system

Abstract

We provide the system. [Solution] A means for receiving images of the inside of the oral cavity taken by a terminal, A means for analyzing the received image and identifying tooth damage present in the image, A means for calculating the number of treatment days and estimated costs based on the identified injury, Means for transmitting the calculated result to a terminal, A means of providing guidance for a device installed in the home to take images of the user's oral cavity, A means of notifying the user of the results audibly and visually based on the captured image, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a busy life, it is difficult to receive regular dental check-ups, and oral problems often progress. Also, due to the limited consultation hours of dental clinics, it is difficult to visit the clinic regularly. Furthermore, the cost of medical treatment is often opaque, and there is a psychological burden that the treatment cost is unclear. As a result, early diagnosis and treatment may be delayed, so it is necessary to provide a means to easily grasp the oral condition and quickly establish a treatment plan.

Means for Solving the Problems

[0005] This invention provides a system that identifies tooth damage by using a terminal to capture images of the oral cavity, which are then received and analyzed by a server. Furthermore, it calculates the number of treatment days and estimated costs based on the identified damage and transmits these results to the terminal. This allows users to understand the health of their teeth from the comfort of their homes and develop appropriate treatment plans early on. In addition, the terminal provides a dental clinic appointment option, enabling users to systematically schedule their appointments. Moreover, by utilizing machine learning models, the accuracy of image analysis can be improved, providing users with highly reliable information.

[0006] A "terminal" is an electronic device used by a user that has the function of taking pictures and communicating.

[0007] "Means of receiving" refers to the function that allows the server to acquire images or data transmitted from a terminal.

[0008] "Means for analyzing and identifying images" refers to a method or program for processing received images and determining whether there is tooth damage or health information present within the image.

[0009] "Means for calculating treatment duration and estimated costs" refers to a method or formula for estimating the required treatment duration and its costs based on the identified injury.

[0010] "Means of transmission" refers to a communication method that has the function of sending the calculated result back to the terminal.

[0011] A "system" is a collection of devices and programs having a series of interrelated functions, and is a combination of components for realizing the specific processing in this invention. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units 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), and the like.

[0016] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a storage with a reference numeral is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, a communication I / F (Interface) with a reference numeral is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark), etc.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention relates to a system that allows a user to take images of their oral cavity, diagnose tooth damage from those images, and even predict the number of days and costs required for treatment. The system consists of a user terminal, a server, and a communication infrastructure.

[0034] First, the user takes pictures of their upper and lower teeth, left and right, using a smartphone or other device. A dedicated application provides guidance during the shooting process, ensuring that images are acquired at the appropriate angle and distance.

[0035] The device uploads the captured images to a server in the cloud. The uploaded images are received and processed on the server side and stored appropriately in the database.

[0036] The server performs image analysis on the received images using machine learning. It utilizes deep learning technologies such as convolutional neural networks (CNNs) to accurately identify tooth damage from the images. In this process, the server uses a model trained on a vast amount of historical data to determine the location and progression of the damage.

[0037] Based on the analysis results, the server calculates the number of days required for treatment and the estimated cost. This uses statistical methods based on historical data. For example, it estimates the average number of treatment days and cost from past treatment cases with similar levels of injury.

[0038] Next, the server sends the calculated results back to the terminal. The terminal has the function of notifying the user of the diagnosis results, estimated treatment duration, costs, etc., and the user can use this information to create a treatment plan.

[0039] Furthermore, if the user wishes, they can also make dental appointments directly from their device. This feature allows them to schedule appointments at appropriate times and manage their treatment schedule efficiently.

[0040] As a concrete example, suppose a user experiences pain in their upper right molar and takes an intraoral image. The system analyzes the image and diagnoses a small cavity in the upper right molar. The server calculates that one visit to the dentist is necessary for treatment, costing approximately 8,000 yen, and notifies the user of this information. By receiving this information, the user can understand the treatment plan in advance and make an appropriate appointment, enabling efficient and economical dental care.

[0041] Thus, the present invention can reduce the burden of dental visits by providing users with rapid and accurate oral diagnostic information and supporting the formulation of treatment plans.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user takes images of the inside of their mouth using a device. A dedicated application is launched, and a shooting guide is displayed, allowing the user to acquire images at the appropriate angle and distance.

[0045] Step 2:

[0046] The device uploads the captured image to a server in the cloud. The image is securely transmitted to the server using an encrypted communication protocol.

[0047] Step 3:

[0048] The server analyzes the received images. Using machine learning models, particularly convolutional neural networks, it identifies tooth damage in the images and determines its location and progression.

[0049] Step 4:

[0050] The server calculates the number of days required for treatment and the estimated cost based on the image analysis results. It uses statistical methods to calculate the results by referring to a database of similar past cases.

[0051] Step 5:

[0052] The server sends the calculated results to the terminal. The results include information about the diagnosed injury, estimated treatment duration, and predicted cost.

[0053] Step 6:

[0054] The terminal receives the results from the server and notifies the user. The notification includes the diagnostic results and an option to schedule the next step.

[0055] Step 7:

[0056] Users can check notifications and, if necessary, make dental appointments on their devices. Users can set appointments for the most suitable date and time by following the application's guide.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] Traditional oral diagnoses required face-to-face consultations with specialists, resulting in lengthy diagnostic processes. Furthermore, accurately analyzing oral images to predict treatment needs and costs was difficult. Additionally, there was a lack of readily available means for users to easily perform diagnoses at home and obtain immediate results. Against this backdrop, there is a need for a system that supports faster and more accurate oral diagnosis and treatment planning.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes processing means for receiving images of the oral cavity captured by a terminal, processing means for analyzing the received images using deep learning technology to identify tooth damage present in the images, and processing means for calculating the number of treatment days and estimated costs based on the identified damage. This enables users to quickly and accurately diagnose the condition of their oral cavity at home and obtain predictions of the number of treatment days and costs required.

[0062] A "terminal" is a portable electronic device owned by a user that has the functionality to take pictures and communicate.

[0063] "Intraoral images" are photographic data that visually records the condition of the teeth, gums, and other tissues present in the user's oral cavity.

[0064] "Receiving processing means" refers to a function or device that allows an electronic device or server to receive data transmitted from an external source.

[0065] "Deep learning technology" is a machine learning technique that uses large amounts of data to train multi-layered neural networks and recognize complex patterns.

[0066] "Image analysis" is a technology that uses computers to process information within digital images and extract and recognize specific features or anomalies.

[0067] "Calculation processing means" refers to a function or device for calculating and deriving specific numerical values ​​or results based on input data.

[0068] "Treatment period" refers to the duration of time including the number of hospital visits and treatments required to treat a specific injury.

[0069] "Predicted cost" refers to the estimated financial cost of performing a particular treatment.

[0070] A "convolutional neural network" is a multi-layered machine learning model primarily used for analyzing image data, and it is a technology that excels in object recognition and pattern extraction.

[0071] "To be equipped with a function" means to have the processing power, equipment, or software necessary to achieve a specific purpose built in.

[0072] "Reservation options" refers to providing users with choices to select the date, time, and location that suits their convenience, allowing them to receive the service in advance.

[0073] This invention is a system that uses a user's device and a cloud-based server to capture and analyze images of the oral cavity, identify tooth damage, and predict the duration and cost of treatment. The user uses a portable device such as a smartphone or tablet. A dedicated application is installed on this device, allowing the user to easily capture images of their oral cavity using this application.

[0074] The device's application provides guidance to the user during shooting. The user uses the device's camera to capture images, and by following the app's guidance, they can take photos at the appropriate angle and distance. The captured images are uploaded to the server via data communication.

[0075] The server analyzes the received images using deep learning techniques based on convolutional neural networks (CNNs). This technique allows for highly accurate identification of tooth damage within the images, determining its location and progression. The server enables this analysis by using machine learning models trained on historical data.

[0076] Furthermore, the server calculates the number of days required for treatment and the estimated cost based on the analysis results, and sends this information back to the terminal. This calculation is based on statistical methods, and refers to the average number of days and costs derived from similar past cases.

[0077] As a concrete example, suppose a user experiences pain in their upper right molar and takes an intraoral image using a dedicated app. The server analyzes the image and diagnoses a small cavity in the upper right molar. Treatment requires one visit to the dentist, and the estimated cost is calculated to be approximately 8,000 yen. This information is then communicated to the user via their device. This allows the user to develop a more rapid and accurate treatment plan.

[0078] An example of a prompt to be input into the generating AI model is, "Explain how to analyze the oral cavity image, identify the tooth damage, and predict the necessary treatment duration and cost." Through this prompt, the system is able to quickly provide an appropriate diagnosis and treatment plan.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user uses a device to take images of their oral cavity. A dedicated application launches and displays guides indicating the appropriate angle and distance for taking the images. The input is the state of the user's oral cavity, and the output is the captured image data. This data is stored on the device for use in the next step.

[0082] Step 2:

[0083] The device uploads the captured image to the server. During this process, the user transmits the image data via data communication. The input is the image data stored on the user's device, and the output is the image data transferred to the server. The uploaded data is transmitted securely using a secure protocol (e.g., HTTPS).

[0084] Step 3:

[0085] The server prepares the received image data for analysis. It extracts image metadata (such as the date and time of capture and user ID) and saves it to the database. The input is the raw image data received by the server, and the output is the image data saved in an analyzable format.

[0086] Step 4:

[0087] The server applies deep learning technology to analyze the image. Specifically, it uses a convolutional neural network to identify tooth damage. The input is the image data prepared in step 3, and the output is identification information of the damaged areas. Here, each pixel is scanned and the damage is labeled throughout the entire image.

[0088] Step 5:

[0089] The server calculates the number of days required for treatment and the estimated cost based on the analysis results. The input is tooth damage identification information, and the output is data on the number of treatment days and estimated cost. Here, historical treatment data is referenced to calculate statistically average values.

[0090] Step 6:

[0091] The server sends the calculated results to the terminal. The input is the calculated treatment information, and the output is the diagnostic result received on the terminal. The data is transferred quickly and securely and displayed on the user's terminal.

[0092] Step 7:

[0093] The device notifies the user of the received diagnostic results. The user can view the results within the app and, if necessary, make an appointment at a dental clinic. The input is the diagnostic result data sent from the server, and the output is treatment plan information displayed to the user. This allows the user to develop an appropriate treatment plan.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] Conventional oral diagnostic systems present challenges because users often find it difficult to capture appropriate images themselves, and the information obtained is limited, making continuous oral care and timely treatment planning difficult. Furthermore, there is a lack of support systems for easily managing health at home.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for receiving images of the oral cavity captured by a terminal, means for analyzing the received images to identify tooth damage present in the images, and means for calculating the number of treatment days and estimated costs based on the identified damage. This makes it possible for a device installed in the home to provide guidance for capturing images of the user's oral cavity. This makes it possible to notify the user of the results based on the captured images audibly and visually, thereby supporting daily oral care.

[0099] A "terminal" is an electronic device used by a user that has the function of taking images of the inside of the mouth and connecting to a server via communication.

[0100] "Means of receiving" refers to the function of acquiring captured image data via a network and preparing it for processing.

[0101] "Methods for analyzing and identifying images" refers to algorithms that use machine learning models to identify tooth damage in received intraoral images.

[0102] The "means of calculation" refers to a function that uses statistical methods to calculate the number of days required for treatment and the estimated cost based on the identified injury.

[0103] "Means of transmission" refers to a function that uses a communication protocol to return the calculated result to the user's terminal and provide the user with information.

[0104] "Devices installed in the home" refers to electronic devices that are permanently installed in the user's living space and are used for health management or diagnostic assistance.

[0105] "Means of providing guidance" refers to interfaces that provide audio and visual instructions to guide users in taking the best possible images.

[0106] "Means of notifying by sound and visual means" refers to functions that use speakers and displays to present information in order to communicate analysis results and calculation results to the user.

[0107] This invention is a system for routinely checking the health of the oral cavity and supporting the planning of necessary treatments. The system uses a terminal installed in the user's living space to capture images of the user's oral cavity and derives diagnostic results in conjunction with a server in the cloud.

[0108] First, the user uses the camera on a device installed in their home to take images of their oral cavity. Appropriate guidance is provided, enabling them to take images at the optimal angle and distance. The device then transmits the captured image data to a cloud server via a receiving device.

[0109] Next, the server analyzes the received intraoral images. Machine learning models using deep learning frameworks such as TENSORFLOW® and PyTorch are used for image analysis to identify tooth damage. This process allows for highly accurate identification of the location and progression of the damage. Based on the identification results, the server uses statistical methods to calculate the number of treatment days and the estimated cost.

[0110] The diagnostic results and calculated information are sent back to the terminal via a transmission method and notified to the user via voice and visual means. Based on this notification, the user can then develop a treatment plan. The terminal also offers a dental clinic appointment option upon request, allowing the user to easily make an appointment.

[0111] For example, if a small cavity is detected in the upper right molar from an image taken by the user with their device, the system will notify the user that it can be treated in one visit and that the cost will be approximately 8,000 yen. Using this information, the user can set an appropriate treatment schedule and make an appointment.

[0112] An example of a prompt message is, "Please suggest improvements to the system that analyzes user-recorded images of the oral cavity and identifies even small areas of damage." This clarifies the overall embodiment of the invention and makes it possible to provide users with a more accessible health support environment.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user takes images of their oral cavity using a device installed in their home. The device guides the user through voice and display to determine the appropriate shooting position and angle, resulting in high-quality image data. The input is an image of the oral cavity acquired through the camera, and the output is image data.

[0116] Step 2:

[0117] The terminal transmits the acquired intraoral image data to a server in the cloud. Internet communication protocols are used to connect to the server and transfer the data. The input is the image data acquired in the previous step, and the output is the image data transferred to the server.

