System

Abstract

An object of the system according to the embodiment is to analyze emotions and symptoms of family members who do not speak to each other and provide appropriate feedback.SOLUTION: A system includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects sound or a moving image. The analysis unit analyzes the sound or the moving image collected by the collection unit. The feedback unit performs feedback based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to accurately grasp the emotions and symptoms of family members who could not communicate verbally, making it difficult to take appropriate action.

[0005] The system according to the embodiment aims to analyze the emotions and symptoms of family members who cannot communicate verbally and provide appropriate feedback. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects audio or video. The analysis unit analyzes the audio or video collected by the collection unit. The feedback unit provides feedback based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the emotions and symptoms of family members who cannot communicate verbally and provide appropriate feedback. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A system according to an embodiment of the present invention collects audio and video recordings of babies and pets (e.g., dogs, cats, birds) and learns the emotions and symptoms that can be inferred from them. This system uses AI to input the cries and cries of babies and pets, analyze the audio data, and predict emotions such as "happy," "hungry," and "sleepy." Based on the predicted emotions, the system then provides feedback via an app or other means. For example, if the system predicts "hungry," it instructs the user to feed the baby or pet. If the baby or pet appears unwell, a video is recorded and input into the AI. Based on past cases, the AI ​​then easily analyzes suspected symptoms and provides information to determine whether a visit to the hospital is necessary. For example, if the AI ​​determines that the baby or pet appears unwell, it recommends a visit to the hospital. This allows the system to understand the emotions and health of important family members who cannot communicate verbally and take appropriate action. For example, if a baby is crying and the AI ​​predicts that the baby is hungry, it instructs the user to feed the baby. If the AI ​​determines that a pet appears unwell, it recommends a visit to the hospital. This will help protect the health of your baby and pets.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects audio or video. The collection unit can collect audio or video using, for example, a microphone or a camera. The collection unit can collect, for example, the sounds of a baby crying or a pet barking. The collection unit can also collect videos of babies or pets. The analysis unit analyzes the audio or video collected by the collection unit. The analysis unit can analyze the audio or video using, for example, an AI algorithm or model. The analysis unit can, for example, analyze audio data to predict emotions such as "happy," "hungry," or "sleepy." The analysis unit can also analyze video data to easily analyze suspected symptoms when the user appears unwell. The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback unit provides information to the user using, for example, an application or a notification system. For example, if the feedback unit predicts that the user is "hungry," the feedback unit instructs the user to feed the user. If the feedback unit determines that the user appears unwell, the feedback unit recommends that the user go to the hospital. As a result, the system according to the embodiment can collect, analyze, and provide feedback on audio and video to understand the emotions and symptoms of babies and pets and take appropriate action.

[0030] The collection unit can collect audio or video using a microphone or a camera. The collection unit collects audio using, for example, a microphone. For example, the collection unit can collect the sound of a baby crying or a pet barking. The collection unit can also collect video using a camera. For example, the collection unit can collect video of a baby or a pet. This makes it possible to collect audio or video using a microphone or a camera.

[0031] The analysis unit can analyze audio or video using an AI algorithm or model. The analysis unit can analyze audio data using, for example, deep learning. For example, the analysis unit can analyze a baby's cry and predict emotions such as "happy," "hungry," or "sleepy." The analysis unit can also analyze video data using a support vector machine. For example, the analysis unit can analyze a video of a pet and easily analyze suspected symptoms if the pet appears to be unwell. This improves the accuracy of audio and video analysis by using AI algorithms and models.

[0032] The feedback unit can provide information to the user using an application or a notification system. The feedback unit can provide information to the user using, for example, a smartphone app. For example, if the feedback unit predicts that the user is "hungry," it instructs the user to feed. The feedback unit can also provide information to the user using an email notification. For example, if the feedback unit determines that the user "looks unwell," it recommends that the user go to the hospital. The feedback unit can also provide information to the user using a push notification. For example, the feedback unit can notify the user of the analysis results in real time. This allows the user to be provided with information quickly using an application or a notification system.

[0033] The analysis unit can analyze symptoms using a case database. The analysis unit analyzes symptoms using, for example, past case data. For example, the analysis unit can analyze a video of a pet and compare it with past case data to easily analyze suspected symptoms when the pet appears unwell. The analysis unit can also analyze symptoms using an SQL database. For example, the analysis unit can analyze a baby's cry and predict emotions by comparing it with past case data stored in an SQL database. The analysis unit can also analyze symptoms using a NoSQL database. For example, the analysis unit can analyze a pet's cry and predict emotions by comparing it with past case data stored in a NoSQL database. In this way, the use of a past case database improves the accuracy of symptom analysis.

[0034] The feedback unit can provide information for determining whether or not to go to the hospital based on the analysis results. The feedback unit provides information for determining whether or not to go to the hospital based on the analysis results. For example, the feedback unit analyzes a video of a pet and, if it is determined that the pet "looks unwell," recommends going to the hospital. The feedback unit can also analyze a baby's crying and, if it is determined that the baby "is hungry," instruct the user to feed the baby. The feedback unit can also evaluate the severity and urgency of symptoms based on the analysis results and provide information for determining whether or not to go to the hospital. For example, the feedback unit analyzes the pet's crying, evaluates the severity and urgency of symptoms, and recommends going to the hospital. This allows for appropriate action to be taken by providing information for determining whether or not to go to the hospital based on the analysis results.

[0035] The collection unit may add a filtering function that automatically removes environmental noise when collecting audio or video. For example, the collection unit filters background noise in real time when collecting audio. For example, when collecting a baby's crying, the collection unit removes background noise to obtain clear audio data. The collection unit may also automatically remove noise from video when collecting video. For example, when collecting a video of a pet, the collection unit removes noise from the video to obtain clear video data. The collection unit may also simultaneously filter noise from both audio and video when collecting both audio and video. For example, when collecting a baby's crying and video simultaneously, the collection unit removes noise from the audio and video to obtain high-quality data. In this way, clear audio and clear video data can be obtained by removing environmental noise.

[0036] When collecting audio or video, the collection unit can detect a specific behavior or facial expression of the target animal or baby and start collection. The collection unit can, for example, detect the moment a baby starts crying and start collecting audio. For example, the collection unit can detect the baby's cry and start collecting audio. The collection unit can also start collecting video when a dog performs a specific behavior (for example, wagging its tail). For example, the collection unit can detect the dog's behavior of wagging its tail and start collecting video. The collection unit can also start simultaneous collection of audio and video when a cat shows a specific facial expression (for example, squinting its eyes). For example, the collection unit can detect the cat's expression of squinting its eyes and start simultaneous collection of audio and video. In this way, by detecting specific behaviors and facial expressions, data can be collected without missing important moments.

