System

Abstract

An object of a system according to an embodiment is to appropriately arrange nails of children and animals.SOLUTION: A system includes an imaging unit, an analysis unit, and a guideline display unit. The photographing unit photographs an image of a target nail. The analysis unit analyzes the image captured by the imaging unit and measures the shape and length of the nail. The guide line display unit displays a guide line at the predetermined portion to be cut based on the information specified by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have made it difficult to properly trim the nails of children and animals, posing the risk of ingrown nails.

[0005] The system of the embodiment is intended to properly trim the nails of children and animals. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an analysis unit, and a guideline display unit. The photographing unit photographs an image of the target nail. The analysis unit analyzes the image photographed by the photographing unit and measures the shape and length of the nail. The guideline display unit displays a guideline at a predetermined portion to be cut based on information identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows for proper trimming of nails of children and animals. [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 nail clipping assistance system according to an embodiment of the present invention provides guidelines for properly trimming a target's nails, reducing the risk of ingrown nails. The nail clipping assistance system takes an image of the target's nails, analyzes it using a generation AI, and displays guidelines on the appropriate areas to trim. Text support is also provided as needed. For example, the nail clipping assistance system takes an image of the target's nails. For example, the nail clipping assistance system can take an image of the nail using a camera. The nail clipping assistance system then analyzes the captured image of the nail using a generation AI. The input to the generation AI is the captured image of the nail, and the generation AI determines the shape and length of the nail based on the image. For example, the generation AI receives a prompt such as "Please analyze the shape and length of this nail" and determines the shape and length of the nail. The nail clipping assistance system then displays guidelines on the appropriate areas to trim based on the information identified by the generation AI. The guidelines are displayed on a display device screen. For example, the guidelines are displayed on the screen, allowing the user to follow them when trimming their nails. The nail clipping assistance system also provides text support as needed. For example, instructions such as "Cut up to here" or "Do not cut this part" are displayed. This allows the nail clipping assistance system to safely and properly trim the nails of children and animals, enabling parents, nurses, caregivers, pet owners, zookeepers, and others to do so. This reduces the risk of ingrown nails and alleviates the stress of nail clipping. For example, parents, nurses, caregivers, pet owners, zookeepers, and others to quickly and accurately trim the nails of children and animals.

[0029] The nail trimming assistance system according to the embodiment includes a photographing unit, an analysis unit, and a guideline display unit. The photographing unit photographs an image of a target nail. Examples of target nail images include, but are not limited to, fingernails, toenails, and animal nails. The photographing unit photographs the image of the nail using, for example, a camera. The photographing unit can also photograph the image of the nail using a smartphone camera. For example, the camera automatically adjusts the focus to capture a clear image. The photographing unit can also photograph the image of the nail in high resolution. For example, the camera photographs in high resolution mode to obtain a detailed image. The analysis unit uses a generation AI to analyze the image of the nail photographed by the photographing unit. The analysis is performed, for example, to identify the shape and length of the nail, but is not limited to, for example. For example, the generation AI identifies the shape and length of the nail using a text generation AI (e.g., LLM). The analysis unit can also identify the shape and length of the nail using a multimodal generation AI. The analysis unit can also identify the edge of the nail using the generation AI. For example, the generation AI identifies the edge of the nail using edge detection technology. The guideline display unit displays guidelines on appropriate nail trimming areas based on the information identified by the analysis unit. The guidelines may be displayed, for example, on the screen of a display device, but are not limited to such examples. For example, the guideline display unit displays the guidelines on the screen, allowing the user to trim their nails according to them. The guideline display unit can also provide text support as needed. For example, instructions such as "Cut up to here" or "Do not cut this part" are displayed. This allows the nail trimming assistance system according to the embodiment to provide guidelines for properly trimming the target nails and reduce the risk of ingrown nails. Some or all of the above-described processing by the guideline display unit may be performed using, for example, AI, or may be performed without AI. For example, the guideline display unit can display the guidelines using an AI model that inputs the information identified by the analysis unit and outputs guidelines.

[0030] The analysis unit can analyze the shape and length of the nail using image processing technology. Image processing technology includes, but is not limited to, edge detection, shape recognition, filtering, and the like. For example, the analysis unit can identify the edge of the nail using edge detection technology. The analysis unit can also identify the shape of the nail using shape recognition technology. For example, the analysis unit can identify the shape of the nail using template matching technology. The analysis unit can also preprocess the nail image using filtering technology. For example, the analysis unit can apply a noise removal filter to improve image quality. This allows the use of image processing technology to accurately analyze the shape and length of the nail. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the image of the nail captured by the imaging unit to the generation AI and have the generation AI analyze the shape and length of the nail.

[0031] The guideline display unit can display the guideline on the screen of the display device. Examples of the display device include, but are not limited to, a display, a monitor, a tablet, etc. The guideline display unit, for example, displays the guideline on the screen of a display. The guideline display unit can also display the guideline on the screen of a monitor. For example, the guideline display unit displays the guideline on the screen of a tablet. By displaying the guideline on the screen of the display device, the user can visually confirm the guideline. Some or all of the above-described processing in the guideline display unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline display unit can display the guideline using an AI model that receives information identified by the analysis unit as input and outputs a guideline.

[0032] The guideline display unit can provide text support based on predetermined conditions. Predetermined conditions include, but are not limited to, user operations, environmental conditions, and analysis results. The guideline display unit can provide text support based on, for example, user operations. The guideline display unit can also provide text support based on environmental conditions. For example, the guideline display unit can provide text support based on analysis results. This allows the user to receive more specific instructions through text support. Some or all of the above-described processing in the guideline display unit can be performed using, for example, AI, or can be performed without using AI. For example, the guideline display unit can provide text support using an AI model that receives information identified by the analysis unit as input and outputs text support.

