system

Abstract

Provide a system. [Solution] means for recording audio data collected by a device worn by the child; means for transmitting the voice data to a server; means for converting the voice data into text data in the server; means for analyzing the text data and generating a summary; means for notifying the parent / guardian of the summary; A system including:

Description

[Technical Field]

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

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

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

[0004] Conventionally, oral reports or paper daily reports have been the common means of communicating the daily activities of children attending kindergarten or nursery school to parents, but the information is often subjective, making it difficult for parents to accurately grasp their children's daily activities. Furthermore, the reporting work places a heavy burden on childcare workers, which can lead to reduced work efficiency and information omissions. Furthermore, delays in confirming important information can sometimes prevent prompt action. The present invention aims to solve these problems and provide a system that can automatically and accurately summarize a child's daily activities and quickly communicate them to parents. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for recording voice data collected by a device worn by a child, a means for transmitting the voice data to a server, a means for converting the voice data into text data in the server, a means for analyzing the text data and generating a summary, and a means for notifying the parent of the summary. This system automatically collects the child's utterances and the contents of conversations with caregivers, and stores, analyzes, and summarizes them as text data, allowing parents to quickly and accurately understand how their child is doing throughout the day. Furthermore, by including a means for noise filtering the voice data and a means for generating summaries using natural language processing technology, the system can provide more accurate information, reducing the burden on caregivers and facilitating smoother communication with parents.

[0006] "Children" refers to children of kindergarten or nursery school age.

[0007] "Device" refers to an electronic device worn by a child that includes a microphone and a communications module for collecting audio data.

[0008] "Audio data" refers to digital audio information that records a child's speech and conversations with caregivers.

[0009] A "server" is a computer system for processing and storing audio data, and refers to a device that connects to the device and parent's terminal via a network.

[0010] "Text data" refers to digital information that has been converted from audio data into a string of characters.

[0011] "Analysis" refers to the process of structuring and generating summaries of text data using natural language processing techniques.

[0012] A "summary" refers to information that extracts key information from analyzed text data and summarizes it concisely.

[0013] "Guardian" refers to a parent or legal guardian who needs to know about a child's daily life.

[0014] "Device" refers to a mobile device such as a smartphone or tablet used by a parent or guardian.

[0015] "Notification" refers to push messages and alerts sent from the server to the parent's device to convey information.

[0016] "Noise filtering means" refers to techniques and functions for removing unwanted background noise from audio data.

[0017] "Natural language processing technology" refers to artificial intelligence technology used to analyze text data and generate summaries. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a system for recording the daily activities of children attending kindergarten or nursery school and providing the information to parents quickly and accurately. This system includes the following main components:

[0040] Wearing the device and collecting voice data

[0041] The device is attached to a child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals. For example, if a child says, "I drew a picture today," the device records that voice. The recorded voice data is temporarily stored in a buffer and periodically sent to a server.

[0042] Sending voice data and converting it to text

[0043] The server receives the voice data sent from the terminal. The received voice data is converted into text data using voice recognition technology. For example, the voice recognition engine analyzes the voice saying "I drew a picture today" and converts it into text format "I drew a picture today." This text data is stored in a temporary database.

[0044] Text data analysis and summary generation

[0045] The server analyzes the stored text data using natural language processing (NLP). During this analysis process, important information is extracted and used as input for summarizing. For example, if multiple pieces of text data include "I drew a picture today" and "I played with my friends," the server aggregates the information and generates the summary "I drew a picture today and played with my friends."

[0046] Parental notification

[0047] The server sends the generated summary to the parent's smartphone, tablet, or other user device. This notification is typically sent as a push notification, which the parent receives through an application. For example, when the parent opens the smartphone app, a summary such as "Today, your child drew pictures and played with friends" is displayed. This allows the parent to accurately grasp their child's daily activities.

[0048] This system reduces the burden of reporting work for caregivers and allows parents to quickly and accurately grasp how their child is doing throughout the day. In addition, by using noise filtering means for voice data and natural language processing technology, the accuracy of the recorded information is improved, enabling smooth communication between parents and caregivers.

[0049] The above is a specific embodiment for carrying out the present invention.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The device is attached to a child's clothing or school bag, and the device has a built-in microphone that starts recording audio data at specified intervals, such as recording a few seconds of ambient audio every minute.

[0053] Step 2:

[0054] The device stores the recorded audio data in a temporary buffer, which is then sent to the server at regular intervals.

[0055] Step 3:

[0056] The device sends the buffered audio data to the server over wireless communication (e.g., Wi-Fi or mobile data).

[0057] Step 4:

[0058] The server analyzes the received voice data and converts it into text data using a voice recognition engine (for example, a cloud-based voice recognition service). If the voice is "Today we played on the slide," this content is saved as text data "Today we played on the slide."

[0059] Step 5:

[0060] The server stores the converted text data in a temporary database, along with a timestamp and identifying information (e.g., the child's ID).

[0061] Step 6:

[0062] The server periodically retrieves the text data and analyzes it using natural language processing technology. During the analysis process, the meaning of the text data is understood and important information is extracted.

[0063] Step 7:

[0064] The server generates a summary based on the analyzed data. For example, from multiple utterances such as "Today I played on the slide" and "Today I built a castle in the sandbox with my friends," it generates an integrated summary sentence such as "Today I played on the slide and built a castle in the sandbox."

[0065] Step 8:

[0066] The server generates a summary and sends it to the parent's device as a push message, which is delivered to the parent's smartphone or tablet.

[0067] Step 9:

[0068] The user (parent) receives a push notification and opens the dedicated app to check the summary, allowing parents to quickly and accurately understand their child's activities for the day.

[0069] Step 10:

[0070] Users can use the in-app feedback function to send comments or inquiries to the nursery if necessary, which is a step to facilitate smooth communication between parents and caregivers.

[0071] These are the specific processing steps of the program. Through this process, the child's daily activities are automatically recorded, analyzed, summarized, and notified to the parents.

[0072] Example 1

[0073] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0074] In modern society, it is important to quickly and accurately communicate to parents how their children at kindergarten or nursery school are doing throughout the day. However, manually creating reports by caregivers takes time and effort, and the accuracy of the reports can be compromised by human error. While voice recording can be sent directly to parents, this approach poses privacy and data volume issues. Furthermore, it is desirable to provide a summary of a child's daily activities in a format that is easy for parents to understand. Based on this, a system is needed that quickly and accurately records and communicates children's activities.

[0075] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0076] In this invention, the server includes a means for recording voice data collected by a device worn by the child, a means for temporarily storing the voice data in a buffer and transmitting it to the server, a means for converting the voice data into text data using speech recognition technology in the server, a means for analyzing the text data using natural language processing (NLP) technology to generate a summary, and a means for sending a push notification of the summary to a parent's device. This reduces the burden on caregivers and enables parents to quickly and accurately grasp their child's daily activities.

[0077] A "child-worn device" is a device that can be worn by a child and has the function of collecting voice data.

[0078] "Audio data" is digital information that records a child's speech and surrounding sounds.

[0079] A "recording means" is a device or program capable of capturing and recording audio data in digital form.

[0080] A "buffer" is a memory area for temporarily storing data.

[0081] A "server" is a computer system that receives, processes, and stores data over a network.

[0082] "Transmitting means" refers to the functions and protocols for sending data to other devices or systems.

[0083] "Voice recognition technology" is a technology that analyzes voice signals and converts their contents into text data.

[0084] "Text data" is data that is saved as character information.

[0085] "Natural language processing (NLP)" is a technology that allows computers to understand, analyze, and generate human language.

[0086] "Means for analyzing and generating summaries" refers to the function of extracting important information from text data and creating a concise summary.

[0087] The "means for push notification" is a function for notifying information to a user terminal in real time via a network.

[0088] The present invention is a system that records the daily activities of children attending kindergarten or nursery school and notifies parents of the activities quickly and accurately. This system consists of the following main components:

[0089] Wearing the device and collecting voice data

[0090] The device is attached to the child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals (e.g., every 5 minutes). For example, if a child says, "I drew a picture today," the device records the audio. The recorded audio data is temporarily stored in a buffer and sent to the server periodically (e.g., every day at 3 p.m.).

[0091] Sending voice data and converting it to text

[0092] The server receives the voice data sent from the terminal. The received voice data is converted into text data using speech recognition technology. Specifically, the server uses Google (registered trademark) Cloud Speech-to-Text API to convert the voice data into text such as "I drew a picture today." This text data is stored in a temporary database.

[0093] Text data analysis and summary generation

[0094] The server analyzes the stored text data using natural language processing (NLP) technology. During this analysis process, important information is extracted and used as input for summarizing. For example, if multiple pieces of text data are "Today I drew a picture" and "I played with my friends," the server aggregates that information and generates the summary "Today I drew a picture and played with my friends." Examples of specific software used include SpaCy and NLTK.

[0095] Parental notification

[0096] The server then pushes the generated summary to the parent's smartphone, tablet, or other user device. This notification is typically sent using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNs). For example, when a parent opens the app on their smartphone, a summary such as "Today, your child drew pictures and played with friends" is displayed. This allows parents to accurately track their child's activities throughout the day.

[0097] Specific examples

[0098] For example, if a child says, "Today was fun," "I drew a picture," or "I played with my friends," the process will proceed as follows:

[0099] 1. The device records audio.

[0100] 2. The audio is stored in a buffer and periodically sent to the server.

[0101] 3. The server receives the voice data and converts it into text data using voice recognition technology.

[0102] 4. The server analyzes the text data and generates a summary such as, "Today was fun. I drew pictures. I played with friends."

[0103] 5. This summary will be pushed to the parent's smartphone.

[0104] Prompt Sentence Examples

[0105] "Please send the voice data collected by your device to the server."

[0106] "The server converts the audio data into text, extracts the key information, and generates a summary."

[0107] "Please notify the user terminal of the generated summary."

[0108] This system reduces the burden on caregivers and allows parents to quickly and accurately grasp their child's daily activities. In addition, by using noise filtering of voice data and natural language processing technology, the accuracy of recorded information is improved, facilitating smooth communication between parents and caregivers.

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1:

[0111] Collection of voice data by device

[0112] Input: What your child is saying and the sounds of the surrounding area.

[0113] How it works: The device records audio at regular intervals (e.g., every 5 minutes) using a built-in microphone. The recorded audio is temporarily stored in a digital buffer. For example, if a child says, "Today was fun," the audio is recorded and stored in the buffer.

[0114] Output: Buffered audio data.

[0115] Step 2:

[0116] Sending audio data from the device to the server

[0117] Input: Buffered audio data.

[0118] Specific operation: The device establishes an Internet connection using a built-in communication module (e.g., LTE module). The recorded voice data is encrypted and sent to the server using a secure protocol (e.g., HTTPS). For example, the device is configured to upload the voice data to the server every day at 3:00 p.m.

[0119] Output: The audio data sent to the server.

[0120] Step 3:

[0121] Server-based text conversion of voice data

[0122] Input: The audio data sent to the server.

[0123] Specific operation: The server saves the received voice data as a temporary file. It then converts the voice data into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text API). For example, the voice saying "I had fun today" is converted into text data saying "I had fun today."

[0124] Output: Text data stored in a temporary database.

[0125] Step 4:

[0126] Analysis of text data and generation of summaries by the server

[0127] Input: Text data stored in a temporary database.

[0128] How it works: The server analyzes the text data using natural language processing (NLP) technology. For example, it uses libraries such as SpaCy or NLTK. As a result of the analysis, important information is extracted. For example, if multiple pieces of text data contain content such as "Today I drew a picture" or "I played with friends," they are aggregated to generate a summary such as "Today I drew a picture and played with friends."

[0129] Output: The generated summary.

[0130] Step 5:

[0131] Notification from the server to the user device

[0132] Input: The generated summary.

[0133] Specific operation: The server sends a push notification to the parent's smartphone or tablet. Notifications are sent using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNs). For example, a summary such as "Today, your child drew a picture and played with friends" is sent to the parent's smartphone. The parent can check the summary by opening the smartphone app.

[0134] Output: Summary notification displayed on parent's device.

[0135] (Application example 1)

[0136] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0137] In conventional childcare facilities, there are limited means for accurately informing parents of how their children are doing throughout the day. Furthermore, there is no adequate system in place to immediately detect abnormalities in children and notify parents, so children's safety cannot be fully guaranteed. This leaves parents constantly anxious about their children's safety. Furthermore, there is the problem that childcare workers have to report in detail on each child's condition, which is time-consuming and burdensome.

[0138] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0139] In this invention, the server includes means for recording voice data collected by a device worn by the child, means for transmitting the voice data to the server, means for converting the voice data into text data in the server, means for analyzing the text data and generating a summary, means for notifying the parent / guardian's terminal of the summary, means for analyzing the text data and using a generative AI model to detect abnormal situations, and means for notifying the parent / guardian's terminal in real time when an abnormal situation is detected. This makes it possible to accurately record the child's daily activities and quickly detect and notify abnormal situations.

[0140] A "child-worn device" is a small device that can be attached to a child's clothing or school bag and is used to collect voice data.

[0141] "Audio data" is a digital recording of a child's speech and surrounding sounds collected by the device.

[0142] A "server" is a computer system that receives voice data sent from a device, converts it into text data, and analyzes it.

[0143] "Text data" refers to character information converted from voice data using voice recognition technology.

[0144] A "means for generating a summary" is a technology for analyzing text data, extracting important information, and summarizing it in a concise manner.

[0145] A "parent's device" is a mobile device used by a parent, such as a smartphone or tablet.

[0146] A "generative AI model" is a model that uses artificial intelligence technology to analyze text data and detect specific abnormal situations.

[0147] An "abnormal situation" is a situation that indicates danger or trouble that differs from normal conditions, and includes statements such as "help me" or "I'm scared."

[0148] "Means of real-time notification" refers to technology that immediately sends a push notification to the parent's device when an abnormal situation is detected.

[0149] "Noise filtering means" refers to a technique for removing unwanted background sounds and noise from collected audio data.

[0150] "Natural language processing technology" is an advanced language analysis technology for understanding, analyzing, and generating summaries of text data.

[0151] This invention is a system that uses a device worn by a child to record voice data and notify parents of the data quickly and accurately. It also has the function of detecting abnormal situations in real time and notifying parents.

[0152] Hardware and software used

[0153] Hardware

[0154] Small devices attached to children's clothing or school bags that have voice recording and communication capabilities. Examples include portable audio recorders and Bluetooth communication modules.

[0155] Server: Equipped with a high-performance CPU and memory, it performs speech recognition and natural language processing. Cloud services are also available.

[0156] Parent's device: A mobile device such as a smartphone or tablet.

[0157] software

[0158] Speech recognition engine: Uses Google Cloud Speech-to-Text or a similar service to convert voice data into text data.

[0159] Natural language processing engine: Analyzes text data using OpenAI's (registered trademark) GPT-3 (registered trademark) and other engines to generate summaries and detect abnormalities.

[0160] Notification service: Uses Firebase Cloud Messaging and other services to send push notifications to parents' devices in real time.

[0161] Program processing

[0162] Audio data collection

[0163] A small device attached to a child's clothing or school bag records audio at regular intervals, temporarily storing the data in a buffer and periodically sending it to a server.

[0164] Analysis of audio data

[0165] The server converts the received voice data into text data using Google Cloud Speech-to-Text, which is then stored in a database.

[0166] Text data analysis and anomaly detection

[0167] The server analyzes the text data using OpenAI's GPT-3. At this stage, it generates a summary and detects anomalies using a specific prompt, such as "Please detect anomalies in the following sentence: Help."

[0168] Parental notification

[0169] The generated summary and the results of any abnormalities detected are sent to the parent's device according to their level of importance via real-time push notifications using Firebase Cloud Messaging.

[0170] Specific examples

[0171] For example, if a child says "help me," the device collects this audio and sends it to a server. The server converts the audio into text using Google Cloud Speech-to-Text and uses OpenAI's GPT-3 to detect abnormalities. If "help me" is recognized as an abnormality, a notification is sent to the parent's smartphone using Firebase Cloud Messaging stating, "Your child is asking for help."

[0172] Prompt Sentence Examples

[0173] Detect the anomaly in the following statements:

[0174] "help me"

[0175] This makes it easier for parents to ensure their children's safety in real time.

[0176] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0177] Processing Steps

[0178] Step 1:

[0179] A device worn by the child collects audio data. The device records ambient audio at regular intervals and temporarily stores it in a buffer. The input is the ambient audio, and the output is the recorded audio data.

[0180] Step 2:

[0181] The device sends audio data to the server. At specified intervals, the audio data stored in the buffer from the device is uploaded to the server via the network. The input is audio data, and the output is the audio data stored on the server.

[0182] Step 3:

[0183] The server converts the voice data into text data. The server then analyzes the received voice data using Google Cloud Speech-to-Text and converts it into text information. The input is voice data and the output is text data.

[0184] Step 4:

[0185] The server analyzes the text data and generates a summary. The server analyzes the text data using natural language processing technology, extracts important information, and generates a summary. The input is text data, and the output is summary text.

[0186] Step 5:

[0187] The server analyzes the text data and detects anomalies. The server uses a generative AI model to determine anomalies through specific prompt sentences (e.g., "Please detect anomalies in the following sentences: Help"). The input is the text data, and the output is the result of detecting anomalies.

[0188] Step 6:

[0189] The server notifies the parent's device of the summary and the results of the abnormality detection. The server uses Firebase Cloud Messaging to push the generated summary text and the results of the abnormality detection to the parent's smartphone in real time. The input is the summary text and the results of the abnormality detection, and the output is a notification message on the smartphone.