[0037] The collection unit can automatically adjust the position of the collection device to ensure optimal collection conditions when collecting audio or video. The collection unit, for example, automatically adjusts the position of the camera. For example, the collection unit can adjust the position of the camera so that the baby's face is always within the frame. The collection unit can also automatically adjust the position of the microphone. For example, when collecting a baby's crying, the collection unit can adjust the position of the microphone to ensure optimal audio collection conditions. The collection unit can also automatically adjust the positions of both the camera and the microphone. For example, when collecting a baby's crying and video simultaneously, the collection unit can adjust the positions of the camera and microphone to obtain high-quality data. Thus, optimal collection conditions can be ensured by automatically adjusting the position of the collection device.

[0038] The collection unit can simultaneously collect biometric information of the target animal or baby when collecting audio or video. For example, the collection unit simultaneously measures the heart rate when collecting the baby's crying. For example, the collection unit can measure the heart rate while collecting the baby's crying and acquire data. The collection unit can also simultaneously measure the body temperature when collecting the dog's bark. For example, the collection unit can measure the body temperature while collecting the dog's bark and acquire data. The collection unit can also simultaneously measure the breathing rate when collecting the cat's movements. For example, the collection unit can measure the breathing rate while collecting the cat's movements and acquire data. In this way, by simultaneously collecting biometric information, more detailed data can be acquired.

[0039] When collecting audio and video, the collection unit may adjust the collection method taking into account the remaining battery level of the collection device. For example, when the remaining battery level is low, the collection unit may shorten the collection time to prioritize collecting important data. For example, when the remaining battery level is low, the collection unit may shorten the collection time to prioritize collecting baby cries and pet barks. The collection unit may also maintain the normal collection method when the remaining battery level is sufficient. For example, when the remaining battery level is sufficient, the collection unit may collect audio and video of babies and pets using the normal collection method. The collection unit may also adjust the collection frequency when the remaining battery level is medium to efficiently collect data. For example, when the remaining battery level is medium, the collection unit may adjust the collection frequency to efficiently collect audio and video of babies and pets. This enables efficient data collection by taking the remaining battery level into account.

[0040] The collection unit can record location information of the collection device when collecting audio or video and add it to the collected data. For example, the collection unit automatically records location information of the collection device when collecting audio. For example, the collection unit can record location information of the collection device when collecting a baby's crying and add it to the data. The collection unit can also automatically record location information of the collection device when collecting video. For example, the collection unit can record location information of the collection device when collecting a video of a pet and add it to the data. The collection unit can also automatically record location information of the collection device when simultaneously collecting audio and video. For example, the collection unit can record location information of the collection device when simultaneously collecting a baby's crying and video and add it to the data. In this way, recording location information makes it easier to understand the context of the collected data.

[0041] During analysis, the analysis unit can detect specific patterns in audio or video to enhance an algorithm that predicts emotions or symptoms. For example, the analysis unit can analyze the pattern of a baby's cry to enhance an algorithm that predicts emotions such as "hungry." For example, the analysis unit can analyze the tone and rhythm of a baby's cry to predict emotions such as "hungry." The analysis unit can also analyze the pattern of a dog's bark to enhance an algorithm that predicts emotions such as "happy." For example, the analysis unit can analyze the tone and rhythm of a dog's bark to predict emotions such as "happy." The analysis unit can also analyze the pattern of a cat's behavior to enhance an algorithm that predicts symptoms such as "not feeling well." For example, the analysis unit can analyze the pattern of a cat's behavior to predict symptoms such as "not feeling well." In this way, by enhancing the algorithm that detects specific patterns, the accuracy of emotion and symptom prediction is improved.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past data of the target animal or baby. The analysis unit can improve the analysis accuracy by, for example, referring to past crying data of a baby. For example, the analysis unit can refer to past crying data of a baby and compare it with current crying data to predict emotions. The analysis unit can also improve the analysis accuracy by referring to past barking data of a dog. For example, the analysis unit can refer to past barking data of a dog and compare it with current barking data to predict emotions. The analysis unit can also improve the analysis accuracy by referring to past movement data of a cat. For example, the analysis unit can refer to past movement data of a cat and compare it with current movement data to predict symptoms. In this way, by referring to past data, the analysis accuracy is improved.

[0043] During analysis, the analysis unit updates the analysis results of the audio and video in real time, and can provide instant feedback to the user. For example, the analysis unit analyzes a baby's cry in real time and provides instant feedback. For example, the analysis unit can analyze a baby's cry in real time and provide instant feedback if it determines that the baby is "hungry." The analysis unit can also analyze a dog's bark in real time and provide instant feedback. For example, the analysis unit can analyze a dog's bark in real time and provide instant feedback if it determines that the dog is "happy." The analysis unit can also analyze a cat's behavior in real time and provide instant feedback. For example, the analysis unit can analyze a cat's behavior in real time and provide instant feedback if it determines that the cat is "not feeling well." In this way, by updating the analysis results in real time, instant feedback can be provided.

[0044] During analysis, the analysis unit can integrate multiple data sources to comprehensively evaluate the health condition of the target animal or baby. For example, the analysis unit can integrate a baby's crying data and body temperature data to evaluate the overall health condition. For example, the analysis unit can integrate a baby's crying data and body temperature data to evaluate the overall health condition. The analysis unit can also integrate a dog's barking data and heart rate data to evaluate the overall health condition. For example, the analysis unit can integrate a dog's barking data and heart rate data to evaluate the overall health condition. The analysis unit can also integrate a cat's movement data and respiratory rate data to evaluate the overall health condition. For example, the analysis unit can integrate a cat's movement data and respiratory rate data to evaluate the overall health condition. This makes it possible to comprehensively evaluate the health condition by integrating multiple data sources.