[0118] Step 3:

[0119] The server analyzes the received image data. Deep learning models using TensorFlow or PyTorch are applied to the image analysis to identify tooth damage within the image. During this process, the deep learning model extracts image features and identifies the damage. The input is the transmitted image data, and the output is data regarding the location and progression of the damage.

[0120] Step 4:

[0121] The server calculates the number of treatment days and estimated costs based on identified injury data. Statistical methods using historical treatment data are employed to calculate average values ​​based on similar cases. Input is data on the specific location and progression of the injury, and output is the number of treatment days and estimated costs.

[0122] Step 5:

[0123] The server sends the calculated treatment information to the terminal. It then sends data to the terminal via a communication protocol, preparing to notify the user. The input is data on treatment days and costs, and the output is the diagnostic result data transferred to the terminal.

[0124] Step 6:

[0125] The terminal informs the user of the received diagnostic results. Using an audio output device and display, it visually and audibly notifies the results and, in some cases, suggests treatment appointment options. The input is the diagnostic result data sent from the server, and the output is the treatment information communicated to the user.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention is a system that efficiently diagnoses the condition of a user's oral cavity and provides a treatment plan based on the user's emotional state. This system is comprised of a camera and sensors installed in the user's terminal, a server in the cloud, and a communication infrastructure.

[0128] First, the device allows the user to take images of the inside of their mouth. A dedicated app displays instructions on the screen, and the user follows these instructions to take pictures of the inside of their mouth. At this time, the device uses a facial recognition sensor to collect data on the user's facial expressions. This allows the device to determine the user's emotional state in real time.

[0129] The captured images and facial expression data are transmitted to the server via an encrypted protocol. The server analyzes the received images using machine learning models, particularly convolutional neural networks, to identify tooth damage. Simultaneously, an emotion engine analyzes the facial expression data to assess the user's psychological state. This assessment is used to determine whether the user is anxious about the treatment.

[0130] Next, the server calculates the number of days required for treatment and the estimated cost based on the damage diagnosis. Furthermore, it adjusts and proposes an appropriate treatment plan and advice to the user based on their emotional state. For example, if a user is feeling anxious, it may consider providing more detailed explanations or comments to alleviate their anxiety.

[0131] The server sends this information back to the terminal, which then notifies the user of the diagnosis results and recommended actions. The notification includes not only a treatment plan but also emotionally-based comments, allowing the user to proceed to the next step with psychological reassurance. Furthermore, the terminal's built-in reservation function allows the user to make a dental appointment directly.

[0132] For example, if the system diagnoses a small cavity in the upper right molar from an image taken by the user, it predicts that the treatment will take one day and cost around 8,000 yen. Furthermore, if the emotion engine detects that the user is anxious, it will provide a comment such as, "The treatment will be quick, so please don't worry," to alleviate the user's anxiety.

[0133] Thus, the present invention goes beyond mere oral diagnosis, providing care that takes into account the user's psychological aspects, thereby improving the overall clinical experience.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user takes images of the inside of their mouth using the device's camera. Upon launching the dedicated application, a shooting guide is displayed, allowing the user to record the condition of their mouth at the appropriate angle.

[0137] Step 2:

[0138] The facial recognition sensor equipped in the device captures the user's facial expressions and acquires emotional data in real time. This data is sent to the server along with the captured intraoral image.

[0139] Step 3:

[0140] The server analyzes the received image data. It utilizes machine learning models to identify tooth damage within the images. This process involves using a convolutional neural network to determine the location and extent of the damage.

[0141] Step 4:

[0142] The server analyzes the facial expression data it receives using an emotion engine to evaluate the user's psychological state. This helps determine whether the user is experiencing anxiety or fear regarding the treatment.

[0143] Step 5:

[0144] The server calculates the treatment duration and estimated cost based on image analysis and emotion assessment results. Simultaneously, it prepares appropriate treatment plans and advice tailored to the user's emotional state.

[0145] Step 6:

[0146] The server sends the calculated treatment results and emotion-based advice to the device. The device receives this and notifies the user of the diagnosis, recommended treatment duration, cost, and emotion-based comments.

[0147] Step 7:

[0148] Users can review the provided diagnostic results and advice, and then use their device's functions to make a dental appointment. Since the appointment function is directly available within the app, users can proceed to the next step without any hassle.

[0149] (Example 2)

[0150] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0151] In modern society, there is a need for users to receive quick and accurate diagnoses of oral health problems, as well as medical plans that take their psychological state into consideration. However, conventional diagnostic systems have focused solely on the physical aspects of the problem and have failed to consider the user's emotions and psychological state. As a result, they have not been able to adequately provide users with appropriate treatment plans or a sense of psychological reassurance.

[0152] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0153] In this invention, the server includes means for receiving images of the oral cavity and user facial expressions captured by a terminal, means for analyzing the received images to identify tooth damage present in the images, and means for analyzing the received facial expressions to evaluate the user's emotional state. This makes it possible to provide a diagnosis based on tooth damage and a treatment plan that takes the user's emotional state into consideration.

[0154] A "terminal" is a device operated by the user, equipped with a camera and sensors, and capable of acquiring images of the inside of the mouth and facial expression data.

[0155] A "server" is a central processing system that receives aggregated data, performs analysis, generates diagnostic results and treatment plans, and transmits them to terminals.

[0156] An "intraoral image" is a digital image taken of the inside of the user's mouth, and is used to check the health of their teeth and gums.

[0157] "Facial expression information" refers to data that captures the movements and expressions of a user's face, and is used to evaluate their emotional state through analysis.

[0158] "Damage" refers to abnormalities or defects found in the teeth and gums in the oral cavity, and specifically includes signs of tooth decay and periodontal disease.

[0159] "Emotional state" refers to the state of a user's psychological and emotional responses, which are evaluated through the analysis of facial expression information.

[0160] A "generative AI model" is an algorithm or process used by artificial intelligence to analyze data and generate new information, and is particularly used in image recognition and sentiment analysis.

[0161] A "treatment plan" is a medical action plan developed based on the tooth damage and the user's emotional state, and includes appropriate treatment and preventive measures.

[0162] This invention is a system that efficiently diagnoses the condition of a user's oral cavity and provides a treatment plan based on the user's emotional state. The system primarily utilizes the user's terminal, a cloud-based server, and communication infrastructure. The terminal is equipped with a camera and facial recognition sensor, and a dedicated application is installed.

[0163] The device activates the camera for the user to take an image of the inside of their mouth, and the app displays instructions to guide the user. Furthermore, a facial recognition sensor collects the user's facial data in real time to determine the user's emotional state. This data is transmitted to the server via an encrypted protocol such as HTTPS.

[0164] The server analyzes images using machine learning models (particularly convolutional neural networks) to identify tooth damage. Simultaneously, an emotion engine analyzes facial expression data to assess the user's psychological state. This process is used to determine the user's anxiety about the dental treatment.

[0165] Based on the diagnosis, the server calculates the number of days required for treatment and the estimated cost, and further generates a treatment plan and advice that takes into account the user's emotional state. A generative AI model supports this entire process, creating specific comments and recommended actions for the user based on prompt messages. For example, a prompt message might be, "Analyze the intraoral images submitted by the user, identify possible damage, and propose an appropriate treatment plan based on emotional data."

[0166] The data generated in this way is then sent back to the device, which notifies the user of the diagnosis and recommended actions. The notification includes a comprehensive treatment plan and emotionally-based comments, allowing the user to proceed to the next step with psychological reassurance. Furthermore, the user can use the device's booking function to make an appointment with a dental clinic directly. This system goes beyond mere physical diagnosis, providing care that considers the user's psychological aspects and improving the overall treatment experience.

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user launches a dedicated app on their device and takes an image of their oral cavity. The device activates the camera and assists the user in taking the image at the correct angle and under appropriate lighting conditions by following the on-screen guide. This captured image becomes the input data for the next processing step.

[0170] Step 2:

[0171] The device uses a facial recognition sensor to collect user facial data. By analyzing facial expressions during and after shooting, and identifying movements such as eyebrows and corners of the mouth, it grasps the user's emotional state in real time. This emotional state is then input as data and used for subsequent processing.

[0172] Step 3:

[0173] The device transmits captured images of the oral cavity and collected facial expression data to the server via the HTTPS protocol. This communication is a crucial step in ensuring data security and allowing the server to receive accurate input data.

[0174] Step 4:

[0175] The server analyzes the received intraoral images using a convolutional neural network (CNN). It processes the images to extract prominent features and identify tooth damage. This analysis result is the output data used in the next step for damage diagnosis.

[0176] Step 5:

[0177] The server analyzes facial expression data using an emotion engine to evaluate the user's emotional state. Specifically, it extracts elements that form psychological states such as tension and anxiety, and the evaluation results become output data that influences the treatment plan for the next step.

[0178] Step 6:

[0179] Based on the diagnosis of the injury and the emotional assessment, the server calculates and generates a treatment plan that includes treatment duration, estimated costs, and emotional considerations. Using the generated AI model, prompt messages optimized for the user are constructed.

[0180] Step 7:

[0181] The server sends a calculated treatment plan and emotionally-based comments to the terminal. This information, along with the diagnosis results, is clearly displayed and notified to the user. Based on this information, the user can decide on their next action and arrange an appointment with a dental clinic using the terminal's booking function.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] It is difficult to not only efficiently diagnose a user's oral condition but also to provide a treatment plan that takes into account the user's emotional state. Existing systems only handle information about the physical condition of the oral cavity, so they cannot address the user's psychological anxieties or questions, and therefore cannot improve overall satisfaction.

[0185] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0186] In this invention, the server includes means for receiving images of the oral cavity captured by a terminal, means for analyzing the received images to identify tooth damage present in the images, and means for evaluating emotional state using facial expression data acquired from the terminal. This enables the provision of a detailed treatment plan based on the diagnosis and personalized advice based on emotions.

[0187] A "terminal" is an information processing device used by the user, which has the function of taking images of the inside of the mouth and acquiring facial expression data.

[0188] "Means for receiving images" refers to a function for receiving intraoral image data transmitted from a terminal.

[0189] "Means for identifying tooth damage" refers to technologies that analyze received intraoral image data to identify the condition and damage to teeth contained in the images.

[0190] "Means for calculating treatment duration and estimated costs" refers to a function that estimates the required treatment period and its cost based on the identified tooth damage.

[0191] "Methods for evaluating emotional states using facial expression data" refers to technologies that analyze facial expression data acquired from a device to determine the user's emotional state.

[0192] "Means for adapting treatment plans and advice" refers to functions that adjust treatment plans and advice according to the user's emotional state and propose them to the user.

[0193] A "server" is a central processing unit that receives data from terminals, performs analysis and calculations, and sends the results back to the terminals.

[0194] This system consists of terminals (information processing devices), servers (central processing units), and a communication infrastructure connecting these two. The specific operation of the system will now be explained.

[0195] First, the user takes an image of their oral cavity using the device. The device is equipped with a camera and an expression recognition sensor, which are used to acquire data on the user's oral cavity and facial expressions. This makes it possible to determine the user's emotional state in real time.

[0196] Next, the device sends the captured image and collected facial expression data to the server. The server receives this data and uses a machine learning model to analyze the image. In particular, it applies deep learning algorithms such as convolutional neural networks to identify tooth damage from images of the oral cavity.

[0197] Furthermore, the server uses facial recognition data to evaluate the user's emotional state. The emotion engine determines whether the user is feeling anxious or reassured, and this evaluation result is reflected in the treatment plan. Depending on the emotional state, the server provides reassuring comments and detailed explanations of the treatment plan to the user.

[0198] Finally, the server sends the analysis results and a treatment plan based on emotions back to the device. The device notifies the user of the diagnosis and recommended actions, and assists with booking a dental appointment if necessary.

[0199] As a concrete example, imagine a user launching the "Smart Dental Care Robot" app in the morning and performing an intraoral scan. At that time, the app assesses the user's emotional state and provides feedback such as, "You seem very relaxed this morning." This allows the user to proceed with greater confidence.

[0200] An example of a prompt generated by the AI ​​model is: "Describe a system in which a consumer robot scans a user's oral cavity and performs sentiment analysis."

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The user uses a device to acquire images of the inside of their mouth and facial expression data. The device's camera takes pictures of the inside of the mouth, and a facial expression recognition sensor captures the user's facial expressions. This results in obtaining images of the inside of the mouth and facial expression data as input data.

[0204] Step 2:

[0205] The terminal transmits the acquired intraoral images and facial expression data to the server. The image data and facial expression data are encrypted via a secure protocol to ensure data protection before transmission. The output of this step is the data that arrives on the server via the communication infrastructure.

[0206] Step 3:

[0207] The server receives images of the oral cavity, inputs them into a machine learning model, and performs analysis. In particular, a convolutional neural network is used to analyze the images and identify tooth damage and abnormalities. In this process, feature extraction and classification of the images are performed, and the output is a judgment of the dental health status.

[0208] Step 4:

[0209] The server inputs facial expression data into the emotion engine and analyzes the user's emotional state. Using an emotion recognition algorithm, it estimates the emotions the user is feeling from the facial expression data. The output of this process is an evaluation result indicating the emotional state.

[0210] Step 5:

[0211] The server generates appropriate treatment plans and advice based on the dental damage diagnosis and emotional assessment results. Comments and advice tailored to the emotional state are added, and the treatment content and procedures are adjusted accordingly. The output consists of treatment policies and advice information to be communicated to the user.

[0212] Step 6:

[0213] The generated treatment plan and advice are sent to the device. The server returns the calculation results and emotion-based feedback to the device. The output of this step is the notification information displayed to the user on the device.

[0214] Step 7:

[0215] Users can view treatment plans and advice from their devices and, if necessary, make appointments at dental clinics. Using the device's booking function, users can proceed directly to the next step. As a result, actions corresponding to the user's choices are output.