[0190] Step 7:

[0191] The parent's device receives the notification message and displays it on the screen. This allows the parent to get real-time information about their child's daily life and any abnormal events. The input is the notification message, and the output is the message displayed on the device.

[0192] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0193] This invention is a system that records the daily activities of children attending kindergarten or nursery school and communicates the information to parents quickly and accurately. This system includes the following main components and emotion engine.

[0194] Wearing the device and collecting voice data

[0195] The device is attached to a child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals. For example, if a child says, "I drew a picture today," the device records that voice. The recorded voice data is temporarily stored in a buffer and periodically sent to a server.

[0196] Sending voice data and converting it to text

[0197] The server receives the voice data sent from the terminal. The received voice data is converted into text data using voice recognition technology. For example, the voice recognition engine analyzes the voice saying "I drew a picture today" and converts it into text format "I drew a picture today." This text data is stored in a temporary database.

[0198] Text data analysis and summary generation

[0199] The server analyzes the stored text data using natural language processing (NLP). This analysis process understands the meaning of the text data and extracts important information. It also uses an emotion engine to recognize the user's emotions from the voice data and adds that emotional information to the text data. For example, if a child says, "I drew a picture today," and the emotion is "it looks fun," that emotional information is added as a tag called "it looks fun."

[0200] Generating summaries and utilizing emotional information

[0201] The server generates a summary based on the analyzed data and the attached emotional information. For example, from multiple utterances such as "Today I played on the slide" and "Today I built a castle in the sandbox with my friends," the server takes the emotional information into account and generates a summary sentence such as "Today they played on the slide and built a castle in the sandbox. They looked like they were having a lot of fun."

[0202] Parental notification

[0203] The server then sends the generated summary and emotional information to the parent's smartphone, tablet, or other user device. This notification is sent as a push notification, which the parent receives through an application. For example, when the parent opens the smartphone application, a summary such as "Today, your child played on the slide and built a castle in the sandbox. They seemed to have a lot of fun" is displayed. This allows the parent to quickly and accurately grasp their child's daily activities and their emotions at that time.

[0204] Feedback and Communication

[0205] Users (parents) can use the app's feedback function to send comments or inquiries to the nursery school as needed. This is a step to facilitate smooth communication between parents and caregivers. In addition, detailed feedback based on emotional information allows caregivers to gain a deeper understanding of the child's situation and take appropriate action.

[0206] The above is a specific embodiment for carrying out the present invention. This system reduces the burden of reporting work on caregivers and allows parents to quickly and accurately grasp their child's daily activities and emotions. By using noise filtering means for voice data, natural language processing technology, and an emotion engine, the accuracy of recorded information is improved, enabling smooth communication between parents and caregivers.

[0207] The processing flow will be explained below.

[0208] Step 1:

[0209] The device is attached to a child's clothing or school bag, and the device has a built-in microphone that starts recording audio data at specified intervals, such as recording a few seconds of ambient audio every minute.

[0210] Step 2:

[0211] The device stores the recorded audio data in a temporary buffer, which is then sent to the server at regular intervals.

[0212] Step 3:

[0213] The device sends the buffered audio data to the server over wireless communication (e.g., Wi-Fi or mobile data).

[0214] Step 4:

[0215] The server analyzes the received voice data and converts it into text data using a voice recognition engine (for example, a cloud-based voice recognition service). If the voice is "Today we played on the slide," this content is saved as text data "Today we played on the slide."

[0216] Step 5:

[0217] The server stores the converted text data in a temporary database, along with a timestamp and identifying information (e.g., the child's ID).

[0218] Step 6:

[0219] The server analyzes the voice data and uses an emotion engine to recognize the child's emotional information. For example, if a statement such as "I played on the slide today" is accompanied by an emotion tag such as "It looks fun," that information is added to the text data.

[0220] Step 7:

[0221] The server periodically collects the acquired text data and emotional information, analyzes it using natural language processing technology, and generates summaries. For example, if multiple pieces of text data include "I drew a picture today" and "I played with my friends," it generates a summary that includes emotional information, such as "I drew a picture today and played with my friends. They seemed to be having a lot of fun."

[0222] Step 8:

[0223] The server generates a summary and sends it to the parent's device as a push message, which is delivered to the parent's smartphone or tablet.

[0224] Step 9:

[0225] The user (parent) receives a push notification and opens the dedicated app to check the summary. For example, a summary such as "Today, they played on the slide and built a castle in the sandbox. They looked like they had a lot of fun" is displayed in the app.

[0226] Step 10:

[0227] Users can use the feedback function within the app to send comments or inquiries to the nursery school as needed, facilitating smooth communication between parents and caregivers.

[0228] These are the specific processing steps of the system that combines the emotion engine. Through this process, a child's daily activities and emotions at that time are accurately recorded, analyzed, summarized, and communicated to the parent.

[0229] Example 2

[0230] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0231] Conventional systems for recording children's daily activities at kindergartens and daycare centers and notifying parents of their children's activities are cumbersome and require a lot of manual work to collect and transmit information. Furthermore, filtering to eliminate noise and redundant information from voice data and adding emotional information are difficult, making it difficult to provide parents with sufficient information. As a result, the reporting work burden on childcare workers increases, making it difficult for parents to accurately grasp their children's situations.

[0232] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0233] In this invention, the server includes means for recording voice data collected by a device worn by the child, means for transmitting the voice data to the server, means for converting the voice data to text data in the server, means for analyzing the text data and adding emotional information to generate a summary, means for notifying the parent / guardian of the summary, and means for the parent / guardian to send feedback through an application. This allows efficient collection of voice data, noise filtering, text conversion, addition of emotional information, and generation of a summary to be performed automatically, reducing the burden on caregivers and enabling parents to accurately and quickly grasp their child's daily activities and emotions.

[0234] "Child" refers to a person of kindergarten or nursery school age.

[0235] The "device" is a device that is attached to a child's clothing or school bag and is equipped with a microphone and recording function for collecting voice data.

[0236] "Audio data" refers to the child's speech and surrounding sounds, and is a digital audio signal collected by the device.

[0237] "Server" refers to a central management device that receives, analyzes, converts, stores, notifies, etc., voice data.

[0238] "Text data" refers to text information converted from voice data using voice recognition technology.

[0239] "Analysis" refers to the process of understanding the content of text data using natural language processing technology and extracting important information and keywords.

[0240] "Emotion information" refers to tags and metadata that indicate the type and intensity of emotions extracted from audio data or text data.

[0241] A "summary" refers to a sentence that concisely summarizes important information and is generated based on analyzed text data and emotional information.

[0242] "Parent's device" refers to an electronic device such as a smartphone or tablet used by a parent.

[0243] "Feedback" refers to the exchange of information by parents to send comments or questions to daycare centers or kindergartens.

[0244] "Noise filtering" refers to the process of removing unnecessary environmental sounds and noise from audio data, leaving only the necessary audio information.

[0245] "Natural language processing technology" refers to a series of algorithms and toolsets that use computer technology to analyze, understand, and generate human language.

[0246] The present invention is a system that records the daily activities of children attending kindergarten or nursery school and quickly and accurately communicates the information to parents. This system is composed of a series of means including a wearable device, a server, and a parent terminal.

[0247] Wearing the device and collecting voice data

[0248] The device is attached to a child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals (for example, every 15 minutes). The recorded audio data is temporarily stored in a buffer on the device and periodically sent to a server. Noise filtering technology is used to remove ambient sounds and other distracting noise.

[0249] Sending voice data and converting it to text

[0250] The device sends the saved voice data to a server via the network. The server converts the received voice data into text data using a speech recognition engine such as Google Cloud Speech-to-Text or Amazon Transcribe. For example, voice data such as "I drew a picture today" is converted into text data such as "I drew a picture today." The converted text data is stored in a temporary database.

[0251] Analysis of text data and addition of emotional information

[0252] The server analyzes the stored text data using natural language processing (NLP) technology. For example, SpaCy or Google Natural Language API is used for the analysis. This process understands the meaning of the text data and extracts keywords and important information. It also uses an emotion engine to extract emotional information from the text data. For example, a statement such as "It was fun" is tagged with a positive emotion tag. This emotional information is added to the text data, and the format is, for example, "I drew a picture today (it was fun)."

[0253] Generate a summary

[0254] The server generates summaries based on the text data with analysis and emotional information added. For example, from statements such as "Today I played on the slide" and "Today I built a castle in the sandbox with my friends," a summary sentence such as "Today we played on the slide and built a castle in the sandbox. They looked like they were having a lot of fun" is generated. Natural language processing technology and generative AI models are used to generate summaries.

[0255] Parental notification

[0256] The server sends the generated summary and emotional information to the parent's device (smartphone or tablet) as a push notification. For example, a notification may arrive on the parent's smartphone saying, "Today, your child played on the slide and built a castle in the sandbox. They looked like they had a lot of fun." The parent can then open the app to view the detailed summary.

[0257] Feedback and Communication

[0258] Users (parents) can send comments and inquiries to the nursery school through the feedback function within the app. For example, they can send a comment such as, "I'd like to know more about the activities that my child particularly enjoyed." The server receives this feedback information and notifies the nursery school's staff. This feedback function is a means to facilitate smooth communication between parents and nursery school staff.

[0259] Specific prompt examples

[0260] Below are some examples of prompt sentences that the system inputs into the generative AI model.

[0261] Please convert the following audio data to text:

[0262] "We played on the slide today. It was so much fun!"

[0263] Text data:

[0264] "I played on the slide today."

[0265] Emotion information:

[0266] it was fun

[0267] The above is a specific embodiment for implementing the present invention. This system reduces the burden of reporting work on caregivers and allows parents to quickly and accurately grasp their child's daily activities and emotions. Furthermore, by using noise filtering means for voice data, natural language processing technology, and an emotion engine, the accuracy of recorded information is improved, allowing for smooth communication between parents and caregivers.

[0268] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0269] Step 1: Collecting audio data

[0270] The device is attached to a child's clothing or school bag. The device's built-in microphone records the child's speech and surrounding sounds at regular intervals (for example, every 15 minutes). The recorded audio data is temporarily stored in a buffer.

[0271] Input: Ambient audio

[0272] Output: Buffered audio data

[0273] Step 2: Sending audio data

[0274] The device transmits the audio data stored in the buffer to the server at regular intervals via wireless communication (Wi-Fi or cellular).

[0275] Input: Buffered audio data

[0276] Output: Audio data sent to the server

[0277] Step 3: Convert the audio to text

[0278] The server converts the voice data received from the device into text data using a speech recognition engine such as Google Cloud Speech-to-Text or Amazon Transcribe. For example, it analyzes the speech "I drew a picture today" and converts it into text "I drew a picture today."

[0279] Input: Audio data sent to the server

[0280] Output: Data converted to text format

[0281] Step 4: Saving text data and filtering noise

[0282] The server stores the converted text data in a temporary database, and simultaneously filters out noise and unnecessary information from the stored text data.

[0283] Input: Data converted to text format

[0284] Output: filtered text data, data stored in a temporary database

[0285] Step 5: Analyzing the text data

[0286] The server analyzes the stored text data using natural language processing (NLP) technology. It uses SpaCy and the Google Natural Language API to extract keywords and important information from the text data. It also uses an emotion engine to extract emotional information from the voice data and add it to the text data.

[0287] Input: filtered text data

[0288] Output: Analyzed text data with emotional information added

[0289] Step 6: Generate a summary

[0290] The server extracts important information from the text data with added emotional information and generates summaries. A text summarization algorithm and a generative AI model are used to generate summaries. For example, from multiple statements such as "Today, I played on the slide" and "Today, I built a castle in the sandbox with my friends," the server generates a summary sentence such as "Today, they played on the slide and built a castle in the sandbox. They looked like they were having a lot of fun."

[0291] Input: Analyzed text data with emotional information added

[0292] Output: Summary

[0293] Step 7: Parental Notification

[0294] The server then sends the generated summary and emotion information to the parent's device (smartphone or tablet) as a push notification. The notification sent to the parent's device may include, for example, "Today, the child played on the slide and built a castle in the sandbox. They seemed to be having a lot of fun."

[0295] Input: Generated summary

[0296] Output: Push notification sent to parent's device

[0297] Step 8: Feedback and communication

[0298] Users (parents) can send comments and inquiries to the kindergarten through the feedback function in the app. For example, they can send a comment such as, "I'd like to know more about the activities that my child particularly enjoyed." The server receives this feedback information and notifies the kindergarten's caregivers.

[0299] Input: Parent feedback information

[0300] Output: Feedback information notified to the nursery teacher

[0301] The above is a detailed description of the processing steps of the program for this system. This system makes it possible to efficiently record the daily behavior and emotions of a child and accurately convey this information to the parents.

[0302] (Application example 2)

[0303] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0304] In traditional brick-and-mortar stores, it is difficult for staff to quickly and accurately grasp customer sentiment and satisfaction levels and respond appropriately in real time. This problem hinders efforts to improve the quality of the customer experience, potentially leading to lower satisfaction and fewer repeat customers. It also increases the burden on on-site staff.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0306] In this invention, the server includes means for recording voice data collected by a device worn by the child, means for transmitting the voice data to the server, means for converting the voice data into text data in the server, means for analyzing the text data and generating a summary, means for notifying the summary to the parent's terminal, means for analyzing emotional information of the customer using a smart device, and means for providing feedback in real time based on the emotional information. This allows staff to quickly and accurately grasp the customer's emotions and satisfaction level and take appropriate action in real time.

[0307] A "child-worn device" is a device that can be attached to a child's clothing, bag, etc., for collecting audio.

[0308] "Audio data" means data recorded by a device that is an electronic representation of human speech or ambient sounds.

[0309] A "server" is a computer system that receives and processes audio data sent from devices via the Internet.

[0310] "Text data" is data that has been analyzed using voice recognition technology and converted into text information.

[0311] A "summary" is a concise summary of important information extracted from the analyzed text data.

[0312] "Parent's device" refers to information devices such as smartphones and tablets owned by parents.

[0313] "Smart devices" are devices with advanced processing capabilities, such as smart glasses or smartphones used by staff in brick-and-mortar stores.

[0314] "Emotional information" is data that indicates the emotional state of a speaker, obtained by analyzing voice data or text data.

[0315] "Means for providing feedback in real time" refers to technology that has the ability to immediately notify staff of analyzed emotional information.

[0316] The present invention is a system that analyzes customer emotional information in real time in a physical store, allowing staff to respond quickly and appropriately. How the system is implemented is described in detail below.

[0317] Hardware and software configuration

[0318] 1. Hardware

[0319] Smart devices: These include smart glasses and smartphones worn by store staff, which have built-in microphones and cameras to record what customers say and what they look like.

[0320] Server: A computer system that receives and analyzes voice data sent from smart devices via the Internet.

[0321] 2. Software

[0322] Speech recognition engine: A technology for converting voice data into text data. The speech_recognition library is used.

[0323] Sentiment analysis engine: A technology that analyzes text data and extracts emotional information. It uses the sentiment-analysis pipeline from the transformers library.

[0324] Text-to-speech engine: A technology for providing audio feedback to staff. The pyttsx3 library is used.

[0325] Operation flow

[0326] 1. Collecting and sending customer voice

[0327] The microphone on the smart device records what the customer says and transmits the audio data to a server.

[0328] 2. Converting voice data to text

[0329] The server converts the received voice data into text data using a voice recognition engine.

[0330] 3. Sentiment Analysis of Text Data

[0331] The server analyzes the text data using an emotion analysis engine to extract customer emotional information.

[0332] 4. Notification of emotional information

[0333] The server then provides real-time feedback to staff based on the extracted emotional information, either in audio or visual form.

[0334] Specific examples

[0335] For example, consider a scenario in which a staff member is serving a customer in a physical store. If the customer says, "This product is amazing!", the microphone in the smart device records this voice and immediately sends it to the server. The server converts the voice data into text data such as "This product is amazing!" and inputs it into an emotion analysis engine. The emotion analysis engine determines the customer's emotion from this text as "delighted" and notifies the staff member of the result in real time. This notification allows the staff member to immediately recognize that the customer is highly satisfied and can use this information in their next response.

[0336] Prompt Sentence Examples

[0337] Analyze customer reactions in your brick-and-mortar store today: "This product is amazing!" Generate text for sentiment analysis and feedback.

[0338] In this way, the present invention is expected to improve the efficiency and quality of customer service in brick-and-mortar stores. By instantly understanding customer emotions, better service can be provided, contributing to increased customer satisfaction.

[0339] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0340] Step 1:

[0341] Customer voice recording and transmission

[0342] The smart device on the terminal records what the customer says with a microphone. The recorded voice data is temporarily stored on the terminal and then sent to a server over the Internet. The input is the customer's voice, and the output is the voice data sent from the terminal to the server. This voice data is used in subsequent processing steps.

[0343] Step 2:

[0344] Converting audio data to text

[0345] The server receives the transmitted voice data and converts it into text data using a speech recognition engine (speech_recognition library). The input is voice data and the output is text data. The data processing performed here involves analyzing the voice and converting it into a string of characters.

[0346] Step 3:

[0347] Sentiment analysis of text data

[0348] The server inputs the converted text data into a sentiment analysis engine (the sentiment-analysis pipeline of the transformers library) to extract customer sentiment information. The input is text data, and the output is sentiment information (e.g., joy, sadness, anger, etc.). The sentiment analysis engine analyzes the text data and identifies the sentiments contained within it.