[0045] The analysis unit can add a dashboard function for visually displaying the analysis results. The analysis unit provides, for example, a dashboard that visually displays the analysis results of a baby's cry in graphs and charts. For example, the analysis unit can provide a dashboard that visually displays the analysis results of a baby's cry in graphs and charts. The analysis unit can also provide a dashboard that visually displays the analysis results of a dog's bark in graphs and charts. For example, the analysis unit can provide a dashboard that visually displays the analysis results of a dog's bark in graphs and charts. The analysis unit can also provide a dashboard that visually displays the analysis results of a cat's movement in graphs and charts. For example, the analysis unit can provide a dashboard that visually displays the analysis results of a cat's movement in graphs and charts. In this way, by visually displaying the analysis results, the user can more easily intuitively understand the results.

[0046] The analysis unit can add a social function for sharing the analysis results with other users. The analysis unit provides, for example, a social function for sharing the analysis results of a baby's cry with other users. For example, the analysis unit can provide a social function for sharing the analysis results of a baby's cry with other users. The analysis unit can also provide a social function for sharing the analysis results of a dog's bark with other users. For example, the analysis unit can provide a social function for sharing the analysis results of a dog's bark with other users. The analysis unit can also provide a social function for sharing the analysis results of a cat's movement with other users. For example, the analysis unit can provide a social function for sharing the analysis results of a cat's movement with other users. This promotes information sharing and communication by sharing the analysis results with other users.

[0047] The feedback unit can provide specific behavioral instructions based on the analysis results when providing feedback. For example, the feedback unit can determine that the baby is "hungry" based on the analysis results of a baby's cry and instruct the user to feed the baby. For example, the feedback unit can determine that the baby is "hungry" based on the analysis results of a baby's cry and instruct the user to feed the baby. The feedback unit can also determine that the dog is "happy" based on the analysis results of a dog's bark and instruct the user to set aside time for play. For example, the feedback unit can determine that the dog is "happy" based on the analysis results of a dog's bark and instruct the user to set aside time for play. The feedback unit can also determine that the cat is "not feeling well" based on the analysis results of a cat's movement and instruct the user to take the cat to the hospital. For example, the feedback unit can determine that the cat is "not feeling well" based on the analysis results of a cat's movement and instruct the user to take the cat to the hospital. By providing specific behavioral instructions, the user can take appropriate action.

[0048] When providing feedback, the feedback unit can select an optimal feedback method by referring to the user's past feedback history. For example, the feedback unit can preferentially provide feedback methods that the user has previously preferred. For example, the feedback unit can preferentially provide feedback methods that the user has previously preferred. The feedback unit can also analyze the user's past feedback history and suggest an optimal feedback method. For example, the feedback unit can analyze the user's past feedback history and suggest an optimal feedback method. The feedback unit can also select a feedback method according to a specific situation from the user's past feedback history. For example, the feedback unit can select a feedback method according to a specific situation from the user's past feedback history. In this way, by referring to the past feedback history, it is possible to provide an optimal feedback method for the user.

[0049] When providing feedback, the feedback unit may make the feedback content multilingual and display it according to the user's language setting. The feedback unit may, for example, automatically translate and display the feedback content based on the language setting of the user's device. For example, the feedback unit may automatically translate and display the feedback content based on the language setting of the user's device. The feedback unit may also provide a language switching function when the user uses multiple languages. For example, the feedback unit may provide a language switching function when the user uses multiple languages. The feedback unit may also display the feedback content in a specific language when the user selects that language. For example, the feedback unit may display the feedback content in that language when the user selects that language. This allows the multilingual support to allow the user to receive feedback in the language they use.

[0050] The feedback unit can provide health management advice based on the analysis results at the time of feedback. The feedback unit can provide health management advice based on, for example, the analysis results of a baby's cry. For example, the feedback unit can provide health management advice based on the analysis results of a baby's cry. The feedback unit can also provide health management advice based on the analysis results of a dog's bark. For example, the feedback unit can provide health management advice based on the analysis results of a dog's bark. The feedback unit can also provide health management advice based on the analysis results of a cat's movement. For example, the feedback unit can provide health management advice based on the analysis results of a cat's movement. In this way, by providing health management advice, the user can perform appropriate health management.

[0051] The feedback unit may select an optimal feedback method by taking into consideration device information of the user when providing feedback. For example, if the user is using a smartphone, the feedback unit may provide a feedback method tailored to the screen size. For example, if the user is using a smartphone, the feedback unit may provide a feedback method tailored to the screen size. Furthermore, if the user is using a tablet, the feedback unit may provide a feedback method optimized for a large screen. For example, if the user is using a tablet, the feedback unit may provide a feedback method optimized for a large screen. Furthermore, if the user is using a smartwatch, the feedback unit may provide a simple and highly visible feedback method. For example, if the user is using a smartwatch, the feedback unit may provide a simple and highly visible feedback method. In this way, feedback optimized for the user's device can be provided by taking into consideration device information.

[0052] The feedback unit can adjust the timing of the feedback content based on the user's schedule. The feedback unit, for example, provides feedback at optimal timing based on the user's schedule. For example, the feedback unit can provide feedback at optimal timing based on the user's schedule. The feedback unit can also provide feedback by avoiding time periods when the user is busy. For example, the feedback unit can provide feedback by avoiding time periods when the user is busy. The feedback unit can also adjust the timing of the feedback to match the user's schedule. For example, the feedback unit can adjust the timing of the feedback to match the user's schedule. In this way, by adjusting the timing of the feedback based on the user's schedule, it is possible to provide feedback at appropriate timing.

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

[0054] The analysis unit can learn the behavioral patterns of babies and pets based on the results of audio and video analysis and predict future behavior. For example, the analysis unit can learn the crying patterns of babies and, if they tend to cry at certain times of the day, suggest preventive measures for those times. The analysis unit can also learn the barking patterns of dogs and, if they tend to bark in certain situations, provide advice on how to avoid those situations. Furthermore, the analysis unit can learn the behavioral patterns of cats and, if certain behaviors are frequently observed, predict health risks associated with those behaviors and suggest preventive measures. This allows for more effective responses by learning behavioral patterns and predicting future behavior.

[0055] The collection unit can simultaneously collect biological information of the target animal or baby when collecting audio or video. For example, the collection unit can simultaneously measure the heart rate when collecting a baby's crying. The collection unit can also simultaneously measure the body temperature when collecting a dog's bark. Furthermore, the collection unit can also simultaneously measure the respiratory rate when collecting a cat's movements. By simultaneously collecting biological information, more detailed data can be obtained.