[0033] The analysis unit can identify the edge of the nail using an edge detection technique. Examples of edge detection techniques include, but are not limited to, Canny edge detection and a Sobel filter. The analysis unit can identify the edge of the nail using, for example, Canny edge detection. The analysis unit can also identify the edge of the nail using a Sobel filter. For example, the analysis unit can identify the edge of the nail using an edge detection technique. As a result, the edge of the nail can be accurately identified using the edge detection technique. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input an image of the nail captured by the imaging unit to the generation AI and cause the generation AI to identify the edge of the nail.

[0034] The analysis unit can identify the shape of the nail using shape recognition technology. Shape recognition technology includes, but is not limited to, template matching and deep learning. The analysis unit can identify the shape of the nail using, for example, template matching technology. The analysis unit can also identify the shape of the nail using deep learning technology. For example, the analysis unit can identify the shape of the nail using shape recognition technology. As a result, the shape recognition technology can be used to accurately identify the shape of the nail. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input an image of the nail captured by the imaging unit into the generation AI and cause the generation AI to identify the shape of the nail.

[0035] The imaging unit can automatically adjust the focus according to the movement of the subject when capturing an image. For example, when the subject moves, the imaging unit detects the movement and automatically readjusts the focus to capture a clear image. Furthermore, when the subject moves their hand, the imaging unit can adjust the focus to follow the movement and capture a blur-free image. For example, when the subject moves their fingernail, the imaging unit predicts the direction of the movement and adjusts the focus in advance to capture a smooth image. This allows the focus to be automatically adjusted according to the movement of the subject, thereby capturing a clear image. Some or all of the above-described processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the imaging unit can input movement data to a generation AI and have the generation AI adjust the focus.

[0036] The photographing unit can select a predetermined photographing mode according to the condition of the target nail when photographing. For example, if the target nail is dry, the photographing unit automatically selects a photographing mode suitable for the dry condition and photographs a detailed image. Furthermore, if the target nail is wet, the photographing unit can automatically select a photographing mode suitable for the wet condition and photograph an image with reduced reflection. For example, if the target nail contains oil, the photographing unit automatically selects a photographing mode that takes the oil into consideration and photographs a clear image. In this way, detailed images can be photographed by selecting a photographing mode suitable for the nail condition. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input nail condition data to the generation AI and cause the generation AI to select a photographing mode.

[0037] The image capturing unit can apply a filter to accurately reproduce the color and texture of the target nail when capturing an image. For example, the image capturing unit can apply a color correction filter to accurately reproduce the color of the target nail and capture an image with natural color tones. The image capturing unit can also apply a texture enhancement filter to capture an image with a realistic texture in order to capture the texture of the target nail in detail. For example, the image capturing unit can apply a transparency filter to reproduce the transparency of the target nail and capture a clear image. This allows the color and texture of the nail to be accurately reproduced, resulting in a realistic image. Some or all of the above-described processing in the image capturing unit can be performed using, for example, AI, or without AI. For example, the image capturing unit can input data on the color and texture of the nail into a generation AI and cause the generation AI to apply a filter.

[0038] When capturing an image, the capture unit can set the optimal exposure by taking into account the ambient light around the target nail. For example, when the surroundings are bright, the capture unit adjusts the exposure to reduce excessive brightness and capture an image with appropriate brightness. Furthermore, when the surroundings are dark, the capture unit can adjust the exposure to correct the brightness and capture a clear image. For example, when the ambient light fluctuates, the capture unit adjusts the exposure in real time to capture an image with stable brightness. This allows an image with appropriate brightness to be captured by setting the exposure according to the ambient light. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input ambient light data to the generation AI and have the generation AI execute the exposure setting.

[0039] The image capturing unit may have a function to predict the movement of the subject's nails and prevent blurring when capturing an image. For example, the image capturing unit may predict the direction in which the subject's nails will move and adjust the camera's shutter speed to prevent blurring. The image capturing unit may also predict the timing at which the subject's nails will move and capture an image at the optimal timing. For example, the image capturing unit may predict the speed at which the subject's nails will move and adjust the camera settings to prevent blurring. This allows a clear image to be captured by predicting the movement of the nails and preventing blurring. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI. For example, the image capturing unit may input data on the movement of the nails to a generation AI and have the generation AI perform adjustments to prevent blurring.

[0040] When capturing an image, the image capturing unit can automatically select a different capture angle depending on the shape of the target nail. For example, if the target nail is flat, the image capturing unit selects a capture angle from directly above to capture the entire nail. Furthermore, if the target nail is curved, the image capturing unit can select a capture angle from an oblique angle to capture the detailed shape. For example, if the target nail is long and thin, the image capturing unit selects a capture angle from a vertical direction to capture the overall balance. In this way, by selecting a capture angle according to the nail shape, the detailed shape can be captured. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI. For example, the image capturing unit can input nail shape data to a generation AI and have the generation AI select the capture angle.

[0041] During analysis, the analysis unit can identify a predetermined cutting position by referring to the growth history of the target nail. The analysis unit, for example, analyzes the past growth history of the target nail and identifies the most appropriate cutting position. The analysis unit can also predict the optimal cutting position in the future by taking into account the growth rate of the target nail. For example, the analysis unit analyzes the growth pattern of the target nail and identifies the safest cutting position. This makes it possible to identify the optimal cutting position by referring to the growth history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input nail growth history data into the generation AI and have the generation AI identify the cutting position.

[0042] During analysis, the analysis unit can detect the health condition of the target nail and suggest appropriate treatment. For example, if the target nail has a crack, the analysis unit suggests cutting the nail while avoiding that part. The analysis unit can also suggest appropriate treatment taking into account the health condition if discoloration is observed in the target nail. For example, if an abnormality is observed in the target nail, the analysis unit suggests that the target nail be diagnosed by a specialist. This allows the health of the nail to be detected and appropriate treatment to be suggested, thereby maintaining the health of the nail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input nail health data into the generation AI and have the generation AI detect the health condition and suggest treatment.

