system

Abstract

We provide the system. [Solution] An acoustic detection means that detects a baby's crying and analyzes the sound pattern, An environmental sensor means for acquiring environmental data around a baby, A wearable device for collecting physiological data from babies, A data analysis means that integrates data transmitted from the aforementioned acoustic detection means, environmental sensor means, and wearable device means to identify the cause, A notification means that proposes countermeasures based on the causes identified by the data analysis means, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, as problems faced by parents who are responsible for child-rearing mainly in urban areas, there are chronic sleep deprivation due to a baby's night crying and an accompanying increase in stress. Especially when the cause of the night crying is unclear, parents cannot take appropriate countermeasures, and the child-rearing stress within the family is amplified. As a result, there is a problem that anxiety about child-rearing increases and the mental fatigue of parents accumulates. In response to such a situation that hinders the comfort and sense of fulfillment that parents should obtain in child-rearing, a solution using appropriate technology is required.

Means for Solving the Problems

[0005] This invention provides a system that integrates acoustic detection means for real-time detection and analysis of a baby's cries, environmental sensor means for detecting the surrounding environment of the baby, and wearable device means for collecting physiological data of the baby. The data collected by these means is integrated and analyzed by data analysis means using machine learning algorithms to clearly identify the cause of nighttime crying. Furthermore, it includes a notification means that suggests beneficial measures to parents based on the resulting cause, thereby enhancing support for parents. In addition, the notification means receives feedback from users, continuously learns and improves, thereby realizing a system that enhances the accuracy of childcare support and reduces the burden on parents regarding childcare.

[0006] "Acoustic detection means" refers to a device or system that has the function of capturing a baby's crying in real time, analyzing its sound pattern, and acquiring it as data.

[0007] An "environmental sensor means" is a device or apparatus for acquiring various data such as temperature, humidity, and light level in the environment where a baby is present, in real time.

[0008] A "wearable device" is a device or system that is attached to a baby to continuously collect physiological data such as body temperature, heart rate, and sleep patterns.

[0009] "Data analysis means" refers to a system or program that has the function of comprehensively analyzing acquired crying sounds, physiological data, and environmental data, and identifying the cause of the baby's night crying using machine learning algorithms.

[0010] A "notification device" is a device or system that has the function of suggesting specific countermeasures to parents regarding the causes of their baby's nighttime crying, based on the analysis results obtained from a data analysis device, and displaying them in real time.

[0011] "Feedback" refers to information that users input regarding the results of actions taken based on suggestions from this system, as well as their impressions, which is used to help the system learn and improve.

[0012] A "machine learning algorithm" is a mathematical model and computational method that learns patterns from collected data and performs predictions and classifications based on new data. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is a system that integrates multiple technologies to reduce the burden on parents caused by their baby's nighttime crying and to provide a more comfortable childcare environment. This system collects data on the baby's crying, physiological data, and environmental data, analyzes this data comprehensively to identify the cause of the nighttime crying, and proposes specific countermeasures to parents. Specific embodiments are described below.

[0035] This system primarily consists of three components: a terminal, a server, and a user. First, the terminal captures the baby's cries and transmits them to the server as digital data. It also acquires environmental data such as temperature, humidity, and illuminance through environmental sensors placed in the room, and collects physiological data such as body temperature, heart rate, and sleep patterns from a wearable device attached to the baby. All of this data is transferred to the server in real time.

[0036] Next, the server receives the data sent from the terminal and analyzes it using machine learning algorithms. It identifies patterns in the baby's crying and identifies possible causes of nighttime crying based on those characteristics. Environmental and physiological data are also integrated and analyzed to comprehensively evaluate the baby's condition. If the analysis determines that the crying is due to hunger, the server prepares to notify the user accordingly.

[0037] Finally, the user (the parent) receives notifications from the device. These notifications include the identified cause of nighttime crying and suggested solutions (for example, "time to feed milk" if the baby is hungry, or "need to adjust room temperature" if the temperature is too high), providing the parent with specific actions to take. Users can also submit feedback on the system's suggestions, which is then incorporated into the server's machine learning to improve the accuracy of future suggestions.