[0349] Step 4:

[0350] Emotional information notification

[0351] The server provides real-time feedback to staff based on the extracted emotion information. The feedback is provided audibly using a text-to-speech engine (pyttsx3 library) or visually displayed on a smart device. The input is emotion information, and the output is a feedback message. For example, the staff member may be notified that "The customer's emotion is happy (confidence: 0.95)."

[0352] As a concrete example, consider the case where a customer says, "This product is amazing!" This voice is recorded in step 1, and converted into text in step 2 as "This product is amazing!" Next, emotion analysis is performed in step 3, and the emotion "delight" is extracted. Finally, in step 4, feedback stating "The customer's emotion is delight (confidence level: 0.95)" is notified to the staff.

[0353] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0354] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0355] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0356] [Second embodiment]

[0357] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0358] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0359] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0360] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0361] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0362] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0363] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0364] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0365] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0366] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0367] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0368] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0369] The present invention is a system for recording the daily activities of children attending kindergarten or nursery school and providing the information to parents quickly and accurately. This system includes the following main components:

[0370] Wearing the device and collecting voice data

[0371] The device is attached to a child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals. For example, if a child says, "I drew a picture today," the device records that voice. The recorded voice data is temporarily stored in a buffer and periodically sent to a server.

[0372] Sending voice data and converting it to text

[0373] The server receives the voice data sent from the terminal. The received voice data is converted into text data using voice recognition technology. For example, the voice recognition engine analyzes the voice saying "I drew a picture today" and converts it into text format "I drew a picture today." This text data is stored in a temporary database.

[0374] Text data analysis and summary generation

[0375] The server analyzes the stored text data using natural language processing (NLP). During this analysis process, important information is extracted and used as input for summarizing. For example, if multiple pieces of text data include "I drew a picture today" and "I played with my friends," the server aggregates the information and generates the summary "I drew a picture today and played with my friends."

[0376] Parental notification

[0377] The server sends the generated summary to the parent's smartphone, tablet, or other user device. This notification is typically sent as a push notification, which the parent receives through an application. For example, when the parent opens the smartphone app, a summary such as "Today, your child drew pictures and played with friends" is displayed. This allows the parent to accurately grasp their child's daily activities.

[0378] This system reduces the burden of reporting work for caregivers and allows parents to quickly and accurately grasp how their child is doing throughout the day. In addition, by using noise filtering means for voice data and natural language processing technology, the accuracy of the recorded information is improved, enabling smooth communication between parents and caregivers.

[0379] The above is a specific embodiment for carrying out the present invention.

[0380] The processing flow will be explained below.

[0381] Step 1:

[0382] The device is attached to a child's clothing or school bag, and the device has a built-in microphone that starts recording audio data at specified intervals, such as recording a few seconds of ambient audio every minute.

[0383] Step 2:

[0384] The device stores the recorded audio data in a temporary buffer, which is then sent to the server at regular intervals.

[0385] Step 3:

[0386] The device sends the buffered audio data to the server over wireless communication (e.g., Wi-Fi or mobile data).

[0387] Step 4:

[0388] The server analyzes the received voice data and converts it into text data using a voice recognition engine (for example, a cloud-based voice recognition service). If the voice is "Today we played on the slide," this content is saved as text data "Today we played on the slide."

[0389] Step 5:

[0390] The server stores the converted text data in a temporary database, along with a timestamp and identifying information (e.g., the child's ID).

[0391] Step 6:

[0392] The server periodically retrieves the text data and analyzes it using natural language processing technology. During the analysis process, the meaning of the text data is understood and important information is extracted.

[0393] Step 7:

[0394] The server generates a summary based on the analyzed data. For example, from multiple utterances such as "Today I played on the slide" and "Today I built a castle in the sandbox with my friends," it generates an integrated summary sentence such as "Today I played on the slide and built a castle in the sandbox."

[0395] Step 8:

[0396] The server generates a summary and sends it to the parent's device as a push message, which is delivered to the parent's smartphone or tablet.

[0397] Step 9:

[0398] The user (parent) receives a push notification and opens the dedicated app to check the summary, allowing parents to quickly and accurately understand their child's activities for the day.

[0399] Step 10:

[0400] Users can use the in-app feedback function to send comments or inquiries to the nursery if necessary, which is a step to facilitate smooth communication between parents and caregivers.

[0401] These are the specific processing steps of the program. Through this process, the child's daily activities are automatically recorded, analyzed, summarized, and notified to the parents.

[0402] Example 1

[0403] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0404] In modern society, it is important to quickly and accurately communicate to parents how their children at kindergarten or nursery school are doing throughout the day. However, manually creating reports by caregivers takes time and effort, and the accuracy of the reports can be compromised by human error. While voice recording can be sent directly to parents, this approach poses privacy and data volume issues. Furthermore, it is desirable to provide a summary of a child's daily activities in a format that is easy for parents to understand. Based on this, a system is needed that quickly and accurately records and communicates children's activities.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0406] In this invention, the server includes a means for recording voice data collected by a device worn by the child, a means for temporarily storing the voice data in a buffer and transmitting it to the server, a means for converting the voice data into text data using speech recognition technology in the server, a means for analyzing the text data using natural language processing (NLP) technology to generate a summary, and a means for sending a push notification of the summary to a parent's device. This reduces the burden on caregivers and enables parents to quickly and accurately grasp their child's daily activities.

[0407] A "child-worn device" is a device that can be worn by a child and has the function of collecting voice data.

[0408] "Audio data" is digital information that records a child's speech and surrounding sounds.

[0409] A "recording means" is a device or program capable of capturing and recording audio data in digital form.

[0410] A "buffer" is a memory area for temporarily storing data.

[0411] A "server" is a computer system that receives, processes, and stores data over a network.

[0412] "Transmitting means" refers to the functions and protocols for sending data to other devices or systems.

[0413] "Voice recognition technology" is a technology that analyzes voice signals and converts their contents into text data.

[0414] "Text data" is data that is saved as character information.

[0415] "Natural language processing (NLP)" is a technology that allows computers to understand, analyze, and generate human language.

[0416] "Means for analyzing and generating summaries" refers to the function of extracting important information from text data and creating a concise summary.

[0417] The "means for push notification" is a function for notifying information to a user terminal in real time via a network.

[0418] The present invention is a system that records the daily activities of children attending kindergarten or nursery school and notifies parents of the activities quickly and accurately. This system consists of the following main components:

[0419] Wearing the device and collecting voice data

[0420] The device is attached to the child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals (e.g., every 5 minutes). For example, if a child says, "I drew a picture today," the device records the audio. The recorded audio data is temporarily stored in a buffer and sent to the server periodically (e.g., every day at 3 p.m.).

[0421] Sending voice data and converting it to text

[0422] The server receives the voice data sent from the device. The received voice data is converted into text data using speech recognition technology. Specifically, the Google Cloud Speech-to-Text API is used to convert the voice data into text such as "I drew a picture today." This text data is then stored in a temporary database.

[0423] Text data analysis and summary generation

[0424] The server analyzes the stored text data using natural language processing (NLP) technology. During this analysis process, important information is extracted and used as input for summarizing. For example, if multiple pieces of text data are "Today I drew a picture" and "I played with my friends," the server aggregates that information and generates the summary "Today I drew a picture and played with my friends." Examples of specific software used include SpaCy and NLTK.

[0425] Parental notification

[0426] The server then pushes the generated summary to the parent's smartphone, tablet, or other user device. This notification is typically sent using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNs). For example, when a parent opens the app on their smartphone, a summary such as "Today, your child drew pictures and played with friends" is displayed. This allows parents to accurately track their child's activities throughout the day.

[0427] Specific examples

[0428] For example, if a child says, "Today was fun," "I drew a picture," or "I played with my friends," the process will proceed as follows:

[0429] 1. The device records audio.

[0430] 2. The audio is stored in a buffer and periodically sent to the server.

[0431] 3. The server receives the voice data and converts it into text data using voice recognition technology.

[0432] 4. The server analyzes the text data and generates a summary such as, "Today was fun. I drew pictures. I played with friends."

[0433] 5. This summary will be pushed to the parent's smartphone.

[0434] Prompt Sentence Examples

[0435] "Please send the voice data collected by your device to the server."

[0436] "The server converts the audio data into text, extracts the key information, and generates a summary."

[0437] "Please notify the user terminal of the generated summary."

[0438] This system reduces the burden on caregivers and allows parents to quickly and accurately grasp their child's daily activities. In addition, by using noise filtering of voice data and natural language processing technology, the accuracy of recorded information is improved, facilitating smooth communication between parents and caregivers.

[0439] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0440] Step 1:

[0441] Collection of voice data by device

[0442] Input: What your child is saying and the sounds of the surrounding area.

[0443] How it works: The device records audio at regular intervals (e.g., every 5 minutes) using a built-in microphone. The recorded audio is temporarily stored in a digital buffer. For example, if a child says, "Today was fun," the audio is recorded and stored in the buffer.

[0444] Output: Buffered audio data.

[0445] Step 2:

[0446] Sending audio data from the device to the server

[0447] Input: Buffered audio data.

[0448] Specific operation: The device establishes an Internet connection using a built-in communication module (e.g., LTE module). The recorded voice data is encrypted and sent to the server using a secure protocol (e.g., HTTPS). For example, the device is configured to upload the voice data to the server every day at 3:00 p.m.

[0449] Output: The audio data sent to the server.

[0450] Step 3:

[0451] Server-based text conversion of voice data

[0452] Input: The audio data sent to the server.

[0453] Specific operation: The server saves the received voice data as a temporary file. It then converts the voice data into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text API). For example, the voice saying "I had fun today" is converted into text data saying "I had fun today."

[0454] Output: Text data stored in a temporary database.

[0455] Step 4:

[0456] Analysis of text data and generation of summaries by the server

[0457] Input: Text data stored in a temporary database.

[0458] How it works: The server analyzes the text data using natural language processing (NLP) technology. For example, it uses libraries such as SpaCy or NLTK. As a result of the analysis, important information is extracted. For example, if multiple pieces of text data contain content such as "Today I drew a picture" or "I played with friends," they are aggregated to generate a summary such as "Today I drew a picture and played with friends."

[0459] Output: The generated summary.

[0460] Step 5:

[0461] Notification from the server to the user device

[0462] Input: The generated summary.

[0463] Specific operation: The server sends a push notification to the parent's smartphone or tablet. Notifications are sent using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNs). For example, a summary such as "Today, your child drew a picture and played with friends" is sent to the parent's smartphone. The parent can check the summary by opening the smartphone app.

[0464] Output: Summary notification displayed on parent's device.

[0465] (Application example 1)

[0466] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0467] In conventional childcare facilities, there are limited means for accurately informing parents of how their children are doing throughout the day. Furthermore, there is no adequate system in place to immediately detect abnormalities in children and notify parents, so children's safety cannot be fully guaranteed. This leaves parents constantly anxious about their children's safety. Furthermore, there is the problem that childcare workers have to report in detail on each child's condition, which is time-consuming and burdensome.

[0468] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0469] In this invention, the server includes means for recording voice data collected by a device worn by the child, means for transmitting the voice data to the server, means for converting the voice data into text data in the server, means for analyzing the text data and generating a summary, means for notifying the parent / guardian's terminal of the summary, means for analyzing the text data and using a generative AI model to detect abnormal situations, and means for notifying the parent / guardian's terminal in real time when an abnormal situation is detected. This makes it possible to accurately record the child's daily activities and quickly detect and notify abnormal situations.

[0470] A "child-worn device" is a small device that can be attached to a child's clothing or school bag and is used to collect voice data.

[0471] "Audio data" is a digital recording of a child's speech and surrounding sounds collected by the device.

[0472] A "server" is a computer system that receives voice data sent from a device, converts it into text data, and analyzes it.

[0473] "Text data" refers to character information converted from voice data using voice recognition technology.

[0474] A "means for generating a summary" is a technology for analyzing text data, extracting important information, and summarizing it in a concise manner.

[0475] A "parent's device" is a mobile device used by a parent, such as a smartphone or tablet.

[0476] A "generative AI model" is a model that uses artificial intelligence technology to analyze text data and detect specific abnormal situations.

[0477] An "abnormal situation" is a situation that indicates danger or trouble that differs from normal conditions, and includes statements such as "help me" or "I'm scared."

[0478] "Means of real-time notification" refers to technology that immediately sends a push notification to the parent's device when an abnormal situation is detected.

[0479] "Noise filtering means" refers to a technique for removing unwanted background sounds and noise from collected audio data.

[0480] "Natural language processing technology" is an advanced language analysis technology for understanding, analyzing, and generating summaries of text data.

[0481] This invention is a system that uses a device worn by a child to record voice data and notify parents of the data quickly and accurately. It also has the function of detecting abnormal situations in real time and notifying parents.

[0482] Hardware and software used

[0483] Hardware

[0484] Small devices attached to children's clothing or school bags that have voice recording and communication capabilities. Examples include portable audio recorders and Bluetooth communication modules.

[0485] Server: Equipped with a high-performance CPU and memory, it performs speech recognition and natural language processing. Cloud services are also available.

[0486] Parent's device: A mobile device such as a smartphone or tablet.

[0487] software

[0488] Speech recognition engine: Uses Google Cloud Speech-to-Text or a similar service to convert voice data into text data.

[0489] Natural language processing engine: Analyzes text data using OpenAI's GPT-3 and other engines to summarize and detect abnormalities.

[0490] Notification service: Uses Firebase Cloud Messaging and other services to send push notifications to parents' devices in real time.

[0491] Program processing

[0492] Audio data collection

[0493] A small device attached to a child's clothing or school bag records audio at regular intervals, temporarily storing the data in a buffer and periodically sending it to a server.

[0494] Analysis of audio data

[0495] The server converts the received voice data into text data using Google Cloud Speech-to-Text, which is then stored in a database.

[0496] Text data analysis and anomaly detection

[0497] The server analyzes the text data using OpenAI's GPT-3. At this stage, it generates a summary and detects anomalies using a specific prompt, such as "Please detect anomalies in the following sentence: Help."

[0498] Parental notification

[0499] The generated summary and the results of any abnormalities detected are sent to the parent's device according to their level of importance via real-time push notifications using Firebase Cloud Messaging.

[0500] Specific examples

[0501] For example, if a child says "help me," the device collects this audio and sends it to a server. The server converts the audio into text using Google Cloud Speech-to-Text and uses OpenAI's GPT-3 to detect abnormalities. If "help me" is recognized as an abnormality, a notification is sent to the parent's smartphone using Firebase Cloud Messaging stating, "Your child is asking for help."

[0502] Prompt Sentence Examples

[0503] Detect the anomaly in the following statements:

[0504] "help me"

[0505] This makes it easier for parents to ensure their children's safety in real time.

[0506] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0507] Processing Steps

[0508] Step 1:

[0509] A device worn by the child collects audio data. The device records ambient audio at regular intervals and temporarily stores it in a buffer. The input is the ambient audio, and the output is the recorded audio data.

[0510] Step 2:

[0511] The device sends audio data to the server. At specified intervals, the audio data stored in the buffer from the device is uploaded to the server via the network. The input is audio data, and the output is the audio data stored on the server.

[0512] Step 3:

[0513] The server converts the voice data into text data. The server then analyzes the received voice data using Google Cloud Speech-to-Text and converts it into text information. The input is voice data and the output is text data.

[0514] Step 4:

[0515] The server analyzes the text data and generates a summary. The server analyzes the text data using natural language processing technology, extracts important information, and generates a summary. The input is text data, and the output is summary text.

[0516] Step 5:

[0517] The server analyzes the text data and detects anomalies. The server uses a generative AI model to determine anomalies through specific prompt sentences (e.g., "Please detect anomalies in the following sentences: Help"). The input is the text data, and the output is the result of detecting anomalies.

[0518] Step 6:

[0519] The server notifies the parent's device of the summary and the results of the abnormality detection. The server uses Firebase Cloud Messaging to push the generated summary text and the results of the abnormality detection to the parent's smartphone in real time. The input is the summary text and the results of the abnormality detection, and the output is a notification message on the smartphone.

[0520] Step 7:

[0521] The parent's device receives the notification message and displays it on the screen. This allows the parent to get real-time information about their child's daily life and any abnormal events. The input is the notification message, and the output is the message displayed on the device.

[0522] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0523] This invention is a system that records the daily activities of children attending kindergarten or nursery school and communicates the information to parents quickly and accurately. This system includes the following main components and emotion engine.

[0524] Wearing the device and collecting voice data

[0525] The device is attached to a child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals. For example, if a child says, "I drew a picture today," the device records that voice. The recorded voice data is temporarily stored in a buffer and periodically sent to a server.

[0526] Sending voice data and converting it to text

[0527] The server receives the voice data sent from the terminal. The received voice data is converted into text data using voice recognition technology. For example, the voice recognition engine analyzes the voice saying "I drew a picture today" and converts it into text format "I drew a picture today." This text data is stored in a temporary database.

[0528] Text data analysis and summary generation

[0529] The server analyzes the stored text data using natural language processing (NLP). This analysis process understands the meaning of the text data and extracts important information. It also uses an emotion engine to recognize the user's emotions from the voice data and adds that emotional information to the text data. For example, if a child says, "I drew a picture today," and the emotion is "it looks fun," that emotional information is added as a tag called "it looks fun."

[0530] Generating summaries and utilizing emotional information

[0531] The server generates a summary based on the analyzed data and the attached emotional information. For example, from multiple utterances such as "Today I played on the slide" and "Today I built a castle in the sandbox with my friends," the server takes the emotional information into account and generates a summary sentence such as "Today they played on the slide and built a castle in the sandbox. They looked like they were having a lot of fun."

