system

Abstract

We provide the system. [Solution] Image processing means for acquiring video data from within a childcare facility or home and analyzing the behavior of minors from said video data, A generative AI method that automatically generates natural language text based on analysis results, A data transfer method that outputs generated natural language text in a format that can be edited by educators, and distributes the edited information to parents, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, in childcare facilities, it has become an issue that childcare workers spend a lot of time on daily communication tasks. As a result, the time dedicated to the original tasks of childcare workers is limited, leading to a decrease in work efficiency. In addition, since parents cannot fully grasp the eating situation of their children, there is also a problem that it is difficult to improve meals at home. There is a need to solve such problems and achieve the improvement of childcare work efficiency and the provision of sufficient information to parents.

Means for Solving the Problems

[0005] This invention provides a system that automatically analyzes childcare activities and mealtimes based on image data and automatically generates relevant text and suggestions, in order to improve operational efficiency within childcare facilities and enhance the provision of information to parents. Specifically, it includes means for acquiring image data within childcare facilities, means for analyzing the image data to extract information on children's activities and mealtimes, means for automatically generating communication log entries and mealtime improvement suggestions based on the analysis results, and means for childcare workers to modify the generated information and distribute it to parents. By encompassing these elements, it is possible to improve operational efficiency and the quality of information provision.

[0006] "Image data" refers to visual information recorded by cameras and sensors within childcare facilities, documenting children's activities and eating habits.

[0007] "Analysis means" refers to algorithms and systems that process acquired image data to identify the child's behavior and eating habits.

[0008] A "generation method" refers to a system that includes AI for automatically creating natural language texts and suggestions from analyzed data.

[0009] A "childcare worker" is a professional who is responsible for the care and education of children in a childcare facility.

[0010] "Parent / guardian" refers to the parent or legal guardian of a child attending a childcare facility.

[0011] "Distribution method" refers to a system that provides generated information to users (parents) via devices such as tablets and smartphones.

[0012] A "communication log entry" is a document created to record a child's daily activities and health at a childcare facility and to report them to their parents.

[0013] A "dietary improvement plan" refers to recipes and methods proposed based on analyzed dietary data, aimed at improving children's nutrition and eating habits. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Next, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention presents a specific form of a system that utilizes image data analysis and generation AI to efficiently record and report on activities and mealtimes within childcare facilities. This system is implemented through the collaboration of a server, terminals, and users.

[0036] The server acquires image data in real time from multiple cameras installed within the facility. This image data records the children's behavior during childcare activities and mealtimes, and serves as the basis for analysis. The server uses image analysis AI to analyze the acquired image data and extract specific information about the children's actions and eating habits (e.g., how much they ate, what they showed interest in).

[0037] The extracted data is stored in the cloud and processed by a generative AI. The server activates the generative AI, which, based on the analyzed data, automatically creates communication log entries suitable for reporting to parents and suggestions for improving meals. For example, it generates specific information such as "Today, C-chan was engrossed in playing with blocks" as an activity record, and "C-chan left half of her vegetables" as a meal report.

[0038] Childcare workers using the terminals review the generated content and make corrections as needed. Once the childcare workers have finished inputting the information, the revised text and suggestions are saved again to the cloud and ready to be distributed to parents.

[0039] Parents, as users, can receive the latest reports and suggestions through the app on their smartphones or tablets. This allows parents to easily stay informed about how their children are doing at the childcare facility and to get up-to-date information about their meals.

[0040] This system will reduce the administrative burden on childcare workers and streamline communication with parents. Furthermore, it will provide parents with added value by offering suggestions regarding their child's food preferences and nutritional balance.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The server acquires image data in real time from cameras within the childcare facility. The image data includes footage of children's activities and how they eat.

[0044] Step 2:

[0045] The server uses image analysis AI to analyze the acquired image data and extract information about the child's actions (e.g., playing, eating) and eating habits (e.g., how much they ate, what they ate).

[0046] Step 3:

[0047] The server saves the analysis results as structured data to a cloud database. This saved data is then used for subsequent processing.

[0048] Step 4:

[0049] The server accesses the analysis data stored in the cloud and activates the generation AI. The generation AI automatically generates communication log entries for childcare workers based on the analysis data.

[0050] Step 5:

[0051] Childcare workers using the terminal can review the text generated by the server and modify the content as needed.

[0052] Step 6:

[0053] The server saves the revised text from the childcare worker back to the cloud and prepares it for distribution to the parents.

[0054] Step 7:

[0055] Parents, as users, receive contact log reports via the app on their smartphones or tablets. This allows parents to accurately understand their child's activities and eating habits.

[0056] Step 8:

[0057] The server analyzes image data of the next meal and evaluates the amount of food left over. Based on the analysis results, the AI ​​generates suggestions for improving the diet.

[0058] Step 9:

[0059] Childcare workers using terminals review the proposals and make revisions as needed. The revised proposals are saved to the cloud and prepared for distribution to parents.

[0060] Step 10:

[0061] Parents, as users of the app, can receive suggestions for improving their children's diets and use them to help them implement these changes at home.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] There is a need to efficiently manage records of children's activities and meals in childcare facilities and to report them to parents promptly. However, currently, manual record-keeping and reporting are time-consuming, placing a heavy burden on childcare workers, and there are issues with the accuracy of the information and the frequency of reporting. Furthermore, it is difficult to provide concrete suggestions for improvement regarding meals. A system is needed to address these problems, reduce the burden on childcare workers, and improve the quality of childcare.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes an analysis means for acquiring image information and analyzing the subject's behavior from the image information, a storage means for storing the analyzed behavior data in an information processing device, and a generation means for automatically generating natural language based on the analysis results. This makes it possible to automatically analyze children's activities and eating habits and generate reports, significantly reducing the workload of childcare workers.

[0067] "Image information" refers to visual data acquired by devices such as cameras and sensors, and includes elements that indicate the subject's actions and circumstances.

[0068] "Analysis means" refers to the techniques and processes used to analyze acquired data and identify the behavior or state of the subject.

[0069] An "information processing device" refers to a device or system that stores, processes, and manages data, and generally includes servers and computers.

[0070] "Storage means" refers to functions or systems for recording and saving the analyzed data.

[0071] "Generation means" refers to functions and technologies that automatically create natural language text based on analyzed data.

[0072] "Natural language" refers to the language that humans use on a daily basis, a form of communication that utilizes grammar and vocabulary.

[0073] "Distribution method" refers to the functions and technologies used to deliver generated information to specific recipients.

[0074] This invention is a system for efficiently recording children's activities and mealtimes within childcare facilities and reporting this information to their parents. The system primarily utilizes a server, terminals, and user smart devices. Details are provided below.

[0075] The server acquires image information in real time from multiple cameras installed within the childcare facility. The cameras are positioned in key activity areas within the facility, and image processing libraries such as OpenCV are used to analyze the image data. For specific data analysis, an image analysis AI is used to identify children's behavior and eating habits. The server stores the analyzed data in cloud storage, which is an information processing device, and further uses a generative AI model, such as GPT-3®, to generate reports and suggestions in natural language based on the analyzed information. The server inputs prompt sentences into the generative AI, which automatically creates reports for parents and suggestions for improving meals.

[0076] As a concrete example, the server generates a report by inputting the following prompt message into the AI:

[0077] Activity report generation prompt: "Please record today's activities and create a report."

[0078] Prompt for generating meal report: "Please record what C-chan ate today and create a report including any necessary advice."

[0079] Childcare workers using the terminals review the generated reports and make corrections as needed. They can access the text fields and make corrections through a dedicated application. Once corrections are complete, the information is saved to the cloud again.

[0080] Parents, who are the users, receive reports through a dedicated app on their smartphones or tablets. The server utilizes a notification function to inform parents in real time when the latest reports or suggestions have arrived.

[0081] The above configuration reduces the administrative burden on childcare workers and enables efficient communication with parents. Furthermore, it can provide specific advice regarding children's diets and deliver value-added information to parents.

[0082] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0083] Step 1:

[0084] The server acquires image information in real time from cameras installed within the childcare facility. The input is real-time video transmitted from multiple cameras. The server receives this video data via the network and performs initial processing to save it as digital data. The output of this step is image data converted into a format necessary for analysis.

[0085] Step 2:

[0086] The server analyzes the acquired image information using an image analysis AI. The input is the image data obtained in step 1. Specifically, the server uses the OpenCV library to extract features from the image and perform tasks such as face recognition and motion analysis. As a result of the analysis, data related to the child's behavior and eating habits is output.

[0087] Step 3:

[0088] The server stores the data obtained from the analysis in cloud storage. The input is the behavioral and dietary data extracted in step 2. The server transfers the data using the cloud API and stores it securely and efficiently. The output of this step is the analysis data stored in the cloud.

[0089] Step 4:

[0090] The server automatically generates reports and proposals based on the analysis results. The input is the analysis data saved in step 3. Specifically, the server inputs prompt sentences into the generation AI model and obtains natural language sentences in response. The output is a report or proposal to be distributed to the parents.

[0091] Step 5:

[0092] Childcare workers using the terminal review the generated report content and make corrections as needed. The input is the text generated in step 4. Childcare workers check the content through a dedicated application and edit it as necessary. The output is the revised report or proposal document.

[0093] Step 6:

[0094] The server saves the information confirmed and corrected on the device back to the cloud and prepares it for distribution. The input is the corrected data obtained in step 5. The server receives the corrected data and overwrites it in cloud storage. The output of this step is the data ready to be distributed to the parent / guardian.

[0095] Step 7:

[0096] The user (parent) receives the latest reports and suggestions on their smart device. The input is the corrected data saved in step 6. Parents receive information through a dedicated app on their smart device and are notified of new information arrivals via push notifications. The output is the reports and suggestions displayed to the parent.

[0097] (Application Example 1)

[0098] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0099] In modern homes and childcare facilities, it is crucial to understand the behavior and eating habits of minors and provide appropriate guidance and advice based on that understanding. However, efficiently implementing this is a significant burden. In addition, it is not easy for parents to receive accurate daily reports and gain insights into their child's development and eating habits. In this situation, there is a need to automate the analysis of minors' behavior and eating habits and to provide effective information.

[0100] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0101] In this invention, the server includes image processing means for acquiring video data from within a childcare facility or home and analyzing the behavior of minors from the video data; generation AI means for automatically generating natural language text based on the analysis results; and data transfer means for outputting the generated natural language text in a format that can be edited by educators and distributing the edited information to parents. This makes it possible to accurately and efficiently grasp the activities and eating habits of minors and to provide appropriate information to parents.

[0102] A "childcare facility" refers to a facility that provides care and education for minors, and is primarily an institution that supports daytime activities.

[0103] "Within the home" generally refers to the living space in which an individual resides, and is the primary environment in which a child's activities and development are observed.

[0104] "Video data" refers to visual information acquired using cameras or other recording devices, and is the content that forms the basis for analysis.

[0105] The term "minor" refers to individuals who have not yet reached the legal age of majority, and usually refers to children and adolescents.

[0106] "Image processing means for analyzing behavior" refers to technical processing methods used to extract specific behavioral patterns or characteristics from video data.

[0107] "Generative AI methods for automatically generating natural language text" refers to artificial intelligence technology used to generate text in human language based on analyzed data.

[0108] "Outputting information in a format that can be edited by the training staff" means that the generated information is provided in a format that can be easily modified or corrected by the relevant experts.

[0109] "Data transfer means" refers to communication methods used to deliver generated information to users in different locations, and typically involves using the internet.

[0110] "Network storage devices" refer to digital storage systems for storing data that are accessible via the internet or intranet.

[0111] An "application" refers to a software program designed to perform a specific function, and is typically used on smartphones and tablets.

[0112] The system for carrying out this invention consists of a server, a terminal, and a user.

[0113] The server acquires video data in real time from multiple cameras installed within childcare facilities or homes. These cameras capture the behavior and eating habits of minors, and the video data is analyzed using image processing technology. This analysis utilizes AI models based on TENSORFLOW® to extract specific behavioral patterns and eating habits. The analyzed data is stored in a network-based storage device.

[0114] Next, the server uses a GPT model from OpenAI (registered trademark) as a generation AI means to generate natural language reports and dietary improvement suggestions from the analysis results. The generated content can be reviewed by educators and childcare workers via their devices and modified as needed. A general-purpose tablet device or desktop computer is used as the platform for this purpose.

[0115] Parents, as users, can receive reports and suggestions delivered from the server via an application using their smartphones or tablets. This makes it easier for parents to understand their child's daily activities and eating habits.

[0116] As a concrete example, a server acquires video data of a child playing at home, and an AI automatically generates natural language messages such as "Today, the child played with blocks a lot" or "The child ate almost all of their salad." These generated messages are then sent to the parents via an app.

[0117] An example of a prompt message is, "Generate a daily report for parents based on the following data: Behavior: Playing with blocks, Expression: Happy, Meal: Finished the salad." In this way, a system is built that achieves the objectives of the present invention through the collaboration of the server, terminal, and user.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] The server acquires video data in real time from cameras installed within childcare facilities or homes. This video data captures the behavior and eating habits of minors and is used as basic data for analysis. The input is video data from the cameras, and the output is raw video data stored on the server. Specifically, the server continuously receives feeds from the cameras and saves them to storage.

[0121] Step 2:

[0122] The server analyzes the acquired video data using image processing tools. This process uses an image recognition model based on TensorFlow to extract specific behavioral patterns and eating patterns. The input is the video data saved in step 1, and the output is the analysis results data of behavior and eating. Specifically, the AI ​​model recognizes specific movements and objects in the video data (e.g., identifying food being eaten) and outputs that information as numerical data.