[0056] The feedback unit can provide specific behavioral instructions based on the analysis results when providing feedback. For example, based on the analysis results of a baby's cry, it can determine that the baby is hungry and instruct the user to feed the baby. Based on the analysis results of a dog's bark, it can determine that the dog is happy and instruct the user to set aside time to play. Furthermore, based on the analysis results of a cat's behavior, it can determine that the cat is not feeling well and instruct the user to take the cat to the vet. By providing specific behavioral instructions, the user can take appropriate action.

[0057] The collection unit can add a filtering function that automatically removes environmental noise when collecting audio or video. For example, background noise can be filtered in real time when collecting audio. When collecting a baby's cry, background noise can be removed to obtain clear audio data. Video noise can also be automatically removed when collecting video. When collecting a video of a pet, video noise can be removed to obtain clear video data. Furthermore, when collecting audio and video simultaneously, noise from both can be filtered simultaneously. When collecting a baby's cry and video simultaneously, noise from both the audio and video can be removed to obtain high-quality data. By removing environmental noise, clear audio and video data can be obtained.

[0058] During analysis, the analysis unit can detect specific patterns in audio or video to strengthen the algorithm that predicts emotions and symptoms. For example, the pattern of a baby's cry can be analyzed to strengthen the algorithm that predicts emotions such as "hungry." The tone and rhythm of a baby's cry can be analyzed to predict emotions such as "hungry." Also, the pattern of a dog's bark can be analyzed to strengthen the algorithm that predicts emotions such as "happy." The tone and rhythm of a dog's bark can be analyzed to predict emotions such as "happy." Furthermore, the pattern of a cat's behavior can be analyzed to strengthen the algorithm that predicts symptoms such as "not feeling well." The pattern of a cat's behavior can be analyzed to predict symptoms such as "not feeling well." In this way, by strengthening the algorithm that detects specific patterns, the accuracy of emotion and symptom prediction can be improved.

[0059] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past data of the target animal or baby. For example, the analysis accuracy can be improved by referring to past crying data of a baby. By referring to past crying data of a baby and comparing it with current crying data, emotions can be predicted. The analysis accuracy can also be improved by referring to past barking data of a dog. By referring to past barking data of a dog and comparing it with current barking data, emotions can be predicted. Furthermore, the analysis accuracy can also be improved by referring to past movement data of a cat. By referring to past movement data of a cat and comparing it with current movement data, symptoms can be predicted. In this way, by referring to past data, the analysis accuracy is improved.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects audio or video. The collection unit can collect audio or video using, for example, a microphone or a camera. The collection unit can collect, for example, the crying of a baby or the barking of a pet. The collection unit can also collect videos of babies and pets. Step 2: The analysis unit analyzes the audio or video collected by the collection unit. The analysis unit analyzes the audio or video using, for example, an AI algorithm or model. For example, the analysis unit analyzes the audio data and predicts emotions such as "happy," "hungry," or "sleepy." The analysis unit can also analyze the video data and easily analyze suspected symptoms if the person appears to be unwell. Step 3: The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback unit provides information to the user using, for example, an application or a notification system. For example, if the feedback unit predicts that the user is "hungry," it instructs the user to feed the user. If the feedback unit determines that the user "seems unwell," it recommends that the user go to the hospital.

[0062] (Example 2) A system according to an embodiment of the present invention collects audio and video recordings of babies and pets (e.g., dogs, cats, birds) and learns the emotions and symptoms that can be inferred from them. This system uses AI to input the cries and cries of babies and pets, analyze the audio data, and predict emotions such as "happy," "hungry," and "sleepy." Based on the predicted emotions, the system then provides feedback via an app or other means. For example, if the system predicts "hungry," it instructs the user to feed the baby or pet. If the baby or pet appears unwell, a video is recorded and input into the AI. Based on past cases, the AI ​​then easily analyzes suspected symptoms and provides information to determine whether a visit to the hospital is necessary. For example, if the AI ​​determines that the baby or pet appears unwell, it recommends a visit to the hospital. This allows the system to understand the emotions and health of important family members who cannot communicate verbally and take appropriate action. For example, if a baby is crying and the AI ​​predicts that the baby is hungry, it instructs the user to feed the baby. If the AI ​​determines that a pet appears unwell, it recommends a visit to the hospital. This will help protect the health of your baby and pets.

[0063] The system according to the embodiment includes a collection unit, an analysis unit, and a feedback unit. The collection unit collects audio or video. The collection unit can collect audio or video using, for example, a microphone or a camera. The collection unit can collect, for example, the sounds of a baby crying or a pet barking. The collection unit can also collect videos of babies or pets. The analysis unit analyzes the audio or video collected by the collection unit. The analysis unit can analyze the audio or video using, for example, an AI algorithm or model. The analysis unit can, for example, analyze audio data to predict emotions such as "happy," "hungry," or "sleepy." The analysis unit can also analyze video data to easily analyze suspected symptoms when the user appears unwell. The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback unit provides information to the user using, for example, an application or a notification system. For example, if the feedback unit predicts that the user is "hungry," the feedback unit instructs the user to feed the user. If the feedback unit determines that the user appears unwell, the feedback unit recommends that the user go to the hospital. As a result, the system according to the embodiment can collect, analyze, and provide feedback on audio and video to understand the emotions and symptoms of babies and pets and take appropriate action.

[0064] The collection unit can collect audio or video using a microphone or a camera. The collection unit collects audio using, for example, a microphone. For example, the collection unit can collect the sound of a baby crying or a pet barking. The collection unit can also collect video using a camera. For example, the collection unit can collect video of a baby or a pet. This makes it possible to collect audio or video using a microphone or a camera.

[0065] The analysis unit can analyze audio or video using an AI algorithm or model. The analysis unit can analyze audio data using, for example, deep learning. For example, the analysis unit can analyze a baby's cry and predict emotions such as "happy," "hungry," or "sleepy." The analysis unit can also analyze video data using a support vector machine. For example, the analysis unit can analyze a video of a pet and easily analyze suspected symptoms if the pet appears to be unwell. This improves the accuracy of audio and video analysis by using AI algorithms and models.

[0066] The feedback unit can provide information to the user using an application or a notification system. The feedback unit can provide information to the user using, for example, a smartphone app. For example, if the feedback unit predicts that the user is "hungry," it instructs the user to feed. The feedback unit can also provide information to the user using an email notification. For example, if the feedback unit determines that the user "looks unwell," it recommends that the user go to the hospital. The feedback unit can also provide information to the user using a push notification. For example, the feedback unit can notify the user of the analysis results in real time. This allows the user to be provided with information quickly using an application or a notification system.