[0038] In this way, the system of the present invention provides embodiments that enable accurate identification of the cause of nighttime crying, provide appropriate support to parents, and reduce the burden of childcare. For example, if the crying is intermittent and follows a certain pattern, the server determines that "hunger is likely" and sends a notification to the user via the terminal prompting them to prepare milk. Also, if the environmental sensor detects a high temperature, it makes a suggestion to take cooling measures. This allows the user to take quick and appropriate action.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device captures the baby's cries with a microphone and records them as digital data. Simultaneously, it acquires environmental data such as room temperature, humidity, and illuminance via environmental sensors, and further collects data on the baby's body temperature, heart rate, and physiological state through a wearable device.

[0042] Step 2:

[0043] The device transmits collected baby crying data, physiological data, and environmental data to the server in real time.

[0044] Step 3:

[0045] The server inputs the received crying data into a machine learning algorithm to analyze the characteristics of the crying. This allows it to compare the data with pre-learned patterns and identify possible causes of nighttime crying (such as hunger, poor health, or environmental maladjustment).

[0046] Step 4:

[0047] The server simultaneously analyzes physiological and environmental data to comprehensively assess the baby's current condition. Based on the analysis results, it determines whether there are any factors hindering the baby's comfort.

[0048] Step 5:

[0049] Based on the identified cause of the baby crying at night, the server generates specific countermeasures for the parents. For example, suggestions such as "give milk" or "lower the room temperature" are decided at this stage.

[0050] Step 6:

[0051] The device receives suggested solutions from the server and notifies the user (parent). The notification includes the cause of the baby's crying and specific recommended actions.

[0052] Step 7:

[0053] Users check the notifications on their devices and take the suggested actions. At the same time, they send feedback information, such as the results and their impressions, to the system, which is used as training data for the server to improve the accuracy of future suggestions.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] Parents raising young children often experience a great deal of stress due to their baby's nighttime crying. While it's crucial to quickly and accurately identify the cause of the crying and take appropriate action, this is currently difficult. To address these issues, a system is needed that comprehensively analyzes the crying, the environment, and the baby's own physiological state, and then provides concrete solutions.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes an acoustic acquisition means, an environmental acquisition means for collecting ambient environmental information, and a wearable acquisition means for collecting physiological information. This makes it possible to comprehensively evaluate the baby's condition and propose quick and accurate countermeasures based on the results.

[0059] "Sound acquisition means" refers to devices or methods for detecting a baby's crying and other sound information and collecting it as digital information.

[0060] "Environmental acquisition means" refers to a device or method for measuring and collecting environmental information such as ambient temperature, humidity, and illuminance as data.

[0061] "Wearable data acquisition methods" refer to devices or methods that are attached to babies to measure and digitize physiological information (such as body temperature, heart rate, and sleep patterns).

[0062] "Information analysis means" refers to techniques for integrating and analyzing collected acoustic data, environmental data, and physiological data to extract specific causes.

[0063] "Notification means" refers to a device or method for notifying the user of countermeasures based on the analysis results.

[0064] In data analysis techniques, the "learning process" refers to a method for recognizing patterns in data and improving its accuracy based on past data.

[0065] "User" refers to an individual who operates this system and acts in accordance with the measures indicated.

[0066] Modes for carrying out the invention

[0067] This invention is a system that identifies the causes of nighttime crying in babies and proposes countermeasures to parents. This system provides rapid and effective childcare support by aggregating and analyzing acoustic data, environmental data, and physiological data. The embodiments of this invention are described in detail below.

[0068] Device configuration and functions

[0069] The device is equipped with a microphone to detect the baby's cries. The acoustic data recorded by this microphone is digitized in real time. The device also has sensors to measure the room's temperature, humidity, and illuminance. The environmental data acquired by these sensors is also transmitted to the server in digital format. Furthermore, physiological data such as body temperature, heart rate, and sleep patterns obtained from wearable devices attached to the baby are also collected on the server via the device.

