System

Abstract

An object of a system according to an embodiment is to analyze data of an oral cavity of a user and propose an optimal brushing method or timing.SOLUTION: A system includes a sensor, a generation AI, and a suggestion unit. The sensor collects data in the oral cavity of the user. The production AI analyzes the information collected by the sensors. The suggestion unit suggests an optimal brushing method or timing based on the result analyzed by the generated AI.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 do not adequately suggest optimal brushing methods and timing based on data from the user's oral cavity, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze data in the user's oral cavity and propose the optimal brushing method or timing. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor, a generation AI, and a suggestion unit. The sensor collects data from the user's oral cavity. The generation AI analyzes the data collected by the sensor. The suggestion unit suggests an optimal brushing method or timing based on the results of the analysis by the generation AI. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data in the user's oral cavity and suggest the optimal brushing method or timing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The oral health improvement system according to an embodiment of the present invention uses a toothbrush equipped with a generative AI to analyze the condition of the user's oral cavity and propose the optimal brushing method and timing, thereby enabling the oral health improvement system to effectively improve the user's oral health.

[0029] An oral health condition improvement system according to an embodiment includes a sensor, a generation AI, and a suggestion unit. The sensor collects data from the user's oral cavity. For example, the sensor detects stains on the surface of the teeth. The sensor can also detect the condition of the gums. The sensor can also detect the presence or absence of bad breath. The generation AI analyzes the data collected by the sensor. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM). The generation AI can also analyze the data using a multimodal generation AI. The generation AI can also analyze the data using a machine learning algorithm. The suggestion unit suggests an optimal brushing method and timing based on the results of the analysis by the generation AI. For example, the suggestion unit may suggest, "There is a lot of staining on the surface of your teeth, so brush this area intensively." The suggestion unit may also suggest, "Your gums are swollen, so brush gently." The suggestion unit may also suggest, "Brush your teeth within 30 minutes after eating." This enables the oral health condition improvement system according to an embodiment to effectively improve the oral health of the user. For example, by brushing according to the instructions of the suggestion unit, the user can effectively remove stains from the surface of the teeth. Also, by brushing according to the instructions of the suggestion unit, the user can maintain healthy gums. Also, by brushing according to the instructions of the suggestion unit, the user can prevent bad breath.

[0030] The sensor can detect stains on the surface of the teeth, the condition of the gums, or the presence or absence of bad breath. The sensor, for example, detects stains on the surface of the teeth. For example, the sensor detects stains on the surface of the teeth using an optical sensor. The sensor can also detect the condition of the gums. For example, the sensor detects swelling of the gums using a pressure sensor. The sensor can also detect the presence or absence of bad breath. For example, the sensor detects the presence or absence of bad breath using a gas sensor. This makes it possible to grasp the detailed condition of the user's oral cavity.

[0031] When analyzing the condition of the oral cavity, the generating AI can compare it with past data to detect changes and provide health status trends. For example, when analyzing the condition of a user's oral cavity, the generating AI can compare it with past data to detect changes. For example, the generating AI can analyze changes in the condition of the gums and tooth surface stains over the past year and provide health status trends. The generating AI can also predict changes in the user's oral health status based on past data. For example, the generating AI can use past data to predict future changes in the condition of the gums and tooth surface stains. This makes it possible to understand changes in long-term health status.

[0032] Based on the analysis results, the generating AI can provide advice regarding the user's diet or lifestyle habits, thereby improving oral health overall. The generating AI can, for example, provide advice regarding the user's diet based on the analysis results. For example, the generating AI can point out that a diet high in sugar causes increased stains on the surface of teeth and suggest improvements to the diet. The generating AI can also provide advice regarding the user's lifestyle habits. For example, the generating AI can point out that lack of sleep has a negative impact on gum health and suggest improving sleeping habits. This improves the user's overall lifestyle habits and improves oral health.

[0033] When analyzing the condition of the oral cavity, the generating AI can perform video analysis in real time using a camera mounted on the toothbrush and provide visual feedback. For example, the generating AI uses a camera mounted on the toothbrush to analyze video of the user's oral cavity in real time. For example, the generating AI can check the dirt on the surface of the teeth and the condition of the gums through video and provide visual feedback. The generating AI can also use video analysis technology to analyze the condition of the user's oral cavity in detail. For example, the generating AI can use image recognition technology to analyze the dirt on the surface of the teeth and the condition of the gums. This allows the user to visually check the condition of their oral cavity.