[0123] Step 3:

[0124] The server generates prompts using a generative AI based on the analysis results, and then uses these prompts to generate reports and improvement suggestions in natural language. Here, the OpenAI GPT model is used. The input is the analysis result data from step 2 and the generated prompt text, and the output is a report and suggestion text in natural language format. Specifically, the generative AI translates the analysis results into text, selects the appropriate language according to the prompt, and constructs the text.

[0125] Step 4:

[0126] The educator using the device will review the generated text and make corrections as needed. Here, the text generated by the generation AI is editable by the educator via the UI. The input is the text generated in step 3, and the output is the final corrected text. Specifically, the educator previews the text on the screen and makes corrections using the editing tools as needed.

[0127] Step 5:

[0128] The user (parent) receives the corrected results via the app. Here, the server sends the corrected report and suggestions to the user's application via the cloud. The input is the corrected text from step 4, and the output is the report content displayed on the parent's device. Specifically, the server pushes the data via the cloud service, and the user's app displays it as a notification.

[0129] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0130] This invention provides a system for efficiently recording children's activities and eating habits in childcare facilities and analyzing their emotional states, thereby enabling more sophisticated responses. This system is implemented in a form that includes the following elements.

[0131] The server acquires image data in real time from cameras installed within the childcare facility and analyzes the image data. This analysis is unique in that, in addition to observing children's behavior and eating habits, it uses an emotion engine to recognize emotions from children's facial expressions and gestures. Specifically, it can detect smiles, crying faces, and expressions of concentration.

[0132] Based on the analysis results, the server activates a generation AI to automatically generate activity suggestions and environmental adjustment proposals that reflect the emotional state. For example, if the analysis results indicate that the child is satisfied with their toys, it will generate a suggestion such as, "We recommend providing a different type of toy." Regarding meals, if an emotion such as "the child is making a displeased face" is recognized, it will suggest improvements to the meal environment, such as, "Please consider changing the type of food."

[0133] Childcare workers using the terminals review the generated suggestions and make corrections as needed. This revised data is saved to the cloud and prepared for distribution to parents.

[0134] Parents, as users, receive reports and suggestions through the app on their smartphones or tablets. This allows parents to comprehensively understand their child's care situation, including their emotional state, and utilize this information to improve their parenting at home.

[0135] This system supports the work of childcare workers and provides a better child-rearing environment by suggesting childcare activities that take into account the psychological state of children. Furthermore, it aims to deepen communication with parents and jointly support children's growth through insights and suggestions based on emotional data.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The server acquires image data in real time from cameras within the childcare facility. This image data includes footage of children's activities and mealtimes.

[0139] Step 2:

[0140] The server inputs image data into an image analysis AI and emotion engine to analyze the child's behavior and emotions. For example, it extracts information such as "playing with toys" or "smiling."

[0141] Step 3:

[0142] The server structures the analyzed behavioral and emotional data and stores it in a cloud database. The stored data is then used for subsequent generation processes.

[0143] Step 4:

[0144] The server activates a generation AI based on analysis data stored in the cloud. The generation AI automatically generates suggestions and environmental adjustment proposals for childcare workers based on children's activities and emotional states.

[0145] Step 5:

[0146] Childcare workers using the terminals review the suggestions generated by the server and consider whether the content is appropriate. They revise the suggestions as needed.

[0147] Step 6:

[0148] Using a terminal, the childcare worker sends the corrected data to the server and saves it again to the cloud. The server then prepares for transmission.

[0149] Step 7:

[0150] Parents, as users, receive reports and generated suggestions from childcare providers through a smartphone or tablet app. This allows them to understand their child's activity level and emotional state.

[0151] Step 8:

[0152] The server then acquires image data from the next meal and analyzes the behavior and emotions in the same way. Based on the analysis results, it generates improvement suggestions based on the emotional state related to the meal.

[0153] Step 9:

[0154] Using a terminal, childcare workers review the meal improvement proposals and make revisions if necessary. The revised proposals are then sent back to the server.

[0155] Step 10:

[0156] Parents, as users of the app, can receive suggestions for improving meals and use them to improve meals at home.

[0157] (Example 2)

[0158] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0159] In current childcare facilities, it is difficult to closely monitor children's activities and emotional states and provide appropriate support based on that information. Furthermore, there is a lack of means to provide parents with comprehensive information, including their child's emotional state. This leads to increased burdens on childcare workers and limited communication with parents.

[0160] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0161] In this invention, the server includes an analysis means for acquiring image information within a childcare facility and analyzing the child's behavior and emotions from the image information; a generation means for automatically generating activity suggestions corresponding to the emotional state based on the analysis results; and a means for outputting the generated activity suggestions in a format that can be modified by caregivers and distributing the modified information to caregivers. This makes it possible to closely track the emotional state and activities of children and to make suggestions based on them in a timely manner.

[0162] A "childcare facility" is a place where infants and children are temporarily entrusted to caregivers and receive education and assistance.

[0163] "Image information" refers to visual data acquired using a camera or other imaging device.

[0164] "Analysis of behavior and emotion" is a process of observing a child's movements and facial expressions from image information and determining their psychological state based on that.

[0165] "Analysis means" refers to equipment and software used for handling, verifying, and evaluating data according to specific purposes.

[0166] "Generative means" refers to functions that include programs and algorithms that create new proposals or content based on analysis results.

[0167] An "activity suggestion" is a recommendation for appropriate activities and support based on a child's emotional state and behavior.

[0168] A "childcare worker" refers to a professional who is responsible for the care and education of children within a childcare facility.

[0169] "Caregiver" refers to a parent or guardian who entrusts their child to a childcare facility.

[0170] "Outputting in a modifiable state" refers to the process of presenting information in a way that allows users to change or adjust the content.

[0171] "Storing in the cloud" refers to securely storing data on a remote server via the internet.

[0172] A "terminal" refers to an electronic device used by a user to receive or manipulate information.

[0173] This invention is a system that efficiently records and analyzes children's activities and emotions in childcare facilities to suggest appropriate activities.

[0174] Hardware and software used

[0175] Server Role

[0176] The server acquires image information in real time from cameras installed within the facility. The server uses an image processing library (e.g., OpenCV) to convert the video into still images and extracts specific frames for analysis. During this analysis process, the server utilizes a deep learning model (e.g., TensorFlow) to analyze the child's facial expressions from the image information and recognize their emotions.

[0177] Generative AI Models

[0178] Based on the analysis results, the server uses a generative AI model to create activity suggestions based on emotional states. The generative AI model used here is one that excels at natural language generation (e.g., a GPT-based model).

[0179] Terminal role

[0180] The device is used by caregivers to review and modify activity suggestions generated by the AI ​​model. Caregivers modify the suggestions on a tablet or other device, and the modified information is uploaded to the cloud.

[0181] User roles

[0182] The user, who is the caregiver, receives the revised suggestions from the app using their own communication device (e.g., smartphone or tablet). The app displays the suggestions in an easy-to-understand manner and provides the user with a comprehensive overview of their child's care situation.

[0183] Specific example

[0184] From a video showing a child repeatedly smiling, a generative AI model creates a suggestion that "the child may be satisfied with the toy." The actual activity suggestion then includes specific details such as, "We recommend providing a different type of toy."

[0185] Example of a prompt

[0186] The generation AI model is prompted with the following message: "Based on the analysis of the child's facial expressions, please generate activity suggestions that reflect their emotional state."

[0187] This invention allows caregivers to propose activities tailored to children's needs based on analyzed emotional data, and to accurately communicate the child's situation to caregivers. As a result, the quality of childcare and upbringing can be improved.

[0188] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0189] Step 1:

[0190] The server acquires image information in real time from cameras installed within the facility. It receives raw video data from the cameras as input. Specifically, the server uses an image processing library to convert the video into still images and extracts specific important frames. The output is still image data converted into an analyzable format.

[0191] Step 2:

[0192] The server performs emotion analysis using the acquired still image data. At this stage, the still image data serves as input. Specifically, the server uses a deep learning model to analyze the child's facial expressions and assigns emotion labels such as smile, crying, and surprised. The output is data representing the emotional state corresponding to each image.

[0193] Step 3:

[0194] The server generates activity suggestions using a generative AI model based on emotional state data. Emotional state data is provided as input. The server incorporates this into prompt statements, inputs them into the generative AI model, and generates activity and environmental suggestions that are appropriate for the child's emotions. The output is a specific activity suggestion. For example, a suggestion such as "recommend providing a wider variety of toys."

[0195] Step 4:

[0196] The terminal allows caregivers to review and modify generated activity proposals. Input consists of activity proposals sent from the server. Caregivers can view the displayed proposals on a tablet and make modifications as needed. Output is the modified proposal data, which is uploaded to the cloud.

[0197] Step 5:

[0198] The user, a caregiver, receives revised suggestions via their device. The input is revised suggestion data retrieved from the cloud. The user views the suggestions using a smartphone app and uses them to improve parenting at home. The output is useful parenting information that the caregiver can obtain.

[0199] (Application Example 2)

[0200] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0201] In modern households, there is a need for efficient means to appropriately understand children's activities and emotional states and to provide appropriate childcare support. However, current technologies have made it difficult to accurately analyze a child's situation and provide appropriate information to parents. In particular, a system that automatically generates and distributes activity suggestions and dietary improvement plans based on each child's individual emotional state has not yet been developed. As a result, there is a challenge in effectively providing childcare support within the home and reducing the burden on parents.

[0202] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0203] In this invention, the server includes an analysis means for acquiring image information and analyzing a person's activities from the image information; a generation means for automatically generating text based on the analysis results; a means for outputting the generated text in a format that can be edited by a caregiver and distributing the edited information to other users; and a generation means for analyzing a person's emotional state and generating activity-related suggestions based on the emotional analysis results. This enables guardians to more accurately understand their child's activities and emotions within the home and to provide childcare support more efficiently.

[0204] "Image information" refers to visual data acquired using cameras or other imaging devices.

[0205] "Analysis means" refers to a device or software that has the function of analyzing the operation or state using a specific algorithm or program with the acquired data.

[0206] "Generation means" refers to a device or software that has the function of automatically creating new proposals, reports, and other content from analyzed data and information.

[0207] A "caregiver" is an individual who takes on the role of caring for others or supporting childcare in a home or institutional setting.

[0208] "Editable" means that users can make changes or corrections to the generated text or data.

[0209] "Distribution means" refers to a communication device or software used to transmit generated information to the intended recipient.

[0210] "Emotional state" refers to an individual's internal psychological condition, encompassing the type and intensity of emotions that can be perceived through facial expressions and behavior.

[0211] A "remote storage device" is a data storage medium located in a physically distant location, such as a cloud server, and is accessible via the internet.

[0212] "Application software" refers to programs that provide functions tailored to specific purposes and are primarily installed on the user's device.

[0213] The system used to implement this invention operates in conjunction with consumer robots that are compatible with home environments. The central components of the system are a camera and analysis engine for acquiring image information and analyzing human activity and emotions from that information. The camera is positioned to capture various scenes within the home. Specifically, an open-source computer vision library (e.g., OpenCV) can be used as the analysis engine.

[0214] The server receives image information from the camera and performs analysis using specific software modules such as EmotionEngine and ActivityAI. EmotionEngine runs an algorithm that analyzes and recognizes the child's emotional state based on the images, identifying emotions such as smiles and anxiety. ActivityAI has the function of automatically generating appropriate activity suggestions based on these analysis results.

[0215] Furthermore, the generation AI model receives the analysis results and constructs appropriate sentences and suggestions. This makes it possible to provide parents, who are the users, with intuitive and useful information about their children's activities. For example, if a child is engrossed in playing with a particular toy, a suggestion such as, "It seems that the child is enjoying playing with this toy. Perhaps you could try other types of toys?" might be generated.

[0216] As an example, here is an example of a prompt statement:

[0217] "A child's smile has been detected. Please generate new play suggestions."

[0218] These entire systems utilize cloud computing to generate information, store it in remote storage, and deliver it to the parent's device. The information can be delivered to the user via application software for common smartphones. This allows users to easily receive the latest information about their children at any time.

[0219] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0220] Step 1:

[0221] The server acquires image information from the camera in real time. The input is video data captured by the camera. The server receives this data and loads it into buffer memory in preparation for the next analysis step. The output is video frames that can be analyzed.

[0222] Step 2:

[0223] The server uses EmotionEngine to analyze the acquired video frames. The input is the video frames obtained in step 1. EmotionEngine processes this data and runs an algorithm to recognize emotions from facial expressions. The output is the identified emotion information (e.g., smile, anxiety, etc.).

[0224] Step 3:

[0225] The server uses ActivityAI to generate activity suggestions based on emotional information. The input is the emotional information obtained in step 2. ActivityAI analyzes this information and generates relevant activity suggestions. The output is a suggestion such as, "Why not try some other types of toys?"

[0226] Step 4:

[0227] The generated proposal is further improved using a generative AI model. The input is the proposal generated in step 3. The generative AI uses natural language processing techniques to translate it into more user-friendly language and include additional information. The output is the final proposal.

[0228] Step 5:

[0229] The final proposal is delivered to the user's device. The server stores this information in remote storage and provides it to the user via a smartphone or tablet. The input is the final proposal obtained in step 4. The output is the notification or proposal presented to the user on their device.

[0230] By ensuring that each step is processed correctly, the entire system functions smoothly, and users can obtain useful information for childcare support at home.