[0067] The analysis unit can analyze symptoms using a case database. The analysis unit analyzes symptoms using, for example, past case data. For example, the analysis unit can analyze a video of a pet and compare it with past case data to easily analyze suspected symptoms when the pet appears unwell. The analysis unit can also analyze symptoms using an SQL database. For example, the analysis unit can analyze a baby's cry and predict emotions by comparing it with past case data stored in an SQL database. The analysis unit can also analyze symptoms using a NoSQL database. For example, the analysis unit can analyze a pet's cry and predict emotions by comparing it with past case data stored in a NoSQL database. In this way, the use of a past case database improves the accuracy of symptom analysis.

[0068] The feedback unit can provide information for determining whether or not to go to the hospital based on the analysis results. The feedback unit provides information for determining whether or not to go to the hospital based on the analysis results. For example, the feedback unit analyzes a video of a pet and, if it is determined that the pet "looks unwell," recommends going to the hospital. The feedback unit can also analyze a baby's crying and, if it is determined that the baby "is hungry," instruct the user to feed the baby. The feedback unit can also evaluate the severity and urgency of symptoms based on the analysis results and provide information for determining whether or not to go to the hospital. For example, the feedback unit analyzes the pet's crying, evaluates the severity and urgency of symptoms, and recommends going to the hospital. This allows for appropriate action to be taken by providing information for determining whether or not to go to the hospital based on the analysis results.

[0069] The collection unit can estimate a user's emotions and adjust the timing of collecting audio and video based on the estimated user emotions. The collection unit, for example, analyzes the user's voice tone to estimate emotions. For example, if the user is feeling stressed, the collection unit automatically adjusts the collection timing to reduce the user's burden. The collection unit can also analyze the user's facial expressions to estimate emotions. For example, if the user is relaxed, the collection unit flexibly sets the collection timing to collect audio and video in a natural situation. The collection unit can also analyze the user's behavioral patterns to estimate emotions. For example, if the user is in a hurry, the collection unit quickly sets the collection timing to quickly collect necessary data. This allows for more appropriate data collection by adjusting the collection timing based on the user's emotions.

[0070] The collection unit may add a filtering function that automatically removes environmental noise when collecting audio or video. For example, the collection unit filters background noise in real time when collecting audio. For example, when collecting a baby's crying, the collection unit removes background noise to obtain clear audio data. The collection unit may also automatically remove noise from video when collecting video. For example, when collecting a video of a pet, the collection unit removes noise from the video to obtain clear video data. The collection unit may also simultaneously filter noise from both audio and video when collecting both audio and video. For example, when collecting a baby's crying and video simultaneously, the collection unit removes noise from the audio and video to obtain high-quality data. In this way, clear audio and clear video data can be obtained by removing environmental noise.

[0071] When collecting audio or video, the collection unit can detect a specific behavior or facial expression of the target animal or baby and start collection. The collection unit can, for example, detect the moment a baby starts crying and start collecting audio. For example, the collection unit can detect the baby's cry and start collecting audio. The collection unit can also start collecting video when a dog performs a specific behavior (for example, wagging its tail). For example, the collection unit can detect the dog's behavior of wagging its tail and start collecting video. The collection unit can also start simultaneous collection of audio and video when a cat shows a specific facial expression (for example, squinting its eyes). For example, the collection unit can detect the cat's expression of squinting its eyes and start simultaneous collection of audio and video. In this way, by detecting specific behaviors and facial expressions, data can be collected without missing important moments.

[0072] The collection unit can automatically adjust the position of the collection device to ensure optimal collection conditions when collecting audio or video. The collection unit, for example, automatically adjusts the position of the camera. For example, the collection unit can adjust the position of the camera so that the baby's face is always within the frame. The collection unit can also automatically adjust the position of the microphone. For example, when collecting a baby's crying, the collection unit can adjust the position of the microphone to ensure optimal audio collection conditions. The collection unit can also automatically adjust the positions of both the camera and the microphone. For example, when collecting a baby's crying and video simultaneously, the collection unit can adjust the positions of the camera and microphone to obtain high-quality data. Thus, optimal collection conditions can be ensured by automatically adjusting the position of the collection device.

[0073] The collection unit can estimate the user's emotions and determine the priority of audio and video to be collected based on the estimated user's emotions. The collection unit, for example, analyzes the user's voice tone to estimate the emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting important audio and video. The collection unit can also analyze the user's facial expressions to estimate the emotions. For example, if the user is relaxed, the collection unit prioritizes collecting normal audio and video. The collection unit can also analyze the user's behavioral patterns to estimate the emotions. For example, if the user is in a hurry, the collection unit prioritizes collecting urgent audio and video. In this way, by determining the priority of audio and video to be collected based on the user's emotions, important data can be collected preferentially.

[0074] The collection unit can simultaneously collect biometric information of the target animal or baby when collecting audio or video. For example, the collection unit simultaneously measures the heart rate when collecting the baby's crying. For example, the collection unit can measure the heart rate while collecting the baby's crying and acquire data. The collection unit can also simultaneously measure the body temperature when collecting the dog's bark. For example, the collection unit can measure the body temperature while collecting the dog's bark and acquire data. The collection unit can also simultaneously measure the breathing rate when collecting the cat's movements. For example, the collection unit can measure the breathing rate while collecting the cat's movements and acquire data. In this way, by simultaneously collecting biometric information, more detailed data can be acquired.

[0075] When collecting audio and video, the collection unit may adjust the collection method taking into account the remaining battery level of the collection device. For example, when the remaining battery level is low, the collection unit may shorten the collection time to prioritize collecting important data. For example, when the remaining battery level is low, the collection unit may shorten the collection time to prioritize collecting baby cries and pet barks. The collection unit may also maintain the normal collection method when the remaining battery level is sufficient. For example, when the remaining battery level is sufficient, the collection unit may collect audio and video of babies and pets using the normal collection method. The collection unit may also adjust the collection frequency when the remaining battery level is medium to efficiently collect data. For example, when the remaining battery level is medium, the collection unit may adjust the collection frequency to efficiently collect audio and video of babies and pets. This enables efficient data collection by taking the remaining battery level into account.