[0034] Based on the analysis results, the generative AI can display the condition of the user's oral cavity in a 3D model, making it easier to visually understand. For example, the generative AI can display the condition of the user's oral cavity in a 3D model based on the analysis results. For example, the generative AI can visually show the dirt on the surface of the teeth and the condition of the gums in a 3D model. The generative AI can also use 3D modeling technology to display the condition of the user's oral cavity in detail. For example, the generative AI can use 3D scanning technology to scan the condition of the user's oral cavity and create a 3D model. This allows the user to visually understand the condition of their oral cavity in a 3D model.

[0035] The generative AI can provide real-time feedback on the user's hand movements and pressure, supporting optimal brushing. The generative AI, for example, analyzes the user's hand movements and pressure in real time. For example, the generative AI can analyze the user's hand movements using a motion sensor. The generative AI can also analyze the user's pressure using a pressure sensor. The generative AI then suggests the optimal brushing method based on the analysis results. For example, the generative AI can instruct the user to ease up on the pressure if the pressure is too strong. The generative AI can also instruct the user to slow down if the hand movements are too fast. This allows the user to brush optimally.

[0036] The generating AI can provide a customized brushing pattern that matches the alignment and shape of the user's teeth. The generating AI, for example, analyzes the alignment and shape of the user's teeth. For example, the generating AI can analyze the alignment and shape of the user's teeth using 3D scanning technology. The generating AI can also analyze the alignment and shape of the user's teeth using image analysis technology. The generating AI provides a customized brushing pattern based on the analysis results. For example, the generating AI can instruct the user to focus on brushing areas with poor alignment. The generating AI can also suggest brushing methods that match the shape of the teeth. This allows the user to brush optimally for the alignment and shape of their teeth.

[0037] The generating AI can analyze the color and transparency of the user's teeth and provide a brushing method to enhance the whitening effect. The generating AI, for example, analyzes the color and transparency of the user's teeth. For example, the generating AI can analyze the color of the user's teeth using a colorimeter. The generating AI can also analyze the transparency of the user's teeth using image analysis technology. Based on the analysis results, the generating AI provides a brushing method to enhance the whitening effect. For example, the generating AI can instruct the user to use a specific toothpaste. The generating AI can also suggest adjusting the brushing time and frequency. This can enhance the whitening effect of the user's teeth.

[0038] The generating AI can provide the optimal brushing timing by taking into account the user's stress level and physical condition. The generating AI, for example, analyzes the user's stress level. For example, the generating AI can analyze the user's stress level using heart rate variability. The generating AI can also analyze the user's stress level using electrodermal activity. The generating AI can also analyze the user's physical condition using body temperature and blood pressure. The generating AI provides the optimal brushing timing based on the analysis results. For example, when stress is high, the generating AI recommends brushing at a time when you can relax. The generating AI can also suggest brushing when you are feeling good. This allows the generating AI to suggest the optimal brushing timing based on the user's stress level and physical condition.

[0039] The generating AI can provide the optimal brushing timing by taking into account the user's activity level and exercise habits. The generating AI, for example, analyzes the user's activity level. For example, the generating AI can analyze the user's activity level using a pedometer. The generating AI can also analyze the user's activity level using an accelerometer. The generating AI can also analyze the user's exercise habits. For example, the generating AI can analyze the frequency and intensity of exercise. The generating AI provides the optimal brushing timing based on the analysis results. For example, the generating AI can recommend brushing after exercise. The generating AI can also suggest brushing when activity levels are low. This makes it possible to suggest the optimal brushing timing based on the user's activity level and exercise habits.