[0043] During analysis, the analysis unit can measure the thickness of the target nail and recommend a predetermined cutting force. The analysis unit, for example, measures the thickness of the target nail and recommends an appropriate cutting force. The analysis unit can also recommend a lighter cutting force if the target nail is thin. For example, the analysis unit can recommend a stronger cutting force if the target nail is thick. This allows for appropriate cutting by recommending a cutting force according to the nail thickness. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input nail thickness data into the generation AI and have the generation AI recommend a cutting force.

[0044] During analysis, the analysis unit can improve the analysis accuracy by taking into account the skin condition around the target nail. For example, if the skin around the target nail is dry, the analysis unit improves the analysis accuracy by taking into account that condition. Furthermore, if the skin around the target nail is moist, the analysis unit can also improve the analysis accuracy by taking into account that condition. For example, if the skin around the target nail is inflamed, the analysis unit improves the analysis accuracy by taking into account that condition. In this way, by taking the skin condition into account, the analysis accuracy is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input skin condition data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0045] During analysis, the analysis unit can apply different analysis algorithms based on the shape of the target nail. For example, if the target nail is flat, the analysis unit applies an analysis algorithm suitable for flat nails. Furthermore, if the target nail is curved, the analysis unit can also apply an analysis algorithm suitable for curved nails. For example, if the target nail is elongated, the analysis unit applies an analysis algorithm suitable for elongated nails. This improves analysis accuracy by applying an analysis algorithm according to the nail shape. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input nail shape data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0046] During the analysis, the analysis unit can predict the growth rate of the target's nails and suggest a predetermined timing for the next nail clipping. For example, the analysis unit can predict the growth rate of the target's nails and suggest the optimal timing for the next nail clipping. The analysis unit can also suggest frequent nail clipping if the target's nails grow quickly. For example, the analysis unit can suggest nail clipping at appropriate intervals if the target's nails grow slowly. In this way, by predicting the nail growth rate, the optimal timing for the next nail clipping can be suggested. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input nail growth rate data into the generation AI and have the generation AI suggest the timing for the next nail clipping.

[0047] When displaying the guideline, the guideline display unit can generate a guideline customized according to the shape of the target nail. For example, if the target nail is flat, the guideline display unit generates a guideline suitable for the flat nail. Furthermore, if the target nail is curved, the guideline display unit can also generate a guideline suitable for the curved nail. For example, if the target nail is elongated, the guideline display unit generates a guideline suitable for the elongated nail. This allows appropriate guidelines to be provided by generating a guideline customized according to the nail shape. Some or all of the above-described processing in the guideline display unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline display unit can input nail shape data to a generation AI and cause the generation AI to generate customized guidelines.

[0048] When displaying the guideline, the guideline display unit can display a predetermined guideline by referring to the cutting history of the target nail. The guideline display unit, for example, refers to the past cutting history of the target nail and displays the most appropriate guideline. The guideline display unit can also predict the optimal guideline in the future based on the cutting history of the target nail. For example, the guideline display unit analyzes the cutting pattern of the target nail and displays the safest guideline. This makes it possible to provide the optimal guideline by referring to the cutting history. Some or all of the above-described processing in the guideline display unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline display unit can input nail cutting history data into a generation AI and cause the generation AI to display the guideline.

[0049] When displaying the guidelines, the guideline display unit can update the guidelines in real time according to the movement of the target nail. For example, when the target nail moves, the guideline display unit detects the movement and updates the guidelines in real time. Furthermore, when the target nail moves, the guideline display unit can also update the guidelines to follow the movement of the target nail. For example, the guideline display unit predicts the direction in which the target nail will move and updates the guidelines in advance. This allows the guidelines to be updated in real time according to the movement of the nail, thereby always providing appropriate guidelines. Some or all of the above-described processing in the guideline display unit may be performed, for example, using AI, or may be performed without using AI. For example, the guideline display unit can input data on the movement of the nail to a generation AI and have the generation AI update the guidelines.

[0050] When displaying the guideline, the guideline display unit can adjust the brightness of the display taking into account the ambient light around the target nail. For example, when the surroundings are bright, the guideline display unit adjusts the brightness of the guideline to ensure visibility. Furthermore, when the surroundings are dark, the guideline display unit can also adjust the brightness of the guideline to make it easier to see. For example, when the ambient light fluctuates, the guideline display unit adjusts the brightness of the guideline in real time to provide stable visibility. This improves visibility by adjusting the brightness according to the ambient light. Some or all of the above-described processing in the guideline display unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline display unit can input ambient light data into the generation AI and have the generation AI adjust the brightness.

[0051] When displaying the guideline, the guideline display unit can select different display modes based on the shape of the target nail. For example, if the target nail is flat, the guideline display unit selects a display mode suitable for flat nails. Furthermore, if the target nail is curved, the guideline display unit can also select a display mode suitable for curved nails. For example, if the target nail is elongated, the guideline display unit selects a display mode suitable for elongated nails. In this way, by selecting a display mode according to the shape of the nail, an appropriate guideline can be provided. Some or all of the above-described processing in the guideline display unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline display unit can input nail shape data to a generation AI and cause the generation AI to select a display mode.

[0052] When displaying the guideline, the guideline display unit can predict the next predetermined guideline by referring to the target nail's cutting history. The guideline display unit, for example, can predict the next optimal guideline by referring to the target nail's past cutting history. The guideline display unit can also predict the future optimal guideline from the target nail's cutting history. For example, the guideline display unit analyzes the target nail's cutting pattern and predicts the next safest guideline. This makes it possible to predict the next optimal guideline by referring to the cutting history. Some or all of the above-described processing in the guideline display unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline display unit can input nail cutting history data into a generation AI and cause the generation AI to predict the next guideline.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] During analysis, the analysis unit can identify a predetermined cutting position by referring to the growth history of the target nail. For example, the analysis unit can analyze the past growth history of the target nail to identify the most appropriate cutting position. It can also predict the optimal cutting position in the future by taking into account the growth rate of the target nail. For example, the analysis unit can analyze the growth pattern of the target nail to identify the safest cutting position. In this way, the optimal cutting position can be identified by referring to the growth history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input nail growth history data into the generation AI and have the generation AI identify the cutting position.

