[0076] The collection unit can estimate the user's emotions and determine the priority of actions to be detected based on the estimated user emotions. For example, if the user is nervous, the collection unit can prioritize detecting important actions. Furthermore, if the user is relaxed, the collection unit can also prioritize detecting normal actions. Furthermore, if the user is in a hurry, the collection unit can also prioritize detecting quick actions. Thus, by determining the priority of actions according to the user's emotions, important actions can be detected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of actions.

[0077] When detecting a movement, the collection unit can prioritize detecting highly relevant actions based on the user's geographical location information. For example, if the user is in a specific location, the collection unit prioritizes detecting actions at that location. Furthermore, if the user is moving, the collection unit can prioritize detecting actions related to movement. Furthermore, if the user is in a specific area, the collection unit can prioritize detecting actions in that area. This allows highly relevant actions to be detected preferentially by detecting actions based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to a generation AI and cause the generation AI to determine the priority of highly relevant actions.

[0078] When detecting a movement, the collection unit can analyze the user's social media activity and detect related movements. For example, the collection unit can detect movements related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts to detect related movements. The collection unit can also detect related movements by referring to the activities of the user's friends on social media. This allows for accurate detection of related movements through movement detection based on social media activity. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to detect related movements.

[0079] When detecting a movement, the collection unit can customize the detection method by reflecting the user's past feedback. The collection unit customizes the detection method, for example, based on feedback provided by the user in the past. The collection unit can also select a method to preferentially detect a specific movement based on the user's past feedback. The collection unit can also analyze the user's past feedback and optimize the detection method. This improves the accuracy of movement detection by customizing the detection method based on the past feedback. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the detection method.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This makes it easier to understand the analysis results by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the action. For example, the analysis unit performs a detailed analysis on important actions. The analysis unit can also perform a standard analysis on normal actions. The analysis unit can also perform a simplified analysis on actions with low importance. In this way, by adjusting the level of detail of the analysis according to the importance of the action, important actions can be analyzed in detail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input action importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the movement. For example, the analysis unit can apply an analysis algorithm that emphasizes stability to the movement of sitting down. The analysis unit can also apply an analysis algorithm that emphasizes balance to the movement of standing up. The analysis unit can also apply an analysis algorithm that emphasizes hand movement to the movement of picking up toilet paper. In this way, application of an analysis algorithm depending on the movement category improves the accuracy of the analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input movement category data into the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of detecting specific movement patterns from the user's past analysis results. The analysis unit can also analyze the user's past analysis results and optimize the analysis accuracy. This improves the analysis accuracy by referring to the past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can quickly provide the analysis result. This adjustment of the length of the analysis according to the user's emotions makes the analysis result easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the time of occurrence of the motion. For example, the analysis unit prioritizes analysis of motions that occurred recently. The analysis unit can also prioritize analysis of motions that occurred frequently in the past. The analysis unit can also prioritize analysis of motions that occurred during a specific time period. In this way, by determining the analysis priority based on the time of occurrence of the motion, important motions are analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of occurrence of the motion to a generation AI and have the generation AI determine the analysis priority.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of actions. For example, the analysis unit prioritizes analysis of highly relevant actions. The analysis unit can also postpone analysis of less relevant actions. The analysis unit can also optimize the order of analysis based on the relevance of actions. As a result, highly relevant actions are analyzed with priority by adjusting the order of analysis based on the relevance of actions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of actions to a generation AI and cause the generation AI to adjust the order of analysis.

[0087] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This makes it easier to understand the analysis results by providing analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI to adjust the use of technical terminology.

[0088] The providing unit can estimate the user's emotions and adjust the way in which assistance is presented based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide audio guidance in a calm voice. Furthermore, if the user is relaxed, the providing unit can provide audio guidance in a cheerful voice. Furthermore, if the user is in a hurry, the providing unit can provide quick and concise audio guidance. This allows the user to receive assistance with peace of mind by adjusting the way in which assistance is presented according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, 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, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way in which assistance is presented.

[0089] The providing unit can adjust the level of detail of the assistance based on the importance of the action when providing assistance. For example, the providing unit provides detailed assistance for an important action. The providing unit can also provide standard assistance for a normal action. The providing unit can also provide simplified assistance for an action with low importance. In this way, detailed assistance is provided for an important action by adjusting the level of detail of the assistance according to the importance of the action. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input action importance data to the generating AI and cause the generating AI to adjust the level of detail of the assistance.