[0040] The generating AI can provide the optimal brushing timing by taking into account the user's water intake and dietary content. The generating AI, for example, analyzes the user's water intake. For example, the generating AI can analyze the user's water intake using a water drinking log. The generating AI can also analyze the user's water intake using a sensor. The generating AI can also analyze the user's dietary content. For example, the generating AI can analyze the user's dietary content using a dietary log. The generating AI can also analyze the user's dietary content using nutritional analysis technology. The generating AI provides the optimal brushing timing based on the analysis results. For example, the generating AI recommends brushing after drinking water. The generating AI can also suggest brushing after eating. This makes it possible to suggest the optimal brushing timing based on the user's water intake and dietary content.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The oral health improvement system may further include a temperature sensor that detects the temperature inside the user's oral cavity. The temperature sensor, for example, can detect changes in temperature inside the oral cavity and detect signs of inflammation or infection early. For example, the temperature sensor may indicate the possibility of inflammation if the temperature of the gums is higher than normal. The temperature sensor may also warn of the risk of infection if the temperature inside the oral cavity changes suddenly. This allows the user to take appropriate measures early.

[0043] The oral health improvement system may further include a humidity sensor that detects humidity in the user's oral cavity. The humidity sensor may, for example, monitor the humidity level in the oral cavity and detect problems such as dryness or excessive humidity. For example, the humidity sensor may provide advice on promoting saliva production if the oral cavity is dry. The humidity sensor may also warn of the risk of mold or bacterial growth if there is excessive humidity. This allows the user to maintain humidity balance in the oral cavity.

[0044] The oral health improvement system can further include a pH sensor that detects the pH level in the user's oral cavity. The pH sensor can, for example, monitor the acidity in the oral cavity and evaluate the risk of acid erosion and tooth decay. For example, the pH sensor can advise the user to consume alkaline foods and beverages if the oral cavity is acidic. The pH sensor can also suggest appropriate oral care products if the oral pH level exceeds the normal range. This allows the user to maintain the pH balance in the oral cavity.

[0045] The oral health improvement system may further include a bacterial sensor that detects the number of bacteria in the user's oral cavity. The bacterial sensor may, for example, monitor the number of bacteria in the oral cavity in real time and evaluate the risk of infection. For example, the bacterial sensor may advise the user to use antibacterial toothpaste or mouthwash if the number of bacteria is increasing. The bacterial sensor may also suggest consulting a specialist if a specific bacteria is detected. This allows the user to maintain the bacterial balance in the oral cavity.

[0046] The oral health improvement system may further include a blood flow sensor that detects blood flow in the user's mouth. The blood flow sensor may, for example, monitor blood flow in the gums and evaluate their health. For example, the blood flow sensor may advise the user to massage their gums or eat certain foods if blood flow is low. The blood flow sensor may also warn of inflammation or other health issues if blood flow is abnormally high, allowing the user to maintain healthy gums.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The sensor collects data from the user's mouth. For example, the sensor detects stains on the surface of the teeth, the condition of the gums, and whether or not there is bad breath. Step 2: Generative AI analyzes the data collected by the sensors. For example, generative AI may analyze the data using text generation AI (e.g., LLM), multimodal generation AI, or machine learning algorithms. Step 3: The suggestion section suggests the optimal brushing method and timing based on the results of the analysis by the generative AI. For example, it might say, "There is a lot of dirt on the surface of your teeth, so brush this area intensively," or "Your gums are swollen, so brush gently," or "Brush your teeth within 30 minutes after eating."

[0049] (Example 2) The oral health improvement system according to an embodiment of the present invention uses a toothbrush equipped with a generative AI to analyze the condition of the user's oral cavity and propose the optimal brushing method and timing, thereby enabling the oral health improvement system to effectively improve the user's oral health.

[0050] An oral health condition improvement system according to an embodiment includes a sensor, a generation AI, and a suggestion unit. The sensor collects data from the user's oral cavity. For example, the sensor detects stains on the surface of the teeth. The sensor can also detect the condition of the gums. The sensor can also detect the presence or absence of bad breath. The generation AI analyzes the data collected by the sensor. For example, the generation AI analyzes the data using a text generation AI (e.g., LLM). The generation AI can also analyze the data using a multimodal generation AI. The generation AI can also analyze the data using a machine learning algorithm. The suggestion unit suggests an optimal brushing method and timing based on the results of the analysis by the generation AI. For example, the suggestion unit may suggest, "There is a lot of staining on the surface of your teeth, so brush this area intensively." The suggestion unit may also suggest, "Your gums are swollen, so brush gently." The suggestion unit may also suggest, "Brush your teeth within 30 minutes after eating." This enables the oral health condition improvement system according to an embodiment to effectively improve the oral health of the user. For example, by brushing according to the instructions of the suggestion unit, the user can effectively remove stains from the surface of the teeth. Also, by brushing according to the instructions of the suggestion unit, the user can maintain healthy gums. Also, by brushing according to the instructions of the suggestion unit, the user can prevent bad breath.