[0532] Parental notification

[0533] The server then sends the generated summary and emotional information to the parent's smartphone, tablet, or other user device. This notification is sent as a push notification, which the parent receives through an application. For example, when the parent opens the smartphone application, a summary such as "Today, your child played on the slide and built a castle in the sandbox. They seemed to have a lot of fun" is displayed. This allows the parent to quickly and accurately grasp their child's daily activities and their emotions at that time.

[0534] Feedback and Communication

[0535] Users (parents) can use the app's feedback function to send comments or inquiries to the nursery school as needed. This is a step to facilitate smooth communication between parents and caregivers. In addition, detailed feedback based on emotional information allows caregivers to gain a deeper understanding of the child's situation and take appropriate action.

[0536] The above is a specific embodiment for carrying out the present invention. This system reduces the burden of reporting work on caregivers and allows parents to quickly and accurately grasp their child's daily activities and emotions. By using noise filtering means for voice data, natural language processing technology, and an emotion engine, the accuracy of recorded information is improved, enabling smooth communication between parents and caregivers.

[0537] The processing flow will be explained below.

[0538] Step 1:

[0539] The device is attached to a child's clothing or school bag, and the device has a built-in microphone that starts recording audio data at specified intervals, such as recording a few seconds of ambient audio every minute.

[0540] Step 2:

[0541] The device stores the recorded audio data in a temporary buffer, which is then sent to the server at regular intervals.

[0542] Step 3:

[0543] The device sends the buffered audio data to the server over wireless communication (e.g., Wi-Fi or mobile data).

[0544] Step 4:

[0545] The server analyzes the received voice data and converts it into text data using a voice recognition engine (for example, a cloud-based voice recognition service). If the voice is "Today we played on the slide," this content is saved as text data "Today we played on the slide."

[0546] Step 5:

[0547] The server stores the converted text data in a temporary database, along with a timestamp and identifying information (e.g., the child's ID).

[0548] Step 6:

[0549] The server analyzes the voice data and uses an emotion engine to recognize the child's emotional information. For example, if a statement such as "I played on the slide today" is accompanied by an emotion tag such as "It looks fun," that information is added to the text data.

[0550] Step 7:

[0551] The server periodically collects the acquired text data and emotional information, analyzes it using natural language processing technology, and generates summaries. For example, if multiple pieces of text data include "I drew a picture today" and "I played with my friends," it generates a summary that includes emotional information, such as "I drew a picture today and played with my friends. They seemed to be having a lot of fun."

[0552] Step 8:

[0553] The server generates a summary and sends it to the parent's device as a push message, which is delivered to the parent's smartphone or tablet.

[0554] Step 9:

[0555] The user (parent) receives a push notification and opens the dedicated app to check the summary. For example, a summary such as "Today, they played on the slide and built a castle in the sandbox. They looked like they had a lot of fun" is displayed in the app.

[0556] Step 10:

[0557] Users can use the feedback function within the app to send comments or inquiries to the nursery school as needed, facilitating smooth communication between parents and caregivers.

[0558] These are the specific processing steps of the system that combines the emotion engine. Through this process, a child's daily activities and emotions at that time are accurately recorded, analyzed, summarized, and communicated to the parent.

[0559] Example 2

[0560] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0561] Conventional systems for recording children's daily activities at kindergartens and daycare centers and notifying parents of their children's activities are cumbersome and require a lot of manual work to collect and transmit information. Furthermore, filtering to eliminate noise and redundant information from voice data and adding emotional information are difficult, making it difficult to provide parents with sufficient information. As a result, the reporting work burden on childcare workers increases, making it difficult for parents to accurately grasp their children's situations.

[0562] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0563] In this invention, the server includes means for recording voice data collected by a device worn by the child, means for transmitting the voice data to the server, means for converting the voice data to text data in the server, means for analyzing the text data and adding emotional information to generate a summary, means for notifying the parent / guardian of the summary, and means for the parent / guardian to send feedback through an application. This allows efficient collection of voice data, noise filtering, text conversion, addition of emotional information, and generation of a summary to be performed automatically, reducing the burden on caregivers and enabling parents to accurately and quickly grasp their child's daily activities and emotions.

[0564] "Child" refers to a person of kindergarten or nursery school age.

[0565] The "device" is a device that is attached to a child's clothing or school bag and is equipped with a microphone and recording function for collecting voice data.

[0566] "Audio data" refers to the child's speech and surrounding sounds, and is a digital audio signal collected by the device.

[0567] "Server" refers to a central management device that receives, analyzes, converts, stores, notifies, etc., voice data.

[0568] "Text data" refers to text information converted from voice data using voice recognition technology.

[0569] "Analysis" refers to the process of understanding the content of text data using natural language processing technology and extracting important information and keywords.

[0570] "Emotion information" refers to tags and metadata that indicate the type and intensity of emotions extracted from audio data or text data.

[0571] A "summary" refers to a sentence that concisely summarizes important information and is generated based on analyzed text data and emotional information.

[0572] "Parent's device" refers to an electronic device such as a smartphone or tablet used by a parent.

[0573] "Feedback" refers to the exchange of information by parents to send comments or questions to daycare centers or kindergartens.

[0574] "Noise filtering" refers to the process of removing unnecessary environmental sounds and noise from audio data, leaving only the necessary audio information.

[0575] "Natural language processing technology" refers to a series of algorithms and toolsets that use computer technology to analyze, understand, and generate human language.

[0576] The present invention is a system that records the daily activities of children attending kindergarten or nursery school and quickly and accurately communicates the information to parents. This system is composed of a series of means including a wearable device, a server, and a parent terminal.

[0577] Wearing the device and collecting voice data

[0578] The device is attached to a child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals (for example, every 15 minutes). The recorded audio data is temporarily stored in a buffer on the device and periodically sent to a server. Noise filtering technology is used to remove ambient sounds and other distracting noise.

[0579] Sending voice data and converting it to text

[0580] The device sends the saved voice data to a server via the network. The server converts the received voice data into text data using a speech recognition engine such as Google Cloud Speech-to-Text or Amazon Transcribe. For example, voice data such as "I drew a picture today" is converted into text data such as "I drew a picture today." The converted text data is stored in a temporary database.

[0581] Analysis of text data and addition of emotional information

[0582] The server analyzes the stored text data using natural language processing (NLP) technology. For example, SpaCy or Google Natural Language API is used for the analysis. This process understands the meaning of the text data and extracts keywords and important information. It also uses an emotion engine to extract emotional information from the text data. For example, a statement such as "It was fun" is tagged with a positive emotion tag. This emotional information is added to the text data, and the format is, for example, "I drew a picture today (it was fun)."

[0583] Generate a summary

[0584] The server generates summaries based on the text data with analysis and emotional information added. For example, from statements such as "Today I played on the slide" and "Today I built a castle in the sandbox with my friends," a summary sentence such as "Today we played on the slide and built a castle in the sandbox. They looked like they were having a lot of fun" is generated. Natural language processing technology and generative AI models are used to generate summaries.

[0585] Parental notification

[0586] The server sends the generated summary and emotional information to the parent's device (smartphone or tablet) as a push notification. For example, a notification may arrive on the parent's smartphone saying, "Today, your child played on the slide and built a castle in the sandbox. They looked like they had a lot of fun." The parent can then open the app to view the detailed summary.

[0587] Feedback and Communication

[0588] Users (parents) can send comments and inquiries to the nursery school through the feedback function within the app. For example, they can send a comment such as, "I'd like to know more about the activities that my child particularly enjoyed." The server receives this feedback information and notifies the nursery school's staff. This feedback function is a means to facilitate smooth communication between parents and nursery school staff.

[0589] Specific prompt examples

[0590] Below are some examples of prompt sentences that the system inputs into the generative AI model.

[0591] Please convert the following audio data to text:

[0592] "We played on the slide today. It was so much fun!"

[0593] Text data:

[0594] "I played on the slide today."

[0595] Emotion information:

[0596] it was fun

[0597] The above is a specific embodiment for implementing the present invention. This system reduces the burden of reporting work on caregivers and allows parents to quickly and accurately grasp their child's daily activities and emotions. Furthermore, by using noise filtering means for voice data, natural language processing technology, and an emotion engine, the accuracy of recorded information is improved, allowing for smooth communication between parents and caregivers.

[0598] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0599] Step 1: Collecting audio data

[0600] The device is attached to a child's clothing or school bag. The device's built-in microphone records the child's speech and surrounding sounds at regular intervals (for example, every 15 minutes). The recorded audio data is temporarily stored in a buffer.

[0601] Input: Ambient audio

[0602] Output: Buffered audio data

[0603] Step 2: Sending audio data

[0604] The device transmits the audio data stored in the buffer to the server at regular intervals via wireless communication (Wi-Fi or cellular).

[0605] Input: Buffered audio data

[0606] Output: Audio data sent to the server

[0607] Step 3: Convert the audio to text

[0608] The server converts the voice data received from the device into text data using a speech recognition engine such as Google Cloud Speech-to-Text or Amazon Transcribe. For example, it analyzes the speech "I drew a picture today" and converts it into text "I drew a picture today."

[0609] Input: Audio data sent to the server

[0610] Output: Data converted to text format

[0611] Step 4: Saving text data and filtering noise

[0612] The server stores the converted text data in a temporary database, and simultaneously filters out noise and unnecessary information from the stored text data.

[0613] Input: Data converted to text format

[0614] Output: filtered text data, data stored in a temporary database

[0615] Step 5: Analyzing the text data

[0616] The server analyzes the stored text data using natural language processing (NLP) technology. It uses SpaCy and the Google Natural Language API to extract keywords and important information from the text data. It also uses an emotion engine to extract emotional information from the voice data and add it to the text data.

[0617] Input: filtered text data

[0618] Output: Analyzed text data with emotional information added

[0619] Step 6: Generate a summary

[0620] The server extracts important information from the text data with added emotional information and generates summaries. A text summarization algorithm and a generative AI model are used to generate summaries. For example, from multiple statements such as "Today, I played on the slide" and "Today, I built a castle in the sandbox with my friends," the server generates a summary sentence such as "Today, they played on the slide and built a castle in the sandbox. They looked like they were having a lot of fun."

[0621] Input: Analyzed text data with emotional information added

[0622] Output: Summary

[0623] Step 7: Parental Notification

[0624] The server then sends the generated summary and emotion information to the parent's device (smartphone or tablet) as a push notification. The notification sent to the parent's device may include, for example, "Today, the child played on the slide and built a castle in the sandbox. They seemed to be having a lot of fun."

[0625] Input: Generated summary

[0626] Output: Push notification sent to parent's device

[0627] Step 8: Feedback and communication

[0628] Users (parents) can send comments and inquiries to the kindergarten through the feedback function in the app. For example, they can send a comment such as, "I'd like to know more about the activities that my child particularly enjoyed." The server receives this feedback information and notifies the kindergarten's caregivers.

[0629] Input: Parent feedback information

[0630] Output: Feedback information notified to the nursery teacher

[0631] The above is a detailed description of the processing steps of the program for this system. This system makes it possible to efficiently record the daily behavior and emotions of a child and accurately convey this information to the parents.

[0632] (Application example 2)

[0633] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0634] In traditional brick-and-mortar stores, it is difficult for staff to quickly and accurately grasp customer sentiment and satisfaction levels and respond appropriately in real time. This problem hinders efforts to improve the quality of the customer experience, potentially leading to lower satisfaction and fewer repeat customers. It also increases the burden on on-site staff.

[0635] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0636] In this invention, the server includes means for recording voice data collected by a device worn by the child, means for transmitting the voice data to the server, means for converting the voice data into text data in the server, means for analyzing the text data and generating a summary, means for notifying the summary to the parent's terminal, means for analyzing emotional information of the customer using a smart device, and means for providing feedback in real time based on the emotional information. This allows staff to quickly and accurately grasp the customer's emotions and satisfaction level and take appropriate action in real time.

[0637] A "child-worn device" is a device that can be attached to a child's clothing, bag, etc., for collecting audio.

[0638] "Audio data" means data recorded by a device that is an electronic representation of human speech or ambient sounds.

[0639] A "server" is a computer system that receives and processes audio data sent from devices via the Internet.

[0640] "Text data" is data that has been analyzed using voice recognition technology and converted into text information.

[0641] A "summary" is a concise summary of important information extracted from the analyzed text data.

[0642] "Parent's device" refers to information devices such as smartphones and tablets owned by parents.

[0643] "Smart devices" are devices with advanced processing capabilities, such as smart glasses or smartphones used by staff in brick-and-mortar stores.

[0644] "Emotional information" is data that indicates the emotional state of a speaker, obtained by analyzing voice data or text data.

[0645] "Means for providing feedback in real time" refers to technology that has the ability to immediately notify staff of analyzed emotional information.

[0646] The present invention is a system that analyzes customer emotional information in real time in a physical store, allowing staff to respond quickly and appropriately. How the system is implemented is described in detail below.

[0647] Hardware and software configuration

[0648] 1. Hardware

[0649] Smart devices: These include smart glasses and smartphones worn by store staff, which have built-in microphones and cameras to record what customers say and what they look like.

[0650] Server: A computer system that receives and analyzes voice data sent from smart devices via the Internet.

[0651] 2. Software

[0652] Speech recognition engine: A technology for converting voice data into text data. The speech_recognition library is used.

[0653] Sentiment analysis engine: A technology that analyzes text data and extracts emotional information. It uses the sentiment-analysis pipeline from the transformers library.

[0654] Text-to-speech engine: A technology for providing audio feedback to staff. The pyttsx3 library is used.

[0655] Operation flow

[0656] 1. Collecting and sending customer voice

[0657] The microphone on the smart device records what the customer says and transmits the audio data to a server.

[0658] 2. Converting voice data to text

[0659] The server converts the received voice data into text data using a voice recognition engine.

[0660] 3. Sentiment Analysis of Text Data

[0661] The server analyzes the text data using an emotion analysis engine to extract customer emotional information.

[0662] 4. Notification of emotional information

[0663] The server then provides real-time feedback to staff based on the extracted emotional information, either in audio or visual form.

[0664] Specific examples

[0665] For example, consider a scenario in which a staff member is serving a customer in a physical store. If the customer says, "This product is amazing!", the microphone in the smart device records this voice and immediately sends it to the server. The server converts the voice data into text data such as "This product is amazing!" and inputs it into an emotion analysis engine. The emotion analysis engine determines the customer's emotion from this text as "delighted" and notifies the staff member of the result in real time. This notification allows the staff member to immediately recognize that the customer is highly satisfied and can use this information in their next response.

[0666] Prompt Sentence Examples

[0667] Analyze customer reactions in your brick-and-mortar store today: "This product is amazing!" Generate text for sentiment analysis and feedback.

[0668] In this way, the present invention is expected to improve the efficiency and quality of customer service in brick-and-mortar stores. By instantly understanding customer emotions, better service can be provided, contributing to increased customer satisfaction.

[0669] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0670] Step 1:

[0671] Customer voice recording and transmission

[0672] The smart device on the terminal records what the customer says with a microphone. The recorded voice data is temporarily stored on the terminal and then sent to a server over the Internet. The input is the customer's voice, and the output is the voice data sent from the terminal to the server. This voice data is used in subsequent processing steps.

[0673] Step 2:

[0674] Converting audio data to text

[0675] The server receives the transmitted voice data and converts it into text data using a speech recognition engine (speech_recognition library). The input is voice data and the output is text data. The data processing performed here involves analyzing the voice and converting it into a string of characters.

[0676] Step 3:

[0677] Sentiment analysis of text data

[0678] The server inputs the converted text data into a sentiment analysis engine (the sentiment-analysis pipeline of the transformers library) to extract customer sentiment information. The input is text data, and the output is sentiment information (e.g., joy, sadness, anger, etc.). The sentiment analysis engine analyzes the text data and identifies the sentiments contained within it.

[0679] Step 4:

[0680] Emotional information notification

[0681] The server provides real-time feedback to staff based on the extracted emotion information. The feedback is provided audibly using a text-to-speech engine (pyttsx3 library) or visually displayed on a smart device. The input is emotion information, and the output is a feedback message. For example, the staff member may be notified that "The customer's emotion is happy (confidence: 0.95)."

[0682] As a concrete example, consider the case where a customer says, "This product is amazing!" This voice is recorded in step 1, and converted into text in step 2 as "This product is amazing!" Next, emotion analysis is performed in step 3, and the emotion "delight" is extracted. Finally, in step 4, feedback stating "The customer's emotion is delight (confidence level: 0.95)" is notified to the staff.

[0683] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0684] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0685] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0686] [Third embodiment]

[0687] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0688] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0689] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0690] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0691] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0692] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0693] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0694] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0695] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0696] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0697] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0698] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0699] The present invention is a system for recording the daily activities of children attending kindergarten or nursery school and providing the information to parents quickly and accurately. This system includes the following main components:

[0700] Wearing the device and collecting voice data

[0701] The device is attached to a child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals. For example, if a child says, "I drew a picture today," the device records that voice. The recorded voice data is temporarily stored in a buffer and periodically sent to a server.

[0702] Sending voice data and converting it to text

[0703] The server receives the voice data sent from the terminal. The received voice data is converted into text data using voice recognition technology. For example, the voice recognition engine analyzes the voice saying "I drew a picture today" and converts it into text format "I drew a picture today." This text data is stored in a temporary database.

[0704] Text data analysis and summary generation

[0705] The server analyzes the stored text data using natural language processing (NLP). During this analysis process, important information is extracted and used as input for summarizing. For example, if multiple pieces of text data include "I drew a picture today" and "I played with my friends," the server aggregates the information and generates the summary "I drew a picture today and played with my friends."