[0216] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0217] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] As shown in Figure 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.

[0222] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0223] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0224] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0225] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0226] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0227] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0228] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0229] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0230] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0231] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0232] This invention relates to a system that allows a user to take images of their oral cavity, diagnose tooth damage from those images, and even predict the number of days and costs required for treatment. The system consists of a user terminal, a server, and a communication infrastructure.

[0233] First, the user takes pictures of their upper and lower teeth, left and right, using a smartphone or other device. A dedicated application provides guidance during the shooting process, ensuring that images are acquired at the appropriate angle and distance.

[0234] The device uploads the captured images to a server in the cloud. The uploaded images are received and processed on the server side and stored appropriately in the database.

[0235] The server performs image analysis on the received images using machine learning. It utilizes deep learning technologies such as convolutional neural networks (CNNs) to accurately identify tooth damage from the images. In this process, the server uses a model trained on a vast amount of historical data to determine the location and progression of the damage.

[0236] Based on the analysis results, the server calculates the number of days required for treatment and the estimated cost. This uses statistical methods based on historical data. For example, it estimates the average number of treatment days and cost from past treatment cases with similar levels of injury.

[0237] Next, the server sends the calculated results back to the terminal. The terminal has the function of notifying the user of the diagnosis results, estimated treatment duration, costs, etc., and the user can use this information to create a treatment plan.

[0238] Furthermore, if the user wishes, they can also make dental appointments directly from their device. This feature allows them to schedule appointments at appropriate times and manage their treatment schedule efficiently.

[0239] As a concrete example, suppose a user experiences pain in their upper right molar and takes an intraoral image. The system analyzes the image and diagnoses a small cavity in the upper right molar. The server calculates that one visit to the dentist is necessary for treatment, costing approximately 8,000 yen, and notifies the user of this information. By receiving this information, the user can understand the treatment plan in advance and make an appropriate appointment, enabling efficient and economical dental care.

[0240] Thus, the present invention can reduce the burden of dental visits by providing users with rapid and accurate oral diagnostic information and supporting the formulation of treatment plans.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The user takes images of the inside of their mouth using a device. A dedicated application is launched, and a shooting guide is displayed, allowing the user to acquire images at the appropriate angle and distance.

[0244] Step 2:

[0245] The device uploads the captured image to a server in the cloud. The image is securely transmitted to the server using an encrypted communication protocol.

[0246] Step 3:

[0247] The server analyzes the received images. Using machine learning models, particularly convolutional neural networks, it identifies tooth damage in the images and determines its location and progression.

[0248] Step 4:

[0249] The server calculates the number of days required for treatment and the estimated cost based on the image analysis results. It uses statistical methods to calculate the results by referring to a database of similar past cases.

[0250] Step 5:

[0251] The server sends the calculated results to the terminal. The results include information about the diagnosed injury, estimated treatment duration, and predicted cost.

[0252] Step 6:

[0253] The terminal receives the results from the server and notifies the user. The notification includes the diagnostic results and an option to schedule the next step.

[0254] Step 7:

[0255] Users can check notifications and, if necessary, make dental appointments on their devices. Users can set appointments for the most suitable date and time by following the application's guide.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0258] Traditional oral diagnoses required face-to-face consultations with specialists, resulting in lengthy diagnostic processes. Furthermore, accurately analyzing oral images to predict treatment needs and costs was difficult. Additionally, there was a lack of readily available means for users to easily perform diagnoses at home and obtain immediate results. Against this backdrop, there is a need for a system that supports faster and more accurate oral diagnosis and treatment planning.

[0259] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0260] In this invention, the server includes processing means for receiving images of the oral cavity captured by a terminal, processing means for analyzing the received images using deep learning technology to identify tooth damage present in the images, and processing means for calculating the number of treatment days and estimated costs based on the identified damage. This enables users to quickly and accurately diagnose the condition of their oral cavity at home and obtain predictions of the number of treatment days and costs required.

[0261] A "terminal" is a portable electronic device owned by a user that has the functionality to take pictures and communicate.

[0262] "Intraoral images" are photographic data that visually records the condition of the teeth, gums, and other tissues present in the user's oral cavity.

[0263] "Receiving processing means" refers to a function or device that allows an electronic device or server to receive data transmitted from an external source.

[0264] "Deep learning technology" is a machine learning technique that uses large amounts of data to train multi-layered neural networks and recognize complex patterns.

[0265] "Image analysis" is a technology that uses computers to process information within digital images and extract and recognize specific features or anomalies.

[0266] "Calculation processing means" refers to a function or device for calculating and deriving specific numerical values ​​or results based on input data.

[0267] "Treatment period" refers to the duration of time including the number of hospital visits and treatments required to treat a specific injury.

[0268] "Predicted cost" refers to the estimated financial cost of performing a particular treatment.

[0269] A "convolutional neural network" is a multi-layered machine learning model primarily used for analyzing image data, and it is a technology that excels in object recognition and pattern extraction.

[0270] "To be equipped with a function" means to have the processing power, equipment, or software necessary to achieve a specific purpose built in.

[0271] "Reservation options" refers to providing users with choices to select the date, time, and location that suits their convenience, allowing them to receive the service in advance.

[0272] This invention is a system that uses a user's device and a cloud-based server to capture and analyze images of the oral cavity, identify tooth damage, and predict the duration and cost of treatment. The user uses a portable device such as a smartphone or tablet. A dedicated application is installed on this device, allowing the user to easily capture images of their oral cavity using this application.

[0273] The device's application provides guidance to the user during shooting. The user uses the device's camera to capture images, and by following the app's guidance, they can take photos at the appropriate angle and distance. The captured images are uploaded to the server via data communication.

[0274] The server analyzes the received images using deep learning techniques based on convolutional neural networks (CNNs). This technique allows for highly accurate identification of tooth damage within the images, determining its location and progression. The server enables this analysis by using machine learning models trained on historical data.

[0275] Furthermore, the server calculates the number of days required for treatment and the estimated cost based on the analysis results, and sends this information back to the terminal. This calculation is based on statistical methods, and refers to the average number of days and costs derived from similar past cases.

[0276] As a concrete example, suppose a user experiences pain in their upper right molar and takes an intraoral image using a dedicated app. The server analyzes the image and diagnoses a small cavity in the upper right molar. Treatment requires one visit to the dentist, and the estimated cost is calculated to be approximately 8,000 yen. This information is then communicated to the user via their device. This allows the user to develop a more rapid and accurate treatment plan.

[0277] As an example of a prompt sentence to be input into the generative AI model, "Please explain how to analyze an image inside the oral cavity, identify the damage to the teeth, and predict the number of treatment days and costs required." can be cited. Through this prompt, the system enables the rapid provision of appropriate diagnoses and treatment plans.

[0278] The flow of the specific process in Example 1 will be described using FIG. 11.

[0279] Step 1:

[0280] The user uses the terminal to take an image inside the oral cavity. A dedicated application is launched, and a guide showing an appropriate angle and distance at the time of shooting is displayed. The input is the state inside the user's oral cavity, and the output is the captured image data. This data is saved in the terminal for use in the next step.

[0281] Step 2:

[0282] The terminal uploads the captured image to the server. At this time, the user transmits the image data via data communication. The input is the image data saved in the user's terminal, and the output is the image data transferred to the server. The uploaded data is securely transmitted using a protocol with security measures (e.g., HTTPS).

[0283] Step 3:

[0284] The server prepares the received image data for analysis. The metadata of the image (shooting date and time, user ID) is extracted and saved in the database. The input is the raw image data received by the server, and the output is the image data saved in an analyzable format.

[0285] Step 4:

[0286] The server applies deep learning technology to analyze images. Specifically, it uses a convolutional neural network to identify tooth damage. The input is the image data prepared in step 3, and the output is the identification information of the damaged location. Here, each pixel is scanned to label the damage throughout the image.

[0287] Step 5:

[0288] The server calculates the number of days required for treatment and the predicted cost based on the analysis results. The input is the tooth damage identification information, and the output is the data of the number of treatment days and the predicted cost. Here, past treatment data is referred to, and statistically average values are calculated.

[0289] Step 6:

[0290] The server transmits the calculated result to the terminal. The input is the calculated treatment information, and the output is the diagnostic result received by the terminal. The data is transferred quickly and safely and is displayed on the user's terminal.

[0291] Step 7:

[0292] The terminal notifies the user of the received diagnostic result. The user can view the result within the app and can also make a reservation at a dental clinic if necessary. The input is the data of the diagnostic result sent from the server, and the output is the treatment plan information displayed to the user. Thus, the user can formulate an appropriate treatment plan.

[0293] (Application Example 1)

[0294] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0295] Conventional oral diagnostic systems present challenges because users often find it difficult to capture appropriate images themselves, and the information obtained is limited, making continuous oral care and timely treatment planning difficult. Furthermore, there is a lack of support systems for easily managing health at home.

[0296] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0297] In this invention, the server includes means for receiving images of the oral cavity captured by a terminal, means for analyzing the received images to identify tooth damage present in the images, and means for calculating the number of treatment days and estimated costs based on the identified damage. This makes it possible for a device installed in the home to provide guidance for capturing images of the user's oral cavity. This makes it possible to notify the user of the results based on the captured images audibly and visually, thereby supporting daily oral care.

[0298] A "terminal" is an electronic device used by a user that has the function of taking images of the inside of the mouth and connecting to a server via communication.

[0299] "Means of receiving" refers to the function of acquiring captured image data via a network and preparing it for processing.

[0300] "Methods for analyzing and identifying images" refers to algorithms that use machine learning models to identify tooth damage in received intraoral images.

[0301] The "means of calculation" refers to a function that uses statistical methods to calculate the number of days required for treatment and the estimated cost based on the identified injury.

[0302] "Means of transmission" refers to a function that uses a communication protocol to return the calculated result to the user's terminal and provide the user with information.

[0303] The "equipment installed in the home" refers to electronic equipment that is permanently installed in the user's living space and is used for physical condition management and diagnostic assistance.

[0304] The "means for providing guidance" refers to an interface for giving voice and visual instructions to guide the user to take an optimal image.

[0305] The "means for notifying audibly and visually" refers to a function of presenting information using a speaker or display in order to convey the analysis results and calculation results to the user.

[0306] This invention is a system for routinely checking the health condition of the oral cavity and assisting in the planning of necessary treatments. The system uses a terminal installed in the user's living space to take an image of the user's oral cavity and cooperate with a server on the cloud to derive a diagnosis result.

[0307] First, the user uses the camera of the terminal installed in the home to take an image of the oral cavity. Since appropriate guidance is provided, it is possible to take the image at an optimal angle and distance. The terminal transmits the captured image data to the cloud server through the receiving means.

[0308] Next, the server analyzes the received oral cavity image. For image analysis, a machine learning model using a deep learning framework such as TensorFlow or PyTorch is used to identify tooth damage. By this process, the position and progress of the damage can be specified with high accuracy. Based on the identification result, the server calculates the number of treatment days and the predicted cost using statistical methods.

[0309] The diagnosis result and the calculated information are returned to the terminal by the transmitting means and notified to the user audibly and visually. Based on this notification, the user can make a treatment plan. In addition, the terminal provides a reservation option for a dental clinic according to the user's wish, and the user can easily make a reservation.

[0310] For example, if a small cavity is detected in the upper right molar from an image taken by the user with their device, the system will notify the user that it can be treated in one visit and that the cost will be approximately 8,000 yen. Using this information, the user can set an appropriate treatment schedule and make an appointment.

[0311] An example of a prompt message is, "Please suggest improvements to the system that analyzes user-recorded images of the oral cavity and identifies even small areas of damage." This clarifies the overall embodiment of the invention and makes it possible to provide users with a more accessible health support environment.

[0312] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0313] Step 1:

[0314] The user takes images of their oral cavity using a device installed in their home. The device guides the user through voice and display to determine the appropriate shooting position and angle, resulting in high-quality image data. The input is an image of the oral cavity acquired through the camera, and the output is image data.

[0315] Step 2:

[0316] The terminal transmits the acquired intraoral image data to a server in the cloud. Internet communication protocols are used to connect to the server and transfer the data. The input is the image data acquired in the previous step, and the output is the image data transferred to the server.

[0317] Step 3:

[0318] The server analyzes the received image data. Deep learning models using TensorFlow or PyTorch are applied to the image analysis to identify tooth damage within the image. During this process, the deep learning model extracts image features and identifies the damage. The input is the transmitted image data, and the output is data regarding the location and progression of the damage.

[0319] Step 4:

[0320] The server calculates the number of treatment days and estimated costs based on identified injury data. Statistical methods using historical treatment data are employed to calculate average values ​​based on similar cases. Input is data on the specific location and progression of the injury, and output is the number of treatment days and estimated costs.

[0321] Step 5:

[0322] The server sends the calculated treatment information to the terminal. It then sends data to the terminal via a communication protocol, preparing to notify the user. The input is data on treatment days and costs, and the output is the diagnostic result data transferred to the terminal.

[0323] Step 6:

[0324] The terminal informs the user of the received diagnostic results. Using an audio output device and display, it visually and audibly notifies the results and, in some cases, suggests treatment appointment options. The input is the diagnostic result data sent from the server, and the output is the treatment information communicated to the user.

[0325] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0326] This invention is a system that efficiently diagnoses the condition of a user's oral cavity and provides a treatment plan based on the user's emotional state. This system is comprised of a camera and sensors installed in the user's terminal, a server in the cloud, and a communication infrastructure.

[0327] First, the device allows the user to take images of the inside of their mouth. A dedicated app displays instructions on the screen, and the user follows these instructions to take pictures of the inside of their mouth. At this time, the device uses a facial recognition sensor to collect data on the user's facial expressions. This allows the device to determine the user's emotional state in real time.