[0076] The collection unit can record location information of the collection device when collecting audio or video and add it to the collected data. For example, the collection unit automatically records location information of the collection device when collecting audio. For example, the collection unit can record location information of the collection device when collecting a baby's crying and add it to the data. The collection unit can also automatically record location information of the collection device when collecting video. For example, the collection unit can record location information of the collection device when collecting a video of a pet and add it to the data. The collection unit can also automatically record location information of the collection device when simultaneously collecting audio and video. For example, the collection unit can record location information of the collection device when simultaneously collecting a baby's crying and video and add it to the data. In this way, recording location information makes it easier to understand the context of the collected data.

[0077] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user's emotions. The analysis unit, for example, analyzes the user's voice tone to estimate the emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. The analysis unit can also analyze the user's facial expressions to estimate the emotions. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also analyze the user's behavioral patterns to estimate the emotions. For example, if the user is in a hurry, the analysis unit provides a concise analysis result. In this way, by adjusting the way in which the analysis results are presented based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0078] During analysis, the analysis unit can detect specific patterns in audio or video to enhance an algorithm that predicts emotions or symptoms. For example, the analysis unit can analyze the pattern of a baby's cry to enhance an algorithm that predicts emotions such as "hungry." For example, the analysis unit can analyze the tone and rhythm of a baby's cry to predict emotions such as "hungry." The analysis unit can also analyze the pattern of a dog's bark to enhance an algorithm that predicts emotions such as "happy." For example, the analysis unit can analyze the tone and rhythm of a dog's bark to predict emotions such as "happy." The analysis unit can also analyze the pattern of a cat's behavior to enhance an algorithm that predicts symptoms such as "not feeling well." For example, the analysis unit can analyze the pattern of a cat's behavior to predict symptoms such as "not feeling well." In this way, by enhancing the algorithm that detects specific patterns, the accuracy of emotion and symptom prediction is improved.

[0079] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past data of the target animal or baby. The analysis unit can improve the analysis accuracy by, for example, referring to past crying data of a baby. For example, the analysis unit can refer to past crying data of a baby and compare it with current crying data to predict emotions. The analysis unit can also improve the analysis accuracy by referring to past barking data of a dog. For example, the analysis unit can refer to past barking data of a dog and compare it with current barking data to predict emotions. The analysis unit can also improve the analysis accuracy by referring to past movement data of a cat. For example, the analysis unit can refer to past movement data of a cat and compare it with current movement data to predict symptoms. In this way, by referring to past data, the analysis accuracy is improved.

[0080] During analysis, the analysis unit updates the analysis results of the audio and video in real time, and can provide instant feedback to the user. For example, the analysis unit analyzes a baby's cry in real time and provides instant feedback. For example, the analysis unit can analyze a baby's cry in real time and provide instant feedback if it determines that the baby is "hungry." The analysis unit can also analyze a dog's bark in real time and provide instant feedback. For example, the analysis unit can analyze a dog's bark in real time and provide instant feedback if it determines that the dog is "happy." The analysis unit can also analyze a cat's behavior in real time and provide instant feedback. For example, the analysis unit can analyze a cat's behavior in real time and provide instant feedback if it determines that the cat is "not feeling well." In this way, by updating the analysis results in real time, instant feedback can be provided.

[0081] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated user's emotions. The analysis unit, for example, analyzes the user's voice tone to estimate emotions. For example, if the user is feeling stressed, the analysis unit prioritizes displaying important analysis results. The analysis unit can also analyze the user's facial expressions to estimate emotions. For example, if the user is relaxed, the analysis unit prioritizes displaying normal analysis results. The analysis unit can also analyze the user's behavioral patterns to estimate emotions. For example, if the user is in a hurry, the analysis unit prioritizes displaying analysis results with a high degree of urgency. In this way, by determining the priority of analysis results based on the user's emotions, important analysis results can be displayed preferentially.

[0082] During analysis, the analysis unit can integrate multiple data sources to comprehensively evaluate the health condition of the target animal or baby. For example, the analysis unit can integrate a baby's crying data and body temperature data to evaluate the overall health condition. For example, the analysis unit can integrate a baby's crying data and body temperature data to evaluate the overall health condition. The analysis unit can also integrate a dog's barking data and heart rate data to evaluate the overall health condition. For example, the analysis unit can integrate a dog's barking data and heart rate data to evaluate the overall health condition. The analysis unit can also integrate a cat's movement data and respiratory rate data to evaluate the overall health condition. For example, the analysis unit can integrate a cat's movement data and respiratory rate data to evaluate the overall health condition. This makes it possible to comprehensively evaluate the health condition by integrating multiple data sources.

[0083] The analysis unit can add a dashboard function for visually displaying the analysis results. The analysis unit provides, for example, a dashboard that visually displays the analysis results of a baby's cry in graphs and charts. For example, the analysis unit can provide a dashboard that visually displays the analysis results of a baby's cry in graphs and charts. The analysis unit can also provide a dashboard that visually displays the analysis results of a dog's bark in graphs and charts. For example, the analysis unit can provide a dashboard that visually displays the analysis results of a dog's bark in graphs and charts. The analysis unit can also provide a dashboard that visually displays the analysis results of a cat's movement in graphs and charts. For example, the analysis unit can provide a dashboard that visually displays the analysis results of a cat's movement in graphs and charts. In this way, by visually displaying the analysis results, the user can more easily intuitively understand the results.

[0084] The analysis unit can add a social function for sharing the analysis results with other users. The analysis unit provides, for example, a social function for sharing the analysis results of a baby's cry with other users. For example, the analysis unit can provide a social function for sharing the analysis results of a baby's cry with other users. The analysis unit can also provide a social function for sharing the analysis results of a dog's bark with other users. For example, the analysis unit can provide a social function for sharing the analysis results of a dog's bark with other users. The analysis unit can also provide a social function for sharing the analysis results of a cat's movement with other users. For example, the analysis unit can provide a social function for sharing the analysis results of a cat's movement with other users. This promotes information sharing and communication by sharing the analysis results with other users.

[0085] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. The feedback unit, for example, analyzes the user's voice tone to estimate the emotions. For example, if the user is nervous, the feedback unit provides simple, highly visible feedback. The feedback unit can also analyze the user's facial expressions to estimate the emotions. For example, if the user is relaxed, the feedback unit provides detailed feedback. The feedback unit can also analyze the user's behavioral patterns to estimate the emotions. For example, if the user is in a hurry, the feedback unit provides feedback that focuses on the main points. In this way, by adjusting the content of the feedback based on the user's emotions, it is possible to provide optimal feedback for the user.