[0231] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0232] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0233] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0234] [Second Embodiment]

[0235] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0236] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0237] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0238] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0239] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0240] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0241] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0242] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0243] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0244] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0245] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0246] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0247] This invention presents a specific form of a system that utilizes image data analysis and generation AI to efficiently record and report on activities and mealtimes within childcare facilities. This system is implemented through the collaboration of a server, terminals, and users.

[0248] The server acquires image data in real time from multiple cameras installed within the facility. This image data records the children's behavior during childcare activities and mealtimes, and serves as the basis for analysis. The server uses image analysis AI to analyze the acquired image data and extract specific information about the children's actions and eating habits (e.g., how much they ate, what they showed interest in).

[0249] The extracted data is stored in the cloud and processed by a generative AI. The server activates the generative AI, which, based on the analyzed data, automatically creates communication log entries suitable for reporting to parents and suggestions for improving meals. For example, it generates specific information such as "Today, C-chan was engrossed in playing with blocks" as an activity record, and "C-chan left half of her vegetables" as a meal report.

[0250] Childcare workers using the terminals review the generated content and make corrections as needed. Once the childcare workers have finished inputting the information, the revised text and suggestions are saved again to the cloud and ready to be distributed to parents.

[0251] Parents, as users, can receive the latest reports and suggestions through the app on their smartphones or tablets. This allows parents to easily stay informed about how their children are doing at the childcare facility and to get up-to-date information about their meals.

[0252] This system will reduce the administrative burden on childcare workers and streamline communication with parents. Furthermore, it will provide parents with added value by offering suggestions regarding their child's food preferences and nutritional balance.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The server acquires image data in real time from cameras within the childcare facility. The image data includes footage of children's activities and how they eat.

[0256] Step 2:

[0257] The server uses image analysis AI to analyze the acquired image data and extract information about the child's actions (e.g., playing, eating) and eating habits (e.g., how much they ate, what they ate).

[0258] Step 3:

[0259] The server saves the analysis results as structured data to a cloud database. This saved data is then used for subsequent processing.

[0260] Step 4:

[0261] The server accesses the analysis data stored in the cloud and activates the generation AI. The generation AI automatically generates communication log entries for childcare workers based on the analysis data.

[0262] Step 5:

[0263] Childcare workers using the terminal can review the text generated by the server and modify the content as needed.

[0264] Step 6:

[0265] The server saves the revised text from the childcare worker back to the cloud and prepares it for distribution to the parents.

[0266] Step 7:

[0267] Parents, as users, receive contact log reports via the app on their smartphones or tablets. This allows parents to accurately understand their child's activities and eating habits.

[0268] Step 8:

[0269] The server analyzes image data of the next meal and evaluates the amount of food left over. Based on the analysis results, the AI ​​generates suggestions for improving the diet.

[0270] Step 9:

[0271] Childcare workers using terminals review the proposals and make revisions as needed. The revised proposals are saved to the cloud and prepared for distribution to parents.

[0272] Step 10:

[0273] Parents, as users of the app, can receive suggestions for improving their children's diets and use them to help them implement these changes at home.

[0274] (Example 1)

[0275] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0276] There is a need to efficiently manage records of children's activities and meals in childcare facilities and to report them to parents promptly. However, currently, manual record-keeping and reporting are time-consuming, placing a heavy burden on childcare workers, and there are issues with the accuracy of the information and the frequency of reporting. Furthermore, it is difficult to provide concrete suggestions for improvement regarding meals. A system is needed to address these problems, reduce the burden on childcare workers, and improve the quality of childcare.

[0277] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0278] In this invention, the server includes an analysis means for acquiring image information and analyzing the subject's behavior from the image information, a storage means for storing the analyzed behavior data in an information processing device, and a generation means for automatically generating natural language based on the analysis results. This makes it possible to automatically analyze children's activities and eating habits and generate reports, significantly reducing the workload of childcare workers.

[0279] "Image information" refers to visual data acquired by devices such as cameras and sensors, and includes elements that indicate the subject's actions and circumstances.

[0280] The "analysis means" refers to the technology or process for analyzing the acquired data to identify the target behavior or state.

[0281] The "information processing device" refers to a device or system for storing, processing, and managing data, generally corresponding to a server or a computer.

[0282] The "storage means" refers to the function or system for recording and storing the analyzed data.

[0283] The "generation means" refers to the function or technology for automatically creating a natural language sentence based on the analysis data.

[0284] The "natural language" refers to the language that humans use daily, which is a form of information transmission using grammar and vocabulary.

[0285] The "distribution means" refers to the function or technology for delivering the generated information to specific recipients.

[0286] This invention is a system for efficiently recording the activities and meal situations of children in a childcare facility and reporting them to the guardians. This system mainly uses a server, a terminal, and the user's smart device. The details are shown below.

[0287] The server acquires image information in real time from a plurality of cameras installed in the childcare facility. The cameras are arranged in the main activity areas within the facility and utilize an image processing library such as OpenCV to analyze the image data. For specific data analysis, an image analysis AI is used to identify the behavior and meal status of children. The server stores the analyzed data in a cloud storage, which is an information processing device, and further uses a generation AI model, such as GPT-3, to generate a report or proposal in natural language based on the analyzed information. The server inputs a prompt sentence into the generation AI to automatically create a report to the guardians and a proposal for meal improvement.

[0288] As a concrete example, the server generates a report by inputting the following prompt message into the AI:

[0289] Activity report generation prompt: "Please record today's activities and create a report."

[0290] Prompt for generating meal report: "Please record what C-chan ate today and create a report including any necessary advice."

[0291] Childcare workers using the terminals review the generated reports and make corrections as needed. They can access the text fields and make corrections through a dedicated application. Once corrections are complete, the information is saved to the cloud again.

[0292] Parents, who are the users, receive reports through a dedicated app on their smartphones or tablets. The server utilizes a notification function to inform parents in real time when the latest reports or suggestions have arrived.

[0293] The above configuration reduces the administrative burden on childcare workers and enables efficient communication with parents. Furthermore, it can provide specific advice regarding children's diets and deliver value-added information to parents.

[0294] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0295] Step 1:

[0296] The server acquires image information in real time from cameras installed within the childcare facility. The input is real-time video transmitted from multiple cameras. The server receives this video data via the network and performs initial processing to save it as digital data. The output of this step is image data converted into a format necessary for analysis.

[0297] Step 2:

[0298] The server analyzes the acquired image information using an image analysis AI. The input is the image data obtained in step 1. Specifically, the server uses the OpenCV library to extract features from the image and perform tasks such as face recognition and motion analysis. As a result of the analysis, data related to the child's behavior and eating habits is output.

[0299] Step 3:

[0300] The server stores the data obtained from the analysis in cloud storage. The input is the behavioral and dietary data extracted in step 2. The server transfers the data using the cloud API and stores it securely and efficiently. The output of this step is the analysis data stored in the cloud.

[0301] Step 4:

[0302] The server automatically generates reports and proposals based on the analysis results. The input is the analysis data saved in step 3. Specifically, the server inputs prompt sentences into the generation AI model and obtains natural language sentences in response. The output is a report or proposal to be distributed to the parents.

[0303] Step 5:

[0304] Childcare workers using the terminal review the generated report content and make corrections as needed. The input is the text generated in step 4. Childcare workers check the content through a dedicated application and edit it as necessary. The output is the revised report or proposal document.

[0305] Step 6:

[0306] The server saves the information confirmed and corrected on the terminal back to the cloud again and prepares for distribution. The input is the corrected data obtained in step 5. The server receives the corrected data and overwrites and saves it in cloud storage. The output of this step is the data ready to be distributed to the guardian.

[0307] Step 7:

[0308] The guardian, who is the user, receives the latest reports and suggestions on their smart terminal. The input is the corrected data saved in step 6. The guardian receives the information through the dedicated app on the smart device and can know the arrival of new information through Push notifications. The output is the reports and suggestions displayed to the guardian.

[0309] (Application Example 1)

[0310] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0311] In modern families and childcare facilities, it is important to grasp the behavior and eating patterns of minors and provide appropriate guidance and advice based on them. However, efficiently implementing this is a huge burden. In addition, it is not easy for guardians to accurately receive daily reports and gain insights into their children's development and diet. In such a situation, it is required to automate the analysis of minors' behavior and diet and realize effective information provision.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0313] In this invention, the server includes image processing means for acquiring video data from within a childcare facility or home and analyzing the behavior of minors from the video data; generation AI means for automatically generating natural language text based on the analysis results; and data transfer means for outputting the generated natural language text in a format that can be edited by educators and distributing the edited information to parents. This makes it possible to accurately and efficiently grasp the activities and eating habits of minors and to provide appropriate information to parents.

[0314] A "childcare facility" refers to a facility that provides care and education for minors, and is primarily an institution that supports daytime activities.

[0315] "Within the home" generally refers to the living space in which an individual resides, and is the primary environment in which a child's activities and development are observed.

[0316] "Video data" refers to visual information acquired using cameras or other recording devices, and is the content that forms the basis for analysis.

[0317] The term "minor" refers to individuals who have not yet reached the legal age of majority, and usually refers to children and adolescents.

[0318] "Image processing means for analyzing behavior" refers to technical processing methods used to extract specific behavioral patterns or characteristics from video data.

[0319] "Generative AI methods for automatically generating natural language text" refers to artificial intelligence technology used to generate text in human language based on analyzed data.

[0320] "Outputting information in a format that can be edited by the training staff" means that the generated information is provided in a format that can be easily modified or corrected by the relevant experts.

[0321] "Data transfer means" refers to communication methods used to deliver generated information to users in different locations, and typically involves using the internet.

[0322] "Network storage devices" refer to digital storage systems for storing data that are accessible via the internet or intranet.

[0323] An "application" refers to a software program designed to perform a specific function, and is typically used on smartphones and tablets.

[0324] The system for carrying out this invention consists of a server, a terminal, and a user.

[0325] The server acquires video data in real time from multiple cameras installed within childcare facilities or homes. These cameras capture the behavior and eating habits of minors, and the video data is analyzed using image processing tools. This analysis utilizes AI models based on TensorFlow to extract specific behavioral patterns and eating habits. The analyzed data is stored in a storage device on the network.

[0326] Next, the server uses a generation AI method, such as OpenAI's GPT model, to generate natural language reports and dietary improvement suggestions from the analysis results. The generated content can be reviewed by educators and childcare workers via their devices and modified as needed. A standard tablet or desktop computer is used as the platform for this purpose.

[0327] Parents, as users, can receive reports and suggestions delivered from the server via an application using their smartphones or tablets. This makes it easier for parents to understand their child's daily activities and eating habits.

[0328] As a concrete example, a server acquires video data of a child playing at home, and an AI automatically generates natural language messages such as "Today, the child played with blocks a lot" or "The child ate almost all of their salad." These generated messages are then sent to the parents via an app.

[0329] An example of a prompt message is, "Generate a daily report for parents based on the following data: Behavior: Playing with blocks, Expression: Happy, Meal: Finished the salad." In this way, a system is built that achieves the objectives of the present invention through the collaboration of the server, terminal, and user.

[0330] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0331] Step 1:

[0332] The server acquires video data in real time from cameras installed within childcare facilities or homes. This video data captures the behavior and eating habits of minors and is used as basic data for analysis. The input is video data from the cameras, and the output is raw video data stored on the server. Specifically, the server continuously receives feeds from the cameras and saves them to storage.

[0333] Step 2:

[0334] The server analyzes the acquired video data using image processing tools. This process uses an image recognition model based on TensorFlow to extract specific behavioral patterns and eating patterns. The input is the video data saved in step 1, and the output is the analysis results data of behavior and eating. Specifically, the AI ​​model recognizes specific movements and objects in the video data (e.g., identifying food being eaten) and outputs that information as numerical data.

[0335] Step 3:

[0336] The server generates prompts using a generative AI based on the analysis results, and then uses these prompts to generate reports and improvement suggestions in natural language. Here, the OpenAI GPT model is used. The input is the analysis result data from step 2 and the generated prompt text, and the output is a report and suggestion text in natural language format. Specifically, the generative AI translates the analysis results into text, selects the appropriate language according to the prompt, and constructs the text.

[0337] Step 4:

[0338] The educator using the device will review the generated text and make corrections as needed. Here, the text generated by the generation AI is editable by the educator via the UI. The input is the text generated in step 3, and the output is the final corrected text. Specifically, the educator previews the text on the screen and makes corrections using the editing tools as needed.

[0339] Step 5:

[0340] The user (parent) receives the corrected results via the app. Here, the server sends the corrected report and suggestions to the user's application via the cloud. The input is the corrected text from step 4, and the output is the report content displayed on the parent's device. Specifically, the server pushes the data via the cloud service, and the user's app displays it as a notification.

[0341] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0342] This invention provides a system for efficiently recording children's activities and eating habits in childcare facilities and analyzing their emotional states, thereby enabling more sophisticated responses. This system is implemented in a form that includes the following elements.

[0343] The server acquires image data in real time from cameras installed within the childcare facility and analyzes the image data. This analysis is unique in that, in addition to observing children's behavior and eating habits, it uses an emotion engine to recognize emotions from children's facial expressions and gestures. Specifically, it can detect smiles, crying faces, and expressions of concentration.

[0344] Based on the analysis results, the server activates a generation AI to automatically generate activity suggestions and environmental adjustment proposals that reflect the emotional state. For example, if the analysis results indicate that the child is satisfied with their toys, it will generate a suggestion such as, "We recommend providing a different type of toy." Regarding meals, if an emotion such as "the child is making a displeased face" is recognized, it will suggest improvements to the meal environment, such as, "Please consider changing the type of food."