[0328] The captured images and facial expression data are transmitted to the server via an encrypted protocol. The server analyzes the received images using machine learning models, particularly convolutional neural networks, to identify tooth damage. Simultaneously, an emotion engine analyzes the facial expression data to assess the user's psychological state. This assessment is used to determine whether the user is anxious about the treatment.

[0329] Next, the server calculates the number of days required for treatment and the estimated cost based on the damage diagnosis. Furthermore, it adjusts and proposes an appropriate treatment plan and advice to the user based on their emotional state. For example, if a user is feeling anxious, it may consider providing more detailed explanations or comments to alleviate their anxiety.

[0330] The server sends this information back to the terminal, which then notifies the user of the diagnosis results and recommended actions. The notification includes not only a treatment plan but also emotionally-based comments, allowing the user to proceed to the next step with psychological reassurance. Furthermore, the terminal's built-in reservation function allows the user to make a dental appointment directly.

[0331] For example, if the system diagnoses a small cavity in the upper right molar from an image taken by the user, it predicts that the treatment will take one day and cost around 8,000 yen. Furthermore, if the emotion engine detects that the user is anxious, it will provide a comment such as, "The treatment will be quick, so please don't worry," to alleviate the user's anxiety.

[0332] Thus, the present invention goes beyond mere oral diagnosis, providing care that takes into account the user's psychological aspects, thereby improving the overall clinical experience.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] The user takes images of the inside of their mouth using the device's camera. Upon launching the dedicated application, a shooting guide is displayed, allowing the user to record the condition of their mouth at the appropriate angle.

[0336] Step 2:

[0337] The facial recognition sensor equipped in the device captures the user's facial expressions and acquires emotional data in real time. This data is sent to the server along with the captured intraoral image.

[0338] Step 3:

[0339] The server analyzes the received image data. It utilizes machine learning models to identify tooth damage within the images. This process involves using a convolutional neural network to determine the location and extent of the damage.

[0340] Step 4:

[0341] The server analyzes the facial expression data it receives using an emotion engine to evaluate the user's psychological state. This helps determine whether the user is experiencing anxiety or fear regarding the treatment.

[0342] Step 5:

[0343] The server calculates the treatment duration and estimated cost based on image analysis and emotion assessment results. Simultaneously, it prepares appropriate treatment plans and advice tailored to the user's emotional state.

[0344] Step 6:

[0345] The server sends the calculated treatment results and emotion-based advice to the device. The device receives this and notifies the user of the diagnosis, recommended treatment duration, cost, and emotion-based comments.

[0346] Step 7:

[0347] Users can review the provided diagnostic results and advice, and then use their device's functions to make a dental appointment. Since the appointment function is directly available within the app, users can proceed to the next step without any hassle.

[0348] (Example 2)

[0349] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0350] In modern society, there is a need for users to receive quick and accurate diagnoses of oral health problems, as well as medical plans that take their psychological state into consideration. However, conventional diagnostic systems have focused solely on the physical aspects of the problem and have failed to consider the user's emotions and psychological state. As a result, they have not been able to adequately provide users with appropriate treatment plans or a sense of psychological reassurance.

[0351] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0352] In this invention, the server includes means for receiving images of the oral cavity and user facial expressions captured by a terminal, means for analyzing the received images to identify tooth damage present in the images, and means for analyzing the received facial expressions to evaluate the user's emotional state. This makes it possible to provide a diagnosis based on tooth damage and a treatment plan that takes the user's emotional state into consideration.

[0353] A "terminal" is a device operated by the user, equipped with a camera and sensors, and capable of acquiring images of the inside of the mouth and facial expression data.

[0354] A "server" is a central processing system that receives aggregated data, performs analysis, generates diagnostic results and treatment plans, and transmits them to terminals.

[0355] An "intraoral image" is a digital image taken of the inside of the user's mouth, and is used to check the health of their teeth and gums.

[0356] "Facial expression information" refers to data that captures the movements and expressions of a user's face, and is used to evaluate their emotional state through analysis.

[0357] "Damage" refers to abnormalities or defects found in the teeth and gums in the oral cavity, and specifically includes signs of tooth decay and periodontal disease.

[0358] "Emotional state" refers to the state of a user's psychological and emotional responses, which are evaluated through the analysis of facial expression information.

[0359] A "generative AI model" is an algorithm or process used by artificial intelligence to analyze data and generate new information, and is particularly used in image recognition and sentiment analysis.

[0360] A "treatment plan" is a medical action plan developed based on the tooth damage and the user's emotional state, and includes appropriate treatment and preventive measures.

[0361] This invention is a system that efficiently diagnoses the condition of a user's oral cavity and provides a treatment plan based on the user's emotional state. The system primarily utilizes the user's terminal, a cloud-based server, and communication infrastructure. The terminal is equipped with a camera and facial recognition sensor, and a dedicated application is installed.

[0362] The device activates the camera for the user to take an image of the inside of their mouth, and the app displays instructions to guide the user. Furthermore, a facial recognition sensor collects the user's facial data in real time to determine the user's emotional state. This data is transmitted to the server via an encrypted protocol such as HTTPS.

[0363] The server analyzes images using machine learning models (particularly convolutional neural networks) to identify tooth damage. Simultaneously, an emotion engine analyzes facial expression data to assess the user's psychological state. This process is used to determine the user's anxiety about the dental treatment.

[0364] Based on the diagnosis, the server calculates the number of days required for treatment and the estimated cost, and further generates a treatment plan and advice that takes into account the user's emotional state. A generative AI model supports this entire process, creating specific comments and recommended actions for the user based on prompt messages. For example, a prompt message might be, "Analyze the intraoral images submitted by the user, identify possible damage, and propose an appropriate treatment plan based on emotional data."

[0365] The data generated in this way is then sent back to the device, which notifies the user of the diagnosis and recommended actions. The notification includes a comprehensive treatment plan and emotionally-based comments, allowing the user to proceed to the next step with psychological reassurance. Furthermore, the user can use the device's booking function to make an appointment with a dental clinic directly. This system goes beyond mere physical diagnosis, providing care that considers the user's psychological aspects and improving the overall treatment experience.

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] The user launches a dedicated app on their device and takes an image of their oral cavity. The device activates the camera and assists the user in taking the image at the correct angle and under appropriate lighting conditions by following the on-screen guide. This captured image becomes the input data for the next processing step.

[0369] Step 2:

[0370] The device uses a facial recognition sensor to collect user facial data. By analyzing facial expressions during and after shooting, and identifying movements such as eyebrows and corners of the mouth, it grasps the user's emotional state in real time. This emotional state is then input as data and used for subsequent processing.

[0371] Step 3:

[0372] The device transmits captured images of the oral cavity and collected facial expression data to the server via the HTTPS protocol. This communication is a crucial step in ensuring data security and allowing the server to receive accurate input data.

[0373] Step 4:

[0374] The server analyzes the received intraoral images using a convolutional neural network (CNN). It processes the images to extract prominent features and identify tooth damage. This analysis result is the output data used in the next step for damage diagnosis.

[0375] Step 5:

[0376] The server analyzes facial expression data using an emotion engine to evaluate the user's emotional state. Specifically, it extracts elements that form psychological states such as tension and anxiety, and the evaluation results become output data that influences the treatment plan for the next step.

[0377] Step 6:

[0378] Based on the diagnosis of the injury and the emotional assessment, the server calculates and generates a treatment plan that includes treatment duration, estimated costs, and emotional considerations. Using the generated AI model, prompt messages optimized for the user are constructed.

[0379] Step 7:

[0380] The server sends a calculated treatment plan and emotionally-based comments to the terminal. This information, along with the diagnosis results, is clearly displayed and notified to the user. Based on this information, the user can decide on their next action and arrange an appointment with a dental clinic using the terminal's booking function.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] It is difficult to not only efficiently diagnose a user's oral condition but also to provide a treatment plan that takes into account the user's emotional state. Existing systems only handle information about the physical condition of the oral cavity, so they cannot address the user's psychological anxieties or questions, and therefore cannot improve overall satisfaction.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes means for receiving images of the oral cavity captured by a terminal, means for analyzing the received images to identify tooth damage present in the images, and means for evaluating emotional state using facial expression data acquired from the terminal. This enables the provision of a detailed treatment plan based on the diagnosis and personalized advice based on emotions.

[0386] A "terminal" is an information processing device used by the user, which has the function of taking images of the inside of the mouth and acquiring facial expression data.

[0387] "Means for receiving images" refers to a function for receiving intraoral image data transmitted from a terminal.

[0388] "Means for identifying tooth damage" refers to technologies that analyze received intraoral image data to identify the condition and damage to teeth contained in the images.

[0389] "Means for calculating treatment duration and estimated costs" refers to a function that estimates the required treatment period and its cost based on the identified tooth damage.

[0390] "Methods for evaluating emotional states using facial expression data" refers to technologies that analyze facial expression data acquired from a device to determine the user's emotional state.

[0391] "Means for adapting treatment plans and advice" refers to functions that adjust treatment plans and advice according to the user's emotional state and propose them to the user.

[0392] A "server" is a central processing unit that receives data from terminals, performs analysis and calculations, and sends the results back to the terminals.

[0393] This system consists of terminals (information processing devices), servers (central processing units), and a communication infrastructure connecting these two. The specific operation of the system will now be explained.

[0394] First, the user takes an image of their oral cavity using the device. The device is equipped with a camera and an expression recognition sensor, which are used to acquire data on the user's oral cavity and facial expressions. This makes it possible to determine the user's emotional state in real time.

[0395] Next, the device sends the captured image and collected facial expression data to the server. The server receives this data and uses a machine learning model to analyze the image. In particular, it applies deep learning algorithms such as convolutional neural networks to identify tooth damage from images of the oral cavity.

[0396] Furthermore, the server uses facial recognition data to evaluate the user's emotional state. The emotion engine determines whether the user is feeling anxious or reassured, and this evaluation result is reflected in the treatment plan. Depending on the emotional state, the server provides reassuring comments and detailed explanations of the treatment plan to the user.

[0397] Finally, the server sends the analysis results and a treatment plan based on emotions back to the device. The device notifies the user of the diagnosis and recommended actions, and assists with booking a dental appointment if necessary.

[0398] As a concrete example, imagine a user launching the "Smart Dental Care Robot" app in the morning and performing an intraoral scan. At that time, the app assesses the user's emotional state and provides feedback such as, "You seem very relaxed this morning." This allows the user to proceed with greater confidence.

[0399] An example of a prompt generated by the AI ​​model is: "Describe a system in which a consumer robot scans a user's oral cavity and performs sentiment analysis."

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The user uses a device to acquire images of the inside of their mouth and facial expression data. The device's camera takes pictures of the inside of the mouth, and a facial expression recognition sensor captures the user's facial expressions. This results in obtaining images of the inside of the mouth and facial expression data as input data.

[0403] Step 2:

[0404] The terminal transmits the acquired intraoral images and facial expression data to the server. The image data and facial expression data are encrypted via a secure protocol to ensure data protection before transmission. The output of this step is the data that arrives on the server via the communication infrastructure.

[0405] Step 3:

[0406] The server receives images of the oral cavity, inputs them into a machine learning model, and performs analysis. In particular, a convolutional neural network is used to analyze the images and identify tooth damage and abnormalities. In this process, feature extraction and classification of the images are performed, and the output is a judgment of the dental health status.

[0407] Step 4:

[0408] The server inputs facial expression data into the emotion engine and analyzes the user's emotional state. Using an emotion recognition algorithm, it estimates the emotions the user is feeling from the facial expression data. The output of this process is an evaluation result indicating the emotional state.

[0409] Step 5:

[0410] The server generates appropriate treatment plans and advice based on the dental damage diagnosis and emotional assessment results. Comments and advice tailored to the emotional state are added, and the treatment content and procedures are adjusted accordingly. The output consists of treatment policies and advice information to be communicated to the user.

[0411] Step 6:

[0412] The generated treatment plan and advice are sent to the device. The server returns the calculation results and emotion-based feedback to the device. The output of this step is the notification information displayed to the user on the device.

[0413] Step 7:

[0414] Users can view treatment plans and advice from their devices and, if necessary, make appointments at dental clinics. Using the device's booking function, users can proceed directly to the next step. As a result, actions corresponding to the user's choices are output.

[0415] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0416] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0417] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0418] [Third Embodiment]

[0419] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0420] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0421] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0422] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0423] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0424] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0425] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0426] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0427] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0428] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0429] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0430] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0431] This invention relates to a system that allows a user to take images of their oral cavity, diagnose tooth damage from those images, and even predict the number of days and costs required for treatment. The system consists of a user terminal, a server, and a communication infrastructure.

[0432] First, the user takes pictures of their upper and lower teeth, left and right, using a smartphone or other device. A dedicated application provides guidance during the shooting process, ensuring that images are acquired at the appropriate angle and distance.

[0433] The device uploads the captured images to a server in the cloud. The uploaded images are received and processed on the server side and stored appropriately in the database.

[0434] The server performs image analysis on the received images using machine learning. It utilizes deep learning technologies such as convolutional neural networks (CNNs) to accurately identify tooth damage from the images. In this process, the server uses a model trained on a vast amount of historical data to determine the location and progression of the damage.

[0435] Based on the analysis results, the server calculates the number of days required for treatment and the estimated cost. This uses statistical methods based on historical data. For example, it estimates the average number of treatment days and cost from past treatment cases with similar levels of injury.

[0436] Next, the server sends the calculated results back to the terminal. The terminal has the function of notifying the user of the diagnosis results, estimated treatment duration, costs, etc., and the user can use this information to create a treatment plan.

[0437] Furthermore, if the user wishes, they can also make dental appointments directly from their device. This feature allows them to schedule appointments at appropriate times and manage their treatment schedule efficiently.