[0086] The feedback unit can provide specific behavioral instructions based on the analysis results when providing feedback. For example, the feedback unit can determine that the baby is "hungry" based on the analysis results of a baby's cry and instruct the user to feed the baby. For example, the feedback unit can determine that the baby is "hungry" based on the analysis results of a baby's cry and instruct the user to feed the baby. The feedback unit can also determine that the dog is "happy" based on the analysis results of a dog's bark and instruct the user to set aside time for play. For example, the feedback unit can determine that the dog is "happy" based on the analysis results of a dog's bark and instruct the user to set aside time for play. The feedback unit can also determine that the cat is "not feeling well" based on the analysis results of a cat's movement and instruct the user to take the cat to the hospital. For example, the feedback unit can determine that the cat is "not feeling well" based on the analysis results of a cat's movement and instruct the user to take the cat to the hospital. By providing specific behavioral instructions, the user can take appropriate action.

[0087] When providing feedback, the feedback unit can select an optimal feedback method by referring to the user's past feedback history. For example, the feedback unit can preferentially provide feedback methods that the user has previously preferred. For example, the feedback unit can preferentially provide feedback methods that the user has previously preferred. The feedback unit can also analyze the user's past feedback history and suggest an optimal feedback method. For example, the feedback unit can analyze the user's past feedback history and suggest an optimal feedback method. The feedback unit can also select a feedback method according to a specific situation from the user's past feedback history. For example, the feedback unit can select a feedback method according to a specific situation from the user's past feedback history. In this way, by referring to the past feedback history, it is possible to provide an optimal feedback method for the user.

[0088] When providing feedback, the feedback unit may make the feedback content multilingual and display it according to the user's language setting. The feedback unit may, for example, automatically translate and display the feedback content based on the language setting of the user's device. For example, the feedback unit may automatically translate and display the feedback content based on the language setting of the user's device. The feedback unit may also provide a language switching function when the user uses multiple languages. For example, the feedback unit may provide a language switching function when the user uses multiple languages. The feedback unit may also display the feedback content in a specific language when the user selects that language. For example, the feedback unit may display the feedback content in that language when the user selects that language. This allows the multilingual support to allow the user to receive feedback in the language they use.

[0089] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. The feedback unit, for example, analyzes the user's voice tone to estimate the emotions. For example, if the user is feeling stressed, the feedback unit prioritizes displaying important feedback. The feedback unit can also estimate the emotions by analyzing the user's facial expressions. For example, if the user is relaxed, the feedback unit prioritizes displaying normal feedback. The feedback unit can also estimate the emotions by analyzing the user's behavioral patterns. For example, if the user is in a hurry, the feedback unit prioritizes displaying urgent feedback. In this way, by determining the priority of feedback based on the user's emotions, important feedback can be displayed preferentially.

[0090] The feedback unit can provide health management advice based on the analysis results at the time of feedback. The feedback unit can provide health management advice based on, for example, the analysis results of a baby's cry. For example, the feedback unit can provide health management advice based on the analysis results of a baby's cry. The feedback unit can also provide health management advice based on the analysis results of a dog's bark. For example, the feedback unit can provide health management advice based on the analysis results of a dog's bark. The feedback unit can also provide health management advice based on the analysis results of a cat's movement. For example, the feedback unit can provide health management advice based on the analysis results of a cat's movement. In this way, by providing health management advice, the user can perform appropriate health management.

[0091] The feedback unit may select an optimal feedback method by taking into consideration device information of the user when providing feedback. For example, if the user is using a smartphone, the feedback unit may provide a feedback method tailored to the screen size. For example, if the user is using a smartphone, the feedback unit may provide a feedback method tailored to the screen size. Furthermore, if the user is using a tablet, the feedback unit may provide a feedback method optimized for a large screen. For example, if the user is using a tablet, the feedback unit may provide a feedback method optimized for a large screen. Furthermore, if the user is using a smartwatch, the feedback unit may provide a simple and highly visible feedback method. For example, if the user is using a smartwatch, the feedback unit may provide a simple and highly visible feedback method. In this way, feedback optimized for the user's device can be provided by taking into consideration device information.

[0092] The feedback unit can adjust the timing of the feedback content based on the user's schedule. The feedback unit, for example, provides feedback at optimal timing based on the user's schedule. For example, the feedback unit can provide feedback at optimal timing based on the user's schedule. The feedback unit can also provide feedback by avoiding time periods when the user is busy. For example, the feedback unit can provide feedback by avoiding time periods when the user is busy. The feedback unit can also adjust the timing of the feedback to match the user's schedule. For example, the feedback unit can adjust the timing of the feedback to match the user's schedule. In this way, by adjusting the timing of the feedback based on the user's schedule, it is possible to provide feedback at appropriate timing. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and feedback unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect audio and video of a baby or pet using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected audio and video using an AI algorithm or model. The feedback unit is realized, for example, by the control unit 46A of the smart device 14, and provides information to the user through an application or a notification system based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and feedback unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect audio and video of a baby or pet using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected audio and video using an AI algorithm or model. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides information to the user through an application or a notification system based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect audio and video of a baby or pet using the camera 42 or microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected audio and video using an AI algorithm or model. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides information to the user through an application or a notification system based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and feedback unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect audio and video of a baby or pet using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected audio and video using an AI algorithm or model. The feedback unit is realized, for example, by the control unit 46A of the robot 414, and provides information to the user through an application or a notification system based on the analysis results.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The analysis unit can learn the behavioral patterns of babies and pets based on the results of audio and video analysis and predict future behavior. For example, the analysis unit can learn the crying patterns of babies and, if they tend to cry at certain times of the day, suggest preventive measures for those times. The analysis unit can also learn the barking patterns of dogs and, if they tend to bark in certain situations, provide advice on how to avoid those situations. Furthermore, the analysis unit can learn the behavioral patterns of cats and, if certain behaviors are frequently observed, predict health risks associated with those behaviors and suggest preventive measures. This allows for more effective responses by learning behavioral patterns and predicting future behavior.

[0095] The collection unit can simultaneously collect biological information of the target animal or baby when collecting audio or video. For example, the collection unit can simultaneously measure the heart rate when collecting a baby's crying. The collection unit can also simultaneously measure the body temperature when collecting a dog's bark. Furthermore, the collection unit can also simultaneously measure the respiratory rate when collecting a cat's movements. By simultaneously collecting biological information, more detailed data can be obtained.

