System

Abstract

An object of a system according to an embodiment is to monitor the safety of an infant in real time and prevent danger.SOLUTION: A system includes a camera, a AI processing unit, a warning unit, and a suggestion unit. The camera acquires video data. The processor is configured to analyze video AI acquired by the camera. The warning unit detects an approach to a dangerous area and issues a warning based on the AI analyzed by the data-processing unit. The proposing section predicts and proposes an idea of safe play or a potential danger based on the AI analyzed by the data-processing section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately monitor the safety of young children in real time and prevent danger before it occurs, so there is room for improvement.

[0005] The system according to the embodiment aims to monitor the safety of young children in real time and prevent danger before it occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, an AI processing unit, a warning unit, and a suggestion unit. The camera acquires video data. The AI ​​processing unit analyzes the video data acquired by the camera. The warning unit detects approach to a dangerous area and issues a warning based on the data analyzed by the AI ​​processing unit. The suggestion unit predicts and suggests ideas for safe play and potential dangers based on the data analyzed by the AI ​​processing unit. [Effects of the Invention]

[0007] The system according to the embodiment can monitor the safety of young children in real time and prevent danger before it occurs. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​surveillance system according to an embodiment of the present invention tracks the movements of young children in real time, detects their approach to dangerous areas, issues a warning, and predicts and suggests safe play ideas and potential dangers. This ensures the safety of young children and reduces the burden on parents.

[0029] An AI monitoring system according to an embodiment includes a camera, an AI processing unit, a warning unit, and a suggestion unit. The camera monitors a child's movements in real time. For example, the camera captures high-resolution video and sends it to the AI ​​processing unit. The AI ​​processing unit analyzes the video data captured by the camera. For example, the AI ​​processing unit uses an image recognition algorithm to identify the child's location and movements. The AI ​​processing unit can also learn the child's movement patterns and detect abnormal movements. The warning unit detects approach to a dangerous area and issues a warning based on the data analyzed by the AI ​​processing unit. For example, the warning unit sends a notification to a smartphone or tablet, displaying a warning message such as "Your child is approaching the stairs." The suggestion unit predicts and suggests safe play ideas and potential dangers based on the data analyzed by the AI ​​processing unit. For example, if a child is playing in the living room, the suggestion unit may suggest safe play activities such as "playing with blocks" or "reading a picture book" and display the suggestions to the child on a smart device or a screen in the home. As a result, the AI ​​monitoring system of the embodiment can track the movements of young children in real time, avoid danger, and suggest safe play activities, thereby ensuring the safety of young children and reducing the burden on parents.

[0030] The AI ​​processing unit can monitor the infant's body temperature and heart rate and notify parents if any abnormalities are detected. For example, the AI ​​processing unit analyzes video data from a smart camera and tracks the infant's movements in real time. At the same time, it can monitor the infant's body temperature and heart rate using a wearable device and notify parents if any abnormalities are detected. For example, if the infant's body temperature rises suddenly, an alert can be sent to the parent's smartphone. This allows abnormalities in the infant's body temperature or heart rate to be detected and responded to quickly.

[0031] The AI ​​processing unit learns the movement patterns of infants, and if abnormal movement is detected, the warning unit can issue a warning to parents. The AI ​​processing unit, for example, analyzes video data from a smart camera and learns the movement patterns of infants. For example, it detects abnormal movement based on normal movement patterns and issues a warning to parents. Specifically, if an infant moves in a way that is different from normal, it sends a message saying, "There is something abnormal with your child's movements. Please check." This allows for the detection of abnormal movements of infants and a quick response.

[0032] The AI ​​processing unit can work with other smart devices in the home to adjust the environment according to the infant's movements. For example, the AI ​​processing unit analyzes video data from a smart camera and tracks the infant's movements. At the same time, it works with smart lights and smart speakers in the home to adjust the environment according to the infant's movements. For example, if the infant moves to the living room, the living room lights can be automatically turned on. This allows the environment to be automatically adjusted according to the infant's movements.

[0033] The AI ​​processing unit can simultaneously monitor the movements of all family members, learn their movement patterns, and suggest optimal times for housework. For example, the AI ​​processing unit analyzes video data from a smart camera and tracks the movements of young children. At the same time, it monitors the movements of all family members and learns their movement patterns. For example, it can identify the time when all family members gather in the living room and suggest doing housework at that time. This allows the system to understand the movements of all family members and suggest optimal times for housework.