[0051] The sensor can detect stains on the surface of the teeth, the condition of the gums, or the presence or absence of bad breath. The sensor, for example, detects stains on the surface of the teeth. For example, the sensor detects stains on the surface of the teeth using an optical sensor. The sensor can also detect the condition of the gums. For example, the sensor detects swelling of the gums using a pressure sensor. The sensor can also detect the presence or absence of bad breath. For example, the sensor detects the presence or absence of bad breath using a gas sensor. This makes it possible to grasp the detailed condition of the user's oral cavity.

[0052] When analyzing the condition of the oral cavity, the generating AI can compare it with past data to detect changes and provide health status trends. For example, when analyzing the condition of a user's oral cavity, the generating AI can compare it with past data to detect changes. For example, the generating AI can analyze changes in the condition of the gums and tooth surface stains over the past year and provide health status trends. The generating AI can also predict changes in the user's oral health status based on past data. For example, the generating AI can use past data to predict future changes in the condition of the gums and tooth surface stains. This makes it possible to understand changes in long-term health status.

[0053] Based on the analysis results, the generating AI can provide advice regarding the user's diet or lifestyle habits, thereby improving oral health overall. The generating AI can, for example, provide advice regarding the user's diet based on the analysis results. For example, the generating AI can point out that a diet high in sugar causes increased stains on the surface of teeth and suggest improvements to the diet. The generating AI can also provide advice regarding the user's lifestyle habits. For example, the generating AI can point out that lack of sleep has a negative impact on gum health and suggest improving sleeping habits. This improves the user's overall lifestyle habits and improves oral health.

[0054] The generation AI can use the emotion estimation function to analyze the user's emotions toward brushing their teeth and suggest brushing methods that elicit positive emotions. The generation AI, for example, uses the emotion estimation function to analyze the user's emotions toward brushing their teeth. For example, the generation AI analyzes the user's emotions using facial expression recognition technology. The generation AI can also analyze the user's emotions using voice analysis technology. The generation AI can also analyze the user's emotions using text analysis technology. Based on the analysis results, the generation AI suggests brushing methods that elicit positive emotions. For example, if the user has negative emotions toward brushing their teeth, the generation AI can suggest ideas such as playing fun music. This allows the user to enjoy brushing their teeth.

[0055] When analyzing the condition of the oral cavity, the generating AI can perform video analysis in real time using a camera mounted on the toothbrush and provide visual feedback. For example, the generating AI uses a camera mounted on the toothbrush to analyze video of the user's oral cavity in real time. For example, the generating AI can check the dirt on the surface of the teeth and the condition of the gums through video and provide visual feedback. The generating AI can also use video analysis technology to analyze the condition of the user's oral cavity in detail. For example, the generating AI can use image recognition technology to analyze the dirt on the surface of the teeth and the condition of the gums. This allows the user to visually check the condition of their oral cavity.

[0056] Based on the analysis results, the generative AI can display the condition of the user's oral cavity in a 3D model, making it easier to visually understand. For example, the generative AI can display the condition of the user's oral cavity in a 3D model based on the analysis results. For example, the generative AI can visually show the dirt on the surface of the teeth and the condition of the gums in a 3D model. The generative AI can also use 3D modeling technology to display the condition of the user's oral cavity in detail. For example, the generative AI can use 3D scanning technology to scan the condition of the user's oral cavity and create a 3D model. This allows the user to visually understand the condition of their oral cavity in a 3D model.