[0438] As a concrete example, suppose a user experiences pain in their upper right molar and takes an intraoral image. The system analyzes the image and diagnoses a small cavity in the upper right molar. The server calculates that one visit to the dentist is necessary for treatment, costing approximately 8,000 yen, and notifies the user of this information. By receiving this information, the user can understand the treatment plan in advance and make an appropriate appointment, enabling efficient and economical dental care.

[0439] Thus, the present invention can reduce the burden of dental visits by providing users with rapid and accurate oral diagnostic information and supporting the formulation of treatment plans.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The user takes images of the inside of their mouth using a device. A dedicated application is launched, and a shooting guide is displayed, allowing the user to acquire images at the appropriate angle and distance.

[0443] Step 2:

[0444] The device uploads the captured image to a server in the cloud. The image is securely transmitted to the server using an encrypted communication protocol.

[0445] Step 3:

[0446] The server analyzes the received images. Using machine learning models, particularly convolutional neural networks, it identifies tooth damage in the images and determines its location and progression.

[0447] Step 4:

[0448] The server calculates the number of days required for treatment and the estimated cost based on the image analysis results. It uses statistical methods to calculate the results by referring to a database of similar past cases.

[0449] Step 5:

[0450] The server sends the calculated results to the terminal. The results include information about the diagnosed injury, estimated treatment duration, and predicted cost.

[0451] Step 6:

[0452] The terminal receives the results from the server and notifies the user. The notification includes the diagnostic results and an option to schedule the next step.

[0453] Step 7:

[0454] Users can check notifications and, if necessary, make dental appointments on their devices. Users can set appointments for the most suitable date and time by following the application's guide.

[0455] (Example 1)

[0456] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0457] Traditional oral diagnoses required face-to-face consultations with specialists, resulting in lengthy diagnostic processes. Furthermore, accurately analyzing oral images to predict treatment needs and costs was difficult. Additionally, there was a lack of readily available means for users to easily perform diagnoses at home and obtain immediate results. Against this backdrop, there is a need for a system that supports faster and more accurate oral diagnosis and treatment planning.

[0458] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0459] In this invention, the server includes processing means for receiving images of the oral cavity captured by a terminal, processing means for analyzing the received images using deep learning technology to identify tooth damage present in the images, and processing means for calculating the number of treatment days and estimated costs based on the identified damage. This enables users to quickly and accurately diagnose the condition of their oral cavity at home and obtain predictions of the number of treatment days and costs required.

[0460] A "terminal" is a portable electronic device owned by a user that has the functionality to take pictures and communicate.

[0461] "Intraoral images" are photographic data that visually records the condition of the teeth, gums, and other tissues present in the user's oral cavity.

[0462] "Receiving processing means" refers to a function or device that allows an electronic device or server to receive data transmitted from an external source.

[0463] "Deep learning technology" is a machine learning technique that uses large amounts of data to train multi-layered neural networks and recognize complex patterns.

[0464] "Image analysis" is a technology that uses computers to process information within digital images and extract and recognize specific features or anomalies.

[0465] "Calculation processing means" refers to a function or device for calculating and deriving specific numerical values ​​or results based on input data.

[0466] "Treatment period" refers to the duration of time including the number of hospital visits and treatments required to treat a specific injury.

[0467] "Predicted cost" refers to the estimated financial cost of performing a particular treatment.

[0468] A "convolutional neural network" is a multi-layered machine learning model primarily used for analyzing image data, and it is a technology that excels in object recognition and pattern extraction.

[0469] "To be equipped with a function" means to have the processing power, equipment, or software necessary to achieve a specific purpose built in.

[0470] "Reservation options" refers to providing users with choices to select the date, time, and location that suits their convenience, allowing them to receive the service in advance.

[0471] This invention is a system that uses a user's device and a cloud-based server to capture and analyze images of the oral cavity, identify tooth damage, and predict the duration and cost of treatment. The user uses a portable device such as a smartphone or tablet. A dedicated application is installed on this device, allowing the user to easily capture images of their oral cavity using this application.

[0472] The device's application provides guidance to the user during shooting. The user uses the device's camera to capture images, and by following the app's guidance, they can take photos at the appropriate angle and distance. The captured images are uploaded to the server via data communication.

[0473] The server analyzes the received images using deep learning techniques based on convolutional neural networks (CNNs). This technique allows for highly accurate identification of tooth damage within the images, determining its location and progression. The server enables this analysis by using machine learning models trained on historical data.

[0474] Furthermore, the server calculates the number of days required for treatment and the estimated cost based on the analysis results, and sends this information back to the terminal. This calculation is based on statistical methods, and refers to the average number of days and costs derived from similar past cases.

[0475] As a concrete example, suppose a user experiences pain in their upper right molar and takes an intraoral image using a dedicated app. The server analyzes the image and diagnoses a small cavity in the upper right molar. Treatment requires one visit to the dentist, and the estimated cost is calculated to be approximately 8,000 yen. This information is then communicated to the user via their device. This allows the user to develop a more rapid and accurate treatment plan.

[0476] An example of a prompt to be input into the generating AI model is, "Explain how to analyze the oral cavity image, identify the tooth damage, and predict the necessary treatment duration and cost." Through this prompt, the system is able to quickly provide an appropriate diagnosis and treatment plan.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The user uses a device to take images of their oral cavity. A dedicated application launches and displays guides indicating the appropriate angle and distance for taking the images. The input is the state of the user's oral cavity, and the output is the captured image data. This data is stored on the device for use in the next step.

[0480] Step 2:

[0481] The device uploads the captured image to the server. During this process, the user transmits the image data via data communication. The input is the image data stored on the user's device, and the output is the image data transferred to the server. The uploaded data is transmitted securely using a secure protocol (e.g., HTTPS).

[0482] Step 3:

[0483] The server prepares the received image data for analysis. It extracts image metadata (such as the date and time of capture and user ID) and saves it to the database. The input is the raw image data received by the server, and the output is the image data saved in an analyzable format.

[0484] Step 4:

[0485] The server applies deep learning technology to analyze the image. Specifically, it uses a convolutional neural network to identify tooth damage. The input is the image data prepared in step 3, and the output is identification information of the damaged areas. Here, each pixel is scanned and the damage is labeled throughout the entire image.

[0486] Step 5:

[0487] The server calculates the number of days required for treatment and the estimated cost based on the analysis results. The input is tooth damage identification information, and the output is data on the number of treatment days and estimated cost. Here, historical treatment data is referenced to calculate statistically average values.

[0488] Step 6:

[0489] The server sends the calculated results to the terminal. The input is the calculated treatment information, and the output is the diagnostic result received on the terminal. The data is transferred quickly and securely and displayed on the user's terminal.

[0490] Step 7:

[0491] The device notifies the user of the received diagnostic results. The user can view the results within the app and, if necessary, make an appointment at a dental clinic. The input is the diagnostic result data sent from the server, and the output is treatment plan information displayed to the user. This allows the user to develop an appropriate treatment plan.

[0492] (Application Example 1)

[0493] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0494] Conventional oral diagnostic systems present challenges because users often find it difficult to capture appropriate images themselves, and the information obtained is limited, making continuous oral care and timely treatment planning difficult. Furthermore, there is a lack of support systems for easily managing health at home.

[0495] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0496] In this invention, the server includes means for receiving images of the oral cavity captured by a terminal, means for analyzing the received images to identify tooth damage present in the images, and means for calculating the number of treatment days and estimated costs based on the identified damage. This makes it possible for a device installed in the home to provide guidance for capturing images of the user's oral cavity. This makes it possible to notify the user of the results based on the captured images audibly and visually, thereby supporting daily oral care.

[0497] A "terminal" is an electronic device used by a user that has the function of taking images of the inside of the mouth and connecting to a server via communication.

[0498] "Means of receiving" refers to the function of acquiring captured image data via a network and preparing it for processing.

[0499] "Methods for analyzing and identifying images" refers to algorithms that use machine learning models to identify tooth damage in received intraoral images.

[0500] The "means of calculation" refers to a function that uses statistical methods to calculate the number of days required for treatment and the estimated cost based on the identified injury.

[0501] "Means of transmission" refers to a function that uses a communication protocol to return the calculated result to the user's terminal and provide the user with information.

[0502] "Devices installed in the home" refers to electronic devices that are permanently installed in the user's living space and are used for health management or diagnostic assistance.

[0503] "Means of providing guidance" refers to interfaces that provide audio and visual instructions to guide users in taking the best possible images.

[0504] "Means of notifying by sound and visual means" refers to functions that use speakers and displays to present information in order to communicate analysis results and calculation results to the user.

[0505] This invention is a system for routinely checking the health of the oral cavity and supporting the planning of necessary treatments. The system uses a terminal installed in the user's living space to capture images of the user's oral cavity and derives diagnostic results in conjunction with a server in the cloud.

[0506] First, the user uses the camera on a device installed in their home to take images of their oral cavity. Appropriate guidance is provided, enabling them to take images at the optimal angle and distance. The device then transmits the captured image data to a cloud server via a receiving device.

[0507] Next, the server analyzes the received intraoral images. Machine learning models using deep learning frameworks such as TensorFlow and PyTorch are employed for image analysis, thereby identifying tooth damage. This process allows for highly accurate identification of the location and progression of the damage. Based on the identification results, the server uses statistical methods to calculate the number of treatment days and the estimated cost.

[0508] The diagnostic results and calculated information are sent back to the terminal via a transmission method and notified to the user via voice and visual means. Based on this notification, the user can then develop a treatment plan. The terminal also offers a dental clinic appointment option upon request, allowing the user to easily make an appointment.

[0509] For example, if a small cavity is detected in the upper right molar from an image taken by the user with their device, the system will notify the user that it can be treated in one visit and that the cost will be approximately 8,000 yen. Using this information, the user can set an appropriate treatment schedule and make an appointment.

[0510] An example of a prompt message is, "Please suggest improvements to the system that analyzes user-recorded images of the oral cavity and identifies even small areas of damage." This clarifies the overall embodiment of the invention and makes it possible to provide users with a more accessible health support environment.

[0511] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0512] Step 1:

[0513] The user takes images of their oral cavity using a device installed in their home. The device guides the user through voice and display to determine the appropriate shooting position and angle, resulting in high-quality image data. The input is an image of the oral cavity acquired through the camera, and the output is image data.

[0514] Step 2:

[0515] The terminal transmits the acquired intraoral image data to a server in the cloud. Internet communication protocols are used to connect to the server and transfer the data. The input is the image data acquired in the previous step, and the output is the image data transferred to the server.

[0516] Step 3:

[0517] The server analyzes the received image data. Deep learning models using TensorFlow or PyTorch are applied to the image analysis to identify tooth damage within the image. During this process, the deep learning model extracts image features and identifies the damage. The input is the transmitted image data, and the output is data regarding the location and progression of the damage.

[0518] Step 4:

[0519] The server calculates the number of treatment days and estimated costs based on identified injury data. Statistical methods using historical treatment data are employed to calculate average values ​​based on similar cases. Input is data on the specific location and progression of the injury, and output is the number of treatment days and estimated costs.

[0520] Step 5:

[0521] The server sends the calculated treatment information to the terminal. It then sends data to the terminal via a communication protocol, preparing to notify the user. The input is data on treatment days and costs, and the output is the diagnostic result data transferred to the terminal.

[0522] Step 6:

[0523] The terminal informs the user of the received diagnostic results. Using an audio output device and display, it visually and audibly notifies the results and, in some cases, suggests treatment appointment options. The input is the diagnostic result data sent from the server, and the output is the treatment information communicated to the user.

[0524] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0525] This invention is a system that efficiently diagnoses the condition of a user's oral cavity and provides a treatment plan based on the user's emotional state. This system is comprised of a camera and sensors installed in the user's terminal, a server in the cloud, and a communication infrastructure.

[0526] First, the device allows the user to take images of the inside of their mouth. A dedicated app displays instructions on the screen, and the user follows these instructions to take pictures of the inside of their mouth. At this time, the device uses a facial recognition sensor to collect data on the user's facial expressions. This allows the device to determine the user's emotional state in real time.

[0527] The captured images and facial expression data are transmitted to the server via an encrypted protocol. The server analyzes the received images using machine learning models, particularly convolutional neural networks, to identify tooth damage. Simultaneously, an emotion engine analyzes the facial expression data to assess the user's psychological state. This assessment is used to determine whether the user is anxious about the treatment.

[0528] Next, the server calculates the number of days required for treatment and the estimated cost based on the damage diagnosis. Furthermore, it adjusts and proposes an appropriate treatment plan and advice to the user based on their emotional state. For example, if a user is feeling anxious, it may consider providing more detailed explanations or comments to alleviate their anxiety.

[0529] The server sends this information back to the terminal, which then notifies the user of the diagnosis results and recommended actions. The notification includes not only a treatment plan but also emotionally-based comments, allowing the user to proceed to the next step with psychological reassurance. Furthermore, the terminal's built-in reservation function allows the user to make a dental appointment directly.

[0530] For example, if the system diagnoses a small cavity in the upper right molar from an image taken by the user, it predicts that the treatment will take one day and cost around 8,000 yen. Furthermore, if the emotion engine detects that the user is anxious, it will provide a comment such as, "The treatment will be quick, so please don't worry," to alleviate the user's anxiety.

[0531] Thus, the present invention goes beyond mere oral diagnosis, providing care that takes into account the user's psychological aspects, thereby improving the overall clinical experience.

[0532] The following describes the processing flow.

[0533] Step 1:

[0534] The user takes images of the inside of their mouth using the device's camera. Upon launching the dedicated application, a shooting guide is displayed, allowing the user to record the condition of their mouth at the appropriate angle.

[0535] Step 2:

[0536] The facial recognition sensor equipped in the device captures the user's facial expressions and acquires emotional data in real time. This data is sent to the server along with the captured intraoral image.