[0055] During analysis, the analysis unit can detect the health condition of the target nail and suggest appropriate treatment. For example, if the target nail has a crack, it can suggest cutting the nail while avoiding that part. Also, if the target nail is discolored, it can suggest appropriate treatment taking the health condition into consideration. For example, if an abnormality is found in the target nail, it can suggest receiving a diagnosis from a specialist. In this way, nail health can be maintained by detecting the health condition of the nail and suggesting appropriate treatment. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI, for example. For example, the analysis unit can input nail health data into the generation AI and have the generation AI detect the health condition and suggest treatment.

[0056] During analysis, the analysis unit can measure the thickness of the target nail and recommend a predetermined cutting force. For example, it can measure the thickness of the target nail and recommend an appropriate cutting force. It can also recommend a lighter cutting force if the target nail is thin. For example, it can recommend a stronger cutting force if the target nail is thick. This allows for appropriate cutting by recommending a cutting force according to the nail thickness. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input nail thickness data into the generation AI and have the generation AI recommend a cutting force.

[0057] During analysis, the analysis unit can improve the analysis accuracy by taking into account the skin condition around the target nail. For example, if the skin around the target nail is dry, the analysis accuracy can be improved by taking that condition into account. Also, if the skin around the target nail is moist, the analysis accuracy can be improved by taking that condition into account. For example, if the skin around the target nail is inflamed, the analysis accuracy can be improved by taking that condition into account. In this way, by taking the skin condition into account, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input skin condition data into the generation AI and have the generation AI improve the analysis accuracy.

[0058] During analysis, the analysis unit can predict the growth rate of the target's nails and suggest a predetermined timing for the next nail clipping. For example, the analysis unit can predict the growth rate of the target's nails and suggest the optimal timing for the next nail clipping. Furthermore, if the target's nails grow quickly, the analysis unit can suggest frequent nail clipping. For example, if the target's nails grow slowly, the analysis unit can suggest appropriate intervals for nail clipping. By predicting the nail growth rate, the analysis unit can suggest the optimal timing for the next nail clipping. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input nail growth rate data into the generation AI and have the generation AI suggest the timing for the next nail clipping.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The camera captures an image of the target nail. Images of target nails include fingernails, toenails, and animal claws. The camera captures the image of the nail using a camera or smartphone camera. The camera automatically adjusts the focus and can capture clear images in high-resolution mode. Step 2: The analysis unit uses the generation AI to analyze the nail image captured by the image capture unit. The analysis is performed to identify the nail shape and length. The generation AI can also use text generation AI (e.g., LLM) or multimodal generation AI to identify the nail shape and length, and can also use edge detection technology to identify the nail edges. Step 3: The guideline display unit displays guidelines for the appropriate areas to cut based on the information identified by the analysis unit. The guidelines are displayed on the screen of the display device, and the user can follow them to cut their nails. If necessary, text support is provided, with instructions such as "Cut up to here" or "Do not cut this part." The guideline display unit can also display guidelines using an AI model that takes the information identified by the analysis unit as input and outputs guidelines.

[0061] (Example 2) A nail clipping assistance system according to an embodiment of the present invention provides guidelines for properly trimming a target's nails, reducing the risk of ingrown nails. The nail clipping assistance system takes an image of the target's nails, analyzes it using a generation AI, and displays guidelines on the appropriate areas to trim. Text support is also provided as needed. For example, the nail clipping assistance system takes an image of the target's nails. For example, the nail clipping assistance system can take an image of the nail using a camera. The nail clipping assistance system then analyzes the captured image of the nail using a generation AI. The input to the generation AI is the captured image of the nail, and the generation AI determines the shape and length of the nail based on the image. For example, the generation AI receives a prompt such as "Please analyze the shape and length of this nail" and determines the shape and length of the nail. The nail clipping assistance system then displays guidelines on the appropriate areas to trim based on the information identified by the generation AI. The guidelines are displayed on a display device screen. For example, the guidelines are displayed on the screen, allowing the user to follow them when trimming their nails. The nail clipping assistance system also provides text support as needed. For example, instructions such as "Cut up to here" or "Do not cut this part" are displayed. This allows the nail clipping assistance system to safely and properly trim the nails of children and animals, enabling parents, nurses, caregivers, pet owners, zookeepers, and others to do so. This reduces the risk of ingrown nails and alleviates the stress of nail clipping. For example, parents, nurses, caregivers, pet owners, zookeepers, and others to quickly and accurately trim the nails of children and animals.

[0062] The nail trimming assistance system according to the embodiment includes a photographing unit, an analysis unit, and a guideline display unit. The photographing unit photographs an image of a target nail. Examples of target nail images include, but are not limited to, fingernails, toenails, and animal nails. The photographing unit photographs the image of the nail using, for example, a camera. The photographing unit can also photograph the image of the nail using a smartphone camera. For example, the camera automatically adjusts the focus to capture a clear image. The photographing unit can also photograph the image of the nail in high resolution. For example, the camera photographs in high resolution mode to obtain a detailed image. The analysis unit uses a generation AI to analyze the image of the nail photographed by the photographing unit. The analysis is performed, for example, to identify the shape and length of the nail, but is not limited to, for example. For example, the generation AI identifies the shape and length of the nail using a text generation AI (e.g., LLM). The analysis unit can also identify the shape and length of the nail using a multimodal generation AI. The analysis unit can also identify the edge of the nail using the generation AI. For example, the generation AI identifies the edge of the nail using edge detection technology. The guideline display unit displays guidelines on appropriate nail trimming areas based on the information identified by the analysis unit. The guidelines may be displayed, for example, on the screen of a display device, but are not limited to such examples. For example, the guideline display unit displays the guidelines on the screen, allowing the user to trim their nails according to them. The guideline display unit can also provide text support as needed. For example, instructions such as "Cut up to here" or "Do not cut this part" are displayed. This allows the nail trimming assistance system according to the embodiment to provide guidelines for properly trimming the target nails and reduce the risk of ingrown nails. Some or all of the above-described processing by the guideline display unit may be performed using, for example, AI, or may be performed without AI. For example, the guideline display unit can display the guidelines using an AI model that inputs the information identified by the analysis unit and outputs guidelines.