[0090] When providing assistance, the providing unit can apply different assistance algorithms depending on the category of the action. For example, the providing unit can apply an assistance algorithm that emphasizes stability to the action of sitting down. The providing unit can also apply an assistance algorithm that emphasizes balance to the action of standing up. The providing unit can also apply an assistance algorithm that emphasizes hand movement to the action of picking up toilet paper. This improves the accuracy of assistance by applying an assistance algorithm according to the category of the action. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input action category data into the generating AI and cause the generating AI to apply an appropriate assistance algorithm.

[0091] When providing assistance, the providing unit can improve the accuracy of the assistance by referring to the user's past assistance results. The providing unit, for example, adjusts the assistance algorithm based on the user's past assistance results. The providing unit can also improve the accuracy of detecting a specific movement pattern from the user's past assistance results. The providing unit can also analyze the user's past assistance results and optimize the accuracy of the assistance. As a result, the accuracy of the assistance is improved by referring to the past assistance results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past assistance result data into the generating AI and cause the generating AI to improve the accuracy of the assistance.

[0092] The providing unit can estimate the user's emotions and adjust the length of assistance based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide short, concise assistance. Furthermore, if the user is relaxed, the providing unit can provide longer assistance with detailed explanations. Furthermore, if the user is in a hurry, the providing unit can provide quick assistance. This allows the user to receive assistance with peace of mind by adjusting the length of assistance according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, 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, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of assistance.

[0093] When providing assistance, the providing unit can determine the priority of assistance based on the time of occurrence of the action. For example, the providing unit can provide assistance preferentially to an action that occurred recently. The providing unit can also provide standard assistance to an action that occurred frequently in the past. The providing unit can also provide assistance preferentially to an action that occurred during a specific time period. In this way, by determining the priority of assistance based on the time of occurrence of the action, important actions are given priority in assistance. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the time of occurrence of the action into the generating AI and cause the generating AI to determine the priority of assistance.

[0094] The providing unit can adjust the order of assistance based on the relevance of the actions when providing assistance. For example, the providing unit can provide assistance preferentially to actions with high relevance. The providing unit can also provide assistance later to actions with low relevance. The providing unit can also optimize the order of assistance based on the relevance of the actions. As a result, assistance is given priority to actions with high relevance by adjusting the order of assistance based on the relevance of the actions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input action relevance data to a generating AI and cause the generating AI to adjust the order of assistance.

[0095] When providing assistance, the providing unit can adjust the use of technical terminology for the assistance depending on the user's level of expertise. For example, if the user has technical expertise, the providing unit can use detailed technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide assistance in simple language. Furthermore, the providing unit can adjust the way the assistance is presented depending on the user's level of expertise. This makes it easier to understand the assistance result by providing an assistance result that is appropriate for the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generating AI to adjust the use of technical terminology.

[0096] The protection unit can estimate the user's emotions and adjust the privacy protection method based on the estimated user emotions. For example, the protection unit can increase the level of privacy protection when the user is nervous. The protection unit can also maintain a normal privacy protection level when the user is relaxed. The protection unit can also quickly provide privacy protection when the user is in a hurry. This allows the user to use the restroom with peace of mind by adjusting the privacy protection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the protection unit can be performed using AI, for example, or without AI. For example, the protection unit can input the user's emotion data into the generation AI and have the generation AI adjust the privacy protection method.

[0097] During privacy protection, the protection unit can select an appropriate protection method by referring to the user's past privacy settings. For example, the protection unit selects the optimal protection method based on the privacy protection level previously set by the user. The protection unit can also select a specific protection method from the user's past privacy settings. The protection unit can also analyze the user's past privacy settings and select the optimal protection method. In this way, the user's privacy is appropriately protected by selecting a protection method based on the past privacy settings. Some or all of the above-described processing in the protection unit may be performed using, for example, AI, or may be performed without using AI. For example, the protection unit can input the user's past privacy setting data into a generation AI and have the generation AI select the optimal protection method.

[0098] During privacy protection, the protection unit can customize protection measures based on the user's current situation. For example, the protection unit can increase the level of privacy protection when the user is in a public place. The protection unit can also maintain a normal level of privacy protection when the user is at home. The protection unit can also provide privacy protection measures according to the situation when the user is in a specific situation. This allows the user's privacy to be appropriately protected by customizing the privacy protection measures according to the current situation. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the user's current situation data to the generation AI and have the generation AI customize the protection measures.

[0099] The protection unit can improve the protection method by reflecting user feedback during privacy protection. For example, the protection unit improves the privacy protection method based on feedback provided by the user. The protection unit can also improve a specific privacy protection method based on the user's past feedback. The protection unit can also analyze the user's feedback and optimize the privacy protection method. This allows the user's privacy to be appropriately protected by improving the protection method based on the feedback. Some or all of the above-mentioned processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input user feedback data into a generation AI and have the generation AI improve the protection method.