[0537] Step 3:

[0538] The server analyzes the received image data. It utilizes machine learning models to identify tooth damage within the images. This process involves using a convolutional neural network to determine the location and extent of the damage.

[0539] Step 4:

[0540] The server analyzes the facial expression data it receives using an emotion engine to evaluate the user's psychological state. This helps determine whether the user is experiencing anxiety or fear regarding the treatment.

[0541] Step 5:

[0542] The server calculates the treatment duration and estimated cost based on image analysis and emotion assessment results. Simultaneously, it prepares appropriate treatment plans and advice tailored to the user's emotional state.

[0543] Step 6:

[0544] The server sends the calculated treatment results and emotion-based advice to the device. The device receives this and notifies the user of the diagnosis, recommended treatment duration, cost, and emotion-based comments.

[0545] Step 7:

[0546] Users can review the provided diagnostic results and advice, and then use their device's functions to make a dental appointment. Since the appointment function is directly available within the app, users can proceed to the next step without any hassle.

[0547] (Example 2)

[0548] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0549] In modern society, there is a need for users to receive quick and accurate diagnoses of oral health problems, as well as medical plans that take their psychological state into consideration. However, conventional diagnostic systems have focused solely on the physical aspects of the problem and have failed to consider the user's emotions and psychological state. As a result, they have not been able to adequately provide users with appropriate treatment plans or a sense of psychological reassurance.

[0550] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0551] In this invention, the server includes means for receiving images of the oral cavity and user facial expressions captured by a terminal, means for analyzing the received images to identify tooth damage present in the images, and means for analyzing the received facial expressions to evaluate the user's emotional state. This makes it possible to provide a diagnosis based on tooth damage and a treatment plan that takes the user's emotional state into consideration.

[0552] A "terminal" is a device operated by the user, equipped with a camera and sensors, and capable of acquiring images of the inside of the mouth and facial expression data.

[0553] A "server" is a central processing system that receives aggregated data, performs analysis, generates diagnostic results and treatment plans, and transmits them to terminals.

[0554] An "intraoral image" is a digital image taken of the inside of the user's mouth, and is used to check the health of their teeth and gums.

[0555] "Facial expression information" refers to data that captures the movements and expressions of a user's face, and is used to evaluate their emotional state through analysis.

[0556] "Damage" refers to abnormalities or defects found in the teeth and gums in the oral cavity, and specifically includes signs of tooth decay and periodontal disease.

[0557] "Emotional state" refers to the state of a user's psychological and emotional responses, which are evaluated through the analysis of facial expression information.

[0558] A "generative AI model" is an algorithm or process used by artificial intelligence to analyze data and generate new information, and is particularly used in image recognition and sentiment analysis.

[0559] A "treatment plan" is a medical action plan developed based on the tooth damage and the user's emotional state, and includes appropriate treatment and preventive measures.

[0560] This invention is a system that efficiently diagnoses the condition of a user's oral cavity and provides a treatment plan based on the user's emotional state. The system primarily utilizes the user's terminal, a cloud-based server, and communication infrastructure. The terminal is equipped with a camera and facial recognition sensor, and a dedicated application is installed.

[0561] The device activates the camera for the user to take an image of the inside of their mouth, and the app displays instructions to guide the user. Furthermore, a facial recognition sensor collects the user's facial data in real time to determine the user's emotional state. This data is transmitted to the server via an encrypted protocol such as HTTPS.

[0562] The server analyzes images using machine learning models (particularly convolutional neural networks) to identify tooth damage. Simultaneously, an emotion engine analyzes facial expression data to assess the user's psychological state. This process is used to determine the user's anxiety about the dental treatment.

[0563] Based on the diagnosis, the server calculates the number of days required for treatment and the estimated cost, and further generates a treatment plan and advice that takes into account the user's emotional state. A generative AI model supports this entire process, creating specific comments and recommended actions for the user based on prompt messages. For example, a prompt message might be, "Analyze the intraoral images submitted by the user, identify possible damage, and propose an appropriate treatment plan based on emotional data."

[0564] The data generated in this way is then sent back to the device, which notifies the user of the diagnosis and recommended actions. The notification includes a comprehensive treatment plan and emotionally-based comments, allowing the user to proceed to the next step with psychological reassurance. Furthermore, the user can use the device's booking function to make an appointment with a dental clinic directly. This system goes beyond mere physical diagnosis, providing care that considers the user's psychological aspects and improving the overall treatment experience.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The user launches a dedicated app on their device and takes an image of their oral cavity. The device activates the camera and assists the user in taking the image at the correct angle and under appropriate lighting conditions by following the on-screen guide. This captured image becomes the input data for the next processing step.

[0568] Step 2:

[0569] The device uses a facial recognition sensor to collect user facial data. By analyzing facial expressions during and after shooting, and identifying movements such as eyebrows and corners of the mouth, it grasps the user's emotional state in real time. This emotional state is then input as data and used for subsequent processing.

[0570] Step 3:

[0571] The device transmits captured images of the oral cavity and collected facial expression data to the server via the HTTPS protocol. This communication is a crucial step in ensuring data security and allowing the server to receive accurate input data.

[0572] Step 4:

[0573] The server analyzes the received intraoral images using a convolutional neural network (CNN). It processes the images to extract prominent features and identify tooth damage. This analysis result is the output data used in the next step for damage diagnosis.

[0574] Step 5:

[0575] The server analyzes facial expression data using an emotion engine to evaluate the user's emotional state. Specifically, it extracts elements that form psychological states such as tension and anxiety, and the evaluation results become output data that influences the treatment plan for the next step.

[0576] Step 6:

[0577] Based on the diagnosis of the injury and the emotional assessment, the server calculates and generates a treatment plan that includes treatment duration, estimated costs, and emotional considerations. Using the generated AI model, prompt messages optimized for the user are constructed.

[0578] Step 7:

[0579] The server sends a calculated treatment plan and emotionally-based comments to the terminal. This information, along with the diagnosis results, is clearly displayed and notified to the user. Based on this information, the user can decide on their next action and arrange an appointment with a dental clinic using the terminal's booking function.

[0580] (Application Example 2)

[0581] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0582] It is difficult to not only efficiently diagnose a user's oral condition but also to provide a treatment plan that takes into account the user's emotional state. Existing systems only handle information about the physical condition of the oral cavity, so they cannot address the user's psychological anxieties or questions, and therefore cannot improve overall satisfaction.

[0583] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0584] In this invention, the server includes means for receiving images of the oral cavity captured by a terminal, means for analyzing the received images to identify tooth damage present in the images, and means for evaluating emotional state using facial expression data acquired from the terminal. This enables the provision of a detailed treatment plan based on the diagnosis and personalized advice based on emotions.

[0585] A "terminal" is an information processing device used by the user, which has the function of taking images of the inside of the mouth and acquiring facial expression data.

[0586] "Means for receiving images" refers to a function for receiving intraoral image data transmitted from a terminal.

[0587] "Means for identifying tooth damage" refers to technologies that analyze received intraoral image data to identify the condition and damage to teeth contained in the images.

[0588] "Means for calculating treatment duration and estimated costs" refers to a function that estimates the required treatment period and its cost based on the identified tooth damage.

[0589] "Methods for evaluating emotional states using facial expression data" refers to technologies that analyze facial expression data acquired from a device to determine the user's emotional state.

[0590] "Means for adapting treatment plans and advice" refers to functions that adjust treatment plans and advice according to the user's emotional state and propose them to the user.

[0591] A "server" is a central processing unit that receives data from terminals, performs analysis and calculations, and sends the results back to the terminals.

[0592] This system consists of terminals (information processing devices), servers (central processing units), and a communication infrastructure connecting these two. The specific operation of the system will now be explained.

[0593] First, the user takes an image of their oral cavity using the device. The device is equipped with a camera and an expression recognition sensor, which are used to acquire data on the user's oral cavity and facial expressions. This makes it possible to determine the user's emotional state in real time.

[0594] Next, the device sends the captured image and collected facial expression data to the server. The server receives this data and uses a machine learning model to analyze the image. In particular, it applies deep learning algorithms such as convolutional neural networks to identify tooth damage from images of the oral cavity.

[0595] Furthermore, the server uses facial recognition data to evaluate the user's emotional state. The emotion engine determines whether the user is feeling anxious or reassured, and this evaluation result is reflected in the treatment plan. Depending on the emotional state, the server provides reassuring comments and detailed explanations of the treatment plan to the user.

[0596] Finally, the server sends the analysis results and a treatment plan based on emotions back to the device. The device notifies the user of the diagnosis and recommended actions, and assists with booking a dental appointment if necessary.

[0597] As a concrete example, imagine a user launching the "Smart Dental Care Robot" app in the morning and performing an intraoral scan. At that time, the app assesses the user's emotional state and provides feedback such as, "You seem very relaxed this morning." This allows the user to proceed with greater confidence.

[0598] An example of a prompt generated by the AI ​​model is: "Describe a system in which a consumer robot scans a user's oral cavity and performs sentiment analysis."

[0599] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0600] Step 1:

[0601] The user uses a device to acquire images of the inside of their mouth and facial expression data. The device's camera takes pictures of the inside of the mouth, and a facial expression recognition sensor captures the user's facial expressions. This results in obtaining images of the inside of the mouth and facial expression data as input data.

[0602] Step 2:

[0603] The terminal transmits the acquired intraoral images and facial expression data to the server. The image data and facial expression data are encrypted via a secure protocol to ensure data protection before transmission. The output of this step is the data that arrives on the server via the communication infrastructure.

[0604] Step 3:

[0605] The server receives images of the oral cavity, inputs them into a machine learning model, and performs analysis. In particular, a convolutional neural network is used to analyze the images and identify tooth damage and abnormalities. In this process, feature extraction and classification of the images are performed, and the output is a judgment of the dental health status.

[0606] Step 4:

[0607] The server inputs facial expression data into the emotion engine and analyzes the user's emotional state. Using an emotion recognition algorithm, it estimates the emotions the user is feeling from the facial expression data. The output of this process is an evaluation result indicating the emotional state.

[0608] Step 5:

[0609] The server generates appropriate treatment plans and advice based on the dental damage diagnosis and emotional assessment results. Comments and advice tailored to the emotional state are added, and the treatment content and procedures are adjusted accordingly. The output consists of treatment policies and advice information to be communicated to the user.

[0610] Step 6:

[0611] The generated treatment plan and advice are sent to the device. The server returns the calculation results and emotion-based feedback to the device. The output of this step is the notification information displayed to the user on the device.

[0612] Step 7:

[0613] Users can view treatment plans and advice from their devices and, if necessary, make appointments at dental clinics. Using the device's booking function, users can proceed directly to the next step. As a result, actions corresponding to the user's choices are output.

[0614] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0615] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0616] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0617] [Fourth Embodiment]

[0618] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0619] As shown in Figure 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.

[0620] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0621] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0622] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0623] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0624] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0625] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0626] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0627] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0628] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0629] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0630] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0631] This invention relates to a system that allows a user to take images of their oral cavity, diagnose tooth damage from those images, and even predict the number of days and costs required for treatment. The system consists of a user terminal, a server, and a communication infrastructure.

[0632] First, the user takes pictures of their upper and lower teeth, left and right, using a smartphone or other device. A dedicated application provides guidance during the shooting process, ensuring that images are acquired at the appropriate angle and distance.

[0633] The device uploads the captured images to a server in the cloud. The uploaded images are received and processed on the server side and stored appropriately in the database.

[0634] The server performs image analysis on the received images using machine learning. It utilizes deep learning technologies such as convolutional neural networks (CNNs) to accurately identify tooth damage from the images. In this process, the server uses a model trained on a vast amount of historical data to determine the location and progression of the damage.

[0635] Based on the analysis results, the server calculates the number of days required for treatment and the estimated cost. This uses statistical methods based on historical data. For example, it estimates the average number of treatment days and cost from past treatment cases with similar levels of injury.

[0636] Next, the server sends the calculated results back to the terminal. The terminal has the function of notifying the user of the diagnosis results, estimated treatment duration, costs, etc., and the user can use this information to create a treatment plan.

[0637] Furthermore, if the user wishes, they can also make dental appointments directly from their device. This feature allows them to schedule appointments at appropriate times and manage their treatment schedule efficiently.

[0638] As a concrete example, suppose a user experiences pain in their upper right molar and takes an intraoral image. The system analyzes the image and diagnoses a small cavity in the upper right molar. The server calculates that one visit to the dentist is necessary for treatment, costing approximately 8,000 yen, and notifies the user of this information. By receiving this information, the user can understand the treatment plan in advance and make an appropriate appointment, enabling efficient and economical dental care.

[0639] Thus, the present invention can reduce the burden of dental visits by providing users with rapid and accurate oral diagnostic information and supporting the formulation of treatment plans.

[0640] The following describes the processing flow.

[0641] Step 1:

[0642] The user takes images of the inside of their mouth using a device. A dedicated application is launched, and a shooting guide is displayed, allowing the user to acquire images at the appropriate angle and distance.

[0643] Step 2:

[0644] The device uploads the captured image to a server in the cloud. The image is securely transmitted to the server using an encrypted communication protocol.

[0645] Step 3:

[0646] The server analyzes the received images. Using machine learning models, particularly convolutional neural networks, it identifies tooth damage in the images and determines its location and progression.

[0647] Step 4:

[0648] The server calculates the number of days required for treatment and the estimated cost based on the image analysis results. It uses statistical methods to calculate the results by referring to a database of similar past cases.

[0649] Step 5:

[0650] The server sends the calculated results to the terminal. The results include information about the diagnosed injury, estimated treatment duration, and predicted cost.

[0651] Step 6:

[0652] The terminal receives the results from the server and notifies the user. The notification includes the diagnostic results and an option to schedule the next step.