[0706] Parental notification

[0707] The server sends the generated summary to the parent's smartphone, tablet, or other user device. This notification is typically sent as a push notification, which the parent receives through an application. For example, when the parent opens the smartphone app, a summary such as "Today, your child drew pictures and played with friends" is displayed. This allows the parent to accurately grasp their child's daily activities.

[0708] This system reduces the burden of reporting work for caregivers and allows parents to quickly and accurately grasp how their child is doing throughout the day. In addition, by using noise filtering means for voice data and natural language processing technology, the accuracy of the recorded information is improved, enabling smooth communication between parents and caregivers.

[0709] The above is a specific embodiment for carrying out the present invention.

[0710] The processing flow will be explained below.

[0711] Step 1:

[0712] The device is attached to a child's clothing or school bag, and the device has a built-in microphone that starts recording audio data at specified intervals, such as recording a few seconds of ambient audio every minute.

[0713] Step 2:

[0714] The device stores the recorded audio data in a temporary buffer, which is then sent to the server at regular intervals.

[0715] Step 3:

[0716] The device sends the buffered audio data to the server over wireless communication (e.g., Wi-Fi or mobile data).

[0717] Step 4:

[0718] The server analyzes the received voice data and converts it into text data using a voice recognition engine (for example, a cloud-based voice recognition service). If the voice is "Today we played on the slide," this content is saved as text data "Today we played on the slide."

[0719] Step 5:

[0720] The server stores the converted text data in a temporary database, along with a timestamp and identifying information (e.g., the child's ID).

[0721] Step 6:

[0722] The server periodically retrieves the text data and analyzes it using natural language processing technology. During the analysis process, the meaning of the text data is understood and important information is extracted.

[0723] Step 7:

[0724] The server generates a summary based on the analyzed data. For example, from multiple utterances such as "Today I played on the slide" and "Today I built a castle in the sandbox with my friends," it generates an integrated summary sentence such as "Today I played on the slide and built a castle in the sandbox."

[0725] Step 8:

[0726] The server generates a summary and sends it to the parent's device as a push message, which is delivered to the parent's smartphone or tablet.

[0727] Step 9:

[0728] The user (parent) receives a push notification and opens the dedicated app to check the summary, allowing parents to quickly and accurately understand their child's activities for the day.

[0729] Step 10:

[0730] Users can use the in-app feedback function to send comments or inquiries to the nursery if necessary, which is a step to facilitate smooth communication between parents and caregivers.

[0731] These are the specific processing steps of the program. Through this process, the child's daily activities are automatically recorded, analyzed, summarized, and notified to the parents.

[0732] Example 1

[0733] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0734] In modern society, it is important to quickly and accurately communicate to parents how their children at kindergarten or nursery school are doing throughout the day. However, manually creating reports by caregivers takes time and effort, and the accuracy of the reports can be compromised by human error. While voice recording can be sent directly to parents, this approach poses privacy and data volume issues. Furthermore, it is desirable to provide a summary of a child's daily activities in a format that is easy for parents to understand. Based on this, a system is needed that quickly and accurately records and communicates children's activities.

[0735] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0736] In this invention, the server includes a means for recording voice data collected by a device worn by the child, a means for temporarily storing the voice data in a buffer and transmitting it to the server, a means for converting the voice data into text data using speech recognition technology in the server, a means for analyzing the text data using natural language processing (NLP) technology to generate a summary, and a means for sending a push notification of the summary to a parent's device. This reduces the burden on caregivers and enables parents to quickly and accurately grasp their child's daily activities.

[0737] A "child-worn device" is a device that can be worn by a child and has the function of collecting voice data.

[0738] "Audio data" is digital information that records a child's speech and surrounding sounds.

[0739] A "recording means" is a device or program capable of capturing and recording audio data in digital form.

[0740] A "buffer" is a memory area for temporarily storing data.

[0741] A "server" is a computer system that receives, processes, and stores data over a network.

[0742] "Transmitting means" refers to the functions and protocols for sending data to other devices or systems.

[0743] "Voice recognition technology" is a technology that analyzes voice signals and converts their contents into text data.

[0744] "Text data" is data that is saved as character information.

[0745] "Natural language processing (NLP)" is a technology that allows computers to understand, analyze, and generate human language.

[0746] "Means for analyzing and generating summaries" refers to the function of extracting important information from text data and creating a concise summary.

[0747] The "means for push notification" is a function for notifying information to a user terminal in real time via a network.

[0748] The present invention is a system that records the daily activities of children attending kindergarten or nursery school and notifies parents of the activities quickly and accurately. This system consists of the following main components:

[0749] Wearing the device and collecting voice data

[0750] The device is attached to the child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals (e.g., every 5 minutes). For example, if a child says, "I drew a picture today," the device records the audio. The recorded audio data is temporarily stored in a buffer and sent to the server periodically (e.g., every day at 3 p.m.).

[0751] Sending voice data and converting it to text

[0752] The server receives the voice data sent from the device. The received voice data is converted into text data using speech recognition technology. Specifically, the Google Cloud Speech-to-Text API is used to convert the voice data into text such as "I drew a picture today." This text data is then stored in a temporary database.

[0753] Text data analysis and summary generation

[0754] The server analyzes the stored text data using natural language processing (NLP) technology. During this analysis process, important information is extracted and used as input for summarizing. For example, if multiple pieces of text data are "Today I drew a picture" and "I played with my friends," the server aggregates that information and generates the summary "Today I drew a picture and played with my friends." Examples of specific software used include SpaCy and NLTK.

[0755] Parental notification

[0756] The server then pushes the generated summary to the parent's smartphone, tablet, or other user device. This notification is typically sent using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNs). For example, when a parent opens the app on their smartphone, a summary such as "Today, your child drew pictures and played with friends" is displayed. This allows parents to accurately track their child's activities throughout the day.

[0757] Specific examples

[0758] For example, if a child says, "Today was fun," "I drew a picture," or "I played with my friends," the process will proceed as follows:

[0759] 1. The device records audio.

[0760] 2. The audio is stored in a buffer and periodically sent to the server.

[0761] 3. The server receives the voice data and converts it into text data using voice recognition technology.

[0762] 4. The server analyzes the text data and generates a summary such as, "Today was fun. I drew pictures. I played with friends."

[0763] 5. This summary will be pushed to the parent's smartphone.

[0764] Prompt Sentence Examples

[0765] "Please send the voice data collected by your device to the server."

[0766] "The server converts the audio data into text, extracts the key information, and generates a summary."

[0767] "Please notify the user terminal of the generated summary."

[0768] This system reduces the burden on caregivers and allows parents to quickly and accurately grasp their child's daily activities. In addition, by using noise filtering of voice data and natural language processing technology, the accuracy of recorded information is improved, facilitating smooth communication between parents and caregivers.

[0769] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0770] Step 1:

[0771] Collection of voice data by device

[0772] Input: What your child is saying and the sounds of the surrounding area.

[0773] How it works: The device records audio at regular intervals (e.g., every 5 minutes) using a built-in microphone. The recorded audio is temporarily stored in a digital buffer. For example, if a child says, "Today was fun," the audio is recorded and stored in the buffer.

[0774] Output: Buffered audio data.

[0775] Step 2:

[0776] Sending audio data from the device to the server

[0777] Input: Buffered audio data.

[0778] Specific operation: The device establishes an Internet connection using a built-in communication module (e.g., LTE module). The recorded voice data is encrypted and sent to the server using a secure protocol (e.g., HTTPS). For example, the device is configured to upload the voice data to the server every day at 3:00 p.m.

[0779] Output: The audio data sent to the server.

[0780] Step 3:

[0781] Server-based text conversion of voice data

[0782] Input: The audio data sent to the server.

[0783] Specific operation: The server saves the received voice data as a temporary file. It then converts the voice data into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text API). For example, the voice saying "I had fun today" is converted into text data saying "I had fun today."

[0784] Output: Text data stored in a temporary database.

[0785] Step 4:

[0786] Analysis of text data and generation of summaries by the server

[0787] Input: Text data stored in a temporary database.

[0788] How it works: The server analyzes the text data using natural language processing (NLP) technology. For example, it uses libraries such as SpaCy or NLTK. As a result of the analysis, important information is extracted. For example, if multiple pieces of text data contain content such as "Today I drew a picture" or "I played with friends," they are aggregated to generate a summary such as "Today I drew a picture and played with friends."

[0789] Output: The generated summary.

[0790] Step 5:

[0791] Notification from the server to the user device

[0792] Input: The generated summary.

[0793] Specific operation: The server sends a push notification to the parent's smartphone or tablet. Notifications are sent using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNs). For example, a summary such as "Today, your child drew a picture and played with friends" is sent to the parent's smartphone. The parent can check the summary by opening the smartphone app.

[0794] Output: Summary notification displayed on parent's device.

[0795] (Application example 1)

[0796] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0797] In conventional childcare facilities, there are limited means for accurately informing parents of how their children are doing throughout the day. Furthermore, there is no adequate system in place to immediately detect abnormalities in children and notify parents, so children's safety cannot be fully guaranteed. This leaves parents constantly anxious about their children's safety. Furthermore, there is the problem that childcare workers have to report in detail on each child's condition, which is time-consuming and burdensome.

[0798] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0799] In this invention, the server includes means for recording voice data collected by a device worn by the child, means for transmitting the voice data to the server, means for converting the voice data into text data in the server, means for analyzing the text data and generating a summary, means for notifying the parent / guardian's terminal of the summary, means for analyzing the text data and using a generative AI model to detect abnormal situations, and means for notifying the parent / guardian's terminal in real time when an abnormal situation is detected. This makes it possible to accurately record the child's daily activities and quickly detect and notify abnormal situations.

[0800] A "child-worn device" is a small device that can be attached to a child's clothing or school bag and is used to collect voice data.

[0801] "Audio data" is a digital recording of a child's speech and surrounding sounds collected by the device.

[0802] A "server" is a computer system that receives voice data sent from a device, converts it into text data, and analyzes it.

[0803] "Text data" refers to character information converted from voice data using voice recognition technology.

[0804] A "means for generating a summary" is a technology for analyzing text data, extracting important information, and summarizing it in a concise manner.

[0805] A "parent's device" is a mobile device used by a parent, such as a smartphone or tablet.

[0806] A "generative AI model" is a model that uses artificial intelligence technology to analyze text data and detect specific abnormal situations.

[0807] An "abnormal situation" is a situation that indicates danger or trouble that differs from normal conditions, and includes statements such as "help me" or "I'm scared."

[0808] "Means of real-time notification" refers to technology that immediately sends a push notification to the parent's device when an abnormal situation is detected.

[0809] "Noise filtering means" refers to a technique for removing unwanted background sounds and noise from collected audio data.

[0810] "Natural language processing technology" is an advanced language analysis technology for understanding, analyzing, and generating summaries of text data.

[0811] This invention is a system that uses a device worn by a child to record voice data and notify parents of the data quickly and accurately. It also has the function of detecting abnormal situations in real time and notifying parents.

[0812] Hardware and software used

[0813] Hardware

[0814] Small devices attached to children's clothing or school bags that have voice recording and communication capabilities. Examples include portable audio recorders and Bluetooth communication modules.

[0815] Server: Equipped with a high-performance CPU and memory, it performs speech recognition and natural language processing. Cloud services are also available.

[0816] Parent's device: A mobile device such as a smartphone or tablet.

[0817] software

[0818] Speech recognition engine: Uses Google Cloud Speech-to-Text or a similar service to convert voice data into text data.

[0819] Natural language processing engine: Analyzes text data using OpenAI's GPT-3 and other engines to summarize and detect abnormalities.

[0820] Notification service: Uses Firebase Cloud Messaging and other services to send push notifications to parents' devices in real time.

[0821] Program processing

[0822] Audio data collection

[0823] A small device attached to a child's clothing or school bag records audio at regular intervals, temporarily storing the data in a buffer and periodically sending it to a server.

[0824] Analysis of audio data

[0825] The server converts the received voice data into text data using Google Cloud Speech-to-Text, which is then stored in a database.

[0826] Text data analysis and anomaly detection

[0827] The server analyzes the text data using OpenAI's GPT-3. At this stage, it generates a summary and detects anomalies using a specific prompt, such as "Please detect anomalies in the following sentence: Help."

[0828] Parental notification

[0829] The generated summary and the results of any abnormalities detected are sent to the parent's device according to their level of importance via real-time push notifications using Firebase Cloud Messaging.

[0830] Specific examples

[0831] For example, if a child says "help me," the device collects this audio and sends it to a server. The server converts the audio into text using Google Cloud Speech-to-Text and uses OpenAI's GPT-3 to detect abnormalities. If "help me" is recognized as an abnormality, a notification is sent to the parent's smartphone using Firebase Cloud Messaging stating, "Your child is asking for help."

[0832] Prompt Sentence Examples

[0833] Detect the anomaly in the following statements:

[0834] "help me"

[0835] This makes it easier for parents to ensure their children's safety in real time.

[0836] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0837] Processing Steps

[0838] Step 1:

[0839] A device worn by the child collects audio data. The device records ambient audio at regular intervals and temporarily stores it in a buffer. The input is the ambient audio, and the output is the recorded audio data.

[0840] Step 2:

[0841] The device sends audio data to the server. At specified intervals, the audio data stored in the buffer from the device is uploaded to the server via the network. The input is audio data, and the output is the audio data stored on the server.

[0842] Step 3:

[0843] The server converts the voice data into text data. The server then analyzes the received voice data using Google Cloud Speech-to-Text and converts it into text information. The input is voice data and the output is text data.

[0844] Step 4:

[0845] The server analyzes the text data and generates a summary. The server analyzes the text data using natural language processing technology, extracts important information, and generates a summary. The input is text data, and the output is summary text.

[0846] Step 5:

[0847] The server analyzes the text data and detects anomalies. The server uses a generative AI model to determine anomalies through specific prompt sentences (e.g., "Please detect anomalies in the following sentences: Help"). The input is the text data, and the output is the result of detecting anomalies.

[0848] Step 6:

[0849] The server notifies the parent's device of the summary and the results of the abnormality detection. The server uses Firebase Cloud Messaging to push the generated summary text and the results of the abnormality detection to the parent's smartphone in real time. The input is the summary text and the results of the abnormality detection, and the output is a notification message on the smartphone.

[0850] Step 7:

[0851] The parent's device receives the notification message and displays it on the screen. This allows the parent to get real-time information about their child's daily life and any abnormal events. The input is the notification message, and the output is the message displayed on the device.

[0852] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0853] This invention is a system that records the daily activities of children attending kindergarten or nursery school and communicates the information to parents quickly and accurately. This system includes the following main components and emotion engine.

[0854] Wearing the device and collecting voice data

[0855] The device is attached to a child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals. For example, if a child says, "I drew a picture today," the device records that voice. The recorded voice data is temporarily stored in a buffer and periodically sent to a server.

[0856] Sending voice data and converting it to text

[0857] The server receives the voice data sent from the terminal. The received voice data is converted into text data using voice recognition technology. For example, the voice recognition engine analyzes the voice saying "I drew a picture today" and converts it into text format "I drew a picture today." This text data is stored in a temporary database.

[0858] Text data analysis and summary generation

[0859] The server analyzes the stored text data using natural language processing (NLP). This analysis process understands the meaning of the text data and extracts important information. It also uses an emotion engine to recognize the user's emotions from the voice data and adds that emotional information to the text data. For example, if a child says, "I drew a picture today," and the emotion is "it looks fun," that emotional information is added as a tag called "it looks fun."

[0860] Generating summaries and utilizing emotional information

[0861] The server generates a summary based on the analyzed data and the attached emotional information. For example, from multiple utterances such as "Today I played on the slide" and "Today I built a castle in the sandbox with my friends," the server takes the emotional information into account and generates a summary sentence such as "Today they played on the slide and built a castle in the sandbox. They looked like they were having a lot of fun."

[0862] Parental notification

[0863] The server then sends the generated summary and emotional information to the parent's smartphone, tablet, or other user device. This notification is sent as a push notification, which the parent receives through an application. For example, when the parent opens the smartphone application, a summary such as "Today, your child played on the slide and built a castle in the sandbox. They seemed to have a lot of fun" is displayed. This allows the parent to quickly and accurately grasp their child's daily activities and their emotions at that time.

[0864] Feedback and Communication

[0865] Users (parents) can use the app's feedback function to send comments or inquiries to the nursery school as needed. This is a step to facilitate smooth communication between parents and caregivers. In addition, detailed feedback based on emotional information allows caregivers to gain a deeper understanding of the child's situation and take appropriate action.

[0866] The above is a specific embodiment for carrying out the present invention. This system reduces the burden of reporting work on caregivers and allows parents to quickly and accurately grasp their child's daily activities and emotions. By using noise filtering means for voice data, natural language processing technology, and an emotion engine, the accuracy of recorded information is improved, enabling smooth communication between parents and caregivers.

[0867] The processing flow will be explained below.

[0868] Step 1:

[0869] The device is attached to a child's clothing or school bag, and the device has a built-in microphone that starts recording audio data at specified intervals, such as recording a few seconds of ambient audio every minute.

[0870] Step 2:

[0871] The device stores the recorded audio data in a temporary buffer, which is then sent to the server at regular intervals.

[0872] Step 3:

[0873] The device sends the buffered audio data to the server over wireless communication (e.g., Wi-Fi or mobile data).

[0874] Step 4:

[0875] The server analyzes the received voice data and converts it into text data using a voice recognition engine (for example, a cloud-based voice recognition service). If the voice is "Today we played on the slide," this content is saved as text data "Today we played on the slide."