[0034] When the warning unit detects approach to a dangerous area, it can automatically operate smart devices in the house to avoid danger. For example, the warning unit analyzes video data from a smart camera and automatically operates a smart lock to lock a door when a small child approaches a dangerous area. For example, if a small child approaches a staircase, the door to the staircase will be locked. This allows the system to detect approach to a dangerous area and automatically avoid danger.

[0035] When the warning unit detects approaching a dangerous area, it can send a warning to the parent's smartphone as well as other devices in the home. For example, the warning unit analyzes video data from a smart camera and sends a warning to the parent's smartphone when the child approaches a dangerous area. At the same time, it also sends a warning to the smartwatch, allowing the parent to respond immediately. In this way, sending warnings to multiple devices allows the parent to respond quickly.

[0036] The suggestion unit can propose play ideas that are customized according to the age and developmental stage of the child. For example, the suggestion unit uses AI to consider the age and developmental stage of the child and propose appropriate play ideas. For example, it suggests age-appropriate play activities, such as "building block play" for a two-year-old child and "simple puzzles" for a four-year-old child. This makes it possible to propose play ideas that are appropriate for the age and developmental stage of the child.

[0037] The suggestion unit can suggest personalized play ideas based on the interests and preferences of young children. For example, the suggestion unit uses AI to analyze a young child's interests and preferences and suggest play ideas based on them. For example, it would suggest "animal picture books" to a young child who likes animals. This makes it possible to suggest play ideas based on the interests and preferences of young children.

[0038] The suggestion unit can suggest play ideas that add visual and auditory effects using other smart devices in the home. For example, the suggestion unit can add visual effects using smart lights to play ideas suggested by AI. For example, when reading a picture book, the color of the light can be changed to create an atmosphere. This allows the suggestion unit to suggest play ideas that add visual and auditory effects.

[0039] The suggestion unit can suggest play ideas that include activities that can be enjoyed by the whole family. For example, the suggestion unit can include activities that can be enjoyed by the whole family in the play ideas suggested by the AI. For example, it can suggest "board games" that the whole family can enjoy together. This makes it possible to suggest play ideas that include activities that can be enjoyed by the whole family.

[0040] The suggestion unit can predict potential dangers using a predictive model based on past data. For example, the suggestion unit uses a predictive model based on past data to predict potential dangers. For example, it analyzes past accident data and issues a warning if a similar situation occurs. This makes it possible to predict potential dangers with high accuracy using a predictive model based on past data.

[0041] The suggestion unit can predict potential dangers by taking into account environmental data inside the home. For example, the suggestion unit uses AI to analyze environmental data inside the home and predict potential dangers. For example, if the temperature is high, it predicts the risk of young children suffering from heatstroke and issues a warning to parents. This makes it possible to predict potential dangers by taking into account environmental data inside the home.

[0042] The suggestion unit can issue visual and audible warnings using other smart devices in the home. For example, when the AI ​​analyzes video data from a smart camera and predicts a potential danger, the suggestion unit issues a visual warning by flashing a smart light. For example, if there is a high risk of a toddler falling, the light will flash to call attention. This allows visual and audible warnings to be issued using other smart devices in the home.

[0043] The suggestion unit can use other smart devices in the home to physically avoid danger. For example, when the AI ​​analyzes video data from a smart camera and predicts a potential danger, the suggestion unit automatically operates a smart lock to lock a door. For example, if there is a high risk of a young child falling in a particular area, the suggestion unit can lock the door in that area. This allows other smart devices in the home to physically avoid danger.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The AI ​​monitoring system can also be equipped with a voice recognition unit. This unit can analyze an infant's voice or crying in real time and detect abnormal voice patterns. For example, if an infant is crying unusually, it can send a message to the parent saying, "Your child is crying abnormally. Please check." This allows for the system to detect abnormalities in an infant's voice or crying and respond quickly.

[0046] The AI ​​monitoring system can also be equipped with a temperature sensor unit. The temperature sensor unit can monitor the temperature in each area of ​​the house in real time and detect abnormal temperature changes. For example, if the temperature in a specific area suddenly rises, a message can be sent to the parents saying, "The temperature in the living room is rising suddenly. Please check." This allows temperature changes in the house to be monitored and responded to quickly.