[0653] Step 7:

[0654] Users can check notifications and, if necessary, make dental appointments on their devices. Users can set appointments for the most suitable date and time by following the application's guide.

[0655] (Example 1)

[0656] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0657] Traditional oral diagnoses required face-to-face consultations with specialists, resulting in lengthy diagnostic processes. Furthermore, accurately analyzing oral images to predict treatment needs and costs was difficult. Additionally, there was a lack of readily available means for users to easily perform diagnoses at home and obtain immediate results. Against this backdrop, there is a need for a system that supports faster and more accurate oral diagnosis and treatment planning.

[0658] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0659] In this invention, the server includes processing means for receiving images of the oral cavity captured by a terminal, processing means for analyzing the received images using deep learning technology to identify tooth damage present in the images, and processing means for calculating the number of treatment days and estimated costs based on the identified damage. This enables users to quickly and accurately diagnose the condition of their oral cavity at home and obtain predictions of the number of treatment days and costs required.

[0660] A "terminal" is a portable electronic device owned by a user that has the functionality to take pictures and communicate.

[0661] "Intraoral images" are photographic data that visually records the condition of the teeth, gums, and other tissues present in the user's oral cavity.

[0662] "Receiving processing means" refers to a function or device that allows an electronic device or server to receive data transmitted from an external source.

[0663] "Deep learning technology" is a machine learning technique that uses large amounts of data to train multi-layered neural networks and recognize complex patterns.

[0664] "Image analysis" is a technology that uses computers to process information within digital images and extract and recognize specific features or anomalies.

[0665] "Calculation processing means" refers to a function or device for calculating and deriving specific numerical values ​​or results based on input data.

[0666] "Treatment period" refers to the duration of time including the number of hospital visits and treatments required to treat a specific injury.

[0667] "Predicted cost" refers to the estimated financial cost of performing a particular treatment.

[0668] A "convolutional neural network" is a multi-layered machine learning model primarily used for analyzing image data, and it is a technology that excels in object recognition and pattern extraction.

[0669] "To be equipped with a function" means to have the processing power, equipment, or software necessary to achieve a specific purpose built in.

[0670] "Reservation options" refers to providing users with choices to select the date, time, and location that suits their convenience, allowing them to receive the service in advance.

[0671] This invention is a system that uses a user's device and a cloud-based server to capture and analyze images of the oral cavity, identify tooth damage, and predict the duration and cost of treatment. The user uses a portable device such as a smartphone or tablet. A dedicated application is installed on this device, allowing the user to easily capture images of their oral cavity using this application.

[0672] The device's application provides guidance to the user during shooting. The user uses the device's camera to capture images, and by following the app's guidance, they can take photos at the appropriate angle and distance. The captured images are uploaded to the server via data communication.

[0673] The server analyzes the received images using deep learning techniques based on convolutional neural networks (CNNs). This technique allows for highly accurate identification of tooth damage within the images, determining its location and progression. The server enables this analysis by using machine learning models trained on historical data.

[0674] Furthermore, the server calculates the number of days required for treatment and the estimated cost based on the analysis results, and sends this information back to the terminal. This calculation is based on statistical methods, and refers to the average number of days and costs derived from similar past cases.

[0675] As a concrete example, suppose a user experiences pain in their upper right molar and takes an intraoral image using a dedicated app. The server analyzes the image and diagnoses a small cavity in the upper right molar. Treatment requires one visit to the dentist, and the estimated cost is calculated to be approximately 8,000 yen. This information is then communicated to the user via their device. This allows the user to develop a more rapid and accurate treatment plan.

[0676] An example of a prompt to be input into the generating AI model is, "Explain how to analyze the oral cavity image, identify the tooth damage, and predict the necessary treatment duration and cost." Through this prompt, the system is able to quickly provide an appropriate diagnosis and treatment plan.

[0677] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0678] Step 1:

[0679] The user uses a device to take images of their oral cavity. A dedicated application launches and displays guides indicating the appropriate angle and distance for taking the images. The input is the state of the user's oral cavity, and the output is the captured image data. This data is stored on the device for use in the next step.

[0680] Step 2:

[0681] The device uploads the captured image to the server. During this process, the user transmits the image data via data communication. The input is the image data stored on the user's device, and the output is the image data transferred to the server. The uploaded data is transmitted securely using a secure protocol (e.g., HTTPS).

[0682] Step 3:

[0683] The server prepares the received image data for analysis. It extracts image metadata (such as the date and time of capture and user ID) and saves it to the database. The input is the raw image data received by the server, and the output is the image data saved in an analyzable format.

[0684] Step 4:

[0685] The server applies deep learning technology to analyze the image. Specifically, it uses a convolutional neural network to identify tooth damage. The input is the image data prepared in step 3, and the output is identification information of the damaged areas. Here, each pixel is scanned and the damage is labeled throughout the entire image.

[0686] Step 5:

[0687] The server calculates the number of days required for treatment and the estimated cost based on the analysis results. The input is tooth damage identification information, and the output is data on the number of treatment days and estimated cost. Here, historical treatment data is referenced to calculate statistically average values.

[0688] Step 6:

[0689] The server sends the calculated results to the terminal. The input is the calculated treatment information, and the output is the diagnostic result received on the terminal. The data is transferred quickly and securely and displayed on the user's terminal.

[0690] Step 7:

[0691] The device notifies the user of the received diagnostic results. The user can view the results within the app and, if necessary, make an appointment at a dental clinic. The input is the diagnostic result data sent from the server, and the output is treatment plan information displayed to the user. This allows the user to develop an appropriate treatment plan.

[0692] (Application Example 1)

[0693] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0694] Conventional oral diagnostic systems present challenges because users often find it difficult to capture appropriate images themselves, and the information obtained is limited, making continuous oral care and timely treatment planning difficult. Furthermore, there is a lack of support systems for easily managing health at home.

[0695] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0696] In this invention, the server includes means for receiving images of the oral cavity captured by a terminal, means for analyzing the received images to identify tooth damage present in the images, and means for calculating the number of treatment days and estimated costs based on the identified damage. This makes it possible for a device installed in the home to provide guidance for capturing images of the user's oral cavity. This makes it possible to notify the user of the results based on the captured images audibly and visually, thereby supporting daily oral care.

[0697] A "terminal" is an electronic device used by a user that has the function of taking images of the inside of the mouth and connecting to a server via communication.

[0698] "Means of receiving" refers to the function of acquiring captured image data via a network and preparing it for processing.

[0699] "Methods for analyzing and identifying images" refers to algorithms that use machine learning models to identify tooth damage in received intraoral images.

[0700] The "means of calculation" refers to a function that uses statistical methods to calculate the number of days required for treatment and the estimated cost based on the identified injury.

[0701] "Means of transmission" refers to a function that uses a communication protocol to return the calculated result to the user's terminal and provide the user with information.

[0702] "Devices installed in the home" refers to electronic devices that are permanently installed in the user's living space and are used for health management or diagnostic assistance.

[0703] "Means of providing guidance" refers to interfaces that provide audio and visual instructions to guide users in taking the best possible images.

[0704] "Means of notifying by sound and visual means" refers to functions that use speakers and displays to present information in order to communicate analysis results and calculation results to the user.

[0705] This invention is a system for routinely checking the health of the oral cavity and supporting the planning of necessary treatments. The system uses a terminal installed in the user's living space to capture images of the user's oral cavity and derives diagnostic results in conjunction with a server in the cloud.

[0706] First, the user uses the camera on a device installed in their home to take images of their oral cavity. Appropriate guidance is provided, enabling them to take images at the optimal angle and distance. The device then transmits the captured image data to a cloud server via a receiving device.

[0707] Next, the server analyzes the received intraoral images. Machine learning models using deep learning frameworks such as TensorFlow and PyTorch are employed for image analysis, thereby identifying tooth damage. This process allows for highly accurate identification of the location and progression of the damage. Based on the identification results, the server uses statistical methods to calculate the number of treatment days and the estimated cost.

[0708] The diagnostic results and calculated information are sent back to the terminal via a transmission method and notified to the user via voice and visual means. Based on this notification, the user can then develop a treatment plan. The terminal also offers a dental clinic appointment option upon request, allowing the user to easily make an appointment.

[0709] For example, if a small cavity is detected in the upper right molar from an image taken by the user with their device, the system will notify the user that it can be treated in one visit and that the cost will be approximately 8,000 yen. Using this information, the user can set an appropriate treatment schedule and make an appointment.

[0710] An example of a prompt message is, "Please suggest improvements to the system that analyzes user-recorded images of the oral cavity and identifies even small areas of damage." This clarifies the overall embodiment of the invention and makes it possible to provide users with a more accessible health support environment.

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The user takes images of their oral cavity using a device installed in their home. The device guides the user through voice and display to determine the appropriate shooting position and angle, resulting in high-quality image data. The input is an image of the oral cavity acquired through the camera, and the output is image data.

[0714] Step 2:

[0715] The terminal transmits the acquired intraoral image data to a server in the cloud. Internet communication protocols are used to connect to the server and transfer the data. The input is the image data acquired in the previous step, and the output is the image data transferred to the server.

[0716] Step 3:

[0717] The server analyzes the received image data. Deep learning models using TensorFlow or PyTorch are applied to the image analysis to identify tooth damage within the image. During this process, the deep learning model extracts image features and identifies the damage. The input is the transmitted image data, and the output is data regarding the location and progression of the damage.

[0718] Step 4:

[0719] The server calculates the number of treatment days and estimated costs based on identified injury data. Statistical methods using historical treatment data are employed to calculate average values ​​based on similar cases. Input is data on the specific location and progression of the injury, and output is the number of treatment days and estimated costs.

[0720] Step 5:

[0721] The server sends the calculated treatment information to the terminal. It then sends data to the terminal via a communication protocol, preparing to notify the user. The input is data on treatment days and costs, and the output is the diagnostic result data transferred to the terminal.

[0722] Step 6:

[0723] The terminal informs the user of the received diagnostic results. Using an audio output device and display, it visually and audibly notifies the results and, in some cases, suggests treatment appointment options. The input is the diagnostic result data sent from the server, and the output is the treatment information communicated to the user.

[0724] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0725] This invention is a system that efficiently diagnoses the condition of a user's oral cavity and provides a treatment plan based on the user's emotional state. This system is comprised of a camera and sensors installed in the user's terminal, a server in the cloud, and a communication infrastructure.

[0726] First, the device allows the user to take images of the inside of their mouth. A dedicated app displays instructions on the screen, and the user follows these instructions to take pictures of the inside of their mouth. At this time, the device uses a facial recognition sensor to collect data on the user's facial expressions. This allows the device to determine the user's emotional state in real time.

[0727] The captured images and facial expression data are transmitted to the server via an encrypted protocol. The server analyzes the received images using machine learning models, particularly convolutional neural networks, to identify tooth damage. Simultaneously, an emotion engine analyzes the facial expression data to assess the user's psychological state. This assessment is used to determine whether the user is anxious about the treatment.

[0728] Next, the server calculates the number of days required for treatment and the estimated cost based on the damage diagnosis. Furthermore, it adjusts and proposes an appropriate treatment plan and advice to the user based on their emotional state. For example, if a user is feeling anxious, it may consider providing more detailed explanations or comments to alleviate their anxiety.

[0729] The server sends this information back to the terminal, which then notifies the user of the diagnosis results and recommended actions. The notification includes not only a treatment plan but also emotionally-based comments, allowing the user to proceed to the next step with psychological reassurance. Furthermore, the terminal's built-in reservation function allows the user to make a dental appointment directly.

[0730] For example, if the system diagnoses a small cavity in the upper right molar from an image taken by the user, it predicts that the treatment will take one day and cost around 8,000 yen. Furthermore, if the emotion engine detects that the user is anxious, it will provide a comment such as, "The treatment will be quick, so please don't worry," to alleviate the user's anxiety.

[0731] Thus, the present invention goes beyond mere oral diagnosis, providing care that takes into account the user's psychological aspects, thereby improving the overall clinical experience.

[0732] The following describes the processing flow.

[0733] Step 1:

[0734] The user takes images of the inside of their mouth using the device's camera. Upon launching the dedicated application, a shooting guide is displayed, allowing the user to record the condition of their mouth at the appropriate angle.

[0735] Step 2:

[0736] The facial recognition sensor equipped in the device captures the user's facial expressions and acquires emotional data in real time. This data is sent to the server along with the captured intraoral image.

[0737] Step 3:

[0738] The server analyzes the received image data. It utilizes machine learning models to identify tooth damage within the images. This process involves using a convolutional neural network to determine the location and extent of the damage.

[0739] Step 4:

[0740] The server analyzes the facial expression data it receives using an emotion engine to evaluate the user's psychological state. This helps determine whether the user is experiencing anxiety or fear regarding the treatment.

[0741] Step 5:

[0742] The server calculates the treatment duration and estimated cost based on image analysis and emotion assessment results. Simultaneously, it prepares appropriate treatment plans and advice tailored to the user's emotional state.

[0743] Step 6:

[0744] The server sends the calculated treatment results and emotion-based advice to the device. The device receives this and notifies the user of the diagnosis, recommended treatment duration, cost, and emotion-based comments.

[0745] Step 7:

[0746] Users can review the provided diagnostic results and advice, and then use their device's functions to make a dental appointment. Since the appointment function is directly available within the app, users can proceed to the next step without any hassle.

[0747] (Example 2)

[0748] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0749] In modern society, there is a need for users to receive quick and accurate diagnoses of oral health problems, as well as medical plans that take their psychological state into consideration. However, conventional diagnostic systems have focused solely on the physical aspects of the problem and have failed to consider the user's emotions and psychological state. As a result, they have not been able to adequately provide users with appropriate treatment plans or a sense of psychological reassurance.