[0876] Step 5:

[0877] The server stores the converted text data in a temporary database, along with a timestamp and identifying information (e.g., the child's ID).

[0878] Step 6:

[0879] The server analyzes the voice data and uses an emotion engine to recognize the child's emotional information. For example, if a statement such as "I played on the slide today" is accompanied by an emotion tag such as "It looks fun," that information is added to the text data.

[0880] Step 7:

[0881] The server periodically collects the acquired text data and emotional information, analyzes it using natural language processing technology, and generates summaries. For example, if multiple pieces of text data include "I drew a picture today" and "I played with my friends," it generates a summary that includes emotional information, such as "I drew a picture today and played with my friends. They seemed to be having a lot of fun."

[0882] Step 8:

[0883] The server generates a summary and sends it to the parent's device as a push message, which is delivered to the parent's smartphone or tablet.

[0884] Step 9:

[0885] The user (parent) receives a push notification and opens the dedicated app to check the summary. For example, a summary such as "Today, they played on the slide and built a castle in the sandbox. They looked like they had a lot of fun" is displayed in the app.

[0886] Step 10:

[0887] Users can use the feedback function within the app to send comments or inquiries to the nursery school as needed, facilitating smooth communication between parents and caregivers.

[0888] These are the specific processing steps of the system that combines the emotion engine. Through this process, a child's daily activities and emotions at that time are accurately recorded, analyzed, summarized, and communicated to the parent.

[0889] Example 2

[0890] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0891] Conventional systems for recording children's daily activities at kindergartens and daycare centers and notifying parents of their children's activities are cumbersome and require a lot of manual work to collect and transmit information. Furthermore, filtering to eliminate noise and redundant information from voice data and adding emotional information are difficult, making it difficult to provide parents with sufficient information. As a result, the reporting work burden on childcare workers increases, making it difficult for parents to accurately grasp their children's situations.

[0892] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0893] In this invention, the server includes means for recording voice data collected by a device worn by the child, means for transmitting the voice data to the server, means for converting the voice data to text data in the server, means for analyzing the text data and adding emotional information to generate a summary, means for notifying the parent / guardian of the summary, and means for the parent / guardian to send feedback through an application. This allows efficient collection of voice data, noise filtering, text conversion, addition of emotional information, and generation of a summary to be performed automatically, reducing the burden on caregivers and enabling parents to accurately and quickly grasp their child's daily activities and emotions.

[0894] "Child" refers to a person of kindergarten or nursery school age.

[0895] The "device" is a device that is attached to a child's clothing or school bag and is equipped with a microphone and recording function for collecting voice data.

[0896] "Audio data" refers to the child's speech and surrounding sounds, and is a digital audio signal collected by the device.

[0897] "Server" refers to a central management device that receives, analyzes, converts, stores, notifies, etc., voice data.

[0898] "Text data" refers to text information converted from voice data using voice recognition technology.

[0899] "Analysis" refers to the process of understanding the content of text data using natural language processing technology and extracting important information and keywords.

[0900] "Emotion information" refers to tags and metadata that indicate the type and intensity of emotions extracted from audio data or text data.

[0901] A "summary" refers to a sentence that concisely summarizes important information and is generated based on analyzed text data and emotional information.

[0902] "Parent's device" refers to an electronic device such as a smartphone or tablet used by a parent.

[0903] "Feedback" refers to the exchange of information by parents to send comments or questions to daycare centers or kindergartens.

[0904] "Noise filtering" refers to the process of removing unnecessary environmental sounds and noise from audio data, leaving only the necessary audio information.

[0905] "Natural language processing technology" refers to a series of algorithms and toolsets that use computer technology to analyze, understand, and generate human language.

[0906] The present invention is a system that records the daily activities of children attending kindergarten or nursery school and quickly and accurately communicates the information to parents. This system is composed of a series of means including a wearable device, a server, and a parent terminal.

[0907] Wearing the device and collecting voice data

[0908] The device is attached to a child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals (for example, every 15 minutes). The recorded audio data is temporarily stored in a buffer on the device and periodically sent to a server. Noise filtering technology is used to remove ambient sounds and other distracting noise.

[0909] Sending voice data and converting it to text

[0910] The device sends the saved voice data to a server via the network. The server converts the received voice data into text data using a speech recognition engine such as Google Cloud Speech-to-Text or Amazon Transcribe. For example, voice data such as "I drew a picture today" is converted into text data such as "I drew a picture today." The converted text data is stored in a temporary database.

[0911] Analysis of text data and addition of emotional information

[0912] The server analyzes the stored text data using natural language processing (NLP) technology. For example, SpaCy or Google Natural Language API is used for the analysis. This process understands the meaning of the text data and extracts keywords and important information. It also uses an emotion engine to extract emotional information from the text data. For example, a statement such as "It was fun" is tagged with a positive emotion tag. This emotional information is added to the text data, and the format is, for example, "I drew a picture today (it was fun)."

[0913] Generate a summary

[0914] The server generates summaries based on the text data with analysis and emotional information added. For example, from statements such as "Today I played on the slide" and "Today I built a castle in the sandbox with my friends," a summary sentence such as "Today we played on the slide and built a castle in the sandbox. They looked like they were having a lot of fun" is generated. Natural language processing technology and generative AI models are used to generate summaries.

[0915] Parental notification

[0916] The server sends the generated summary and emotional information to the parent's device (smartphone or tablet) as a push notification. For example, a notification may arrive on the parent's smartphone saying, "Today, your child played on the slide and built a castle in the sandbox. They looked like they had a lot of fun." The parent can then open the app to view the detailed summary.

[0917] Feedback and Communication

[0918] Users (parents) can send comments and inquiries to the nursery school through the feedback function within the app. For example, they can send a comment such as, "I'd like to know more about the activities that my child particularly enjoyed." The server receives this feedback information and notifies the nursery school's staff. This feedback function is a means to facilitate smooth communication between parents and nursery school staff.

[0919] Specific prompt examples

[0920] Below are some examples of prompt sentences that the system inputs into the generative AI model.

[0921] Please convert the following audio data to text:

[0922] "We played on the slide today. It was so much fun!"

[0923] Text data:

[0924] "I played on the slide today."

[0925] Emotion information:

[0926] it was fun

[0927] The above is a specific embodiment for implementing the present invention. This system reduces the burden of reporting work on caregivers and allows parents to quickly and accurately grasp their child's daily activities and emotions. Furthermore, by using noise filtering means for voice data, natural language processing technology, and an emotion engine, the accuracy of recorded information is improved, allowing for smooth communication between parents and caregivers.

[0928] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0929] Step 1: Collecting audio data

[0930] The device is attached to a child's clothing or school bag. The device's built-in microphone records the child's speech and surrounding sounds at regular intervals (for example, every 15 minutes). The recorded audio data is temporarily stored in a buffer.

[0931] Input: Ambient audio

[0932] Output: Buffered audio data

[0933] Step 2: Sending audio data

[0934] The device transmits the audio data stored in the buffer to the server at regular intervals via wireless communication (Wi-Fi or cellular).

[0935] Input: Buffered audio data

[0936] Output: Audio data sent to the server

[0937] Step 3: Convert the audio to text

[0938] The server converts the voice data received from the device into text data using a speech recognition engine such as Google Cloud Speech-to-Text or Amazon Transcribe. For example, it analyzes the speech "I drew a picture today" and converts it into text "I drew a picture today."

[0939] Input: Audio data sent to the server

[0940] Output: Data converted to text format

[0941] Step 4: Saving text data and filtering noise

[0942] The server stores the converted text data in a temporary database, and simultaneously filters out noise and unnecessary information from the stored text data.

[0943] Input: Data converted to text format

[0944] Output: filtered text data, data stored in a temporary database

[0945] Step 5: Analyzing the text data

[0946] The server analyzes the stored text data using natural language processing (NLP) technology. It uses SpaCy and the Google Natural Language API to extract keywords and important information from the text data. It also uses an emotion engine to extract emotional information from the voice data and add it to the text data.

[0947] Input: filtered text data

[0948] Output: Analyzed text data with emotional information added

[0949] Step 6: Generate a summary

[0950] The server extracts important information from the text data with added emotional information and generates summaries. A text summarization algorithm and a generative AI model are used to generate summaries. For example, from multiple statements such as "Today, I played on the slide" and "Today, I built a castle in the sandbox with my friends," the server generates a summary sentence such as "Today, they played on the slide and built a castle in the sandbox. They looked like they were having a lot of fun."

[0951] Input: Analyzed text data with emotional information added

[0952] Output: Summary

[0953] Step 7: Parental Notification

[0954] The server sends the generated summary and emotion information to the parent's device (smartphone or tablet) as a push notification. The notification sent to the parent's device may include, for example, "Today, the child played on the slide and built a castle in the sandbox. They seemed to be having a lot of fun."

[0955] Input: Generated summary

[0956] Output: Push notification sent to parent's device

[0957] Step 8: Feedback and communication

[0958] Users (parents) can send comments and inquiries to the kindergarten through the feedback function in the app. For example, they can send a comment such as, "I'd like to know more about the activities that my child particularly enjoyed." The server receives this feedback information and notifies the kindergarten's caregivers.

[0959] Input: Parent feedback information

[0960] Output: Feedback information notified to the nursery teacher

[0961] The above is a detailed description of the processing steps of the program for this system. This system makes it possible to efficiently record the daily behavior and emotions of a child and accurately convey this information to the parents.

[0962] (Application example 2)

[0963] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0964] In traditional brick-and-mortar stores, it is difficult for staff to quickly and accurately grasp customer sentiment and satisfaction levels and respond appropriately in real time. This problem hinders efforts to improve the quality of the customer experience, potentially leading to lower satisfaction and fewer repeat customers. It also increases the burden on on-site staff.

[0965] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0966] In this invention, the server includes means for recording voice data collected by a device worn by the child, means for transmitting the voice data to the server, means for converting the voice data into text data in the server, means for analyzing the text data and generating a summary, means for notifying the summary to the parent's terminal, means for analyzing emotional information of the customer using a smart device, and means for providing feedback in real time based on the emotional information. This allows staff to quickly and accurately grasp the customer's emotions and satisfaction level and take appropriate action in real time.

[0967] A "child-worn device" is a device that can be attached to a child's clothing, bag, etc., for collecting audio.

[0968] "Audio data" means data recorded by a device that is an electronic representation of human speech or ambient sounds.

[0969] A "server" is a computer system that receives and processes audio data sent from devices via the Internet.

[0970] "Text data" is data that has been analyzed using voice recognition technology and converted into text information.

[0971] A "summary" is a concise summary of important information extracted from the analyzed text data.

[0972] "Parent's device" refers to information devices such as smartphones and tablets owned by parents.

[0973] "Smart devices" are devices with advanced processing capabilities, such as smart glasses or smartphones used by staff in brick-and-mortar stores.

[0974] "Emotional information" is data that indicates the emotional state of a speaker, obtained by analyzing voice data or text data.

[0975] "Means for providing feedback in real time" refers to technology that has the ability to immediately notify staff of analyzed emotional information.

[0976] The present invention is a system that analyzes customer emotional information in real time in a physical store, allowing staff to respond quickly and appropriately. How the system is implemented is described in detail below.

[0977] Hardware and software configuration

[0978] 1. Hardware

[0979] Smart devices: These include smart glasses and smartphones worn by store staff, which have built-in microphones and cameras to record what customers say and what they look like.

[0980] Server: A computer system that receives and analyzes voice data sent from smart devices via the Internet.

[0981] 2. Software

[0982] Speech recognition engine: A technology for converting voice data into text data. The speech_recognition library is used.

[0983] Sentiment analysis engine: A technology that analyzes text data and extracts emotional information. It uses the sentiment-analysis pipeline from the transformers library.

[0984] Text-to-speech engine: A technology for providing audio feedback to staff. The pyttsx3 library is used.

[0985] Operation flow

[0986] 1. Collecting and sending customer voice

[0987] The microphone on the smart device records what the customer says and transmits the audio data to a server.

[0988] 2. Converting voice data to text

[0989] The server converts the received voice data into text data using a voice recognition engine.

[0990] 3. Sentiment Analysis of Text Data

[0991] The server analyzes the text data using an emotion analysis engine to extract customer emotional information.

[0992] 4. Notification of emotional information

[0993] The server then provides real-time feedback to staff based on the extracted emotional information, either in audio or visual form.

[0994] Specific examples

[0995] For example, consider a scenario in which a staff member is serving a customer in a physical store. If the customer says, "This product is amazing!", the microphone in the smart device records this voice and immediately sends it to the server. The server converts the voice data into text data such as "This product is amazing!" and inputs it into an emotion analysis engine. The emotion analysis engine determines the customer's emotion from this text as "delighted" and notifies the staff member of the result in real time. This notification allows the staff member to immediately recognize that the customer is highly satisfied and can use this information in their next response.

[0996] Prompt Sentence Examples

[0997] Analyze customer reactions in your brick-and-mortar store today: "This product is amazing!" Generate text for sentiment analysis and feedback.

[0998] In this way, the present invention is expected to improve the efficiency and quality of customer service in brick-and-mortar stores. By instantly understanding customer emotions, better service can be provided, contributing to increased customer satisfaction.

[0999] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1000] Step 1:

[1001] Customer voice recording and transmission

[1002] The smart device on the terminal records what the customer says with a microphone. The recorded voice data is temporarily stored on the terminal and then sent to a server over the Internet. The input is the customer's voice, and the output is the voice data sent from the terminal to the server. This voice data is used in subsequent processing steps.

[1003] Step 2:

[1004] Converting audio data to text

[1005] The server receives the transmitted voice data and converts it into text data using a speech recognition engine (speech_recognition library). The input is voice data and the output is text data. The data processing performed here involves analyzing the voice and converting it into a string of characters.

[1006] Step 3:

[1007] Sentiment analysis of text data

[1008] The server inputs the converted text data into a sentiment analysis engine (the sentiment-analysis pipeline of the transformers library) to extract customer sentiment information. The input is text data, and the output is sentiment information (e.g., joy, sadness, anger, etc.). The sentiment analysis engine analyzes the text data and identifies the sentiments contained within it.

[1009] Step 4:

[1010] Emotional information notification

[1011] The server provides real-time feedback to staff based on the extracted emotion information. The feedback is provided audibly using a text-to-speech engine (pyttsx3 library) or visually displayed on a smart device. The input is emotion information, and the output is a feedback message. For example, the staff member may be notified that "The customer's emotion is happy (confidence: 0.95)."

[1012] As a concrete example, consider the case where a customer says, "This product is amazing!" This voice is recorded in step 1, and converted into text in step 2 as "This product is amazing!" Next, emotion analysis is performed in step 3, and the emotion "delight" is extracted. Finally, in step 4, feedback stating "The customer's emotion is delight (confidence level: 0.95)" is notified to the staff.

[1013] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1014] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1015] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1016] [Fourth embodiment]

[1017] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1018] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1019] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1020] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1021] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1022] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1023] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1024] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1025] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1026] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1027] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1028] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1029] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1030] The present invention is a system for recording the daily activities of children attending kindergarten or nursery school and providing the information to parents quickly and accurately. This system includes the following main components:

[1031] Wearing the device and collecting voice data

[1032] The device is attached to a child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals. For example, if a child says, "I drew a picture today," the device records that voice. The recorded voice data is temporarily stored in a buffer and periodically sent to a server.

[1033] Sending voice data and converting it to text

[1034] The server receives the voice data sent from the terminal. The received voice data is converted into text data using voice recognition technology. For example, the voice recognition engine analyzes the voice saying "I drew a picture today" and converts it into text format "I drew a picture today." This text data is stored in a temporary database.

[1035] Text data analysis and summary generation

[1036] The server analyzes the stored text data using natural language processing (NLP). During this analysis process, important information is extracted and used as input for summarizing. For example, if multiple pieces of text data include "I drew a picture today" and "I played with my friends," the server aggregates the information and generates the summary "I drew a picture today and played with my friends."

[1037] Parental notification

[1038] The server sends the generated summary to the parent's smartphone, tablet, or other user device. This notification is typically sent as a push notification, which the parent receives through an application. For example, when the parent opens the smartphone app, a summary such as "Today, your child drew pictures and played with friends" is displayed. This allows the parent to accurately grasp their child's daily activities.

[1039] This system reduces the burden of reporting work for caregivers and allows parents to quickly and accurately grasp how their child is doing throughout the day. In addition, by using noise filtering means for voice data and natural language processing technology, the accuracy of the recorded information is improved, enabling smooth communication between parents and caregivers.

[1040] The above is a specific embodiment for carrying out the present invention.

[1041] The processing flow will be explained below.

[1042] Step 1:

[1043] The device is attached to a child's clothing or school bag, and the device has a built-in microphone that starts recording audio data at specified intervals, such as recording a few seconds of ambient audio every minute.

[1044] Step 2:

[1045] The device stores the recorded audio data in a temporary buffer, which is then sent to the server at regular intervals.

[1046] Step 3:

[1047] The device sends the buffered audio data to the server over wireless communication (e.g., Wi-Fi or mobile data).

[1048] Step 4:

[1049] The server analyzes the received voice data and converts it into text data using a voice recognition engine (for example, a cloud-based voice recognition service). If the voice is "Today we played on the slide," this content is saved as text data "Today we played on the slide."

[1050] Step 5:

[1051] The server stores the converted text data in a temporary database, along with a timestamp and identifying information (e.g., the child's ID).

[1052] Step 6:

[1053] The server periodically retrieves the text data and analyzes it using natural language processing technology. During the analysis process, the meaning of the text data is understood and important information is extracted.