[0047] The AI ​​monitoring system can also be equipped with a humidity sensor. The humidity sensor can monitor the humidity in each area of ​​the house in real time and detect abnormal changes in humidity. For example, if the humidity in a specific area suddenly rises, a message can be sent to the parent saying, "The humidity in the kitchen is rising sharply. Please check." This allows the system to monitor humidity changes in the house and respond quickly.

[0048] The AI ​​monitoring system can also be equipped with an air quality sensor unit. The air quality sensor unit can monitor the air quality in each area of ​​the home in real time and detect abnormal changes in air quality. For example, if the air quality in a specific area suddenly deteriorates, a message can be sent to the parent saying, "The air quality in the bedroom has deteriorated. Please check." This allows changes in air quality in the home to be monitored and responded to quickly.

[0049] The AI ​​surveillance system can also be equipped with an illuminance sensor unit. The illuminance sensor unit can monitor the illuminance in each area of ​​the house in real time and detect abnormal changes in illuminance. For example, if the illuminance in a specific area suddenly drops, a message can be sent to the parents saying, "The illuminance in the living room has dropped. Please check." This allows changes in illuminance in the house to be monitored and responded to quickly.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The camera monitors the infant's movements in real time and captures high-resolution video, which is then sent to the AI ​​processing unit. Step 2: The AI ​​processing unit analyzes the video data captured by the camera. Specifically, it uses an image recognition algorithm to identify the child's position and movements, learns their movement patterns, and detects abnormal movements. Step 3: The warning unit detects approach to a dangerous area based on the data analyzed by the AI ​​processing unit and issues a warning, for example by sending a notification to a smartphone or tablet and displaying a warning message such as "Your child is approaching the stairs." Step 4: The suggestion unit predicts and suggests safe play ideas and potential dangers based on the data analyzed by the AI ​​processing unit. For example, if a child is playing in the living room, it will suggest safe play activities such as "playing with blocks" or "reading picture books" and display them to the child via smart devices or screens around the house.

[0052] (Example 2) The AI ​​surveillance system according to an embodiment of the present invention tracks the movements of young children in real time, detects their approach to dangerous areas, issues a warning, and predicts and suggests safe play ideas and potential dangers. This ensures the safety of young children and reduces the burden on parents.

[0053] An AI monitoring system according to an embodiment includes a camera, an AI processing unit, a warning unit, and a suggestion unit. The camera monitors a child's movements in real time. For example, the camera captures high-resolution video and sends it to the AI ​​processing unit. The AI ​​processing unit analyzes the video data captured by the camera. For example, the AI ​​processing unit uses an image recognition algorithm to identify the child's location and movements. The AI ​​processing unit can also learn the child's movement patterns and detect abnormal movements. The warning unit detects approach to a dangerous area and issues a warning based on the data analyzed by the AI ​​processing unit. For example, the warning unit sends a notification to a smartphone or tablet, displaying a warning message such as "Your child is approaching the stairs." The suggestion unit predicts and suggests safe play ideas and potential dangers based on the data analyzed by the AI ​​processing unit. For example, if a child is playing in the living room, the suggestion unit may suggest safe play activities such as "playing with blocks" or "reading a picture book" and display the suggestions to the child on a smart device or a screen in the home. As a result, the AI ​​monitoring system of the embodiment can track the movements of young children in real time, avoid danger, and suggest safe play activities, thereby ensuring the safety of young children and reducing the burden on parents.

[0054] The AI ​​processing unit can monitor the infant's body temperature and heart rate and notify parents if any abnormalities are detected. For example, the AI ​​processing unit analyzes video data from a smart camera and tracks the infant's movements in real time. At the same time, it can monitor the infant's body temperature and heart rate using a wearable device and notify parents if any abnormalities are detected. For example, if the infant's body temperature rises suddenly, an alert can be sent to the parent's smartphone. This allows abnormalities in the infant's body temperature or heart rate to be detected and responded to quickly.

[0055] The AI ​​processing unit learns the movement patterns of infants, and if abnormal movement is detected, the warning unit can issue a warning to parents. The AI ​​processing unit, for example, analyzes video data from a smart camera and learns the movement patterns of infants. For example, it detects abnormal movement based on normal movement patterns and issues a warning to parents. Specifically, if an infant moves in a way that is different from normal, it sends a message saying, "There is something abnormal with your child's movements. Please check." This allows for the detection of abnormal movements of infants and a quick response.