[0345] Childcare workers using the terminals review the generated suggestions and make corrections as needed. This revised data is saved to the cloud and prepared for distribution to parents.

[0346] Parents, as users, receive reports and suggestions through the app on their smartphones or tablets. This allows parents to comprehensively understand their child's care situation, including their emotional state, and utilize this information to improve their parenting at home.

[0347] This system supports the work of childcare workers and provides a better child-rearing environment by suggesting childcare activities that take into account the psychological state of children. Furthermore, it aims to deepen communication with parents and jointly support children's growth through insights and suggestions based on emotional data.

[0348] The following describes the processing flow.

[0349] Step 1:

[0350] The server acquires image data in real time from cameras within the childcare facility. This image data includes footage of children's activities and mealtimes.

[0351] Step 2:

[0352] The server inputs image data into an image analysis AI and emotion engine to analyze the child's behavior and emotions. For example, it extracts information such as "playing with toys" or "smiling."

[0353] Step 3:

[0354] The server structures the analyzed behavioral and emotional data and stores it in a cloud database. The stored data is then used for subsequent generation processes.

[0355] Step 4:

[0356] The server activates a generation AI based on analysis data stored in the cloud. The generation AI automatically generates suggestions and environmental adjustment proposals for childcare workers based on children's activities and emotional states.

[0357] Step 5:

[0358] Childcare workers using the terminals review the suggestions generated by the server and consider whether the content is appropriate. They revise the suggestions as needed.

[0359] Step 6:

[0360] Using a terminal, the childcare worker sends the corrected data to the server and saves it again to the cloud. The server then prepares for transmission.

[0361] Step 7:

[0362] Parents, as users, receive reports and generated suggestions from childcare providers through a smartphone or tablet app. This allows them to understand their child's activity level and emotional state.

[0363] Step 8:

[0364] The server then acquires image data from the next meal and analyzes the behavior and emotions in the same way. Based on the analysis results, it generates improvement suggestions based on the emotional state related to the meal.

[0365] Step 9:

[0366] Using a terminal, childcare workers review the meal improvement proposals and make revisions if necessary. The revised proposals are then sent back to the server.

[0367] Step 10:

[0368] Parents, as users of the app, can receive suggestions for improving meals and use them to improve meals at home.

[0369] (Example 2)

[0370] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0371] In current childcare facilities, it is difficult to closely monitor children's activities and emotional states and provide appropriate support based on that information. Furthermore, there is a lack of means to provide parents with comprehensive information, including their child's emotional state. This leads to increased burdens on childcare workers and limited communication with parents.

[0372] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0373] In this invention, the server includes an analysis means for acquiring image information within a childcare facility and analyzing the child's behavior and emotions from the image information; a generation means for automatically generating activity suggestions corresponding to the emotional state based on the analysis results; and a means for outputting the generated activity suggestions in a format that can be modified by caregivers and distributing the modified information to caregivers. This makes it possible to closely track the emotional state and activities of children and to make suggestions based on them in a timely manner.

[0374] A "childcare facility" is a place where infants and children are temporarily entrusted to caregivers and receive education and assistance.

[0375] "Image information" refers to visual data acquired using a camera or other imaging device.

[0376] "Analysis of behavior and emotion" is a process of observing a child's movements and facial expressions from image information and determining their psychological state based on that.

[0377] "Analysis means" refers to equipment and software used for handling, verifying, and evaluating data according to specific purposes.

[0378] "Generative means" refers to functions that include programs and algorithms that create new proposals or content based on analysis results.

[0379] An "activity suggestion" is a recommendation for appropriate activities and support based on a child's emotional state and behavior.

[0380] A "childcare worker" refers to a professional who is responsible for the care and education of children within a childcare facility.

[0381] "Caregiver" refers to a parent or guardian who entrusts their child to a childcare facility.

[0382] "Outputting in a modifiable state" refers to the process of presenting information in a way that allows users to change or adjust the content.

[0383] "Storing in the cloud" refers to securely storing data on a remote server via the internet.

[0384] A "terminal" refers to an electronic device used by a user to receive or manipulate information.

[0385] This invention is a system that efficiently records and analyzes children's activities and emotions in childcare facilities to suggest appropriate activities.

[0386] Hardware and software used

[0387] Server Role

[0388] The server acquires image information in real time from cameras installed within the facility. The server uses an image processing library (e.g., OpenCV) to convert the video into still images and extracts specific frames for analysis. During this analysis process, the server utilizes a deep learning model (e.g., TensorFlow) to analyze the child's facial expressions from the image information and recognize their emotions.

[0389] Generative AI Models

[0390] Based on the analysis results, the server uses a generative AI model to create activity suggestions based on emotional states. The generative AI model used here is one that excels at natural language generation (e.g., a GPT-based model).

[0391] Terminal role

[0392] The device is used by caregivers to review and modify activity suggestions generated by the AI ​​model. Caregivers modify the suggestions on a tablet or other device, and the modified information is uploaded to the cloud.

[0393] User roles

[0394] The user, who is the caregiver, receives the revised suggestions from the app using their own communication device (e.g., smartphone or tablet). The app displays the suggestions in an easy-to-understand manner and provides the user with a comprehensive overview of their child's care situation.

[0395] Specific example

[0396] From a video showing a child repeatedly smiling, a generative AI model creates a suggestion that "the child may be satisfied with the toy." The actual activity suggestion then includes specific details such as, "We recommend providing a different type of toy."

[0397] Example of a prompt

[0398] The generation AI model is prompted with the following message: "Based on the analysis of the child's facial expressions, please generate activity suggestions that reflect their emotional state."

[0399] This invention allows caregivers to propose activities tailored to children's needs based on analyzed emotional data, and to accurately communicate the child's situation to caregivers. As a result, the quality of childcare and upbringing can be improved.

[0400] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0401] Step 1:

[0402] The server acquires image information in real time from cameras installed within the facility. It receives raw video data from the cameras as input. Specifically, the server uses an image processing library to convert the video into still images and extracts specific important frames. The output is still image data converted into an analyzable format.

[0403] Step 2:

[0404] The server performs emotion analysis using the acquired still image data. At this stage, the still image data serves as input. Specifically, the server uses a deep learning model to analyze the child's facial expressions and assigns emotion labels such as smile, crying, and surprised. The output is data representing the emotional state corresponding to each image.

[0405] Step 3:

[0406] The server generates activity suggestions using a generative AI model based on emotional state data. Emotional state data is provided as input. The server incorporates this into prompt statements, inputs them into the generative AI model, and generates activity and environmental suggestions that are appropriate for the child's emotions. The output is a specific activity suggestion. For example, a suggestion such as "recommend providing a wider variety of toys."

[0407] Step 4:

[0408] The terminal allows caregivers to review and modify generated activity proposals. Input consists of activity proposals sent from the server. Caregivers can view the displayed proposals on a tablet and make modifications as needed. Output is the modified proposal data, which is uploaded to the cloud.

[0409] Step 5:

[0410] The user, a caregiver, receives revised suggestions via their device. The input is revised suggestion data retrieved from the cloud. The user views the suggestions using a smartphone app and uses them to improve parenting at home. The output is useful parenting information that the caregiver can obtain.

[0411] (Application Example 2)

[0412] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0413] In modern households, there is a need for efficient means to appropriately understand children's activities and emotional states and to provide appropriate childcare support. However, current technologies have made it difficult to accurately analyze a child's situation and provide appropriate information to parents. In particular, a system that automatically generates and distributes activity suggestions and dietary improvement plans based on each child's individual emotional state has not yet been developed. As a result, there is a challenge in effectively providing childcare support within the home and reducing the burden on parents.

[0414] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0415] In this invention, the server includes an analysis means for acquiring image information and analyzing a person's activities from the image information; a generation means for automatically generating text based on the analysis results; a means for outputting the generated text in a format that can be edited by a caregiver and distributing the edited information to other users; and a generation means for analyzing a person's emotional state and generating activity-related suggestions based on the emotional analysis results. This enables guardians to more accurately understand their child's activities and emotions within the home and to provide childcare support more efficiently.

[0416] "Image information" refers to visual data acquired using cameras or other imaging devices.

[0417] "Analysis means" refers to a device or software that has the function of analyzing the operation or state using a specific algorithm or program with the acquired data.

[0418] "Generation means" refers to a device or software that has the function of automatically creating new proposals, reports, and other content from analyzed data and information.

[0419] A "caregiver" is an individual who takes on the role of caring for others or supporting childcare in a home or institutional setting.

[0420] "Editable" means that users can make changes or corrections to the generated text or data.

[0421] "Distribution means" refers to a communication device or software used to transmit generated information to the intended recipient.

[0422] "Emotional state" refers to an individual's internal psychological condition, encompassing the type and intensity of emotions that can be perceived through facial expressions and behavior.

[0423] A "remote storage device" is a data storage medium located in a physically distant location, such as a cloud server, and is accessible via the internet.

[0424] "Application software" refers to programs that provide functions tailored to specific purposes and are primarily installed on the user's device.

[0425] The system used to implement this invention operates in conjunction with consumer robots that are compatible with home environments. The central components of the system are a camera and analysis engine for acquiring image information and analyzing human activity and emotions from that information. The camera is positioned to capture various scenes within the home. Specifically, an open-source computer vision library (e.g., OpenCV) can be used as the analysis engine.

[0426] The server receives image information from the camera and performs analysis using specific software modules such as EmotionEngine and ActivityAI. EmotionEngine runs an algorithm that analyzes and recognizes the child's emotional state based on the images, identifying emotions such as smiles and anxiety. ActivityAI has the function of automatically generating appropriate activity suggestions based on these analysis results.

[0427] Furthermore, the generation AI model receives the analysis results and constructs appropriate sentences and suggestions. This makes it possible to provide parents, who are the users, with intuitive and useful information about their children's activities. For example, if a child is engrossed in playing with a particular toy, a suggestion such as, "It seems that the child is enjoying playing with this toy. Perhaps you could try other types of toys?" might be generated.

[0428] As an example, here is an example of a prompt statement:

[0429] "A child's smile has been detected. Please generate new play suggestions."

[0430] These entire systems utilize cloud computing to generate information, store it in remote storage, and deliver it to the parent's device. The information can be delivered to the user via application software for common smartphones. This allows users to easily receive the latest information about their children at any time.

[0431] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0432] Step 1:

[0433] The server acquires image information from the camera in real time. The input is video data captured by the camera. The server receives this data and loads it into buffer memory in preparation for the next analysis step. The output is video frames that can be analyzed.

[0434] Step 2:

[0435] The server uses EmotionEngine to analyze the acquired video frames. The input is the video frames obtained in step 1. EmotionEngine processes this data and runs an algorithm to recognize emotions from facial expressions. The output is the identified emotion information (e.g., smile, anxiety, etc.).

[0436] Step 3:

[0437] The server uses ActivityAI to generate activity suggestions based on emotional information. The input is the emotional information obtained in step 2. ActivityAI analyzes this information and generates relevant activity suggestions. The output is a suggestion such as, "Why not try some other types of toys?"

[0438] Step 4:

[0439] The generated proposal is further improved using a generative AI model. The input is the proposal generated in step 3. The generative AI uses natural language processing techniques to translate it into more user-friendly language and include additional information. The output is the final proposal.

[0440] Step 5:

[0441] The final proposal is delivered to the user's device. The server stores this information in remote storage and provides it to the user via a smartphone or tablet. The input is the final proposal obtained in step 4. The output is the notification or proposal presented to the user on their device.

[0442] By ensuring that each step is processed correctly, the entire system functions smoothly, and users can obtain useful information for childcare support at home.

[0443] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0444] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0445] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0446] [Third Embodiment]

[0447] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0448] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0449] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0450] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0451] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0452] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0453] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0454] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0455] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0456] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0457] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0458] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0459] This invention presents a specific form of a system that utilizes image data analysis and generation AI to efficiently record and report on activities and mealtimes within childcare facilities. This system is implemented through the collaboration of a server, terminals, and users.

[0460] The server acquires image data in real time from multiple cameras installed within the facility. This image data records the children's behavior during childcare activities and mealtimes, and serves as the basis for analysis. The server uses image analysis AI to analyze the acquired image data and extract specific information about the children's actions and eating habits (e.g., how much they ate, what they showed interest in).

[0461] The extracted data is stored in the cloud and processed by a generative AI. The server activates the generative AI, which, based on the analyzed data, automatically creates communication log entries suitable for reporting to parents and suggestions for improving meals. For example, it generates specific information such as "Today, C-chan was engrossed in playing with blocks" as an activity record, and "C-chan left half of her vegetables" as a meal report.

[0462] Childcare workers using the terminals review the generated content and make corrections as needed. Once the childcare workers have finished inputting the information, the revised text and suggestions are saved again to the cloud and ready to be distributed to parents.

[0463] Parents, as users, can receive the latest reports and suggestions through the app on their smartphones or tablets. This allows parents to easily stay informed about how their children are doing at the childcare facility and to get up-to-date information about their meals.

[0464] This system will reduce the administrative burden on childcare workers and streamline communication with parents. Furthermore, it will provide parents with added value by offering suggestions regarding their child's food preferences and nutritional balance.

[0465] The following describes the processing flow.

[0466] Step 1:

[0467] The server acquires image data in real time from cameras within the childcare facility. The image data includes footage of children's activities and how they eat.

[0468] Step 2:

[0469] The server uses image analysis AI to analyze the acquired image data and extract information about the child's actions (e.g., playing, eating) and eating habits (e.g., how much they ate, what they ate).