[1054] Step 7:

[1055] The server generates a summary based on the analyzed data. For example, from multiple utterances such as "Today I played on the slide" and "Today I built a castle in the sandbox with my friends," it generates an integrated summary sentence such as "Today I played on the slide and built a castle in the sandbox."

[1056] Step 8:

[1057] The server generates a summary and sends it to the parent's device as a push message, which is delivered to the parent's smartphone or tablet.

[1058] Step 9:

[1059] The user (parent) receives a push notification and opens the dedicated app to check the summary, allowing parents to quickly and accurately understand their child's activities for the day.

[1060] Step 10:

[1061] Users can use the in-app feedback function to send comments or inquiries to the nursery if necessary, which is a step to facilitate smooth communication between parents and caregivers.

[1062] These are the specific processing steps of the program. Through this process, the child's daily activities are automatically recorded, analyzed, summarized, and notified to the parents.

[1063] Example 1

[1064] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1065] In modern society, it is important to quickly and accurately communicate to parents how their children at kindergarten or nursery school are doing throughout the day. However, manually creating reports by caregivers takes time and effort, and the accuracy of the reports can be compromised by human error. While voice recording can be sent directly to parents, this approach poses privacy and data volume issues. Furthermore, it is desirable to provide a summary of a child's daily activities in a format that is easy for parents to understand. Based on this, a system is needed that quickly and accurately records and communicates children's activities.

[1066] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1067] In this invention, the server includes a means for recording voice data collected by a device worn by the child, a means for temporarily storing the voice data in a buffer and transmitting it to the server, a means for converting the voice data into text data using speech recognition technology in the server, a means for analyzing the text data using natural language processing (NLP) technology to generate a summary, and a means for sending a push notification of the summary to a parent's device. This reduces the burden on caregivers and enables parents to quickly and accurately grasp their child's daily activities.

[1068] A "child-worn device" is a device that can be worn by a child and has the function of collecting voice data.

[1069] "Audio data" is digital information that records a child's speech and surrounding sounds.

[1070] A "recording means" is a device or program capable of capturing and recording audio data in digital form.

[1071] A "buffer" is a memory area for temporarily storing data.

[1072] A "server" is a computer system that receives, processes, and stores data over a network.

[1073] "Transmitting means" refers to the functions and protocols for sending data to other devices or systems.

[1074] "Voice recognition technology" is a technology that analyzes voice signals and converts their contents into text data.

[1075] "Text data" is data that is saved as character information.

[1076] "Natural language processing (NLP)" is a technology that allows computers to understand, analyze, and generate human language.

[1077] "Means for analyzing and generating summaries" refers to the function of extracting important information from text data and creating a concise summary.

[1078] The "means for push notification" is a function for notifying information to a user terminal in real time via a network.

[1079] The present invention is a system that records the daily activities of children attending kindergarten or nursery school and notifies parents of the activities quickly and accurately. This system consists of the following main components:

[1080] Wearing the device and collecting voice data

[1081] The device is attached to the child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals (e.g., every 5 minutes). For example, if a child says, "I drew a picture today," the device records the audio. The recorded audio data is temporarily stored in a buffer and sent to the server periodically (e.g., every day at 3 p.m.).

[1082] Sending voice data and converting it to text

[1083] The server receives the voice data sent from the device. The received voice data is converted into text data using speech recognition technology. Specifically, the Google Cloud Speech-to-Text API is used to convert the voice data into text such as "I drew a picture today." This text data is then stored in a temporary database.

[1084] Text data analysis and summary generation

[1085] The server analyzes the stored text data using natural language processing (NLP) technology. During this analysis process, important information is extracted and used as input for summarizing. For example, if multiple pieces of text data are "Today I drew a picture" and "I played with my friends," the server aggregates that information and generates the summary "Today I drew a picture and played with my friends." Examples of specific software used include SpaCy and NLTK.

[1086] Parental notification

[1087] The server then pushes the generated summary to the parent's smartphone, tablet, or other user device. This notification is typically sent using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNs). For example, when a parent opens the app on their smartphone, a summary such as "Today, your child drew pictures and played with friends" is displayed. This allows parents to accurately track their child's activities throughout the day.

[1088] Specific examples

[1089] For example, if a child says, "Today was fun," "I drew a picture," or "I played with my friends," the process will proceed as follows:

[1090] 1. The device records audio.

[1091] 2. The audio is stored in a buffer and periodically sent to the server.

[1092] 3. The server receives the voice data and converts it into text data using voice recognition technology.

[1093] 4. The server analyzes the text data and generates a summary such as, "Today was fun. I drew pictures. I played with friends."

[1094] 5. This summary will be pushed to the parent's smartphone.

[1095] Prompt Sentence Examples

[1096] "Please send the voice data collected by your device to the server."

[1097] "The server converts the audio data into text, extracts the key information, and generates a summary."

[1098] "Please notify the user terminal of the generated summary."

[1099] This system reduces the burden on caregivers and allows parents to quickly and accurately grasp their child's daily activities. In addition, by using noise filtering of voice data and natural language processing technology, the accuracy of recorded information is improved, facilitating smooth communication between parents and caregivers.

[1100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1101] Step 1:

[1102] Collection of voice data by device

[1103] Input: What your child is saying and the sounds of the surrounding area.

[1104] How it works: The device records audio at regular intervals (e.g., every 5 minutes) using a built-in microphone. The recorded audio is temporarily stored in a digital buffer. For example, if a child says, "Today was fun," the audio is recorded and stored in the buffer.

[1105] Output: Buffered audio data.

[1106] Step 2:

[1107] Sending audio data from the device to the server

[1108] Input: Buffered audio data.

[1109] Specific operation: The device establishes an Internet connection using a built-in communication module (e.g., LTE module). The recorded voice data is encrypted and sent to the server using a secure protocol (e.g., HTTPS). For example, the device is configured to upload the voice data to the server every day at 3:00 p.m.

[1110] Output: The audio data sent to the server.

[1111] Step 3:

[1112] Server-based text conversion of voice data

[1113] Input: The audio data sent to the server.

[1114] Specific operation: The server saves the received voice data as a temporary file. It then converts the voice data into text data using speech recognition technology (e.g., Google Cloud Speech-to-Text API). For example, the voice saying "I had fun today" is converted into text data saying "I had fun today."

[1115] Output: Text data stored in a temporary database.

[1116] Step 4:

[1117] Analysis of text data and generation of summaries by the server

[1118] Input: Text data stored in a temporary database.

[1119] How it works: The server analyzes the text data using natural language processing (NLP) technology. For example, it uses libraries such as SpaCy or NLTK. As a result of the analysis, important information is extracted. For example, if multiple pieces of text data contain content such as "Today I drew a picture" or "I played with friends," they are aggregated to generate a summary such as "Today I drew a picture and played with friends."

[1120] Output: The generated summary.

[1121] Step 5:

[1122] Notification from the server to the user device

[1123] Input: The generated summary.

[1124] Specific operation: The server sends a push notification to the parent's smartphone or tablet. Notifications are sent using Firebase Cloud Messaging (FCM) or Apple Push Notification Service (APNs). For example, a summary such as "Today, your child drew a picture and played with friends" is sent to the parent's smartphone. The parent can check the summary by opening the smartphone app.

[1125] Output: Summary notification displayed on parent's device.

[1126] (Application example 1)

[1127] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1128] In conventional childcare facilities, there are limited means for accurately informing parents of how their children are doing throughout the day. Furthermore, there is no adequate system in place to immediately detect abnormalities in children and notify parents, so children's safety cannot be fully guaranteed. This leaves parents constantly anxious about their children's safety. Furthermore, there is the problem that childcare workers have to report in detail on each child's condition, which is time-consuming and burdensome.

[1129] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1130] In this invention, the server includes means for recording voice data collected by a device worn by the child, means for transmitting the voice data to the server, means for converting the voice data into text data in the server, means for analyzing the text data and generating a summary, means for notifying the parent / guardian's terminal of the summary, means for analyzing the text data and using a generative AI model to detect abnormal situations, and means for notifying the parent / guardian's terminal in real time when an abnormal situation is detected. This makes it possible to accurately record the child's daily activities and quickly detect and notify abnormal situations.

[1131] A "child-worn device" is a small device that can be attached to a child's clothing or school bag and is used to collect voice data.

[1132] "Audio data" is a digital recording of a child's speech and surrounding sounds collected by the device.

[1133] A "server" is a computer system that receives voice data sent from a device, converts it into text data, and analyzes it.

[1134] "Text data" refers to character information converted from voice data using voice recognition technology.

[1135] A "means for generating a summary" is a technology for analyzing text data, extracting important information, and summarizing it in a concise manner.

[1136] A "parent's device" is a mobile device used by a parent, such as a smartphone or tablet.

[1137] A "generative AI model" is a model that uses artificial intelligence technology to analyze text data and detect specific abnormal situations.

[1138] An "abnormal situation" is a situation that indicates danger or trouble that differs from normal conditions, and includes statements such as "help me" or "I'm scared."

[1139] "Means of real-time notification" refers to technology that immediately sends a push notification to the parent's device when an abnormal situation is detected.

[1140] "Noise filtering means" refers to a technique for removing unwanted background sounds and noise from collected audio data.

[1141] "Natural language processing technology" is an advanced language analysis technology for understanding, analyzing, and generating summaries of text data.

[1142] This invention is a system that uses a device worn by a child to record voice data and notify parents of the data quickly and accurately. It also has the function of detecting abnormal situations in real time and notifying parents.

[1143] Hardware and software used

[1144] Hardware

[1145] Small devices attached to children's clothing or school bags that have voice recording and communication capabilities. Examples include portable audio recorders and Bluetooth communication modules.

[1146] Server: Equipped with a high-performance CPU and memory, it performs speech recognition and natural language processing. Cloud services are also available.

[1147] Parent's device: A mobile device such as a smartphone or tablet.

[1148] software

[1149] Speech recognition engine: Uses Google Cloud Speech-to-Text or a similar service to convert voice data into text data.

[1150] Natural language processing engine: Analyzes text data using OpenAI's GPT-3 and other engines to summarize and detect abnormalities.

[1151] Notification service: Uses Firebase Cloud Messaging and other services to send push notifications to parents' devices in real time.

[1152] Program processing

[1153] Audio data collection

[1154] A small device attached to a child's clothing or school bag records audio at regular intervals, temporarily storing the data in a buffer and periodically sending it to a server.

[1155] Analysis of audio data

[1156] The server converts the received voice data into text data using Google Cloud Speech-to-Text, which is then stored in a database.

[1157] Text data analysis and anomaly detection

[1158] The server analyzes the text data using OpenAI's GPT-3. At this stage, it generates a summary and detects anomalies using a specific prompt, such as "Please detect anomalies in the following sentence: Help."

[1159] Parental notification

[1160] The generated summary and the results of any abnormalities detected are sent to the parent's device according to their level of importance via real-time push notifications using Firebase Cloud Messaging.

[1161] Specific examples

[1162] For example, if a child says "help me," the device collects this audio and sends it to a server. The server converts the audio into text using Google Cloud Speech-to-Text and uses OpenAI's GPT-3 to detect abnormalities. If "help me" is recognized as an abnormality, a notification is sent to the parent's smartphone using Firebase Cloud Messaging stating, "Your child is asking for help."

[1163] Prompt Sentence Examples

[1164] Detect the anomaly in the following statements:

[1165] "help me"

[1166] This makes it easier for parents to ensure their children's safety in real time.

[1167] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1168] Processing Steps

[1169] Step 1:

[1170] A device worn by the child collects audio data. The device records ambient audio at regular intervals and temporarily stores it in a buffer. The input is the ambient audio, and the output is the recorded audio data.

[1171] Step 2:

[1172] The device sends audio data to the server. At specified intervals, the audio data stored in the buffer from the device is uploaded to the server via the network. The input is audio data, and the output is the audio data stored on the server.

[1173] Step 3:

[1174] The server converts the voice data into text data. The server then analyzes the received voice data using Google Cloud Speech-to-Text and converts it into text information. The input is voice data and the output is text data.

[1175] Step 4:

[1176] The server analyzes the text data and generates a summary. The server analyzes the text data using natural language processing technology, extracts important information, and generates a summary. The input is text data, and the output is summary text.

[1177] Step 5:

[1178] The server analyzes the text data and detects anomalies. The server uses a generative AI model to determine anomalies through specific prompt sentences (e.g., "Please detect anomalies in the following sentences: Help"). The input is the text data, and the output is the result of detecting anomalies.

[1179] Step 6:

[1180] The server notifies the parent's device of the summary and the results of the abnormality detection. The server uses Firebase Cloud Messaging to push the generated summary text and the results of the abnormality detection to the parent's smartphone in real time. The input is the summary text and the results of the abnormality detection, and the output is a notification message on the smartphone.

[1181] Step 7:

[1182] The parent's device receives the notification message and displays it on the screen. This allows the parent to get real-time information about their child's daily life and any abnormal events. The input is the notification message, and the output is the message displayed on the device.

[1183] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1184] This invention is a system that records the daily activities of children attending kindergarten or nursery school and communicates the information to parents quickly and accurately. This system includes the following main components and emotion engine.

[1185] Wearing the device and collecting voice data

[1186] The device is attached to a child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals. For example, if a child says, "I drew a picture today," the device records that voice. The recorded voice data is temporarily stored in a buffer and periodically sent to a server.

[1187] Sending voice data and converting it to text

[1188] The server receives the voice data sent from the terminal. The received voice data is converted into text data using voice recognition technology. For example, the voice recognition engine analyzes the voice saying "I drew a picture today" and converts it into text format "I drew a picture today." This text data is stored in a temporary database.

[1189] Text data analysis and summary generation

[1190] The server analyzes the stored text data using natural language processing (NLP). This analysis process understands the meaning of the text data and extracts important information. It also uses an emotion engine to recognize the user's emotions from the voice data and adds that emotional information to the text data. For example, if a child says, "I drew a picture today," and the emotion is "it looks fun," that emotional information is added as a tag called "it looks fun."

[1191] Generating summaries and utilizing emotional information

[1192] The server generates a summary based on the analyzed data and the attached emotional information. For example, from multiple utterances such as "Today I played on the slide" and "Today I built a castle in the sandbox with my friends," the server takes the emotional information into account and generates a summary sentence such as "Today they played on the slide and built a castle in the sandbox. They looked like they were having a lot of fun."

[1193] Parental notification

[1194] The server then sends the generated summary and emotional information to the parent's smartphone, tablet, or other user device. This notification is sent as a push notification, which the parent receives through an application. For example, when the parent opens the smartphone application, a summary such as "Today, your child played on the slide and built a castle in the sandbox. They seemed to have a lot of fun" is displayed. This allows the parent to quickly and accurately grasp their child's daily activities and their emotions at that time.

[1195] Feedback and Communication

[1196] Users (parents) can use the app's feedback function to send comments or inquiries to the nursery school as needed. This is a step to facilitate smooth communication between parents and caregivers. In addition, detailed feedback based on emotional information allows caregivers to gain a deeper understanding of the child's situation and take appropriate action.

[1197] The above is a specific embodiment for carrying out the present invention. This system reduces the burden of reporting work on caregivers and allows parents to quickly and accurately grasp their child's daily activities and emotions. By using noise filtering means for voice data, natural language processing technology, and an emotion engine, the accuracy of recorded information is improved, enabling smooth communication between parents and caregivers.

[1198] The processing flow will be explained below.

[1199] Step 1:

[1200] The device is attached to a child's clothing or school bag, and the device has a built-in microphone that starts recording audio data at specified intervals, such as recording a few seconds of ambient audio every minute.

[1201] Step 2:

[1202] The device stores the recorded audio data in a temporary buffer, which is then sent to the server at regular intervals.

[1203] Step 3:

[1204] The device sends the buffered audio data to the server over wireless communication (e.g., Wi-Fi or mobile data).

[1205] Step 4:

[1206] The server analyzes the received voice data and converts it into text data using a voice recognition engine (for example, a cloud-based voice recognition service). If the voice is "Today we played on the slide," this content is saved as text data "Today we played on the slide."

[1207] Step 5:

[1208] The server stores the converted text data in a temporary database, along with a timestamp and identifying information (e.g., the child's ID).

[1209] Step 6:

[1210] The server analyzes the voice data and uses an emotion engine to recognize the child's emotional information. For example, if a statement such as "I played on the slide today" is accompanied by an emotion tag such as "It looks fun," that information is added to the text data.

[1211] Step 7:

[1212] The server periodically collects the acquired text data and emotional information, analyzes it using natural language processing technology, and generates summaries. For example, if multiple pieces of text data include "I drew a picture today" and "I played with my friends," it generates a summary that includes emotional information, such as "I drew a picture today and played with my friends. They seemed to be having a lot of fun."

[1213] Step 8:

[1214] The server generates a summary and sends it to the parent's device as a push message, which is delivered to the parent's smartphone or tablet.

[1215] Step 9:

[1216] The user (parent) receives a push notification and opens the dedicated app to check the summary. For example, a summary such as "Today, they played on the slide and built a castle in the sandbox. They looked like they had a lot of fun" is displayed in the app.

[1217] Step 10:

[1218] Users can use the feedback function within the app to send comments or inquiries to the nursery school as needed, facilitating smooth communication between parents and caregivers.

[1219] These are the specific processing steps of the system that combines the emotion engine. Through this process, a child's daily activities and emotions at that time are accurately recorded, analyzed, summarized, and communicated to the parent.