[0070] Server configuration and functionality

[0071] The server receives data sent from the terminal and stores it in storage. It then uses a generative AI model to analyze the patterns in the acoustic data. Machine learning algorithms identify the characteristics of crying and pinpoint the cause of nighttime crying. For example, if the crying pattern is consistent and intermittent, it is likely to be due to hunger. Simultaneously, the server comprehensively evaluates environmental and physiological data to make an overall assessment of the baby's health.

[0072] User notifications and interface

[0073] Parents, as users, can receive analysis results from the server as notifications on their devices. These notifications specifically describe the cause of nighttime crying and recommended countermeasures. For example, instructions such as "Prepare milk" or "Lower the room temperature" may be displayed. This allows users to take quick action.

[0074] Examples of specific cases and prompt statements

[0075] For example, if a baby cries continuously and the server determines that the baby is hungry, a notification will be sent to the user via their device saying, "It's time to feed the baby." Also, if the system determines that the room temperature is too high, it will suggest, "Please adjust the air conditioner temperature."

[0076] An example of a prompt for a generative AI model would be:

[0077] "Please propose the optimal algorithm for identifying the cause of a baby's nighttime crying. If possible, please explain an integrated analysis method that also considers environmental and physiological data."

[0078] Thus, the system of the present invention has specific embodiments that analyze a baby's nighttime crying and provide effective childcare support.

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

[0080] Step 1:

[0081] The device detects the baby's cries using a microphone. The input is ambient sound, which is converted into digital audio data. The output is this digitized audio data, which is sent to a server to identify the cause of the nighttime crying.

[0082] Step 2:

[0083] The device measures room temperature, humidity, and illuminance using environmental sensors. The input is an analog signal from the environmental sensors, which is then processed to convert it into digital data. The output is digital environmental data, which is transmitted to a server in real time and used to evaluate the baby's comfort level.

[0084] Step 3:

[0085] The device collects body temperature, heart rate, and sleep patterns from a wearable device attached to the baby. The input consists of physiological indicators from the wearable device, which are then processed to convert them into a digital format. The output is digital physiological data, which is sent to a server to comprehensively assess the baby's health.

[0086] Step 4:

[0087] The server integrates and analyzes acoustic, environmental, and physiological data received from the terminal. The input consists of this digital data, and machine learning algorithms are used for data analysis. The output is a result indicating possible causes of nighttime crying. A generative AI model is used for the analysis to extract and evaluate features of crying patterns.

[0088] Step 5:

[0089] The server generates countermeasures based on the analysis results. The input is the analysis results indicating the cause of the baby's crying at night, and the server processes this to create specific action suggestions. The output is a notification message containing the countermeasures. This notification is sent to the terminal so that the user can take appropriate action quickly.

[0090] Step 6:

[0091] The user receives notifications from the server via their device. The input is the suggested course of action sent from the server, and the user takes action based on this. The output is the user's action. The user ensures the baby's comfort by giving milk or adjusting the room temperature as suggested.

[0092] Step 7:

[0093] Users provide feedback to the server. Input consists of user evaluations and opinions on suggestions, which are digitized and sent to the server. Output is data that contributes to the server's machine learning algorithms, used to improve the accuracy of future analyses.

[0094] (Application Example 1)

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

[0096] In childcare, a baby's nighttime crying is a significant burden for parents, and it is crucial to quickly and accurately identify the cause and take appropriate measures. However, conventional methods make it difficult to understand the cause of nighttime crying and fail to alleviate parenting stress. Therefore, the present invention aims to provide a system that comprehensively analyzes a baby's crying, environment, and physiological data to identify the cause of nighttime crying, enabling parents to respond quickly.

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

[0098] In this invention, the server includes acoustic analysis means, environmental measurement means, and wearable device means. This allows for the analysis of crying patterns, environmental and physiological changes, real-time notification of countermeasures to parents, and identification of the cause of nighttime crying, enabling a rapid response.