[0056] The AI ​​processing unit can infer emotions from the movements of an infant and notify parents if the infant is feeling stressed or anxious. For example, the AI ​​processing unit analyzes video data from a smart camera and infers emotions from the movements of an infant. For example, if an infant is feeling stressed or anxious, it can send a message to the parent saying, "Your child is feeling stressed. Please check on them." This allows the emotional state of the infant to be understood and a prompt response to be made.

[0057] The AI ​​processing unit can work with other smart devices in the home to adjust the environment according to the infant's movements. For example, the AI ​​processing unit analyzes video data from a smart camera and tracks the infant's movements. At the same time, it works with smart lights and smart speakers in the home to adjust the environment according to the infant's movements. For example, if the infant moves to the living room, the living room lights can be automatically turned on. This allows the environment to be automatically adjusted according to the infant's movements.

[0058] The AI ​​processing unit can simultaneously monitor the movements of all family members, learn their movement patterns, and suggest optimal times for housework. For example, the AI ​​processing unit analyzes video data from a smart camera and tracks the movements of young children. At the same time, it monitors the movements of all family members and learns their movement patterns. For example, it can identify the time when all family members gather in the living room and suggest doing housework at that time. This allows the system to understand the movements of all family members and suggest optimal times for housework.

[0059] The AI ​​processing unit can monitor the emotions of all family members and suggest the optimal timing for housework based on their emotional state. For example, the AI ​​processing unit can analyze video data from a smart camera and monitor the emotions of all family members. For example, it can suggest doing housework at a time when all family members are relaxed. This allows the system to grasp the emotional state of all family members and suggest the optimal timing for housework.

[0060] When the warning unit detects approach to a dangerous area, it can automatically operate smart devices in the house to avoid danger. For example, the warning unit analyzes video data from a smart camera and automatically operates a smart lock to lock a door when a small child approaches a dangerous area. For example, if a small child approaches a staircase, the door to the staircase will be locked. This allows the system to detect approach to a dangerous area and automatically avoid danger.

[0061] When the warning unit detects approaching a dangerous area, it can send a warning to the parent's smartphone as well as other devices in the home. For example, the warning unit analyzes video data from a smart camera and sends a warning to the parent's smartphone when the child approaches a dangerous area. At the same time, it also sends a warning to the smartwatch, allowing the parent to respond immediately. In this way, sending warnings to multiple devices allows the parent to respond quickly.

[0062] The warning unit can use the emotion estimation function to estimate the emotion of the infant when approaching a dangerous area and send a warning message to the parent according to the emotion. For example, the warning unit analyzes video data from a smart camera to estimate the emotion of the infant when approaching a dangerous area. For example, if the infant is excited, it sends a message to the parent saying, "Your child is excited. Please be careful." This allows the parent to respond appropriately by sending a warning message according to the infant's emotional state.

[0063] The suggestion unit can propose play ideas that are customized according to the age and developmental stage of the child. For example, the suggestion unit uses AI to consider the age and developmental stage of the child and propose appropriate play ideas. For example, it suggests age-appropriate play activities, such as "building block play" for a two-year-old child and "simple puzzles" for a four-year-old child. This makes it possible to propose play ideas that are appropriate for the age and developmental stage of the child.

[0064] The suggestion unit can suggest personalized play ideas based on the interests and preferences of young children. For example, the suggestion unit uses AI to analyze a young child's interests and preferences and suggest play ideas based on them. For example, it would suggest "animal picture books" to a young child who likes animals. This makes it possible to suggest play ideas based on the interests and preferences of young children.

[0065] The suggestion unit can use the emotion estimation function to suggest play ideas that correspond to the emotional state of the infant. For example, the suggestion unit uses AI to analyze the infant's emotional state and suggest play ideas that correspond to it. For example, if the infant is feeling stressed, it can suggest "reading a relaxing picture book." This makes it possible to suggest play ideas that correspond to the infant's emotional state.