[0063] The analysis unit can analyze the shape and length of the nail using image processing technology. Image processing technology includes, but is not limited to, edge detection, shape recognition, filtering, and the like. For example, the analysis unit can identify the edge of the nail using edge detection technology. The analysis unit can also identify the shape of the nail using shape recognition technology. For example, the analysis unit can identify the shape of the nail using template matching technology. The analysis unit can also preprocess the nail image using filtering technology. For example, the analysis unit can apply a noise removal filter to improve image quality. This allows the use of image processing technology to accurately analyze the shape and length of the nail. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the image of the nail captured by the imaging unit to the generation AI and have the generation AI analyze the shape and length of the nail.

[0064] The guideline display unit can display the guideline on the screen of the display device. Examples of the display device include, but are not limited to, a display, a monitor, a tablet, etc. The guideline display unit, for example, displays the guideline on the screen of a display. The guideline display unit can also display the guideline on the screen of a monitor. For example, the guideline display unit displays the guideline on the screen of a tablet. By displaying the guideline on the screen of the display device, the user can visually confirm the guideline. Some or all of the above-described processing in the guideline display unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline display unit can display the guideline using an AI model that receives information identified by the analysis unit as input and outputs a guideline.

[0065] The guideline display unit can provide text support based on predetermined conditions. Predetermined conditions include, but are not limited to, user operations, environmental conditions, and analysis results. The guideline display unit can provide text support based on, for example, user operations. The guideline display unit can also provide text support based on environmental conditions. For example, the guideline display unit can provide text support based on analysis results. This allows the user to receive more specific instructions through text support. Some or all of the above-described processing in the guideline display unit can be performed using, for example, AI, or can be performed without using AI. For example, the guideline display unit can provide text support using an AI model that receives information identified by the analysis unit as input and outputs text support.

[0066] The analysis unit can identify the edge of the nail using an edge detection technique. Examples of edge detection techniques include, but are not limited to, Canny edge detection and a Sobel filter. The analysis unit can identify the edge of the nail using, for example, Canny edge detection. The analysis unit can also identify the edge of the nail using a Sobel filter. For example, the analysis unit can identify the edge of the nail using an edge detection technique. As a result, the edge of the nail can be accurately identified using the edge detection technique. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input an image of the nail captured by the imaging unit to the generation AI and cause the generation AI to identify the edge of the nail.

[0067] The analysis unit can identify the shape of the nail using shape recognition technology. Shape recognition technology includes, but is not limited to, template matching and deep learning. The analysis unit can identify the shape of the nail using, for example, template matching technology. The analysis unit can also identify the shape of the nail using deep learning technology. For example, the analysis unit can identify the shape of the nail using shape recognition technology. As a result, the shape recognition technology can be used to accurately identify the shape of the nail. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input an image of the nail captured by the imaging unit into the generation AI and cause the generation AI to identify the shape of the nail.

[0068] The image capture unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. For example, if the user is nervous, the image capture unit displays a message encouraging the user to take a deep breath to relax, and then captures the image. Furthermore, if the user is anxious, the image capture unit can wait until the user calms down and capture the image when the user has calmed down. For example, if the user is relaxed, the image capture unit captures the user's nails in their current state, capturing a natural nail state. This allows the image capture timing to be adjusted according to the user's emotions, thereby capturing a more natural nail state. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the image capture unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the image capture unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] The imaging unit can automatically adjust the focus according to the movement of the subject when capturing an image. For example, when the subject moves, the imaging unit detects the movement and automatically readjusts the focus to capture a clear image. Furthermore, when the subject moves their hand, the imaging unit can adjust the focus to follow the movement and capture a blur-free image. For example, when the subject moves their fingernail, the imaging unit predicts the direction of the movement and adjusts the focus in advance to capture a smooth image. This allows the focus to be automatically adjusted according to the movement of the subject, thereby capturing a clear image. Some or all of the above-described processing in the imaging unit may be performed using, for example, AI, or may be performed without using AI. For example, the imaging unit can input movement data to a generation AI and have the generation AI adjust the focus.

[0070] The photographing unit can select a predetermined photographing mode according to the condition of the target nail when photographing. For example, if the target nail is dry, the photographing unit automatically selects a photographing mode suitable for the dry condition and photographs a detailed image. Furthermore, if the target nail is wet, the photographing unit can automatically select a photographing mode suitable for the wet condition and photograph an image with reduced reflection. For example, if the target nail contains oil, the photographing unit automatically selects a photographing mode that takes the oil into consideration and photographs a clear image. In this way, detailed images can be photographed by selecting a photographing mode suitable for the nail condition. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input nail condition data to the generation AI and cause the generation AI to select a photographing mode.

[0071] The image capturing unit can apply a filter to accurately reproduce the color and texture of the target nail when capturing an image. For example, the image capturing unit can apply a color correction filter to accurately reproduce the color of the target nail and capture an image with natural color tones. The image capturing unit can also apply a texture enhancement filter to capture an image with a realistic texture in order to capture the texture of the target nail in detail. For example, the image capturing unit can apply a transparency filter to reproduce the transparency of the target nail and capture a clear image. This allows the color and texture of the nail to be accurately reproduced, resulting in a realistic image. Some or all of the above-described processing in the image capturing unit can be performed using, for example, AI, or without AI. For example, the image capturing unit can input data on the color and texture of the nail into a generation AI and cause the generation AI to apply a filter.