[0057] The generation AI can use the emotion estimation function to provide relaxation music or guidance to reduce the stress the user feels while brushing their teeth. For example, the generation AI uses the emotion estimation function to analyze the stress the user feels while brushing their teeth. For example, the generation AI can analyze the user's stress using facial expression recognition technology. The generation AI can also analyze the user's stress using voice analysis technology. The generation AI can also analyze the user's stress using text analysis technology. The generation AI provides relaxation music or guidance based on the analysis results. For example, the generation AI can automatically select music that helps the user relax. The generation AI can also provide a guide that helps the user relax. This allows the user to relax while brushing their teeth.

[0058] The generative AI can provide real-time feedback on the user's hand movements and pressure, supporting optimal brushing. The generative AI, for example, analyzes the user's hand movements and pressure in real time. For example, the generative AI can analyze the user's hand movements using a motion sensor. The generative AI can also analyze the user's pressure using a pressure sensor. The generative AI then suggests the optimal brushing method based on the analysis results. For example, the generative AI can instruct the user to ease up on the pressure if the pressure is too strong. The generative AI can also instruct the user to slow down if the hand movements are too fast. This allows the user to brush optimally.

[0059] The generating AI can provide a customized brushing pattern that matches the alignment and shape of the user's teeth. The generating AI, for example, analyzes the alignment and shape of the user's teeth. For example, the generating AI can analyze the alignment and shape of the user's teeth using 3D scanning technology. The generating AI can also analyze the alignment and shape of the user's teeth using image analysis technology. The generating AI provides a customized brushing pattern based on the analysis results. For example, the generating AI can instruct the user to focus on brushing areas with poor alignment. The generating AI can also suggest brushing methods that match the shape of the teeth. This allows the user to brush optimally for the alignment and shape of their teeth.

[0060] The generation AI can use the emotion estimation function to analyze the level of satisfaction the user feels while brushing and provide positive feedback. The generation AI, for example, uses the emotion estimation function to analyze the level of satisfaction the user feels while brushing. For example, the generation AI can analyze the level of satisfaction using facial expression recognition technology. The generation AI can also analyze the level of satisfaction using voice analysis technology. The generation AI can also analyze the level of satisfaction using text analysis technology. The generation AI provides positive feedback based on the analysis results. For example, the generation AI can display words of praise if the user brushes well. The generation AI can also provide feedback that gives the user a sense of satisfaction. This allows the user to feel satisfied with brushing.

[0061] The generating AI can analyze the color and transparency of the user's teeth and provide a brushing method to enhance the whitening effect. The generating AI, for example, analyzes the color and transparency of the user's teeth. For example, the generating AI can analyze the color of the user's teeth using a colorimeter. The generating AI can also analyze the transparency of the user's teeth using image analysis technology. Based on the analysis results, the generating AI provides a brushing method to enhance the whitening effect. For example, the generating AI can instruct the user to use a specific toothpaste. The generating AI can also suggest adjusting the brushing time and frequency. This can enhance the whitening effect of the user's teeth.

[0062] The generation AI can use the emotion estimation function to provide aromatherapy or relaxation guidance to reduce the discomfort the user feels while brushing. For example, the generation AI uses the emotion estimation function to analyze the discomfort the user feels while brushing. For example, the generation AI can analyze the user's discomfort using facial expression recognition technology. The generation AI can also analyze the user's discomfort using voice analysis technology. The generation AI can also analyze the user's discomfort using text analysis technology. The generation AI provides aromatherapy or relaxation guidance based on the analysis results. For example, the generation AI can automatically select a relaxing scent. The generation AI can also provide a guide to help the user relax. This allows the user to relax while brushing.

[0063] The generating AI can provide the optimal brushing timing by taking into account the user's stress level and physical condition. The generating AI, for example, analyzes the user's stress level. For example, the generating AI can analyze the user's stress level using heart rate variability. The generating AI can also analyze the user's stress level using electrodermal activity. The generating AI can also analyze the user's physical condition using body temperature and blood pressure. The generating AI provides the optimal brushing timing based on the analysis results. For example, when stress is high, the generating AI recommends brushing at a time when you can relax. The generating AI can also suggest brushing when you are feeling good. This allows the generating AI to suggest the optimal brushing timing based on the user's stress level and physical condition.