[0470] Step 3:

[0471] The server saves the analysis results as structured data to a cloud database. This saved data is then used for subsequent processing.

[0472] Step 4:

[0473] The server accesses the analysis data stored in the cloud and activates the generation AI. The generation AI automatically generates communication log entries for childcare workers based on the analysis data.

[0474] Step 5:

[0475] Childcare workers using the terminal can review the text generated by the server and modify the content as needed.

[0476] Step 6:

[0477] The server saves the revised text from the childcare worker back to the cloud and prepares it for distribution to the parents.

[0478] Step 7:

[0479] Parents, as users, receive contact log reports via the app on their smartphones or tablets. This allows parents to accurately understand their child's activities and eating habits.

[0480] Step 8:

[0481] The server analyzes image data of the next meal and evaluates the amount of food left over. Based on the analysis results, the AI ​​generates suggestions for improving the diet.

[0482] Step 9:

[0483] Childcare workers using terminals review the proposals and make revisions as needed. The revised proposals are saved to the cloud and prepared for distribution to parents.

[0484] Step 10:

[0485] Parents, as users of the app, can receive suggestions for improving their children's diets and use them to help them implement these changes at home.

[0486] (Example 1)

[0487] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0488] There is a need to efficiently manage records of children's activities and meals in childcare facilities and to report them to parents promptly. However, currently, manual record-keeping and reporting are time-consuming, placing a heavy burden on childcare workers, and there are issues with the accuracy of the information and the frequency of reporting. Furthermore, it is difficult to provide concrete suggestions for improvement regarding meals. A system is needed to address these problems, reduce the burden on childcare workers, and improve the quality of childcare.

[0489] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0490] In this invention, the server includes an analysis means for acquiring image information and analyzing the subject's behavior from the image information, a storage means for storing the analyzed behavior data in an information processing device, and a generation means for automatically generating natural language based on the analysis results. This makes it possible to automatically analyze children's activities and eating habits and generate reports, significantly reducing the workload of childcare workers.

[0491] "Image information" refers to visual data acquired by devices such as cameras and sensors, and includes elements that indicate the subject's actions and circumstances.

[0492] "Analysis means" refers to the techniques and processes used to analyze acquired data and identify the behavior or state of the subject.

[0493] An "information processing device" refers to a device or system that stores, processes, and manages data, and generally includes servers and computers.

[0494] "Storage means" refers to functions or systems for recording and saving the analyzed data.

[0495] "Generation means" refers to functions and technologies that automatically create natural language text based on analyzed data.

[0496] "Natural language" refers to the language that humans use on a daily basis, a form of communication that utilizes grammar and vocabulary.

[0497] "Distribution method" refers to the functions and technologies used to deliver generated information to specific recipients.

[0498] This invention is a system for efficiently recording children's activities and mealtimes within childcare facilities and reporting this information to their parents. The system primarily utilizes a server, terminals, and user smart devices. Details are provided below.

[0499] The server acquires image information in real time from multiple cameras installed within the childcare facility. The cameras are positioned in key activity areas within the facility, and image processing libraries such as OpenCV are used to analyze the image data. For specific data analysis, an image analysis AI is used to identify children's behavior and eating habits. The server stores the analyzed data in cloud storage, which is an information processing device, and further uses a generative AI model, such as GPT-3, to generate reports and suggestions in natural language based on the analyzed information. The server inputs prompt sentences into the generative AI, which automatically creates reports for parents and suggestions for improving meals.

[0500] As a concrete example, the server generates a report by inputting the following prompt message into the AI:

[0501] Activity report generation prompt: "Please record today's activities and create a report."

[0502] Prompt for generating meal report: "Please record what C-chan ate today and create a report including any necessary advice."

[0503] Childcare workers using the terminals review the generated reports and make corrections as needed. They can access the text fields and make corrections through a dedicated application. Once corrections are complete, the information is saved to the cloud again.

[0504] Parents, who are the users, receive reports through a dedicated app on their smartphones or tablets. The server utilizes a notification function to inform parents in real time when the latest reports or suggestions have arrived.

[0505] The above configuration reduces the administrative burden on childcare workers and enables efficient communication with parents. Furthermore, it can provide specific advice regarding children's diets and deliver value-added information to parents.

[0506] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0507] Step 1:

[0508] The server acquires image information in real time from cameras installed within the childcare facility. The input is real-time video transmitted from multiple cameras. The server receives this video data via the network and performs initial processing to save it as digital data. The output of this step is image data converted into a format necessary for analysis.

[0509] Step 2:

[0510] The server analyzes the acquired image information using an image analysis AI. The input is the image data obtained in step 1. Specifically, the server uses the OpenCV library to extract features from the image and perform tasks such as face recognition and motion analysis. As a result of the analysis, data related to the child's behavior and eating habits is output.

[0511] Step 3:

[0512] The server stores the data obtained from the analysis in cloud storage. The input is the behavioral and dietary data extracted in step 2. The server transfers the data using the cloud API and stores it securely and efficiently. The output of this step is the analysis data stored in the cloud.

[0513] Step 4:

[0514] The server automatically generates reports and proposals based on the analysis results. The input is the analysis data saved in step 3. Specifically, the server inputs prompt sentences into the generation AI model and obtains natural language sentences in response. The output is a report or proposal to be distributed to the parents.

[0515] Step 5:

[0516] Childcare workers using the terminal review the generated report content and make corrections as needed. The input is the text generated in step 4. Childcare workers check the content through a dedicated application and edit it as necessary. The output is the revised report or proposal document.

[0517] Step 6:

[0518] The server saves the information confirmed and corrected on the device back to the cloud and prepares it for distribution. The input is the corrected data obtained in step 5. The server receives the corrected data and overwrites it in cloud storage. The output of this step is the data ready to be distributed to the parent / guardian.

[0519] Step 7:

[0520] The user (parent) receives the latest reports and suggestions on their smart device. The input is the corrected data saved in step 6. Parents receive information through a dedicated app on their smart device and are notified of new information arrivals via push notifications. The output is the reports and suggestions displayed to the parent.

[0521] (Application Example 1)

[0522] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] In modern homes and childcare facilities, it is crucial to understand the behavior and eating habits of minors and provide appropriate guidance and advice based on that understanding. However, efficiently implementing this is a significant burden. In addition, it is not easy for parents to receive accurate daily reports and gain insights into their child's development and eating habits. In this situation, there is a need to automate the analysis of minors' behavior and eating habits and to provide effective information.

[0524] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0525] In this invention, the server includes image processing means for acquiring video data from within a childcare facility or home and analyzing the behavior of minors from the video data; generation AI means for automatically generating natural language text based on the analysis results; and data transfer means for outputting the generated natural language text in a format that can be edited by educators and distributing the edited information to parents. This makes it possible to accurately and efficiently grasp the activities and eating habits of minors and to provide appropriate information to parents.

[0526] A "childcare facility" refers to a facility that provides care and education for minors, and is primarily an institution that supports daytime activities.

[0527] "Within the home" generally refers to the living space in which an individual resides, and is the primary environment in which a child's activities and development are observed.

[0528] "Video data" refers to visual information acquired using cameras or other recording devices, and is the content that forms the basis for analysis.

[0529] The term "minor" refers to individuals who have not yet reached the legal age of majority, and usually refers to children and adolescents.

[0530] "Image processing means for analyzing behavior" refers to technical processing methods used to extract specific behavioral patterns or characteristics from video data.

[0531] "Generative AI methods for automatically generating natural language text" refers to artificial intelligence technology used to generate text in human language based on analyzed data.

[0532] "Outputting information in a format that can be edited by the training staff" means that the generated information is provided in a format that can be easily modified or corrected by the relevant experts.

[0533] "Data transfer means" refers to communication methods used to deliver generated information to users in different locations, and typically involves using the internet.

[0534] "Network storage devices" refer to digital storage systems for storing data that are accessible via the internet or intranet.

[0535] An "application" refers to a software program designed to perform a specific function, and is typically used on smartphones and tablets.

[0536] The system for carrying out this invention consists of a server, a terminal, and a user.

[0537] The server acquires video data in real time from multiple cameras installed within childcare facilities or homes. These cameras capture the behavior and eating habits of minors, and the video data is analyzed using image processing tools. This analysis utilizes AI models based on TensorFlow to extract specific behavioral patterns and eating habits. The analyzed data is stored in a storage device on the network.

[0538] Next, the server uses a generation AI method, such as OpenAI's GPT model, to generate natural language reports and dietary improvement suggestions from the analysis results. The generated content can be reviewed by educators and childcare workers via their devices and modified as needed. A standard tablet or desktop computer is used as the platform for this purpose.

[0539] Parents, as users, can receive reports and suggestions delivered from the server via an application using their smartphones or tablets. This makes it easier for parents to understand their child's daily activities and eating habits.

[0540] As a concrete example, a server acquires video data of a child playing at home, and an AI automatically generates natural language messages such as "Today, the child played with blocks a lot" or "The child ate almost all of their salad." These generated messages are then sent to the parents via an app.

[0541] An example of a prompt message is, "Generate a daily report for parents based on the following data: Behavior: Playing with blocks, Expression: Happy, Meal: Finished the salad." In this way, a system is built that achieves the objectives of the present invention through the collaboration of the server, terminal, and user.

[0542] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0543] Step 1:

[0544] The server acquires video data in real time from cameras installed within childcare facilities or homes. This video data captures the behavior and eating habits of minors and is used as basic data for analysis. The input is video data from the cameras, and the output is raw video data stored on the server. Specifically, the server continuously receives feeds from the cameras and saves them to storage.

[0545] Step 2:

[0546] The server analyzes the acquired video data using image processing tools. This process uses an image recognition model based on TensorFlow to extract specific behavioral patterns and eating patterns. The input is the video data saved in step 1, and the output is the analysis results data of behavior and eating. Specifically, the AI ​​model recognizes specific movements and objects in the video data (e.g., identifying food being eaten) and outputs that information as numerical data.

[0547] Step 3:

[0548] The server generates prompts using a generative AI based on the analysis results, and then uses these prompts to generate reports and improvement suggestions in natural language. Here, the OpenAI GPT model is used. The input is the analysis result data from step 2 and the generated prompt text, and the output is a report and suggestion text in natural language format. Specifically, the generative AI translates the analysis results into text, selects the appropriate language according to the prompt, and constructs the text.

[0549] Step 4:

[0550] The educator using the device will review the generated text and make corrections as needed. Here, the text generated by the generation AI is editable by the educator via the UI. The input is the text generated in step 3, and the output is the final corrected text. Specifically, the educator previews the text on the screen and makes corrections using the editing tools as needed.

[0551] Step 5:

[0552] The user (parent) receives the corrected results via the app. Here, the server sends the corrected report and suggestions to the user's application via the cloud. The input is the corrected text from step 4, and the output is the report content displayed on the parent's device. Specifically, the server pushes the data via the cloud service, and the user's app displays it as a notification.

[0553] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0554] This invention provides a system for efficiently recording children's activities and eating habits in childcare facilities and analyzing their emotional states, thereby enabling more sophisticated responses. This system is implemented in a form that includes the following elements.

[0555] The server acquires image data in real time from cameras installed within the childcare facility and analyzes the image data. This analysis is unique in that, in addition to observing children's behavior and eating habits, it uses an emotion engine to recognize emotions from children's facial expressions and gestures. Specifically, it can detect smiles, crying faces, and expressions of concentration.

[0556] Based on the analysis results, the server activates a generation AI to automatically generate activity suggestions and environmental adjustment proposals that reflect the emotional state. For example, if the analysis results indicate that the child is satisfied with their toys, it will generate a suggestion such as, "We recommend providing a different type of toy." Regarding meals, if an emotion such as "the child is making a displeased face" is recognized, it will suggest improvements to the meal environment, such as, "Please consider changing the type of food."

[0557] Childcare workers using the terminals review the generated suggestions and make corrections as needed. This revised data is saved to the cloud and prepared for distribution to parents.

[0558] Parents, as users, receive reports and suggestions through the app on their smartphones or tablets. This allows parents to comprehensively understand their child's care situation, including their emotional state, and utilize this information to improve their parenting at home.

[0559] This system supports the work of childcare workers and provides a better child-rearing environment by suggesting childcare activities that take into account the psychological state of children. Furthermore, it aims to deepen communication with parents and jointly support children's growth through insights and suggestions based on emotional data.

[0560] The following describes the processing flow.

[0561] Step 1:

[0562] The server acquires image data in real time from cameras within the childcare facility. This image data includes footage of children's activities and mealtimes.

[0563] Step 2:

[0564] The server inputs image data into an image analysis AI and emotion engine to analyze the child's behavior and emotions. For example, it extracts information such as "playing with toys" or "smiling."

[0565] Step 3:

[0566] The server structures the analyzed behavioral and emotional data and stores it in a cloud database. The stored data is then used for subsequent generation processes.

[0567] Step 4:

[0568] The server activates a generation AI based on analysis data stored in the cloud. The generation AI automatically generates suggestions and environmental adjustment proposals for childcare workers based on children's activities and emotional states.

[0569] Step 5:

[0570] Childcare workers using the terminals review the suggestions generated by the server and consider whether the content is appropriate. They revise the suggestions as needed.

[0571] Step 6:

[0572] Using a terminal, the childcare worker sends the corrected data to the server and saves it again to the cloud. The server then prepares for transmission.

[0573] Step 7:

[0574] Parents, as users, receive reports and generated suggestions from childcare providers through a smartphone or tablet app. This allows them to understand their child's activity level and emotional state.