[0099] "Acoustic analysis means" refers to a device or system that detects a baby's crying and analyzes its sound pattern.

[0100] "Environmental measurement means" refers to a device or group of sensors that acquires environmental data such as temperature, humidity, and illuminance around the baby.

[0101] A "wearable device" is a device that is attached to a baby's body to collect physiological information (such as body temperature and heart rate).

[0102] "Information processing means" refers to a computer system for receiving data from acoustic analysis means, environmental measurement means, and wearable device means, and performing integrated analysis.

[0103] "Notification provision means" refers to a communication device or software that informs the user of the identified cause of nighttime crying and the countermeasures to be taken.

[0104] "Display means" refers to a device that has a display function for visually showing countermeasure information through a mobile device.

[0105] The system for realizing this invention includes acoustic analysis means, environmental measurement means, wearable device means, information processing means, notification provision means, and display means. At the heart of the system is the information processing means that integrally acquires and analyzes the baby's crying, physiological data, and environmental data. This makes it possible to identify the cause of nighttime crying and provide parents with specific countermeasures.

[0106] The server receives baby crying data acquired from acoustic analysis equipment. For example, it analyzes this crying data using machine learning algorithms employing deep learning technology (such as TENSORFLOW® or PyTorch) to identify specific crying patterns. It also comprehensively processes data such as temperature and humidity acquired from environmental measurement equipment, as well as physiological information such as body temperature and heart rate from wearable devices, to perform a comprehensive evaluation.

[0107] The device receives analysis results from the server and provides information to the user (parent) via smartphone or smart glasses. This notification includes identified causes of nighttime crying and possible countermeasures, such as action suggestions like "prepare milk" or "adjust room temperature." Furthermore, the device functions as a display tool to effectively show this countermeasure information, enabling parents to respond quickly and efficiently.

[0108] Parents, as users, can take action according to the provided solutions. Furthermore, by sending user feedback to the server, the accuracy of the system's machine learning algorithms improves, resulting in more precise suggestions in the future.

[0109] For example, a parent might receive an audio notification while at work, advising them to "use the air conditioner because the baby's temperature is high and the room temperature is too high." Furthermore, if the crying pattern matches the "hungry pattern," the notification might instruct them to "prepare milk."

[0110] Examples of prompts to input into a generative AI model are as follows:

[0111] "Please explain how an app works by analyzing a baby's crying data, body temperature, and ambient temperature to suggest the cause of the crying and how to address it."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server activates an acoustic analysis system to detect a baby's crying. The input is real-time audio data. This audio data is then analyzed using a machine learning algorithm to perform speech pattern analysis. The output is a characteristic pattern of the crying. Specifically, a deep learning model is used to analyze the amplitude and frequency patterns of the audio data.

[0115] Step 2:

[0116] The server receives data from environmental measurement devices and wearable devices. Inputs include temperature, humidity, illuminance data, and physiological data such as body temperature and heart rate. This data is converted to a unified format and stored in a database. This process integrates all sensor data, outputting comprehensive state information. Specifically, it performs normalization and averaging of disparate data.

[0117] Step 3:

[0118] The server integrates crying patterns from acoustic analysis devices with environmental and physiological data to perform data analysis. It uses integrated information from various data sources as input. It utilizes a generative AI model to execute an algorithm to identify the cause of nighttime crying. The output includes the identified cause and recommended behavioral measures. The specific operation includes a cause identification inference process using machine learning libraries.

[0119] Step 4:

[0120] The device receives analysis results sent from the server and presents countermeasures to the user through a notification system. The input is the identified cause of nighttime crying and information on countermeasures. The output shows specific actions to be conveyed to the user, such as "prepare milk" or "adjust the room temperature." As a concrete action, information is quickly conveyed to the user using the smartphone's push notification system.