[1220] Example 2

[1221] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1222] Conventional systems for recording children's daily activities at kindergartens and daycare centers and notifying parents of their children's activities are cumbersome and require a lot of manual work to collect and transmit information. Furthermore, filtering to eliminate noise and redundant information from voice data and adding emotional information are difficult, making it difficult to provide parents with sufficient information. As a result, the reporting work burden on childcare workers increases, making it difficult for parents to accurately grasp their children's situations.

[1223] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1224] In this invention, the server includes means for recording voice data collected by a device worn by the child, means for transmitting the voice data to the server, means for converting the voice data to text data in the server, means for analyzing the text data and adding emotional information to generate a summary, means for notifying the parent / guardian of the summary, and means for the parent / guardian to send feedback through an application. This allows efficient collection of voice data, noise filtering, text conversion, addition of emotional information, and generation of a summary to be performed automatically, reducing the burden on caregivers and enabling parents to accurately and quickly grasp their child's daily activities and emotions.

[1225] "Child" refers to a person of kindergarten or nursery school age.

[1226] The "device" is a device that is attached to a child's clothing or school bag and is equipped with a microphone and recording function for collecting voice data.

[1227] "Audio data" refers to the child's speech and surrounding sounds, and is a digital audio signal collected by the device.

[1228] "Server" refers to a central management device that receives, analyzes, converts, stores, notifies, etc., voice data.

[1229] "Text data" refers to text information converted from voice data using voice recognition technology.

[1230] "Analysis" refers to the process of understanding the content of text data using natural language processing technology and extracting important information and keywords.

[1231] "Emotion information" refers to tags and metadata that indicate the type and intensity of emotions extracted from audio data or text data.

[1232] A "summary" refers to a sentence that concisely summarizes important information and is generated based on analyzed text data and emotional information.

[1233] "Parent's device" refers to an electronic device such as a smartphone or tablet used by a parent.

[1234] "Feedback" refers to the exchange of information by parents to send comments or questions to daycare centers or kindergartens.

[1235] "Noise filtering" refers to the process of removing unnecessary environmental sounds and noise from audio data, leaving only the necessary audio information.

[1236] "Natural language processing technology" refers to a series of algorithms and toolsets that use computer technology to analyze, understand, and generate human language.

[1237] The present invention is a system that records the daily activities of children attending kindergarten or nursery school and quickly and accurately communicates the information to parents. This system is composed of a series of means including a wearable device, a server, and a parent terminal.

[1238] Wearing the device and collecting voice data

[1239] The device is attached to a child's clothing or school bag. The device has a built-in microphone that records the child's speech and surrounding sounds at regular intervals (for example, every 15 minutes). The recorded audio data is temporarily stored in a buffer on the device and periodically sent to a server. Noise filtering technology is used to remove ambient sounds and other distracting noise.

[1240] Sending voice data and converting it to text

[1241] The device sends the saved voice data to a server via the network. The server converts the received voice data into text data using a speech recognition engine such as Google Cloud Speech-to-Text or Amazon Transcribe. For example, voice data such as "I drew a picture today" is converted into text data such as "I drew a picture today." The converted text data is stored in a temporary database.

[1242] Analysis of text data and addition of emotional information

[1243] The server analyzes the stored text data using natural language processing (NLP) technology. For example, SpaCy or Google Natural Language API is used for the analysis. This process understands the meaning of the text data and extracts keywords and important information. It also uses an emotion engine to extract emotional information from the text data. For example, a statement such as "It was fun" is tagged with a positive emotion tag. This emotional information is added to the text data, and the format is, for example, "I drew a picture today (it was fun)."

[1244] Generate a summary

[1245] The server generates summaries based on the text data with analysis and emotional information added. For example, from statements such as "Today I played on the slide" and "Today I built a castle in the sandbox with my friends," a summary sentence such as "Today we played on the slide and built a castle in the sandbox. They looked like they were having a lot of fun" is generated. Natural language processing technology and generative AI models are used to generate summaries.

[1246] Parental notification

[1247] The server sends the generated summary and emotional information to the parent's device (smartphone or tablet) as a push notification. For example, a notification may arrive on the parent's smartphone saying, "Today, your child played on the slide and built a castle in the sandbox. They looked like they had a lot of fun." The parent can then open the app to view the detailed summary.

[1248] Feedback and Communication

[1249] Users (parents) can send comments and inquiries to the nursery school through the feedback function within the app. For example, they can send a comment such as, "I'd like to know more about the activities that my child particularly enjoyed." The server receives this feedback information and notifies the nursery school's staff. This feedback function is a means to facilitate smooth communication between parents and nursery school staff.

[1250] Specific prompt examples

[1251] Below are some examples of prompt sentences that the system inputs into the generative AI model.

[1252] Please convert the following audio data to text:

[1253] "We played on the slide today. It was so much fun!"

[1254] Text data:

[1255] "I played on the slide today."

[1256] Emotion information:

[1257] it was fun

[1258] The above is a specific embodiment for implementing the present invention. This system reduces the burden of reporting work on caregivers and allows parents to quickly and accurately grasp their child's daily activities and emotions. Furthermore, by using noise filtering means for voice data, natural language processing technology, and an emotion engine, the accuracy of recorded information is improved, allowing for smooth communication between parents and caregivers.

[1259] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1260] Step 1: Collecting audio data

[1261] The device is attached to a child's clothing or school bag. The device's built-in microphone records the child's speech and surrounding sounds at regular intervals (for example, every 15 minutes). The recorded audio data is temporarily stored in a buffer.

[1262] Input: Ambient audio

[1263] Output: Buffered audio data

[1264] Step 2: Sending audio data

[1265] The device transmits the audio data stored in the buffer to the server at regular intervals via wireless communication (Wi-Fi or cellular).

[1266] Input: Buffered audio data

[1267] Output: Audio data sent to the server

[1268] Step 3: Convert the audio to text

[1269] The server converts the voice data received from the device into text data using a speech recognition engine such as Google Cloud Speech-to-Text or Amazon Transcribe. For example, it analyzes the speech "I drew a picture today" and converts it into text "I drew a picture today."

[1270] Input: Audio data sent to the server

[1271] Output: Data converted to text format

[1272] Step 4: Saving text data and filtering noise

[1273] The server stores the converted text data in a temporary database, and simultaneously filters out noise and unnecessary information from the stored text data.

[1274] Input: Data converted to text format

[1275] Output: filtered text data, data stored in a temporary database

[1276] Step 5: Analyzing the text data

[1277] The server analyzes the stored text data using natural language processing (NLP) technology. It uses SpaCy and the Google Natural Language API to extract keywords and important information from the text data. It also uses an emotion engine to extract emotional information from the voice data and add it to the text data.

[1278] Input: filtered text data

[1279] Output: Analyzed text data with emotional information added

[1280] Step 6: Generate a summary

[1281] The server extracts important information from the text data with added emotional information and generates summaries. A text summarization algorithm and a generative AI model are used to generate summaries. For example, from multiple statements such as "Today, I played on the slide" and "Today, I built a castle in the sandbox with my friends," the server generates a summary sentence such as "Today, they played on the slide and built a castle in the sandbox. They looked like they were having a lot of fun."

[1282] Input: Analyzed text data with emotional information added

[1283] Output: Summary

[1284] Step 7: Parental Notification

[1285] The server then sends the generated summary and emotion information to the parent's device (smartphone or tablet) as a push notification. The notification sent to the parent's device may include, for example, "Today, the child played on the slide and built a castle in the sandbox. They seemed to be having a lot of fun."

[1286] Input: Generated summary

[1287] Output: Push notification sent to parent's device

[1288] Step 8: Feedback and communication

[1289] Users (parents) can send comments and inquiries to the kindergarten through the feedback function in the app. For example, they can send a comment such as, "I'd like to know more about the activities that my child particularly enjoyed." The server receives this feedback information and notifies the kindergarten's caregivers.

[1290] Input: Parent feedback information

[1291] Output: Feedback information notified to the nursery teacher

[1292] The above is a detailed description of the processing steps of the program for this system. This system makes it possible to efficiently record the daily behavior and emotions of a child and accurately convey this information to the parents.

[1293] (Application example 2)

[1294] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1295] In traditional brick-and-mortar stores, it is difficult for staff to quickly and accurately grasp customer sentiment and satisfaction levels and respond appropriately in real time. This problem hinders efforts to improve the quality of the customer experience, potentially leading to lower satisfaction and fewer repeat customers. It also increases the burden on on-site staff.

[1296] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1297] In this invention, the server includes means for recording voice data collected by a device worn by the child, means for transmitting the voice data to the server, means for converting the voice data into text data in the server, means for analyzing the text data and generating a summary, means for notifying the summary to the parent's terminal, means for analyzing emotional information of the customer using a smart device, and means for providing feedback in real time based on the emotional information. This allows staff to quickly and accurately grasp the customer's emotions and satisfaction level and take appropriate action in real time.

[1298] A "child-worn device" is a device that can be attached to a child's clothing, bag, etc., for collecting audio.

[1299] "Audio data" means data recorded by a device that is an electronic representation of human speech or ambient sounds.

[1300] A "server" is a computer system that receives and processes audio data sent from devices via the Internet.

[1301] "Text data" is data that has been analyzed using voice recognition technology and converted into text information.

[1302] A "summary" is a concise summary of important information extracted from the analyzed text data.

[1303] "Parent's device" refers to information devices such as smartphones and tablets owned by parents.

[1304] "Smart devices" are devices with advanced processing capabilities, such as smart glasses or smartphones used by staff in brick-and-mortar stores.

[1305] "Emotional information" is data that indicates the emotional state of a speaker, obtained by analyzing voice data or text data.

[1306] "Means for providing feedback in real time" refers to technology that has the ability to immediately notify staff of analyzed emotional information.

[1307] The present invention is a system that analyzes customer emotional information in real time in a physical store, allowing staff to respond quickly and appropriately. How the system is implemented is described in detail below.

[1308] Hardware and software configuration

[1309] 1. Hardware

[1310] Smart devices: These include smart glasses and smartphones worn by store staff, which have built-in microphones and cameras to record what customers say and what they look like.

[1311] Server: A computer system that receives and analyzes voice data sent from smart devices via the Internet.

[1312] 2. Software

[1313] Speech recognition engine: A technology for converting voice data into text data. The speech_recognition library is used.

[1314] Sentiment analysis engine: A technology that analyzes text data and extracts emotional information. It uses the sentiment-analysis pipeline from the transformers library.

[1315] Text-to-speech engine: A technology for providing audio feedback to staff. The pyttsx3 library is used.

[1316] Operation flow

[1317] 1. Collecting and sending customer voice

[1318] The microphone on the smart device records what the customer says and transmits the audio data to a server.

[1319] 2. Converting voice data to text

[1320] The server converts the received voice data into text data using a voice recognition engine.

[1321] 3. Sentiment Analysis of Text Data

[1322] The server analyzes the text data using an emotion analysis engine to extract customer emotional information.

[1323] 4. Notification of emotional information

[1324] The server then provides real-time feedback to staff based on the extracted emotional information, either in audio or visual form.

[1325] Specific examples

[1326] For example, consider a scenario in which a staff member is serving a customer in a physical store. If the customer says, "This product is amazing!", the microphone in the smart device records this voice and immediately sends it to the server. The server converts the voice data into text data such as "This product is amazing!" and inputs it into an emotion analysis engine. The emotion analysis engine determines the customer's emotion from this text as "delighted" and notifies the staff member of the result in real time. This notification allows the staff member to immediately recognize that the customer is highly satisfied and can use this information in their next response.

[1327] Prompt Sentence Examples

[1328] Analyze customer reactions in your brick-and-mortar store today: "This product is amazing!" Generate text for sentiment analysis and feedback.

[1329] In this way, the present invention is expected to improve the efficiency and quality of customer service in brick-and-mortar stores. By instantly understanding customer emotions, better service can be provided, contributing to increased customer satisfaction.

[1330] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1331] Step 1:

[1332] Customer voice recording and transmission

[1333] The smart device on the terminal records what the customer says with a microphone. The recorded voice data is temporarily stored on the terminal and then sent to a server over the Internet. The input is the customer's voice, and the output is the voice data sent from the terminal to the server. This voice data is used in subsequent processing steps.

[1334] Step 2:

[1335] Converting audio data to text

[1336] The server receives the transmitted voice data and converts it into text data using a speech recognition engine (speech_recognition library). The input is voice data and the output is text data. The data processing performed here involves analyzing the voice and converting it into a string of characters.

[1337] Step 3:

[1338] Sentiment analysis of text data

[1339] The server inputs the converted text data into a sentiment analysis engine (the sentiment-analysis pipeline of the transformers library) to extract customer sentiment information. The input is text data, and the output is sentiment information (e.g., joy, sadness, anger, etc.). The sentiment analysis engine analyzes the text data and identifies the sentiments contained within it.

[1340] Step 4:

[1341] Emotional information notification

[1342] The server provides real-time feedback to staff based on the extracted emotion information. The feedback is provided audibly using a text-to-speech engine (pyttsx3 library) or visually displayed on a smart device. The input is emotion information, and the output is a feedback message. For example, the staff member may be notified that "The customer's emotion is happy (confidence: 0.95)."

[1343] As a concrete example, consider the case where a customer says, "This product is amazing!" This voice is recorded in step 1, and converted into text in step 2 as "This product is amazing!" Next, emotion analysis is performed in step 3, and the emotion "delight" is extracted. Finally, in step 4, feedback stating "The customer's emotion is delight (confidence level: 0.95)" is notified to the staff.

[1344] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1345] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1346] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1347] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1348] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1349] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1350] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1351] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1352] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1353] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1354] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1355] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1356] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1357] 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.

[1358] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1359] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1360] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1361] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1362] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1363] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1364] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1365] The following is further disclosed regarding the above embodiment.

[1366] (Claim 1)

[1367] means for recording audio data collected by a device worn by the child;

[1368] means for transmitting the voice data to a server;

[1369] means for converting the voice data into text data in the server;

[1370] means for analyzing the text data and generating a summary;

[1371] means for notifying the parent / guardian of the summary;

[1372] A system including:

[1373] (Claim 2)

[1374] 10. The system of claim 1, further comprising means for noise filtering the audio data.

[1375] (Claim 3)

[1376] 10. The system of claim 1, further comprising means for using natural language processing techniques to generate the summary.

[1377] "Example 1"

[1378] (Claim 1)

[1379] means for recording audio data collected by a device worn by the child;

[1380] means for temporarily storing the voice data in a buffer and transmitting the voice data to a server;

[1381] means for converting the voice data into text data using a voice recognition technique in the server;

[1382] means for analyzing the text data using natural language processing (NLP) technology and generating a summary;

[1383] means for sending a push notification of the summary to a parent's terminal;

[1384] A system including:

[1385] (Claim 2)

[1386] 10. The system of claim 1, further comprising means for noise filtering the audio data.

[1387] (Claim 3)

[1388] 10. The system of claim 1, further comprising means for using natural language processing techniques to generate the summary.

[1389] "Application Example 1"

[1390] Rewritten claims

[1391] (Claim 1)

[1392] means for recording audio data collected by a device worn by the child;

[1393] means for transmitting the voice data to a server;

[1394] means for converting the voice data into text data in the server;

[1395] means for analyzing the text data and generating a summary;

[1396] means for notifying the parent / guardian of the summary;

[1397] a means for using a generative AI model to analyze the text data and detect abnormal situations;

[1398] A means to notify parents' devices in real time when an abnormal situation is detected, and

[1399] A system including:

[1400] (Claim 2)

[1401] 10. The system of claim 1, further comprising means for noise filtering the audio data.

[1402] (Claim 3)

[1403] 10. The system of claim 1, further comprising means for using natural language processing techniques for the summarization and anomaly detection.

[1404] "Example 2: Combining Emotion Engines"

[1405] (Claim 1)

[1406] means for recording audio data collected by a device worn by the child;

[1407] means for transmitting the voice data to a server;

[1408] means for converting the voice data into text data in the server;

[1409] means for analyzing the text data and adding emotional information to generate a summary;

[1410] means for notifying the parent / guardian of the summary;

[1411] means for the parent to submit feedback through the application;

[1412] A system including:

[1413] (Claim 2)

[1414] 10. The system of claim 1, further comprising means for noise filtering the audio data.

[1415] (Claim 3)

[1416] 10. The system of claim 1, further comprising means for using natural language processing techniques to generate the summary.

[1417] (Claim 4)

[1418] 10. The system of claim 1, further comprising means for recording and storing said audio data at regular intervals.

[1419] (Claim 5)

[1420] 10. The system of claim 1, further comprising means for extracting emotion information from the audio data.

[1421] "Application example 2 when combining emotion engines"

[1422] (Claim 1)

[1423] means for recording audio data collected by a device worn by the child;

[1424] means for transmitting the voice data to a server;

[1425] means for converting the voice data into text data in the server;

[1426] means for analyzing the text data and generating a summary;

[1427] means for notifying the parent / guardian of the summary;

[1428] A means for analyzing customer sentiment information using a smart device;

[1429] means for providing real-time feedback based on the emotion information;

[1430] A system including:

[1431] (Claim 2)

[1432] 10. The system of claim 1, further comprising means for noise filtering the audio data.

[1433] (Claim 3)

[1434] 10. The system of claim 1, further comprising means for using natural language processing techniques to generate the summary. [Explanation of symbols]

[1435] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for recording audio data collected by a device worn by the child; means for transmitting the voice data to a server; means for converting the voice data into text data in the server; means for analyzing the text data and generating a summary; means for notifying the parent / guardian of the summary; A system including:

2. 10. The system of claim 1, further comprising means for noise filtering said audio data.

3. The system of claim 1 further comprising means for using natural language processing techniques to generate the summary.

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