[0575] Step 8:

[0576] The server then acquires image data from the next meal and analyzes the behavior and emotions in the same way. Based on the analysis results, it generates improvement suggestions based on the emotional state related to the meal.

[0577] Step 9:

[0578] Using a terminal, childcare workers review the meal improvement proposals and make revisions if necessary. The revised proposals are then sent back to the server.

[0579] Step 10:

[0580] Parents, as users of the app, can receive suggestions for improving meals and use them to improve meals at home.

[0581] (Example 2)

[0582] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0583] In current childcare facilities, it is difficult to closely monitor children's activities and emotional states and provide appropriate support based on that information. Furthermore, there is a lack of means to provide parents with comprehensive information, including their child's emotional state. This leads to increased burdens on childcare workers and limited communication with parents.

[0584] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0585] In this invention, the server includes an analysis means for acquiring image information within a childcare facility and analyzing the child's behavior and emotions from the image information; a generation means for automatically generating activity suggestions corresponding to the emotional state based on the analysis results; and a means for outputting the generated activity suggestions in a format that can be modified by caregivers and distributing the modified information to caregivers. This makes it possible to closely track the emotional state and activities of children and to make suggestions based on them in a timely manner.

[0586] A "childcare facility" is a place where infants and children are temporarily entrusted to caregivers and receive education and assistance.

[0587] "Image information" refers to visual data acquired using a camera or other imaging device.

[0588] "Analysis of behavior and emotion" is a process of observing a child's movements and facial expressions from image information and determining their psychological state based on that.

[0589] "Analysis means" refers to equipment and software used for handling, verifying, and evaluating data according to specific purposes.

[0590] "Generative means" refers to functions that include programs and algorithms that create new proposals or content based on analysis results.

[0591] An "activity suggestion" is a recommendation for appropriate activities and support based on a child's emotional state and behavior.

[0592] A "childcare worker" refers to a professional who is responsible for the care and education of children within a childcare facility.

[0593] "Caregiver" refers to a parent or guardian who entrusts their child to a childcare facility.

[0594] "Outputting in a modifiable state" refers to the process of presenting information in a way that allows users to change or adjust the content.

[0595] "Storing in the cloud" refers to securely storing data on a remote server via the internet.

[0596] A "terminal" refers to an electronic device used by a user to receive or manipulate information.

[0597] This invention is a system that efficiently records and analyzes children's activities and emotions in childcare facilities to suggest appropriate activities.

[0598] Hardware and software used

[0599] Server Role

[0600] The server acquires image information in real time from cameras installed within the facility. The server uses an image processing library (e.g., OpenCV) to convert the video into still images and extracts specific frames for analysis. During this analysis process, the server utilizes a deep learning model (e.g., TensorFlow) to analyze the child's facial expressions from the image information and recognize their emotions.

[0601] Generative AI Models

[0602] Based on the analysis results, the server uses a generative AI model to create activity suggestions based on emotional states. The generative AI model used here is one that excels at natural language generation (e.g., a GPT-based model).

[0603] Terminal role

[0604] The device is used by caregivers to review and modify activity suggestions generated by the AI ​​model. Caregivers modify the suggestions on a tablet or other device, and the modified information is uploaded to the cloud.

[0605] User roles

[0606] The user, who is the caregiver, receives the revised suggestions from the app using their own communication device (e.g., smartphone or tablet). The app displays the suggestions in an easy-to-understand manner and provides the user with a comprehensive overview of their child's care situation.

[0607] Specific example

[0608] From a video showing a child repeatedly smiling, a generative AI model creates a suggestion that "the child may be satisfied with the toy." The actual activity suggestion then includes specific details such as, "We recommend providing a different type of toy."

[0609] Example of a prompt

[0610] The generation AI model is prompted with the following message: "Based on the analysis of the child's facial expressions, please generate activity suggestions that reflect their emotional state."

[0611] This invention allows caregivers to propose activities tailored to children's needs based on analyzed emotional data, and to accurately communicate the child's situation to caregivers. As a result, the quality of childcare and upbringing can be improved.

[0612] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0613] Step 1:

[0614] The server acquires image information in real time from cameras installed within the facility. It receives raw video data from the cameras as input. Specifically, the server uses an image processing library to convert the video into still images and extracts specific important frames. The output is still image data converted into an analyzable format.

[0615] Step 2:

[0616] The server performs emotion analysis using the acquired still image data. At this stage, the still image data serves as input. Specifically, the server uses a deep learning model to analyze the child's facial expressions and assigns emotion labels such as smile, crying, and surprised. The output is data representing the emotional state corresponding to each image.

[0617] Step 3:

[0618] The server generates activity suggestions using a generative AI model based on emotional state data. Emotional state data is provided as input. The server incorporates this into prompt statements, inputs them into the generative AI model, and generates activity and environmental suggestions that are appropriate for the child's emotions. The output is a specific activity suggestion. For example, a suggestion such as "recommend providing a wider variety of toys."

[0619] Step 4:

[0620] The terminal allows caregivers to review and modify generated activity proposals. Input consists of activity proposals sent from the server. Caregivers can view the displayed proposals on a tablet and make modifications as needed. Output is the modified proposal data, which is uploaded to the cloud.

[0621] Step 5:

[0622] The user, a caregiver, receives revised suggestions via their device. The input is revised suggestion data retrieved from the cloud. The user views the suggestions using a smartphone app and uses them to improve parenting at home. The output is useful parenting information that the caregiver can obtain.

[0623] (Application Example 2)

[0624] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0625] In modern households, there is a need for efficient means to appropriately understand children's activities and emotional states and to provide appropriate childcare support. However, current technologies have made it difficult to accurately analyze a child's situation and provide appropriate information to parents. In particular, a system that automatically generates and distributes activity suggestions and dietary improvement plans based on each child's individual emotional state has not yet been developed. As a result, there is a challenge in effectively providing childcare support within the home and reducing the burden on parents.

[0626] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0627] In this invention, the server includes an analysis means for acquiring image information and analyzing a person's activities from the image information; a generation means for automatically generating text based on the analysis results; a means for outputting the generated text in a format that can be edited by a caregiver and distributing the edited information to other users; and a generation means for analyzing a person's emotional state and generating activity-related suggestions based on the emotional analysis results. This enables guardians to more accurately understand their child's activities and emotions within the home and to provide childcare support more efficiently.

[0628] "Image information" refers to visual data acquired using cameras or other imaging devices.

[0629] "Analysis means" refers to a device or software that has the function of analyzing the operation or state using a specific algorithm or program with the acquired data.

[0630] "Generation means" refers to a device or software that has the function of automatically creating new proposals, reports, and other content from analyzed data and information.

[0631] A "caregiver" is an individual who takes on the role of caring for others or supporting childcare in a home or institutional setting.

[0632] "Editable" means that users can make changes or corrections to the generated text or data.

[0633] "Distribution means" refers to a communication device or software used to transmit generated information to the intended recipient.

[0634] "Emotional state" refers to an individual's internal psychological condition, encompassing the type and intensity of emotions that can be perceived through facial expressions and behavior.

[0635] A "remote storage device" is a data storage medium located in a physically distant location, such as a cloud server, and is accessible via the internet.

[0636] "Application software" refers to programs that provide functions tailored to specific purposes and are primarily installed on the user's device.

[0637] The system used to implement this invention operates in conjunction with consumer robots that are compatible with home environments. The central components of the system are a camera and analysis engine for acquiring image information and analyzing human activity and emotions from that information. The camera is positioned to capture various scenes within the home. Specifically, an open-source computer vision library (e.g., OpenCV) can be used as the analysis engine.

[0638] The server receives image information from the camera and performs analysis using specific software modules such as EmotionEngine and ActivityAI. EmotionEngine runs an algorithm that analyzes and recognizes the child's emotional state based on the images, identifying emotions such as smiles and anxiety. ActivityAI has the function of automatically generating appropriate activity suggestions based on these analysis results.

[0639] Furthermore, the generation AI model receives the analysis results and constructs appropriate sentences and suggestions. This makes it possible to provide parents, who are the users, with intuitive and useful information about their children's activities. For example, if a child is engrossed in playing with a particular toy, a suggestion such as, "It seems that the child is enjoying playing with this toy. Perhaps you could try other types of toys?" might be generated.

[0640] As an example, here is an example of a prompt statement:

[0641] "A child's smile has been detected. Please generate new play suggestions."

[0642] These entire systems utilize cloud computing to generate information, store it in remote storage, and deliver it to the parent's device. The information can be delivered to the user via application software for common smartphones. This allows users to easily receive the latest information about their children at any time.

[0643] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0644] Step 1:

[0645] The server acquires image information from the camera in real time. The input is video data captured by the camera. The server receives this data and loads it into buffer memory in preparation for the next analysis step. The output is video frames that can be analyzed.

[0646] Step 2:

[0647] The server uses EmotionEngine to analyze the acquired video frames. The input is the video frames obtained in step 1. EmotionEngine processes this data and runs an algorithm to recognize emotions from facial expressions. The output is the identified emotion information (e.g., smile, anxiety, etc.).

[0648] Step 3:

[0649] The server uses ActivityAI to generate activity suggestions based on emotional information. The input is the emotional information obtained in step 2. ActivityAI analyzes this information and generates relevant activity suggestions. The output is a suggestion such as, "Why not try some other types of toys?"

[0650] Step 4:

[0651] The generated proposal is further improved using a generative AI model. The input is the proposal generated in step 3. The generative AI uses natural language processing techniques to translate it into more user-friendly language and include additional information. The output is the final proposal.

[0652] Step 5:

[0653] The final proposal is delivered to the user's device. The server stores this information in remote storage and provides it to the user via a smartphone or tablet. The input is the final proposal obtained in step 4. The output is the notification or proposal presented to the user on their device.

[0654] By ensuring that each step is processed correctly, the entire system functions smoothly, and users can obtain useful information for childcare support at home.

[0655] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0656] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0657] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0658] [Fourth Embodiment]

[0659] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0660] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0661] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0662] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0663] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0664] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0665] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0666] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0667] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0668] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0669] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0670] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0671] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0672] This invention presents a specific form of a system that utilizes image data analysis and generation AI to efficiently record and report on activities and mealtimes within childcare facilities. This system is implemented through the collaboration of a server, terminals, and users.

[0673] The server acquires image data in real time from multiple cameras installed within the facility. This image data records the children's behavior during childcare activities and mealtimes, and serves as the basis for analysis. The server uses image analysis AI to analyze the acquired image data and extract specific information about the children's actions and eating habits (e.g., how much they ate, what they showed interest in).

[0674] The extracted data is stored in the cloud and processed by a generative AI. The server activates the generative AI, which, based on the analyzed data, automatically creates communication log entries suitable for reporting to parents and suggestions for improving meals. For example, it generates specific information such as "Today, C-chan was engrossed in playing with blocks" as an activity record, and "C-chan left half of her vegetables" as a meal report.

[0675] Childcare workers using the terminals review the generated content and make corrections as needed. Once the childcare workers have finished inputting the information, the revised text and suggestions are saved again to the cloud and ready to be distributed to parents.

[0676] Parents, as users, can receive the latest reports and suggestions through the app on their smartphones or tablets. This allows parents to easily stay informed about how their children are doing at the childcare facility and to get up-to-date information about their meals.

[0677] This system will reduce the administrative burden on childcare workers and streamline communication with parents. Furthermore, it will provide parents with added value by offering suggestions regarding their child's food preferences and nutritional balance.

[0678] The following describes the processing flow.

[0679] Step 1:

[0680] The server acquires image data in real time from cameras within the childcare facility. The image data includes footage of children's activities and how they eat.

[0681] Step 2:

[0682] The server uses image analysis AI to analyze the acquired image data and extract information about the child's actions (e.g., playing, eating) and eating habits (e.g., how much they ate, what they ate).

[0683] Step 3:

[0684] The server saves the analysis results as structured data to a cloud database. This saved data is then used for subsequent processing.

[0685] Step 4:

[0686] The server accesses the analysis data stored in the cloud and activates the generation AI. The generation AI automatically generates communication log entries for childcare workers based on the analysis data.

[0687] Step 5:

[0688] Childcare workers using the terminal can review the text generated by the server and modify the content as needed.

[0689] Step 6:

[0690] The server saves the revised text from the childcare worker back to the cloud and prepares it for distribution to the parents.

[0691] Step 7:

[0692] Parents, as users, receive contact log reports via the app on their smartphones or tablets. This allows parents to accurately understand their child's activities and eating habits.

[0693] Step 8:

[0694] The server analyzes image data of the next meal and evaluates the amount of food left over. Based on the analysis results, the AI ​​generates suggestions for improving the diet.

[0695] Step 9:

[0696] Childcare workers using terminals review the proposals and make revisions as needed. The revised proposals are saved to the cloud and prepared for distribution to parents.

[0697] Step 10:

[0698] Parents, as users of the app, can receive suggestions for improving their children's diets and use them to help them implement these changes at home.

[0699] (Example 1)

[0700] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] There is a need to efficiently manage records of children's activities and meals in childcare facilities and to report them to parents promptly. However, currently, manual record-keeping and reporting are time-consuming, placing a heavy burden on childcare workers, and there are issues with the accuracy of the information and the frequency of reporting. Furthermore, it is difficult to provide concrete suggestions for improvement regarding meals. A system is needed to address these problems, reduce the burden on childcare workers, and improve the quality of childcare.

[0702] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0703] In this invention, the server includes an analysis means for acquiring image information and analyzing the subject's behavior from the image information, a storage means for storing the analyzed behavior data in an information processing device, and a generation means for automatically generating natural language based on the analysis results. This makes it possible to automatically analyze children's activities and eating habits and generate reports, significantly reducing the workload of childcare workers.