[0072] The image capture unit can estimate the user's emotions and determine the priority of images to be captured based on the estimated user emotions. For example, if the user is nervous, the image capture unit can prioritize capturing the most important images and capture more detailed images later. Furthermore, if the user is relaxed, the image capture unit can capture all images equally to obtain more detailed information. For example, if the user is anxious, the image capture unit can quickly capture the necessary images and capture additional images later. This allows the priority of images to be captured according to the user's emotions, thereby prioritizing the capture of important images. Emotion estimation is achieved using an emotion estimation function, such as 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 image capture unit can be performed using AI, for example, or without AI. For example, the image capture unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] When capturing an image, the capture unit can set the optimal exposure by taking into account the ambient light around the target nail. For example, when the surroundings are bright, the capture unit adjusts the exposure to reduce excessive brightness and capture an image with appropriate brightness. Furthermore, when the surroundings are dark, the capture unit can adjust the exposure to correct the brightness and capture a clear image. For example, when the ambient light fluctuates, the capture unit adjusts the exposure in real time to capture an image with stable brightness. This allows an image with appropriate brightness to be captured by setting the exposure according to the ambient light. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input ambient light data to the generation AI and have the generation AI execute the exposure setting.

[0074] The image capturing unit may have a function to predict the movement of the subject's nails and prevent blurring when capturing an image. For example, the image capturing unit may predict the direction in which the subject's nails will move and adjust the camera's shutter speed to prevent blurring. The image capturing unit may also predict the timing at which the subject's nails will move and capture an image at the optimal timing. For example, the image capturing unit may predict the speed at which the subject's nails will move and adjust the camera settings to prevent blurring. This allows a clear image to be captured by predicting the movement of the nails and preventing blurring. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI. For example, the image capturing unit may input data on the movement of the nails to a generation AI and have the generation AI perform adjustments to prevent blurring.

[0075] When capturing an image, the image capturing unit can automatically select a different capture angle depending on the shape of the target nail. For example, if the target nail is flat, the image capturing unit selects a capture angle from directly above to capture the entire nail. Furthermore, if the target nail is curved, the image capturing unit can select a capture angle from an oblique angle to capture the detailed shape. For example, if the target nail is long and thin, the image capturing unit selects a capture angle from a vertical direction to capture the overall balance. In this way, by selecting a capture angle according to the nail shape, the detailed shape can be captured. Some or all of the above-described processing in the image capturing unit may be performed using, for example, AI, or may be performed without using AI. For example, the image capturing unit can input nail shape data to a generation AI and have the generation AI select the capture angle.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that focuses on the main points. This improves visibility by providing a display method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can identify a predetermined cutting position by referring to the growth history of the target nail. The analysis unit, for example, analyzes the past growth history of the target nail and identifies the most appropriate cutting position. The analysis unit can also predict the optimal cutting position in the future by taking into account the growth rate of the target nail. For example, the analysis unit analyzes the growth pattern of the target nail and identifies the safest cutting position. This makes it possible to identify the optimal cutting position by referring to the growth history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input nail growth history data into the generation AI and have the generation AI identify the cutting position.

[0078] During analysis, the analysis unit can detect the health condition of the target nail and suggest appropriate treatment. For example, if the target nail has a crack, the analysis unit suggests cutting the nail while avoiding that part. The analysis unit can also suggest appropriate treatment taking into account the health condition if discoloration is observed in the target nail. For example, if an abnormality is observed in the target nail, the analysis unit suggests that the target nail be diagnosed by a specialist. This allows the health of the nail to be detected and appropriate treatment to be suggested, thereby maintaining the health of the nail. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input nail health data into the generation AI and have the generation AI detect the health condition and suggest treatment.

[0079] During analysis, the analysis unit can measure the thickness of the target nail and recommend a predetermined cutting force. The analysis unit, for example, measures the thickness of the target nail and recommends an appropriate cutting force. The analysis unit can also recommend a lighter cutting force if the target nail is thin. For example, the analysis unit can recommend a stronger cutting force if the target nail is thick. This allows for appropriate cutting by recommending a cutting force according to the nail thickness. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input nail thickness data into the generation AI and have the generation AI recommend a cutting force.

[0080] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize and display the most important analysis results. Furthermore, if the user is relaxed, the analysis unit can also display all analysis results equally. For example, if the user is in a hurry, the analysis unit can quickly display the necessary analysis results. This prioritizes the analysis results according to the user's emotions, allowing important information to be provided preferentially. Emotion estimation is achieved using an emotion estimation function, such as 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 facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] During analysis, the analysis unit can improve the analysis accuracy by taking into account the skin condition around the target nail. For example, if the skin around the target nail is dry, the analysis unit improves the analysis accuracy by taking into account that condition. Furthermore, if the skin around the target nail is moist, the analysis unit can also improve the analysis accuracy by taking into account that condition. For example, if the skin around the target nail is inflamed, the analysis unit improves the analysis accuracy by taking into account that condition. In this way, by taking the skin condition into account, the analysis accuracy is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input skin condition data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0082] During analysis, the analysis unit can apply different analysis algorithms based on the shape of the target nail. For example, if the target nail is flat, the analysis unit applies an analysis algorithm suitable for flat nails. Furthermore, if the target nail is curved, the analysis unit can also apply an analysis algorithm suitable for curved nails. For example, if the target nail is elongated, the analysis unit applies an analysis algorithm suitable for elongated nails. This improves analysis accuracy by applying an analysis algorithm according to the nail shape. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input nail shape data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0083] During the analysis, the analysis unit can predict the growth rate of the target's nails and suggest a predetermined timing for the next nail clipping. For example, the analysis unit can predict the growth rate of the target's nails and suggest the optimal timing for the next nail clipping. The analysis unit can also suggest frequent nail clipping if the target's nails grow quickly. For example, the analysis unit can suggest nail clipping at appropriate intervals if the target's nails grow slowly. In this way, by predicting the nail growth rate, the optimal timing for the next nail clipping can be suggested. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input nail growth rate data into the generation AI and have the generation AI suggest the timing for the next nail clipping.