[0750] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0751] In this invention, the server includes means for receiving images of the oral cavity and user facial expressions captured by a terminal, means for analyzing the received images to identify tooth damage present in the images, and means for analyzing the received facial expressions to evaluate the user's emotional state. This makes it possible to provide a diagnosis based on tooth damage and a treatment plan that takes the user's emotional state into consideration.

[0752] A "terminal" is a device operated by the user, equipped with a camera and sensors, and capable of acquiring images of the inside of the mouth and facial expression data.

[0753] A "server" is a central processing system that receives aggregated data, performs analysis, generates diagnostic results and treatment plans, and transmits them to terminals.

[0754] An "intraoral image" is a digital image taken of the inside of the user's mouth, and is used to check the health of their teeth and gums.

[0755] "Facial expression information" refers to data that captures the movements and expressions of a user's face, and is used to evaluate their emotional state through analysis.

[0756] "Damage" refers to abnormalities or defects found in the teeth and gums in the oral cavity, and specifically includes signs of tooth decay and periodontal disease.

[0757] "Emotional state" refers to the state of a user's psychological and emotional responses, which are evaluated through the analysis of facial expression information.

[0758] A "generative AI model" is an algorithm or process used by artificial intelligence to analyze data and generate new information, and is particularly used in image recognition and sentiment analysis.

[0759] A "treatment plan" is a medical action plan developed based on the tooth damage and the user's emotional state, and includes appropriate treatment and preventive measures.

[0760] This invention is a system that efficiently diagnoses the condition of a user's oral cavity and provides a treatment plan based on the user's emotional state. The system primarily utilizes the user's terminal, a cloud-based server, and communication infrastructure. The terminal is equipped with a camera and facial recognition sensor, and a dedicated application is installed.

[0761] The device activates the camera for the user to take an image of the inside of their mouth, and the app displays instructions to guide the user. Furthermore, a facial recognition sensor collects the user's facial data in real time to determine the user's emotional state. This data is transmitted to the server via an encrypted protocol such as HTTPS.

[0762] The server analyzes images using machine learning models (particularly convolutional neural networks) to identify tooth damage. Simultaneously, an emotion engine analyzes facial expression data to assess the user's psychological state. This process is used to determine the user's anxiety about the dental treatment.

[0763] Based on the diagnosis, the server calculates the number of days required for treatment and the estimated cost, and further generates a treatment plan and advice that takes into account the user's emotional state. A generative AI model supports this entire process, creating specific comments and recommended actions for the user based on prompt messages. For example, a prompt message might be, "Analyze the intraoral images submitted by the user, identify possible damage, and propose an appropriate treatment plan based on emotional data."

[0764] The data generated in this way is then sent back to the device, which notifies the user of the diagnosis and recommended actions. The notification includes a comprehensive treatment plan and emotionally-based comments, allowing the user to proceed to the next step with psychological reassurance. Furthermore, the user can use the device's booking function to make an appointment with a dental clinic directly. This system goes beyond mere physical diagnosis, providing care that considers the user's psychological aspects and improving the overall treatment experience.

[0765] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0766] Step 1:

[0767] The user launches a dedicated app on their device and takes an image of their oral cavity. The device activates the camera and assists the user in taking the image at the correct angle and under appropriate lighting conditions by following the on-screen guide. This captured image becomes the input data for the next processing step.

[0768] Step 2:

[0769] The device uses a facial recognition sensor to collect user facial data. By analyzing facial expressions during and after shooting, and identifying movements such as eyebrows and corners of the mouth, it grasps the user's emotional state in real time. This emotional state is then input as data and used for subsequent processing.

[0770] Step 3:

[0771] The device transmits captured images of the oral cavity and collected facial expression data to the server via the HTTPS protocol. This communication is a crucial step in ensuring data security and allowing the server to receive accurate input data.

[0772] Step 4:

[0773] The server analyzes the received intraoral images using a convolutional neural network (CNN). It processes the images to extract prominent features and identify tooth damage. This analysis result is the output data used in the next step for damage diagnosis.

[0774] Step 5:

[0775] The server analyzes facial expression data using an emotion engine to evaluate the user's emotional state. Specifically, it extracts elements that form psychological states such as tension and anxiety, and the evaluation results become output data that influences the treatment plan for the next step.

[0776] Step 6:

[0777] Based on the diagnosis of the injury and the emotional assessment, the server calculates and generates a treatment plan that includes treatment duration, estimated costs, and emotional considerations. Using the generated AI model, prompt messages optimized for the user are constructed.

[0778] Step 7:

[0779] The server sends a calculated treatment plan and emotionally-based comments to the terminal. This information, along with the diagnosis results, is clearly displayed and notified to the user. Based on this information, the user can decide on their next action and arrange an appointment with a dental clinic using the terminal's booking function.

[0780] (Application Example 2)

[0781] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0782] It is difficult to not only efficiently diagnose a user's oral condition but also to provide a treatment plan that takes into account the user's emotional state. Existing systems only handle information about the physical condition of the oral cavity, so they cannot address the user's psychological anxieties or questions, and therefore cannot improve overall satisfaction.

[0783] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0784] In this invention, the server includes means for receiving images of the oral cavity captured by a terminal, means for analyzing the received images to identify tooth damage present in the images, and means for evaluating emotional state using facial expression data acquired from the terminal. This enables the provision of a detailed treatment plan based on the diagnosis and personalized advice based on emotions.

[0785] A "terminal" is an information processing device used by the user, which has the function of taking images of the inside of the mouth and acquiring facial expression data.

[0786] "Means for receiving images" refers to a function for receiving intraoral image data transmitted from a terminal.

[0787] "Means for identifying tooth damage" refers to technologies that analyze received intraoral image data to identify the condition and damage to teeth contained in the images.

[0788] "Means for calculating treatment duration and estimated costs" refers to a function that estimates the required treatment period and its cost based on the identified tooth damage.

[0789] "Methods for evaluating emotional states using facial expression data" refers to technologies that analyze facial expression data acquired from a device to determine the user's emotional state.

[0790] "Means for adapting treatment plans and advice" refers to functions that adjust treatment plans and advice according to the user's emotional state and propose them to the user.

[0791] A "server" is a central processing unit that receives data from terminals, performs analysis and calculations, and sends the results back to the terminals.

[0792] This system consists of terminals (information processing devices), servers (central processing units), and a communication infrastructure connecting these two. The specific operation of the system will now be explained.

[0793] First, the user takes an image of their oral cavity using the device. The device is equipped with a camera and an expression recognition sensor, which are used to acquire data on the user's oral cavity and facial expressions. This makes it possible to determine the user's emotional state in real time.

[0794] Next, the device sends the captured image and collected facial expression data to the server. The server receives this data and uses a machine learning model to analyze the image. In particular, it applies deep learning algorithms such as convolutional neural networks to identify tooth damage from images of the oral cavity.

[0795] Furthermore, the server uses facial recognition data to evaluate the user's emotional state. The emotion engine determines whether the user is feeling anxious or reassured, and this evaluation result is reflected in the treatment plan. Depending on the emotional state, the server provides reassuring comments and detailed explanations of the treatment plan to the user.

[0796] Finally, the server sends the analysis results and a treatment plan based on emotions back to the device. The device notifies the user of the diagnosis and recommended actions, and assists with booking a dental appointment if necessary.

[0797] As a concrete example, imagine a user launching the "Smart Dental Care Robot" app in the morning and performing an intraoral scan. At that time, the app assesses the user's emotional state and provides feedback such as, "You seem very relaxed this morning." This allows the user to proceed with greater confidence.

[0798] An example of a prompt generated by the AI ​​model is: "Describe a system in which a consumer robot scans a user's oral cavity and performs sentiment analysis."

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The user uses a device to acquire images of the inside of their mouth and facial expression data. The device's camera takes pictures of the inside of the mouth, and a facial expression recognition sensor captures the user's facial expressions. This results in obtaining images of the inside of the mouth and facial expression data as input data.

[0802] Step 2:

[0803] The terminal transmits the acquired intraoral images and facial expression data to the server. The image data and facial expression data are encrypted via a secure protocol to ensure data protection before transmission. The output of this step is the data that arrives on the server via the communication infrastructure.

[0804] Step 3:

[0805] The server receives images of the oral cavity, inputs them into a machine learning model, and performs analysis. In particular, a convolutional neural network is used to analyze the images and identify tooth damage and abnormalities. In this process, feature extraction and classification of the images are performed, and the output is a judgment of the dental health status.

[0806] Step 4:

[0807] The server inputs facial expression data into the emotion engine and analyzes the user's emotional state. Using an emotion recognition algorithm, it estimates the emotions the user is feeling from the facial expression data. The output of this process is an evaluation result indicating the emotional state.

[0808] Step 5:

[0809] The server generates appropriate treatment plans and advice based on the dental damage diagnosis and emotional assessment results. Comments and advice tailored to the emotional state are added, and the treatment content and procedures are adjusted accordingly. The output consists of treatment policies and advice information to be communicated to the user.

[0810] Step 6:

[0811] The generated treatment plan and advice are sent to the device. The server returns the calculation results and emotion-based feedback to the device. The output of this step is the notification information displayed to the user on the device.

[0812] Step 7:

[0813] Users can view treatment plans and advice from their devices and, if necessary, make appointments at dental clinics. Using the device's booking function, users can proceed directly to the next step. As a result, actions corresponding to the user's choices are output.

[0814] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0815] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0816] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0817] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0818] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0819] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0820] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0821] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0822] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0823] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0824] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0825] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0826] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0827] 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.

[0828] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0829] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0830] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0831] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0832] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0833] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0834] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0835] The following is further disclosed regarding the embodiments described above.

[0836] (Claim 1)

[0837] A means for receiving images of the inside of the oral cavity taken by a terminal,

[0838] A means for analyzing the received image and identifying tooth damage present in the image,

[0839] A means for calculating the number of treatment days and estimated costs based on the identified injury,

[0840] Means for transmitting the calculated result to a terminal,

[0841] A system that includes this.

[0842] (Claim 2)

[0843] The system according to claim 1, wherein the terminal comprises means for providing the user with a dental clinic appointment option.

[0844] (Claim 3)

[0845] The system according to claim 1, comprising means for performing the image analysis by applying a machine learning model.

[0846] "Example 1"

[0847] (Claim 1)

[0848] Processing means for receiving images of the inside of the oral cavity captured by a terminal,

[0849] A processing means that analyzes the received image using deep learning technology and identifies tooth damage present in the image,

[0850] A processing means for calculating the number of treatment days and estimated costs based on the identified injury,

[0851] A processing means for transmitting the calculated result to a terminal,

[0852] A display means that provides the user with guidance when taking pictures,

[0853] An information processing system that includes this.

[0854] (Claim 2)

[0855] The information processing system according to claim 1, wherein the terminal has a function to provide the user with a selection of medical facility reservation options.

[0856] (Claim 3)

[0857] The information processing system according to claim 1, which includes a function in which the aforementioned image analysis is performed by applying a machine learning model using a convolutional neural network.

[0858] "Application Example 1"

[0859] (Claim 1)

[0860] A means for receiving images of the inside of the oral cavity taken by a terminal,

[0861] A means for analyzing the received image and identifying tooth damage present in the image,

[0862] A means for calculating the number of treatment days and estimated costs based on the identified injury,

[0863] Means for transmitting the calculated result to a terminal,

[0864] A means of providing guidance for a device installed in the home to take images of the user's oral cavity,

[0865] A means of notifying the user of the results audibly and visually based on the captured image,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, wherein the terminal comprises means for providing the user with a dental clinic appointment option.

[0869] (Claim 3)

[0870] The system according to claim 1, comprising means for performing the image analysis by applying a machine learning model.

[0871] "Example 2 of combining an emotion engine"

[0872] (Claim 1)

[0873] A means for receiving images of the inside of the oral cavity and user facial information captured by a terminal,

[0874] A means for analyzing the received image and identifying tooth damage present in the image,

[0875] A means for analyzing the received facial expression information and evaluating the user's emotional state,

[0876] A means for calculating the number of treatment days and estimated costs, as well as an emotion-based treatment plan, based on the identified injury and assessed emotional state.

[0877] Means for transmitting the calculated result to a terminal,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, wherein the terminal provides the user with a means for making a reservation at a dental medical facility.

[0881] (Claim 3)

[0882] The system according to claim 1, comprising means for performing the aforementioned image analysis and emotion information analysis by applying a generative AI model.

[0883] "Application example 2 when combining with an emotional engine"

[0884] (Claim 1)

[0885] A means for receiving images of the inside of the oral cavity taken by a terminal,

[0886] A means for analyzing the received image and identifying tooth damage present in the image,

[0887] A means for calculating the number of treatment days and estimated costs based on the identified injury,

[0888] A means of evaluating emotional state using facial expression data acquired from a terminal,

[0889] Means for adapting treatment plans and advice according to the emotional state,

[0890] A means for transmitting the calculated results and adjusted treatment plan to the terminal,

[0891] A system that includes this.

[0892] (Claim 2)

[0893] The system according to claim 1, wherein the terminal provides the user with means for making reservations at dental medical facilities.

[0894] (Claim 3)

[0895] The system according to claim 1, comprising means for performing the aforementioned image analysis and evaluation of emotional state by applying a machine learning model. [Explanation of symbols]

[0896] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving images of the inside of the oral cavity taken by a terminal, A means for analyzing the received image and identifying tooth damage present in the image, A means for calculating the number of treatment days and estimated costs based on the identified injury, Means for transmitting the calculated result to a terminal, A means of providing guidance for a device installed in the home to take images of the user's oral cavity, A means of notifying the user of the results audibly and visually based on the captured image, A system that includes this.

2. The system according to claim 1, wherein the terminal is provided with means for offering the user a dental clinic appointment option.

3. The system according to claim 1, comprising means for performing the image analysis by applying a machine learning model.

Images