[0100] The protection unit can estimate the user's emotions and determine the priority of privacy protection based on the estimated user's emotions. For example, the protection unit can increase the priority of privacy protection when the user is nervous. The protection unit can also maintain the normal priority of privacy protection when the user is relaxed. The protection unit can also quickly protect privacy when the user is in a hurry. This allows important privacy protection to be prioritized by determining the priority of privacy protection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the protection unit can be performed using AI, for example, or without AI. For example, the protection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of privacy protection.

[0101] The protection unit can select the optimal protection method during privacy protection, taking into account the user's geographical location information. For example, the protection unit can increase the level of privacy protection when the user is in a public place. The protection unit can also maintain a normal level of privacy protection when the user is at home. The protection unit can also provide privacy protection measures appropriate for a specific area when the user is in that area. This allows the user's privacy to be appropriately protected by selecting a protection method based on the geographical location information. Some or all of the above-described processing in the protection unit can be performed using, for example, AI, or can be performed without using AI. For example, the protection unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal protection method.

[0102] During privacy protection, the protection unit can analyze the user's social media activities and suggest protection measures. For example, the protection unit can suggest privacy protection measures related to places where the user has checked in on social media. The protection unit can also analyze the content of the user's social media posts and suggest related privacy protection measures. The protection unit can also suggest related privacy protection measures based on the activities of the user's friends on social media. In this way, the user's privacy is appropriately protected by suggesting protection measures based on social media activities. Some or all of the above-described processing in the protection unit can be performed using, for example, AI, or without AI. For example, the protection unit can input the user's social media activity data into a generation AI and have the generation AI execute the suggestion of protection measures.

[0103] The protection unit can customize the protection method by reflecting the user's past feedback during privacy protection. For example, the protection unit customizes the privacy protection method based on feedback provided by the user. The protection unit can also customize a specific privacy protection method based on the user's past feedback. The protection unit can also analyze the user's feedback and optimize the privacy protection method. This allows the user's privacy to be appropriately protected by customizing the protection method based on the past feedback. Some or all of the above-described processing in the protection unit may be performed using AI, for example, or may be performed without using AI. For example, the protection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the protection method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and protection unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit detects the user's movements using a sensor or camera of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using AI. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and performs automatic toilet paper supply, automatic toilet seat raising and lowering, and voice guidance instructions. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and protects the user's privacy using data encryption, access control, and anonymization technology. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and protection unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit detects the user's movements using a sensor or camera in the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and analyzes the collected information using AI. The provision unit is realized, for example, by the control unit 46A in the smart glasses 214, and performs automatic toilet paper supply, automatic toilet seat raising and lowering, and voice guidance instructions. The protection unit is realized, for example, by the specific processing unit 290 in the data processing device 12, and protects the user's privacy using data encryption, access control, and anonymization technology. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and protection unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit detects the user's movements using a sensor or camera of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using AI. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and performs automatic toilet paper supply, automatic raising and lowering of the toilet seat, and instructions via voice guidance. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and protects the user's privacy using data encryption, access control, and anonymization technology. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and protection unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit detects the user's movements using sensors and cameras of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using AI. The provision unit is realized, for example, by the control unit 46A of the robot 414, and performs automatic toilet paper supply, automatic raising and lowering of the toilet seat, and instructions via voice guidance. The protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and protects the user's privacy using data encryption, access control, and anonymization technology.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The analysis unit can estimate the user's emotions and determine analysis priorities based on the estimated user emotions. For example, if the user is nervous, important actions can be analyzed with priority. Also, if the user is relaxed, normal actions can be analyzed with priority. Furthermore, if the user is in a hurry, quick actions can be analyzed with priority. Thus, by determining the analysis priorities according to the user's emotions, important actions are analyzed with priority. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priorities.

[0106] The providing unit can estimate the user's emotions and adjust the way in which assistance is presented based on the estimated user's emotions. For example, if the user is nervous, the audio guide can be provided in a calm voice. If the user is relaxed, the audio guide can be provided in a cheerful voice. Furthermore, if the user is in a hurry, the audio guide can be provided in a quick and concise voice. This allows the user to receive assistance with peace of mind by adjusting the way in which assistance is presented according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, 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 such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way in which assistance is presented.

[0107] The protection unit can estimate the user's emotions and adjust the privacy protection method based on the estimated user's emotions. For example, if the user is nervous, the level of privacy protection can be increased. Alternatively, if the user is relaxed, the normal level of privacy protection can be maintained. Furthermore, if the user is in a hurry, privacy protection can be quickly implemented. This allows the user to use the restroom with peace of mind by adjusting the privacy protection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the protection unit can be performed using, for example, an AI, or without an AI. For example, the protection unit can input the user's emotion data into the generation AI and have the generation AI adjust the privacy protection method.