[0064] The generation AI can use the emotion estimation function to provide reminders and guides to increase the user's motivation for brushing. For example, the generation AI uses the emotion estimation function to analyze the user's motivation for brushing. For example, the generation AI can analyze the user's motivation using facial expression recognition technology. The generation AI can also analyze the user's motivation using voice analysis technology. The generation AI can also analyze the user's motivation using text analysis technology. The generation AI provides reminders and guides based on the analysis results. For example, the generation AI can display positive messages to encourage brushing. The generation AI can also set reminders to encourage the user to brush. This can increase the user's motivation for brushing.

[0065] The generating AI can provide the optimal brushing timing by taking into account the user's activity level and exercise habits. The generating AI, for example, analyzes the user's activity level. For example, the generating AI can analyze the user's activity level using a pedometer. The generating AI can also analyze the user's activity level using an accelerometer. The generating AI can also analyze the user's exercise habits. For example, the generating AI can analyze the frequency and intensity of exercise. The generating AI provides the optimal brushing timing based on the analysis results. For example, the generating AI can recommend brushing after exercise. The generating AI can also suggest brushing when activity levels are low. This makes it possible to suggest the optimal brushing timing based on the user's activity level and exercise habits.

[0066] The generating AI can provide the optimal brushing timing by taking into account the user's water intake and dietary content. The generating AI, for example, analyzes the user's water intake. For example, the generating AI can analyze the user's water intake using a water drinking log. The generating AI can also analyze the user's water intake using a sensor. The generating AI can also analyze the user's dietary content. For example, the generating AI can analyze the user's dietary content using a dietary log. The generating AI can also analyze the user's dietary content using nutritional analysis technology. The generating AI provides the optimal brushing timing based on the analysis results. For example, the generating AI recommends brushing after drinking water. The generating AI can also suggest brushing after eating. This makes it possible to suggest the optimal brushing timing based on the user's water intake and dietary content.

[0067] The generation AI can use the emotion estimation function to provide relaxation guides and advice to reduce the anxiety and stress the user feels when brushing. For example, the generation AI uses the emotion estimation function to analyze the anxiety and stress the user feels when brushing. For example, the generation AI can analyze the user's anxiety and stress using facial expression recognition technology. The generation AI can also analyze the user's anxiety and stress using voice analysis technology. The generation AI can also analyze the user's anxiety and stress using text analysis technology. The generation AI provides relaxation guides and advice based on the analysis results. For example, the generation AI can recommend brushing at a time when you can relax. The generation AI can also provide advice that will help the user relax. This can reduce the anxiety and stress the user feels when brushing.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The oral health improvement system may further include a temperature sensor that detects the temperature inside the user's oral cavity. The temperature sensor, for example, can detect changes in temperature inside the oral cavity and detect signs of inflammation or infection early. For example, the temperature sensor may indicate the possibility of inflammation if the temperature of the gums is higher than normal. The temperature sensor may also warn of the risk of infection if the temperature inside the oral cavity changes suddenly. This allows the user to take appropriate measures early.

[0070] The oral health improvement system may further include a humidity sensor that detects humidity in the user's oral cavity. The humidity sensor may, for example, monitor the humidity level in the oral cavity and detect problems such as dryness or excessive humidity. For example, the humidity sensor may provide advice on promoting saliva production if the oral cavity is dry. The humidity sensor may also warn of the risk of mold or bacterial growth if there is excessive humidity. This allows the user to maintain humidity balance in the oral cavity.

[0071] The oral health improvement system can further include a pH sensor that detects the pH level in the user's oral cavity. The pH sensor can, for example, monitor the acidity in the oral cavity and evaluate the risk of acid erosion and tooth decay. For example, the pH sensor can advise the user to consume alkaline foods and beverages if the oral cavity is acidic. The pH sensor can also suggest appropriate oral care products if the oral pH level exceeds the normal range. This allows the user to maintain the pH balance in the oral cavity.

[0072] The oral health improvement system may further include a bacterial sensor that detects the number of bacteria in the user's oral cavity. The bacterial sensor may, for example, monitor the number of bacteria in the oral cavity in real time and evaluate the risk of infection. For example, the bacterial sensor may advise the user to use antibacterial toothpaste or mouthwash if the number of bacteria is increasing. The bacterial sensor may also suggest consulting a specialist if a specific bacteria is detected. This allows the user to maintain the bacterial balance in the oral cavity.