[0066] The suggestion unit can suggest play ideas that add visual and auditory effects using other smart devices in the home. For example, the suggestion unit can add visual effects using smart lights to play ideas suggested by AI. For example, when reading a picture book, the color of the light can be changed to create an atmosphere. This allows the suggestion unit to suggest play ideas that add visual and auditory effects.

[0067] The suggestion unit can suggest play ideas that include activities that can be enjoyed by the whole family. For example, the suggestion unit can include activities that can be enjoyed by the whole family in the play ideas suggested by the AI. For example, it can suggest "board games" that the whole family can enjoy together. This makes it possible to suggest play ideas that include activities that can be enjoyed by the whole family.

[0068] The suggestion unit can use the emotion estimation function to suggest play ideas that correspond to the emotional state of each family member. For example, the suggestion unit uses AI to analyze the emotional state of each family member and suggest play ideas that correspond to that. For example, if all family members are relaxed, the suggestion unit will suggest a "relaxing board game." This allows the suggestion unit to suggest play ideas that correspond to the emotional state of each family member.

[0069] The suggestion unit can predict potential dangers using a predictive model based on past data. For example, the suggestion unit uses a predictive model based on past data to predict potential dangers. For example, it analyzes past accident data and issues a warning if a similar situation occurs. This makes it possible to predict potential dangers with high accuracy using a predictive model based on past data.

[0070] The suggestion unit can predict potential dangers by taking into account environmental data inside the home. For example, the suggestion unit uses AI to analyze environmental data inside the home and predict potential dangers. For example, if the temperature is high, it predicts the risk of young children suffering from heatstroke and issues a warning to parents. This makes it possible to predict potential dangers by taking into account environmental data inside the home.

[0071] The suggestion unit can use the emotion estimation function to predict potential danger based on the infant's emotional state and send suggestions to the parent according to the emotion. For example, the suggestion unit uses AI to analyze the infant's emotional state and predict potential danger based on that. For example, if the infant is excited, the risk of falling increases, so the suggestion unit sends the parent a message such as "Your child is excited. Please be careful." This makes it possible to predict potential danger based on the infant's emotional state and send suggestions to the parent according to the emotion.

[0072] The suggestion unit can issue visual and audible warnings using other smart devices in the home. For example, when the AI ​​analyzes video data from a smart camera and predicts a potential danger, the suggestion unit issues a visual warning by flashing a smart light. For example, if there is a high risk of a toddler falling, the light will flash to call attention. This allows visual and audible warnings to be issued using other smart devices in the home.

[0073] The suggestion unit can use other smart devices in the home to physically avoid danger. For example, when the AI ​​analyzes video data from a smart camera and predicts a potential danger, the suggestion unit automatically operates a smart lock to lock a door. For example, if there is a high risk of a young child falling in a particular area, the suggestion unit can lock the door in that area. This allows other smart devices in the home to physically avoid danger.

[0074] The suggestion unit can use the emotion estimation function to predict potential danger based on the infant's emotional state and send suggestions to the parent according to the emotion. For example, the suggestion unit uses AI to analyze the infant's emotional state and predict potential danger based on that. For example, if the infant is excited, the risk of falling increases, so the suggestion unit sends the parent a message such as "Your child is excited. Please be careful." This makes it possible to predict potential danger based on the infant's emotional state and send suggestions to the parent according to the emotion.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The AI ​​monitoring system can also be equipped with a voice recognition unit. This unit can analyze an infant's voice or crying in real time and detect abnormal voice patterns. For example, if an infant is crying unusually, it can send a message to the parent saying, "Your child is crying abnormally. Please check." This allows for the system to detect abnormalities in an infant's voice or crying and respond quickly.

[0077] The AI ​​monitoring system can also be equipped with a temperature sensor unit. The temperature sensor unit can monitor the temperature in each area of ​​the house in real time and detect abnormal temperature changes. For example, if the temperature in a specific area suddenly rises, a message can be sent to the parents saying, "The temperature in the living room is rising suddenly. Please check." This allows temperature changes in the house to be monitored and responded to quickly.

[0078] The AI ​​monitoring system can also be equipped with a humidity sensor. The humidity sensor can monitor the humidity in each area of ​​the house in real time and detect abnormal changes in humidity. For example, if the humidity in a specific area suddenly rises, a message can be sent to the parent saying, "The humidity in the kitchen is rising sharply. Please check." This allows the system to monitor humidity changes in the house and respond quickly.