[0084] The guideline display unit can estimate the user's emotions and adjust the guideline display method based on the estimated user's emotions. For example, if the user is nervous, the guideline display unit displays simple, highly visible guidelines. Furthermore, if the user is relaxed, the guideline display unit can also display guidelines containing detailed information. For example, if the user is in a hurry, the guideline display unit displays guidelines that focus on the main points. This improves visibility by providing a guideline display method that corresponds 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 guideline display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guideline display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When displaying the guideline, the guideline display unit can generate a guideline customized according to the shape of the target nail. For example, if the target nail is flat, the guideline display unit generates a guideline suitable for the flat nail. Furthermore, if the target nail is curved, the guideline display unit can also generate a guideline suitable for the curved nail. For example, if the target nail is elongated, the guideline display unit generates a guideline suitable for the elongated nail. This allows appropriate guidelines to be provided by generating a guideline customized according to the nail shape. Some or all of the above-described processing in the guideline display unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline display unit can input nail shape data to a generation AI and cause the generation AI to generate customized guidelines.

[0086] When displaying the guideline, the guideline display unit can display a predetermined guideline by referring to the cutting history of the target nail. The guideline display unit, for example, refers to the past cutting history of the target nail and displays the most appropriate guideline. The guideline display unit can also predict the optimal guideline in the future based on the cutting history of the target nail. For example, the guideline display unit analyzes the cutting pattern of the target nail and displays the safest guideline. This makes it possible to provide the optimal guideline by referring to the cutting history. Some or all of the above-described processing in the guideline display unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline display unit can input nail cutting history data into a generation AI and cause the generation AI to display the guideline.

[0087] When displaying the guidelines, the guideline display unit can update the guidelines in real time according to the movement of the target nail. For example, when the target nail moves, the guideline display unit detects the movement and updates the guidelines in real time. Furthermore, when the target nail moves, the guideline display unit can also update the guidelines to follow the movement of the target nail. For example, the guideline display unit predicts the direction in which the target nail will move and updates the guidelines in advance. This allows the guidelines to be updated in real time according to the movement of the nail, thereby always providing appropriate guidelines. Some or all of the above-described processing in the guideline display unit may be performed, for example, using AI, or may be performed without using AI. For example, the guideline display unit can input data on the movement of the nail to a generation AI and have the generation AI update the guidelines.

[0088] The guideline display unit can estimate the user's emotions and adjust the color and thickness of the predetermined guideline based on the estimated user's emotions. For example, if the user is nervous, the guideline display unit displays guidelines in a subdued color to reduce visual stress. Furthermore, if the user is relaxed, the guideline display unit can display guidelines in a bright color. For example, if the user is in a hurry, the guideline display unit displays thick, highly visible guidelines. This improves visibility by adjusting the color and thickness of the guidelines 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 guideline display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guideline display unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] When displaying the guideline, the guideline display unit can adjust the brightness of the display taking into account the ambient light around the target nail. For example, when the surroundings are bright, the guideline display unit adjusts the brightness of the guideline to ensure visibility. Furthermore, when the surroundings are dark, the guideline display unit can also adjust the brightness of the guideline to make it easier to see. For example, when the ambient light fluctuates, the guideline display unit adjusts the brightness of the guideline in real time to provide stable visibility. This improves visibility by adjusting the brightness according to the ambient light. Some or all of the above-described processing in the guideline display unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline display unit can input ambient light data into the generation AI and have the generation AI adjust the brightness.

[0090] When displaying the guideline, the guideline display unit can select different display modes based on the shape of the target nail. For example, if the target nail is flat, the guideline display unit selects a display mode suitable for flat nails. Furthermore, if the target nail is curved, the guideline display unit can also select a display mode suitable for curved nails. For example, if the target nail is elongated, the guideline display unit selects a display mode suitable for elongated nails. In this way, by selecting a display mode according to the shape of the nail, an appropriate guideline can be provided. Some or all of the above-described processing in the guideline display unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline display unit can input nail shape data to a generation AI and cause the generation AI to select a display mode.

[0091] When displaying the guideline, the guideline display unit can predict the next predetermined guideline by referring to the target nail's cutting history. The guideline display unit, for example, can predict the next optimal guideline by referring to the target nail's past cutting history. The guideline display unit can also predict the future optimal guideline from the target nail's cutting history. For example, the guideline display unit analyzes the target nail's cutting pattern and predicts the next safest guideline. This makes it possible to predict the next optimal guideline by referring to the cutting history. Some or all of the above-described processing in the guideline display unit may be performed using, for example, AI, or may be performed without using AI. For example, the guideline display unit can input nail cutting history data into a generation AI and cause the generation AI to predict the next guideline. === Hard Collateral 1-1 === Each of the multiple elements, including the above-described photographing unit, analysis unit, and guideline display unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit can take an image of the nails using the camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photographed image of the nails using a generating AI. The guideline display unit displays guidelines using the display 40A of the smart device 14, allowing the user to cut their nails according to the guidelines. The guideline display unit can also provide text support as needed. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described photographing unit, analysis unit, and guideline display unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit can capture an image of the nails using the camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured image of the nails using a generative AI. The guideline display unit displays guidelines using the display of the smart glasses 214, allowing the user to trim their nails according to the guidelines. The guideline display unit can also provide text support as needed. === Hard Collateral 1-3 === Each of the multiple elements including the above-described photographing unit, analysis unit, and guideline display unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the photographing unit can photograph an image of the nails using the camera 42 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photographed image of the nails using a generation AI. The guideline display unit displays guidelines using the display 343 of the headset-type terminal 314, allowing the user to cut their nails according to the guidelines. The guideline display unit can also provide text support as needed. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned photographing unit, analysis unit, and guideline display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit can take an image of the nail using the camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the photographed image of the nail using a generative AI. The guideline display unit displays guidelines using the display of the robot 414, allowing the user to cut their nails according to the guidelines. The guideline display unit can also provide text support as needed.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. For example, if the user is in a hurry, a display method that focuses on the main points can be provided. This improves visibility by providing a display method that corresponds 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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0094] The image capture unit can estimate the user's emotions and adjust the timing of capturing images based on the estimated user emotions. For example, if the user is nervous, a message encouraging the user to take a deep breath to relax can be displayed, and then the image can be captured. Alternatively, if the user is feeling anxious, the image capture unit can wait until the user calms down and then capture the image when the user is calm. For example, if the user is relaxed, the image capture unit can capture the natural state of the nails in that state. This allows the image capture timing to be adjusted according to the user's emotions, thereby capturing a more natural state of the nails. 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 image capture unit can be performed using, for example, an AI, or without an AI. For example, the image capture unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0095] The guideline display unit can estimate the user's emotions and adjust the guideline display method based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible guideline can be displayed. Also, if the user is relaxed, a guideline containing detailed information can be displayed. For example, if the user is in a hurry, a guideline that focuses on the main points can be displayed. This improves visibility by providing a guideline display method that corresponds 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, 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 guideline display unit can be performed using, for example, AI, or without AI. For example, the guideline display unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0096] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. For example, if the user is nervous, the most important analysis results can be displayed first. Alternatively, if the user is relaxed, all analysis results can be displayed evenly. For example, if the user is in a hurry, the necessary analysis results can be displayed quickly. This allows important information to be provided preferentially by prioritizing the analysis results 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 analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0097] The guideline display unit can estimate the user's emotions and adjust the color and thickness of the predetermined guideline based on the estimated user's emotions. For example, if the user is nervous, the guideline can be displayed in a subdued color to reduce visual stress. Alternatively, if the user is relaxed, the guideline can be displayed in a bright color. For example, if the user is in a hurry, the guideline can be displayed in a thick, highly visible color. Adjusting the color and thickness of the guideline according to the user's emotions improves visibility. The emotion estimation is realized using an emotion estimation function, such as 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 guideline display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the guideline display unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0098] During analysis, the analysis unit can identify a predetermined cutting position by referring to the growth history of the target nail. For example, the analysis unit can analyze the past growth history of the target nail to identify the most appropriate cutting position. It can also predict the optimal cutting position in the future by taking into account the growth rate of the target nail. For example, the analysis unit can analyze the growth pattern of the target nail to identify the safest cutting position. In this way, the optimal cutting position can be identified by referring to the growth history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input nail growth history data into the generation AI and have the generation AI identify the cutting position.