[0108] The collection unit can estimate the user's emotions and adjust the movement detection accuracy based on the estimated user's emotions. For example, if the user is nervous, the movement detection accuracy can be increased to accurately capture even small movements. Alternatively, if the user is relaxed, the movement detection accuracy can be returned to normal to capture natural movements. Furthermore, if the user is in a hurry, the movement detection accuracy can be increased to accommodate quick movements. This allows for more accurate movement detection by adjusting the movement detection accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the movement detection accuracy.

[0109] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that focus on the main points. This adjustment of the presentation method of the analysis according to the user's emotions makes the analysis results easier to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0110] The collection unit can analyze the user's past behavior history and select an appropriate detection method. For example, the collection unit can select the optimal detection method based on behaviors frequently performed by the user in the past. The collection unit can also select a method for detecting a specific behavior pattern from the user's past behavior history. Furthermore, the collection unit can analyze the user's past behavior history and adjust the detection method based on the frequency and timing of the behavior. This improves the accuracy of behavior detection by selecting the optimal detection method based on the past behavior history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past behavior history data into the generation AI and have the generation AI select the optimal detection method.

[0111] When detecting a movement, the collection unit can perform filtering based on the user's physical condition. For example, if the user is tired, the movement detection accuracy can be increased to accurately capture even small movements. Also, if the user is in good health, the movement detection accuracy can be returned to normal to capture natural movements. Furthermore, if the user is in poor health, the movement detection accuracy can be increased to detect abnormal movements early. This improves the accuracy of movement detection through filtering according to the user's physical condition. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's physical condition data into the generation AI and have the generation AI adjust the filtering.

[0112] When detecting a movement, the collection unit can select an appropriate detection means according to the user's input method. For example, if the user uses voice input, the movement can be detected using voice recognition technology. Also, if the user uses gesture input, the movement can be detected using gesture recognition technology. Furthermore, if the user uses gaze input, the movement can be detected using gaze tracking technology. This improves the accuracy of movement detection by selecting a detection means according to the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input method data into the generation AI and have the generation AI select the optimal detection means.

[0113] When detecting a movement, the collection unit can prioritize detection of highly relevant movements based on the user's geographical location information. For example, if the user is in a specific location, it can prioritize detection of movements at that location. Also, if the user is moving, it can prioritize detection of movements related to the movement. Furthermore, if the user is in a specific area, it can prioritize detection of movements in that area. This allows highly relevant movements to be detected preferentially by detecting movements based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's geographical location information data to the generation AI and cause the generation AI to determine the priority of highly relevant movements.

[0114] When detecting a movement, the collection unit can analyze the user's social media activity and detect related movements. For example, it can detect movements related to places where the user has checked in on social media. The collection unit can also analyze the content of the user's social media posts to detect related movements. Furthermore, the collection unit can also detect related movements by referring to the activities of the user's friends on social media. This allows for accurate detection of related movements through movement detection based on social media activity. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to detect related movements.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit detects the user's movements. These movements include sitting on the toilet seat and standing up. The collection unit captures the user's movements in detail using sensors and cameras. For example, infrared sensors and pressure sensors are used to detect the user's movements. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis is performed using AI, machine learning algorithms, and deep learning. For example, the analysis unit inputs the collected information into an AI model to determine whether assistance is needed. Step 3: The provision unit provides assistance based on the analysis results obtained by the analysis unit. Assistance includes automatic toilet paper supply, automatic raising and lowering of the toilet seat, and instructions via audio guidance. For example, the provision unit controls the toilet paper supply mechanism, raises and lowers the toilet seat with a motor, and provides audio guidance using a speaker. Step 4: The protection unit protects the user's privacy. Privacy protection includes data encryption, access control, anonymization technology, etc. For example, the protection unit encrypts collected information, controls access, and uses anonymization technology to delete personally identifiable information.

[0117] 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.

[0118] 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 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that detects user movement; an analysis unit that analyzes the information collected by the collection unit; a providing unit that provides assistance based on the analysis result obtained by the analyzing unit; Equipped with a protection section to protect user privacy A system characterized by:

2. The providing unit Automatically dispenses toilet paper 2. The system of claim 1.

3. The providing unit Automatically raises and lowers the toilet seat 2. The system of claim 1.

4. The providing unit Providing instructions to the user using audio guidance 2. The system of claim 1.

5. The protective part is Providing a means to protect user privacy 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the accuracy of motion detection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Analyze the user's past behavior history and select the appropriate detection method 2. The system of claim 1.

8. The collecting unit When motion is detected, filtering is performed based on the user's physical state.

2. The system of claim 1.

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