[0096] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user's emotions. For example, the analysis unit can estimate emotions by analyzing the user's voice tone. If the user is nervous, simple and highly visible analysis results can be provided. The analysis unit can also estimate emotions by analyzing the user's facial expressions. If the user is relaxed, detailed analysis results can be provided. Furthermore, the analysis unit can also estimate emotions by analyzing the user's behavioral patterns. If the user is in a hurry, analysis results that are concise can be provided. In this way, by adjusting the way in which the analysis results are presented based on the user's emotions, analysis results that are easy for the user to understand can be provided.

[0097] The feedback unit can provide specific behavioral instructions based on the analysis results when providing feedback. For example, based on the analysis results of a baby's cry, it can determine that the baby is hungry and instruct the user to feed the baby. Based on the analysis results of a dog's bark, it can determine that the dog is happy and instruct the user to set aside time to play. Furthermore, based on the analysis results of a cat's behavior, it can determine that the cat is not feeling well and instruct the user to take the cat to the vet. By providing specific behavioral instructions, the user can take appropriate action.

[0098] The collection unit can add a filtering function that automatically removes environmental noise when collecting audio or video. For example, background noise can be filtered in real time when collecting audio. When collecting a baby's cry, background noise can be removed to obtain clear audio data. Video noise can also be automatically removed when collecting video. When collecting a video of a pet, video noise can be removed to obtain clear video data. Furthermore, when collecting audio and video simultaneously, noise from both can be filtered simultaneously. When collecting a baby's cry and video simultaneously, noise from both the audio and video can be removed to obtain high-quality data. By removing environmental noise, clear audio and video data can be obtained.

[0099] During analysis, the analysis unit can detect specific patterns in audio or video to strengthen the algorithm that predicts emotions and symptoms. For example, the pattern of a baby's cry can be analyzed to strengthen the algorithm that predicts emotions such as "hungry." The tone and rhythm of a baby's cry can be analyzed to predict emotions such as "hungry." Also, the pattern of a dog's bark can be analyzed to strengthen the algorithm that predicts emotions such as "happy." The tone and rhythm of a dog's bark can be analyzed to predict emotions such as "happy." Furthermore, the pattern of a cat's behavior can be analyzed to strengthen the algorithm that predicts symptoms such as "not feeling well." The pattern of a cat's behavior can be analyzed to predict symptoms such as "not feeling well." In this way, by strengthening the algorithm that detects specific patterns, the accuracy of emotion and symptom prediction can be improved.

[0100] The collection unit can estimate the user's emotions and adjust the timing of audio and video collection based on the estimated user emotions. For example, the collection unit can estimate emotions by analyzing the user's voice tone. If the user is feeling stressed, the collection timing can be automatically adjusted to reduce the user's burden. The collection unit can also estimate emotions by analyzing the user's facial expressions. If the user is relaxed, the collection timing can be flexibly set to collect audio and video in natural situations. Furthermore, the collection unit can analyze the user's behavioral patterns to estimate emotions. If the user is in a hurry, the collection timing can be quickly set to quickly collect the necessary data. This allows more appropriate data collection by adjusting the collection timing based on the user's emotions.

[0101] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, the user's voice tone can be analyzed to estimate the emotions. If the user is nervous, simple, highly visible feedback can be provided. The user's facial expressions can also be analyzed to estimate the emotions. If the user is relaxed, detailed feedback can be provided. Furthermore, the user's behavioral patterns can be analyzed to estimate the emotions. If the user is in a hurry, feedback that focuses on the main points can be provided. In this way, by adjusting the content of the feedback based on the user's emotions, optimal feedback can be provided to the user.

[0102] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past data of the target animal or baby. For example, the analysis accuracy can be improved by referring to past crying data of a baby. By referring to past crying data of a baby and comparing it with current crying data, emotions can be predicted. The analysis accuracy can also be improved by referring to past barking data of a dog. By referring to past barking data of a dog and comparing it with current barking data, emotions can be predicted. Furthermore, the analysis accuracy can also be improved by referring to past movement data of a cat. By referring to past movement data of a cat and comparing it with current movement data, symptoms can be predicted. In this way, by referring to past data, the analysis accuracy is improved.

[0103] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, the user's voice tone can be analyzed to estimate emotions. If the user is feeling stressed, important feedback can be displayed with priority. The feedback unit can also analyze the user's facial expressions to estimate emotions. If the user is relaxed, regular feedback can be displayed with priority. Furthermore, the feedback unit can analyze the user's behavioral patterns to estimate emotions. If the user is in a hurry, urgent feedback can be displayed with priority. Thus, by determining the priority of feedback based on the user's emotions, important feedback can be displayed with priority.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects audio or video. The collection unit can collect audio or video using, for example, a microphone or a camera. The collection unit can collect, for example, the crying of a baby or the barking of a pet. The collection unit can also collect videos of babies and pets. Step 2: The analysis unit analyzes the audio or video collected by the collection unit. The analysis unit analyzes the audio or video using, for example, an AI algorithm or model. For example, the analysis unit analyzes the audio data and predicts emotions such as "happy," "hungry," or "sleepy." The analysis unit can also analyze the video data and easily analyze suspected symptoms if the person appears to be unwell. Step 3: The feedback unit provides feedback based on the analysis results obtained by the analysis unit. The feedback unit provides information to the user using, for example, an application or a notification system. For example, if the feedback unit predicts that the user is "hungry," it instructs the user to feed the user. If the feedback unit determines that the user "seems unwell," it recommends that the user go to the hospital.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a 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.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] 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.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0171] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0175] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a collection unit that collects audio or video; an analysis unit that analyzes the audio or video collected by the collection unit; and a feedback unit that provides feedback based on the analysis results obtained by the analysis unit.

2. The system of claim 1 , wherein the collection unit collects audio or video using a microphone or a camera.

3. The system of claim 1 , wherein the analysis unit analyzes audio or video using an AI algorithm or model.

4. The system of claim 1 , wherein the feedback unit provides information to the user using an application or a notification system.

5. The system according to claim 1 , wherein the analysis unit analyzes symptoms using a case database.

6. The system according to claim 1 , wherein the feedback unit provides information for determining whether or not to go to a hospital based on the analysis result.

7. The collecting unit Estimate user emotions and adjust the timing of audio and video collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Add filtering functionality to automatically remove environmental noise when collecting audio and video.

2. The system of claim 1.

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