[0099] During analysis, the analysis unit can detect the health condition of the target nail and suggest appropriate treatment. For example, if the target nail has a crack, it can suggest cutting the nail while avoiding that part. Also, if the target nail is discolored, it can suggest appropriate treatment taking the health condition into consideration. For example, if an abnormality is found in the target nail, it can suggest receiving a diagnosis from a specialist. In this way, nail health can be maintained by detecting the health condition of the nail and suggesting appropriate treatment. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI, for example. For example, the analysis unit can input nail health data into the generation AI and have the generation AI detect the health condition and suggest treatment.

[0100] During analysis, the analysis unit can measure the thickness of the target nail and recommend a predetermined cutting force. For example, it can measure the thickness of the target nail and recommend an appropriate cutting force. It can also recommend a lighter cutting force if the target nail is thin. For example, it can recommend a stronger cutting force if the target nail is thick. This allows for appropriate cutting by recommending a cutting force according to the nail thickness. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input nail thickness data into the generation AI and have the generation AI recommend a cutting force.

[0101] During analysis, the analysis unit can improve the analysis accuracy by taking into account the skin condition around the target nail. For example, if the skin around the target nail is dry, the analysis accuracy can be improved by taking that condition into account. Also, if the skin around the target nail is moist, the analysis accuracy can be improved by taking that condition into account. For example, if the skin around the target nail is inflamed, the analysis accuracy can be improved by taking that condition into account. In this way, by taking the skin condition into account, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input skin condition data into the generation AI and have the generation AI improve the analysis accuracy.

[0102] During analysis, the analysis unit can predict the growth rate of the target's nails and suggest a predetermined timing for the next nail clipping. For example, the analysis unit can predict the growth rate of the target's nails and suggest the optimal timing for the next nail clipping. Furthermore, if the target's nails grow quickly, the analysis unit can suggest frequent nail clipping. For example, if the target's nails grow slowly, the analysis unit can suggest appropriate intervals for nail clipping. By predicting the nail growth rate, the analysis unit can suggest the optimal timing for the next nail clipping. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input nail growth rate data into the generation AI and have the generation AI suggest the timing for the next nail clipping.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The camera captures an image of the target nail. Images of target nails include fingernails, toenails, and animal claws. The camera captures the image of the nail using a camera or smartphone camera. The camera automatically adjusts the focus and can capture clear images in high-resolution mode. Step 2: The analysis unit uses the generation AI to analyze the nail image captured by the image capture unit. The analysis is performed to identify the nail shape and length. The generation AI can also use text generation AI (e.g., LLM) or multimodal generation AI to identify the nail shape and length, and can also use edge detection technology to identify the nail edges. Step 3: The guideline display unit displays guidelines for the appropriate areas to cut based on the information identified by the analysis unit. The guidelines are displayed on the screen of the display device, and the user can follow them to cut their nails. If necessary, text support is provided, with instructions such as "Cut up to here" or "Do not cut this part." The guideline display unit can also display guidelines using an AI model that takes the information identified by the analysis unit as input and outputs guidelines.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an imaging unit that captures an image of the target nail; an analysis unit that analyzes the image taken by the photographing unit and measures the shape and length of the nail; a guideline display unit that displays a guideline at a predetermined portion to be cut based on the information identified by the analysis unit. A system characterized by:

2. The analysis unit Analyzing nail shape and length using image processing technology The system of claim 1 .

3. The guideline display unit Displaying guidelines on the display device screen The system of claim 1 .

4. The guideline display unit Provide text support based on predefined conditions The system of claim 1 .

5. The analysis unit Identifying nail edges using edge detection technology The system of claim 1 .

6. The analysis unit Identifying nail shapes using shape recognition technology The system of claim 1 .

7. The imaging unit is To estimate a user's emotion and adjust a predetermined photographing timing based on the estimated user's emotion. The system of claim 1 .

8. The imaging unit is Equipped with a function that automatically adjusts focus according to the movement of the subject when shooting. The system of claim 1 .

Images