[0704] "Image information" refers to visual data acquired by devices such as cameras and sensors, and includes elements that indicate the subject's actions and circumstances.

[0705] "Analysis means" refers to the techniques and processes used to analyze acquired data and identify the behavior or state of the subject.

[0706] An "information processing device" refers to a device or system that stores, processes, and manages data, and generally includes servers and computers.

[0707] "Storage means" refers to functions or systems for recording and saving the analyzed data.

[0708] "Generation means" refers to functions and technologies that automatically create natural language text based on analyzed data.

[0709] "Natural language" refers to the language that humans use on a daily basis, a form of communication that utilizes grammar and vocabulary.

[0710] "Distribution method" refers to the functions and technologies used to deliver generated information to specific recipients.

[0711] This invention is a system for efficiently recording children's activities and mealtimes within childcare facilities and reporting this information to their parents. The system primarily utilizes a server, terminals, and user smart devices. Details are provided below.

[0712] The server acquires image information in real time from multiple cameras installed within the childcare facility. The cameras are positioned in key activity areas within the facility, and image processing libraries such as OpenCV are used to analyze the image data. For specific data analysis, an image analysis AI is used to identify children's behavior and eating habits. The server stores the analyzed data in cloud storage, which is an information processing device, and further uses a generative AI model, such as GPT-3, to generate reports and suggestions in natural language based on the analyzed information. The server inputs prompt sentences into the generative AI, which automatically creates reports for parents and suggestions for improving meals.

[0713] As a concrete example, the server generates a report by inputting the following prompt message into the AI:

[0714] Activity report generation prompt: "Please record today's activities and create a report."

[0715] Prompt for generating meal report: "Please record what C-chan ate today and create a report including any necessary advice."

[0716] Childcare workers using the terminals review the generated reports and make corrections as needed. They can access the text fields and make corrections through a dedicated application. Once corrections are complete, the information is saved to the cloud again.

[0717] Parents, who are the users, receive reports through a dedicated app on their smartphones or tablets. The server utilizes a notification function to inform parents in real time when the latest reports or suggestions have arrived.

[0718] The above configuration reduces the administrative burden on childcare workers and enables efficient communication with parents. Furthermore, it can provide specific advice regarding children's diets and deliver value-added information to parents.

[0719] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0720] Step 1:

[0721] The server acquires image information in real time from cameras installed within the childcare facility. The input is real-time video transmitted from multiple cameras. The server receives this video data via the network and performs initial processing to save it as digital data. The output of this step is image data converted into a format necessary for analysis.

[0722] Step 2:

[0723] The server analyzes the acquired image information using an image analysis AI. The input is the image data obtained in step 1. Specifically, the server uses the OpenCV library to extract features from the image and perform tasks such as face recognition and motion analysis. As a result of the analysis, data related to the child's behavior and eating habits is output.

[0724] Step 3:

[0725] The server stores the data obtained from the analysis in cloud storage. The input is the behavioral and dietary data extracted in step 2. The server transfers the data using the cloud API and stores it securely and efficiently. The output of this step is the analysis data stored in the cloud.

[0726] Step 4:

[0727] The server automatically generates reports and proposals based on the analysis results. The input is the analysis data saved in step 3. Specifically, the server inputs prompt sentences into the generation AI model and obtains natural language sentences in response. The output is a report or proposal to be distributed to the parents.

[0728] Step 5:

[0729] Childcare workers using the terminal review the generated report content and make corrections as needed. The input is the text generated in step 4. Childcare workers check the content through a dedicated application and edit it as necessary. The output is the revised report or proposal document.

[0730] Step 6:

[0731] The server saves the information confirmed and corrected on the device back to the cloud and prepares it for distribution. The input is the corrected data obtained in step 5. The server receives the corrected data and overwrites it in cloud storage. The output of this step is the data ready to be distributed to the parent / guardian.

[0732] Step 7:

[0733] The user (parent) receives the latest reports and suggestions on their smart device. The input is the corrected data saved in step 6. Parents receive information through a dedicated app on their smart device and are notified of new information arrivals via push notifications. The output is the reports and suggestions displayed to the parent.

[0734] (Application Example 1)

[0735] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0736] In modern homes and childcare facilities, it is crucial to understand the behavior and eating habits of minors and provide appropriate guidance and advice based on that understanding. However, efficiently implementing this is a significant burden. In addition, it is not easy for parents to receive accurate daily reports and gain insights into their child's development and eating habits. In this situation, there is a need to automate the analysis of minors' behavior and eating habits and to provide effective information.

[0737] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0738] In this invention, the server includes image processing means for acquiring video data from within a childcare facility or home and analyzing the behavior of minors from the video data; generation AI means for automatically generating natural language text based on the analysis results; and data transfer means for outputting the generated natural language text in a format that can be edited by educators and distributing the edited information to parents. This makes it possible to accurately and efficiently grasp the activities and eating habits of minors and to provide appropriate information to parents.

[0739] A "childcare facility" refers to a facility that provides care and education for minors, and is primarily an institution that supports daytime activities.

[0740] "Within the home" generally refers to the living space in which an individual resides, and is the primary environment in which a child's activities and development are observed.

[0741] "Video data" refers to visual information acquired using cameras or other recording devices, and is the content that forms the basis for analysis.

[0742] The term "minor" refers to individuals who have not yet reached the legal age of majority, and usually refers to children and adolescents.

[0743] "Image processing means for analyzing behavior" refers to technical processing methods used to extract specific behavioral patterns or characteristics from video data.

[0744] "Generative AI methods for automatically generating natural language text" refers to artificial intelligence technology used to generate text in human language based on analyzed data.

[0745] "Outputting information in a format that can be edited by the training staff" means that the generated information is provided in a format that can be easily modified or corrected by the relevant experts.

[0746] "Data transfer means" refers to communication methods used to deliver generated information to users in different locations, and typically involves using the internet.

[0747] "Network storage devices" refer to digital storage systems for storing data that are accessible via the internet or intranet.

[0748] An "application" refers to a software program designed to perform a specific function, and is typically used on smartphones and tablets.

[0749] The system for carrying out this invention consists of a server, a terminal, and a user.

[0750] The server acquires video data in real time from multiple cameras installed within childcare facilities or homes. These cameras capture the behavior and eating habits of minors, and the video data is analyzed using image processing tools. This analysis utilizes AI models based on TensorFlow to extract specific behavioral patterns and eating habits. The analyzed data is stored in a storage device on the network.

[0751] Next, the server uses a generation AI method, such as OpenAI's GPT model, to generate natural language reports and dietary improvement suggestions from the analysis results. The generated content can be reviewed by educators and childcare workers via their devices and modified as needed. A standard tablet or desktop computer is used as the platform for this purpose.

[0752] Parents, as users, can receive reports and suggestions delivered from the server via an application using their smartphones or tablets. This makes it easier for parents to understand their child's daily activities and eating habits.

[0753] As a concrete example, a server acquires video data of a child playing at home, and an AI automatically generates natural language messages such as "Today, the child played with blocks a lot" or "The child ate almost all of their salad." These generated messages are then sent to the parents via an app.

[0754] An example of a prompt message is, "Generate a daily report for parents based on the following data: Behavior: Playing with blocks, Expression: Happy, Meal: Finished the salad." In this way, a system is built that achieves the objectives of the present invention through the collaboration of the server, terminal, and user.

[0755] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0756] Step 1:

[0757] The server acquires video data in real time from cameras installed within childcare facilities or homes. This video data captures the behavior and eating habits of minors and is used as basic data for analysis. The input is video data from the cameras, and the output is raw video data stored on the server. Specifically, the server continuously receives feeds from the cameras and saves them to storage.

[0758] Step 2:

[0759] The server analyzes the acquired video data using image processing tools. This process uses an image recognition model based on TensorFlow to extract specific behavioral patterns and eating patterns. The input is the video data saved in step 1, and the output is the analysis results data of behavior and eating. Specifically, the AI ​​model recognizes specific movements and objects in the video data (e.g., identifying food being eaten) and outputs that information as numerical data.

[0760] Step 3:

[0761] The server generates prompts using a generative AI based on the analysis results, and then uses these prompts to generate reports and improvement suggestions in natural language. Here, the OpenAI GPT model is used. The input is the analysis result data from step 2 and the generated prompt text, and the output is a report and suggestion text in natural language format. Specifically, the generative AI translates the analysis results into text, selects the appropriate language according to the prompt, and constructs the text.

[0762] Step 4:

[0763] The educator using the device will review the generated text and make corrections as needed. Here, the text generated by the generation AI is editable by the educator via the UI. The input is the text generated in step 3, and the output is the final corrected text. Specifically, the educator previews the text on the screen and makes corrections using the editing tools as needed.

[0764] Step 5:

[0765] The user (parent) receives the corrected results via the app. Here, the server sends the corrected report and suggestions to the user's application via the cloud. The input is the corrected text from step 4, and the output is the report content displayed on the parent's device. Specifically, the server pushes the data via the cloud service, and the user's app displays it as a notification.

[0766] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0767] This invention provides a system for efficiently recording children's activities and eating habits in childcare facilities and analyzing their emotional states, thereby enabling more sophisticated responses. This system is implemented in a form that includes the following elements.

[0768] The server acquires image data in real time from cameras installed within the childcare facility and analyzes the image data. This analysis is unique in that, in addition to observing children's behavior and eating habits, it uses an emotion engine to recognize emotions from children's facial expressions and gestures. Specifically, it can detect smiles, crying faces, and expressions of concentration.

[0769] Based on the analysis results, the server activates a generation AI to automatically generate activity suggestions and environmental adjustment proposals that reflect the emotional state. For example, if the analysis results indicate that the child is satisfied with their toys, it will generate a suggestion such as, "We recommend providing a different type of toy." Regarding meals, if an emotion such as "the child is making a displeased face" is recognized, it will suggest improvements to the meal environment, such as, "Please consider changing the type of food."

[0770] Childcare workers using the terminals review the generated suggestions and make corrections as needed. This revised data is saved to the cloud and prepared for distribution to parents.

[0771] Parents, as users, receive reports and suggestions through the app on their smartphones or tablets. This allows parents to comprehensively understand their child's care situation, including their emotional state, and utilize this information to improve their parenting at home.

[0772] This system supports the work of childcare workers and provides a better child-rearing environment by suggesting childcare activities that take into account the psychological state of children. Furthermore, it aims to deepen communication with parents and jointly support children's growth through insights and suggestions based on emotional data.

[0773] The following describes the processing flow.

[0774] Step 1:

[0775] The server acquires image data in real time from cameras within the childcare facility. This image data includes footage of children's activities and mealtimes.

[0776] Step 2:

[0777] The server inputs image data into an image analysis AI and emotion engine to analyze the child's behavior and emotions. For example, it extracts information such as "playing with toys" or "smiling."

[0778] Step 3:

[0779] The server structures the analyzed behavioral and emotional data and stores it in a cloud database. The stored data is then used for subsequent generation processes.

[0780] Step 4:

[0781] The server activates a generation AI based on analysis data stored in the cloud. The generation AI automatically generates suggestions and environmental adjustment proposals for childcare workers based on children's activities and emotional states.

[0782] Step 5:

[0783] Childcare workers using the terminals review the suggestions generated by the server and consider whether the content is appropriate. They revise the suggestions as needed.

[0784] Step 6:

[0785] Using a terminal, the childcare worker sends the corrected data to the server and saves it again to the cloud. The server then prepares for transmission.

[0786] Step 7:

[0787] Parents, as users, receive reports and generated suggestions from childcare providers through a smartphone or tablet app. This allows them to understand their child's activity level and emotional state.

[0788] Step 8:

[0789] The server then acquires image data from the next meal and analyzes the behavior and emotions in the same way. Based on the analysis results, it generates improvement suggestions based on the emotional state related to the meal.

[0790] Step 9:

[0791] Using a terminal, childcare workers review the meal improvement proposals and make revisions if necessary. The revised proposals are then sent back to the server.

[0792] Step 10:

[0793] Parents, as users of the app, can receive suggestions for improving meals and use them to improve meals at home.

[0794] (Example 2)

[0795] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0796] In current childcare facilities, it is difficult to closely monitor children's activities and emotional states and provide appropriate support based on that information. Furthermore, there is a lack of means to provide parents with comprehensive information, including their child's emotional state. This leads to increased burdens on childcare workers and limited communication with parents.

[0797] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0798] In this invention, the server includes an analysis means for acquiring image information within a childcare facility and analyzing the child's behavior and emotions from the image information; a generation means for automatically generating activity suggestions corresponding to the emotional state based on the analysis results; and a means for outputting the generated activity suggestions in a format that can be modified by caregivers and distributing the modified information to caregivers. This makes it possible to closely track the emotional state and activities of children and to make suggestions based on them in a timely manner.

[0799] A "childcare facility" is a place where infants and children are temporarily entrusted to caregivers and receive education and assistance.

[0800] "Image information" refers to visual data acquired using a camera or other imaging device.

[0801] "Analysis of behavior and emotion" is a process of observing a child's movements and facial expressions from image information and determining their psychological state based on that.

[0802] "Analysis means" refers to equipment and software used for handling, verifying, and evaluating data according to specific purposes.

[0803] "Generative means" refers to functions that include programs and algorithms that create new proposals or content based on analysis results.

[0804] An "activity suggestion" is a recommendation for appropriate activities and support based on a child's emotional state and behavior.

[0805] A "childcare worker" refers to a professional who is responsible for the care and education of children within a childcare facility.