[0079] The AI ​​monitoring system can also be equipped with an air quality sensor unit. The air quality sensor unit can monitor the air quality in each area of ​​the home in real time and detect abnormal changes in air quality. For example, if the air quality in a specific area suddenly deteriorates, a message can be sent to the parent saying, "The air quality in the bedroom has deteriorated. Please check." This allows changes in air quality in the home to be monitored and responded to quickly.

[0080] The AI ​​surveillance system can also be equipped with an illuminance sensor unit. The illuminance sensor unit can monitor the illuminance in each area of ​​the house in real time and detect abnormal changes in illuminance. For example, if the illuminance in a specific area suddenly drops, a message can be sent to the parents saying, "The illuminance in the living room has dropped. Please check." This allows changes in illuminance in the house to be monitored and responded to quickly.

[0081] The AI ​​monitoring system can also use emotion estimation to play music based on the infant's emotional state. For example, if the infant is feeling stressed, it can play relaxing music. This provides music that matches the infant's emotional state, helping to stabilize their emotions.

[0082] The AI ​​monitoring system can also use emotion estimation to adjust the lighting color based on the infant's emotional state. For example, if the infant is excited, the lighting color will be changed to a calmer color. This provides lighting that suits the infant's emotional state and helps stabilize their emotions.

[0083] The AI ​​monitoring system can also use emotion estimation to suggest toys based on the child's emotional state. For example, if a child is bored, it can suggest a new toy. This allows the system to provide toys that suit the child's emotional state and stabilize their emotions.

[0084] The AI ​​monitoring system can also use emotion estimation to suggest meals based on the infant's emotional state. For example, if the infant is feeling stressed, it can suggest a meal that will help them relax. This allows the system to provide meals that are appropriate for the infant's emotional state, helping to stabilize their emotions.

[0085] The AI ​​monitoring system can also use emotion estimation to suggest play locations based on the child's emotional state. For example, if a child is feeling anxious, it will suggest a safe place to play. This allows the system to provide play locations that suit the child's emotional state and promote emotional stability.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The camera monitors the infant's movements in real time and captures high-resolution video, which is then sent to the AI ​​processing unit. Step 2: The AI ​​processing unit analyzes the video data captured by the camera. Specifically, it uses an image recognition algorithm to identify the child's position and movements, learns their movement patterns, and detects abnormal movements. Step 3: The warning unit detects approach to a dangerous area based on the data analyzed by the AI ​​processing unit and issues a warning, for example by sending a notification to a smartphone or tablet and displaying a warning message such as "Your child is approaching the stairs." Step 4: The suggestion unit predicts and suggests safe play ideas and potential dangers based on the data analyzed by the AI ​​processing unit. For example, if a child is playing in the living room, it will suggest safe play activities such as "playing with blocks" or "reading picture books" and display them to the child via smart devices or screens around the house.

[0088] 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.

[0089] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0155] 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. Equipped with a camera, an AI processing unit that analyzes video data acquired by the camera; a warning unit that detects approach to a dangerous area and issues a warning based on the data analyzed by the AI ​​processing unit; and a suggestion unit that predicts and suggests safe play ideas and potential dangers based on the data analyzed by the AI ​​processing unit. A system characterized by:

2. The AI ​​processing unit Monitors the infant's temperature and heart rate and notifies the parent if any abnormalities occur.

2. The system of claim 1.

3. The AI ​​processing unit Connects with other smart devices in the home and adjusts the environment based on your child's movements 2. The system of claim 1.

4. The warning unit When approaching the dangerous area is detected, the smart device is automatically operated to avoid the danger.

2. The system of claim 1.

5. The proposal unit Propose ideas for the above-mentioned games that are customized according to the age and developmental stage of the child.

2. The system of claim 1.

6. The proposal unit Using an emotion estimation function, the prediction of the potential danger based on the infant's emotional state and the emotion-dependent suggestion are sent to the parent.

2. The system of claim 1.

7. The AI ​​processing unit Inferring emotions from infant movements and notifying parents if the infant is experiencing stress or anxiety 2. The system of claim 1.

8. The warning unit Using an emotion estimation function, the emotion of the infant when approaching the danger area is estimated, and a warning message according to the emotion is sent to the parent.

2. The system of claim 1.

Images