[0073] The oral health improvement system may further include a blood flow sensor that detects blood flow in the user's mouth. The blood flow sensor may, for example, monitor blood flow in the gums and evaluate their health. For example, the blood flow sensor may advise the user to massage their gums or eat certain foods if blood flow is low. The blood flow sensor may also warn of inflammation or other health issues if blood flow is abnormally high, allowing the user to maintain healthy gums.

[0074] The oral health improvement system can use the emotion estimation function to analyze the user's motivation for brushing their teeth and provide a reward system to increase motivation. For example, the generation AI can award points or badges when the user completes brushing. The generation AI can also provide rewards if the user continues to brush their teeth for a certain period of time. This makes it easier for the user to maintain motivation for brushing their teeth.

[0075] The oral health improvement system can use the emotion estimation function to analyze the anxiety the user feels about brushing their teeth and provide a counseling function to reduce the anxiety. For example, if the user feels anxious about brushing their teeth, the generation AI can provide them with advice to help them relax. The generation AI can also suggest breathing exercises and relaxation techniques to help the user reduce their anxiety. This helps the user reduce their anxiety about brushing their teeth.

[0076] The oral health improvement system can use the emotion estimation function to analyze the user's level of satisfaction with brushing their teeth and provide customized feedback to increase satisfaction. For example, if the user feels highly satisfied with brushing their teeth, the generation AI can display praise and encouraging messages. The generation AI can also provide specific advice to increase the user's satisfaction. This allows the user to improve their satisfaction with brushing their teeth.

[0077] The oral health improvement system can use its emotion estimation function to analyze the stress a user feels while brushing their teeth and provide entertainment functions to reduce stress. For example, if a user feels stressed while brushing their teeth, the generation AI can play their favorite music or podcast. The generation AI can also display videos or animations that help users relax. This makes it easier for users to relax while brushing their teeth.

[0078] The oral health improvement system can use its emotion estimation function to analyze a user's interest in brushing their teeth and provide educational content to stimulate that interest. For example, if a user is interested in brushing their teeth, the generation AI can provide information and quizzes about dental health. The generation AI can also play educational videos based on topics that are likely to interest the user. This helps the user maintain their interest in brushing their teeth.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The sensor collects data from the user's mouth. For example, the sensor detects stains on the surface of the teeth, the condition of the gums, and whether or not there is bad breath. Step 2: Generative AI analyzes the data collected by the sensors. For example, generative AI may analyze the data using text generation AI (e.g., LLM), multimodal generation AI, or machine learning algorithms. Step 3: The suggestion section suggests the optimal brushing method and timing based on the results of the analysis by the generative AI. For example, it might say, "There is a lot of dirt on the surface of your teeth, so brush this area intensively," or "Your gums are swollen, so brush gently," or "Brush your teeth within 30 minutes after eating."

[0081] 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.

[0082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0109] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0148] 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 sensor that collects data from the user's oral cavity; A generation AI that analyzes the data collected by the sensor; and a suggestion unit that suggests an optimal brushing method or timing based on the results of analysis by the generating AI. A system characterized by:

2. The sensor Detecting the presence or absence of stains on the surface of the teeth, the condition of the gums, or bad breath 2. The system of claim 1.

3. The generated AI is When analyzing the state of the oral cavity, a camera mounted on the toothbrush is used to perform video analysis in real time to provide visual feedback.

2. The system of claim 1.

4. The generated AI is The user's hand movement or force is fed back in real time to support optimal brushing.

2. The system of claim 1.

5. The generated AI is Using an emotion estimation function, the emotion the user has about brushing their teeth is analyzed, and a brushing method that elicits positive emotions is proposed.

2. The system of claim 1.

6. The generated AI is Providing a customized brushing pattern that is tailored to the alignment or shape of the user's teeth 2. The system of claim 1.

7. The generated AI is Providing advice taking into account the temperature or humidity in the oral cavity of the user 2. The system of claim 1.

8. The generated AI is Using the emotion estimation function, a relaxation guide or advice is provided to the user to reduce anxiety or stress felt by the user regarding brushing timing.

2. The system of claim 1.

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