[0806] "Caregiver" refers to a parent or guardian who entrusts their child to a childcare facility.

[0807] "Outputting in a modifiable state" refers to the process of presenting information in a way that allows users to change or adjust the content.

[0808] "Storing in the cloud" refers to securely storing data on a remote server via the internet.

[0809] A "terminal" refers to an electronic device used by a user to receive or manipulate information.

[0810] This invention is a system that efficiently records and analyzes children's activities and emotions in childcare facilities to suggest appropriate activities.

[0811] Hardware and software used

[0812] Server Role

[0813] The server acquires image information in real time from cameras installed within the facility. The server uses an image processing library (e.g., OpenCV) to convert the video into still images and extracts specific frames for analysis. During this analysis process, the server utilizes a deep learning model (e.g., TensorFlow) to analyze the child's facial expressions from the image information and recognize their emotions.

[0814] Generative AI Models

[0815] Based on the analysis results, the server uses a generative AI model to create activity suggestions based on emotional states. The generative AI model used here is one that excels at natural language generation (e.g., a GPT-based model).

[0816] Terminal role

[0817] The device is used by caregivers to review and modify activity suggestions generated by the AI ​​model. Caregivers modify the suggestions on a tablet or other device, and the modified information is uploaded to the cloud.

[0818] User roles

[0819] The user, who is the caregiver, receives the revised suggestions from the app using their own communication device (e.g., smartphone or tablet). The app displays the suggestions in an easy-to-understand manner and provides the user with a comprehensive overview of their child's care situation.

[0820] Specific example

[0821] From a video showing a child repeatedly smiling, a generative AI model creates a suggestion that "the child may be satisfied with the toy." The actual activity suggestion then includes specific details such as, "We recommend providing a different type of toy."

[0822] Example of a prompt

[0823] The generation AI model is prompted with the following message: "Based on the analysis of the child's facial expressions, please generate activity suggestions that reflect their emotional state."

[0824] This invention allows caregivers to propose activities tailored to children's needs based on analyzed emotional data, and to accurately communicate the child's situation to caregivers. As a result, the quality of childcare and upbringing can be improved.

[0825] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0826] Step 1:

[0827] The server acquires image information in real time from cameras installed within the facility. It receives raw video data from the cameras as input. Specifically, the server uses an image processing library to convert the video into still images and extracts specific important frames. The output is still image data converted into an analyzable format.

[0828] Step 2:

[0829] The server performs emotion analysis using the acquired still image data. At this stage, the still image data serves as input. Specifically, the server uses a deep learning model to analyze the child's facial expressions and assigns emotion labels such as smile, crying, and surprised. The output is data representing the emotional state corresponding to each image.

[0830] Step 3:

[0831] The server generates activity suggestions using a generative AI model based on emotional state data. Emotional state data is provided as input. The server incorporates this into prompt statements, inputs them into the generative AI model, and generates activity and environmental suggestions that are appropriate for the child's emotions. The output is a specific activity suggestion. For example, a suggestion such as "recommend providing a wider variety of toys."

[0832] Step 4:

[0833] The terminal allows caregivers to review and modify generated activity proposals. Input consists of activity proposals sent from the server. Caregivers can view the displayed proposals on a tablet and make modifications as needed. Output is the modified proposal data, which is uploaded to the cloud.

[0834] Step 5:

[0835] The user, a caregiver, receives revised suggestions via their device. The input is revised suggestion data retrieved from the cloud. The user views the suggestions using a smartphone app and uses them to improve parenting at home. The output is useful parenting information that the caregiver can obtain.

[0836] (Application Example 2)

[0837] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0838] In modern households, there is a need for efficient means to appropriately understand children's activities and emotional states and to provide appropriate childcare support. However, current technologies have made it difficult to accurately analyze a child's situation and provide appropriate information to parents. In particular, a system that automatically generates and distributes activity suggestions and dietary improvement plans based on each child's individual emotional state has not yet been developed. As a result, there is a challenge in effectively providing childcare support within the home and reducing the burden on parents.

[0839] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0840] In this invention, the server includes an analysis means for acquiring image information and analyzing a person's activities from the image information; a generation means for automatically generating text based on the analysis results; a means for outputting the generated text in a format that can be edited by a caregiver and distributing the edited information to other users; and a generation means for analyzing a person's emotional state and generating activity-related suggestions based on the emotional analysis results. This enables guardians to more accurately understand their child's activities and emotions within the home and to provide childcare support more efficiently.

[0841] "Image information" refers to visual data acquired using cameras or other imaging devices.

[0842] "Analysis means" refers to a device or software that has the function of analyzing the operation or state using a specific algorithm or program with the acquired data.

[0843] "Generation means" refers to a device or software that has the function of automatically creating new proposals, reports, and other content from analyzed data and information.

[0844] A "caregiver" is an individual who takes on the role of caring for others or supporting childcare in a home or institutional setting.

[0845] "Editable" means that users can make changes or corrections to the generated text or data.

[0846] "Distribution means" refers to a communication device or software used to transmit generated information to the intended recipient.

[0847] "Emotional state" refers to an individual's internal psychological condition, encompassing the type and intensity of emotions that can be perceived through facial expressions and behavior.

[0848] A "remote storage device" is a data storage medium located in a physically distant location, such as a cloud server, and is accessible via the internet.

[0849] "Application software" refers to programs that provide functions tailored to specific purposes and are primarily installed on the user's device.

[0850] The system used to implement this invention operates in conjunction with consumer robots that are compatible with home environments. The central components of the system are a camera and analysis engine for acquiring image information and analyzing human activity and emotions from that information. The camera is positioned to capture various scenes within the home. Specifically, an open-source computer vision library (e.g., OpenCV) can be used as the analysis engine.

[0851] The server receives image information from the camera and performs analysis using specific software modules such as EmotionEngine and ActivityAI. EmotionEngine runs an algorithm that analyzes and recognizes the child's emotional state based on the images, identifying emotions such as smiles and anxiety. ActivityAI has the function of automatically generating appropriate activity suggestions based on these analysis results.

[0852] Furthermore, the generation AI model receives the analysis results and constructs appropriate sentences and suggestions. This makes it possible to provide parents, who are the users, with intuitive and useful information about their children's activities. For example, if a child is engrossed in playing with a particular toy, a suggestion such as, "It seems that the child is enjoying playing with this toy. Perhaps you could try other types of toys?" might be generated.

[0853] As an example, here is an example of a prompt statement:

[0854] "A child's smile has been detected. Please generate new play suggestions."

[0855] These entire systems utilize cloud computing to generate information, store it in remote storage, and deliver it to the parent's device. The information can be delivered to the user via application software for common smartphones. This allows users to easily receive the latest information about their children at any time.

[0856] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0857] Step 1:

[0858] The server acquires image information from the camera in real time. The input is video data captured by the camera. The server receives this data and loads it into buffer memory in preparation for the next analysis step. The output is video frames that can be analyzed.

[0859] Step 2:

[0860] The server uses EmotionEngine to analyze the acquired video frames. The input is the video frames obtained in step 1. EmotionEngine processes this data and runs an algorithm to recognize emotions from facial expressions. The output is the identified emotion information (e.g., smile, anxiety, etc.).

[0861] Step 3:

[0862] The server uses ActivityAI to generate activity suggestions based on emotional information. The input is the emotional information obtained in step 2. ActivityAI analyzes this information and generates relevant activity suggestions. The output is a suggestion such as, "Why not try some other types of toys?"

[0863] Step 4:

[0864] The generated proposal is further improved using a generative AI model. The input is the proposal generated in step 3. The generative AI uses natural language processing techniques to translate it into more user-friendly language and include additional information. The output is the final proposal.

[0865] Step 5:

[0866] The final proposal is delivered to the user's device. The server stores this information in remote storage and provides it to the user via a smartphone or tablet. The input is the final proposal obtained in step 4. The output is the notification or proposal presented to the user on their device.

[0867] By ensuring that each step is processed correctly, the entire system functions smoothly, and users can obtain useful information for childcare support at home.

[0868] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0869] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0870] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0871] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0872] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0873] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0874] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0875] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0876] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0877] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0878] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0879] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0880] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0881] 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.

[0882] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0883] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0884] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0885] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0886] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0887] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0888] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0889] The following is further disclosed regarding the embodiments described above.

[0890] (Claim 1)

[0891] An analysis means for acquiring image data from a childcare facility and analyzing children's activities from said image data,

[0892] A generation means for automatically generating text based on the analysis results,

[0893] A method for outputting the generated text in a format that can be edited by childcare workers, and then distributing the edited data to parents,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] A meal analysis method that analyzes image data during meals and evaluates the amount of food left over,

[0897] A generation means for generating dietary improvement plans based on the evaluation results,

[0898] A means of distributing the generated meal improvement plan to parents,

[0899] The system according to claim 1, including the following:

[0900] (Claim 3)

[0901] A storage method for saving the analyzed activity data to the cloud,

[0902] A distribution method that allows users to receive the generated information through the app,

[0903] The system according to claim 1, including the following:

[0904] "Example 1"

[0905] (Claim 1)

[0906] An analysis means for acquiring image information and analyzing the target's behavior from said image information,

[0907] A storage means for storing the analyzed behavioral data in an information processing device,

[0908] A generation means for automatically generating natural language based on analysis results,

[0909] A means of outputting generated natural language information in a format that can be edited by the person in charge, and distributing the edited information to the recipient,

[0910] A system that includes this.

[0911] (Claim 2)

[0912] A dietary analysis method that analyzes image information during nutrient intake and evaluates the amount of remaining nutrient intake,

[0913] A generation means for generating a nutritional intake improvement plan based on the evaluation results,

[0914] A means of distributing the generated nutritional intake improvement plan to the recipient,

[0915] The system according to claim 1, including the following:

[0916] (Claim 3)

[0917] A storage means for storing the analyzed behavioral data on an information processing device,

[0918] A distribution method that allows users to receive the generated information via their information terminals,

[0919] The system according to claim 1, including the following:

[0920] "Application Example 1"

[0921] (Claim 1)

[0922] Image processing means for acquiring video data from within a childcare facility or home and analyzing the behavior of minors from said video data,

[0923] A generative AI method that automatically generates natural language text based on analysis results,

[0924] A data transfer method that outputs generated natural language text in a format that can be edited by educators, and distributes the edited information to parents,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] A meal analysis method that analyzes video data during meals and evaluates the amount of leftover food,

[0928] Information generation means for generating dietary improvement measures based on the evaluation results,

[0929] A data distribution method for delivering the generated dietary improvement plan to parents,

[0930] The system according to claim 1, including the following:

[0931] (Claim 3)

[0932] A data storage means for storing the analyzed activity information in a storage device on the network,

[0933] Information and communication means that enable users to receive generated information through an application,

[0934] The system according to claim 1, including the following:

[0935] "Example 2 of combining an emotion engine"

[0936] (Claim 1)

[0937] An analytical means for acquiring image information from within a childcare facility and analyzing children's behavior and emotions from said image information,

[0938] A generation means that automatically generates activity suggestions according to the emotional state based on the analysis results,

[0939] A means of outputting the generated activity proposals in a format that can be modified by childcare workers, and distributing the modified information to caregivers,

[0940] A storage method for saving the analyzed emotional data to the cloud,

[0941] A distribution method that allows users to receive suggestions based on emotional data through their devices,

[0942] A system that includes this.

[0943] (Claim 2)

[0944] A meal analysis method that analyzes image information during meals and evaluates emotional state,

[0945] A generation means for generating proposals for improving the dining environment based on the evaluation results,

[0946] A means of distributing the generated suggestions for improving the meal environment to caregivers,

[0947] The system according to claim 1, including the following:

[0948] (Claim 3)

[0949] A storage means for storing analyzed activity and emotion data in remote memory,

[0950] A distribution method that allows customers to receive the generated information via communication devices,

[0951] The system according to claim 1, including the following:

[0952] "Application example 2 when combining with an emotional engine"

[0953] (Claim 1)

[0954] An analysis means for acquiring image information and analyzing a person's activities from said image information,

[0955] A generation means for automatically generating text based on the analysis results,

[0956] A means of outputting the generated text in a format that caregivers can edit, and distributing the edited information to other users,

[0957] A generation means for analyzing a person's emotional state and generating activity suggestions based on the results of the emotional analysis,

[0958] A system that includes this.

[0959] (Claim 2)

[0960] A meal analysis method that analyzes image information during meals and evaluates the amount of food consumed,

[0961] A generation means for generating dietary improvement suggestions based on the evaluation results,

[0962] A means of distributing the generated diet improvement suggestions to other users,

[0963] The system according to claim 1, including the following:

[0964] (Claim 3)

[0965] A storage means for saving the analyzed activity information to a remote storage device,

[0966] A distribution method that allows users to receive the generated information through application software,

[0967] The system according to claim 1, including the following: [Explanation of symbols]

[0968] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. Image processing means for acquiring video data from within a childcare facility or home and analyzing the behavior of minors from said video data, A generative AI method that automatically generates natural language text based on analysis results, A data transfer method that outputs generated natural language text in a format that can be edited by educators, and distributes the edited information to parents, A system that includes this.

2. A meal analysis method that analyzes video data during meals and evaluates the amount of leftover food, Information generation means for generating dietary improvement measures based on the evaluation results, A data distribution method for delivering the generated dietary improvement plan to parents, The system according to claim 1, including the following:

3. A data storage means for storing the analyzed activity information in a storage device on the network, Information and communication means that enable users to receive generated information through an application, The system according to claim 1, including the following:

Images