[0121] Step 5:

[0122] Users take action and implement countermeasures based on notifications from their devices. User feedback is sent back to the server, which then serves as input for improving the accuracy of future analyses. The feedback data is analyzed, and new training data is obtained as output, contributing to future improvements in proposal accuracy. Specifically, this process involves collecting feedback information and evaluating it using data science techniques.

[0123] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0124] This invention combines a system designed to alleviate the burden on parents regarding their baby's nighttime crying and provide a more comfortable childcare environment with an emotion engine that recognizes the user's emotions. This system collects and analyzes the baby's crying, physiological data, and environmental data, and can propose countermeasures while considering the user's emotional state. Specific embodiments are described below.

[0125] This system primarily consists of three components: a terminal, a server, and a user. First, the terminal captures the baby's cries and transmits them to the server as digital data. Furthermore, the terminal is equipped with a camera and microphone, which are used to collect the user's voice and facial expression data. This allows the terminal to provide the server with a realistic picture of the environment the user is in.

[0126] The server receives data from the terminal and uses machine learning algorithms to analyze the baby's crying patterns. This analysis identifies the characteristics of the crying and pinpoints the cause of nighttime crying. It also comprehensively analyzes environmental and physiological data to evaluate the baby's condition. Furthermore, the server has an emotion engine implemented that analyzes the user's voice and facial expression data to recognize their emotional state. This emotional information is considered along with the baby's condition to help suggest the most appropriate countermeasures.

[0127] The device receives suggested solutions from the server and sends notifications to the user. These notifications include not only the suspected cause and recommended actions, but also supportive messages for parents based on the user's current emotional state. For example, messages such as "Take a break when you're tired" or "Don't worry, we'll fix it soon" may be displayed, showing emotional consideration.

[0128] Users review the notification and take the suggested actions. They can also provide feedback to the system through this experience. This feedback is used in the server's further learning process to improve the system's performance.

[0129] For example, when a baby starts crying in the middle of the night, the system detects the user's fatigue and suggests solutions such as, "The temperature is a little high. I recommend adjusting it. Also, try to relax a little." This gives the user concrete ways to soothe the baby, while also addressing their own emotional needs, thus reducing their mental burden.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device captures the baby's cries with a microphone and saves them as audio data. Simultaneously, it uses a camera and microphone to capture the user's facial expressions and voice, collecting data to measure their emotions. In addition, it measures the room's temperature, humidity, and illuminance via environmental sensors, and obtains the baby's body temperature and heart rate from a wearable device.

[0133] Step 2:

[0134] The device sends the collected data to the server. The data sent includes crying data, user voice and facial expression data, environmental data, and physiological data.

[0135] Step 3:

[0136] The server applies machine learning algorithms to analyze the received crying data and identify crying patterns. Based on this analysis, it identifies factors that are likely to be causing nighttime crying.

[0137] Step 4:

[0138] The server analyzes the received user voice and facial expression data using an emotion engine to recognize the user's emotional state. For example, if patterns suggesting fatigue or stress are detected, the server evaluates the situation based on that.

[0139] Step 5:

[0140] The server integrates the analysis results and generates the optimal response based on the baby's condition and the user's emotions. This response includes specific actions related to the cause of the baby's crying (e.g., "It's time for milk," "Let's lower the room temperature") and messages that take the user's emotions into consideration (e.g., "Let's relax a little").

[0141] Step 6:

[0142] The device receives a response generated from the server and notifies the user. The notification includes the cause, recommended actions, and mental support.

[0143] Step 7:

[0144] Users check notifications on their devices and take action according to the suggested measures. Simultaneously, they provide feedback on the results and experience, sending their thoughts and suggestions for improvement to the system. This feedback is sent to the server and used as learning data for future analysis and suggestions.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] A baby's crying is often a major source of stress for parents, especially at night, leading to sleep deprivation and emotional distress. Furthermore, identifying the cause of a baby's crying and addressing it appropriately requires not only sound analysis but also consideration of environmental data, the baby's physiological state, and even the parents' emotional state. However, previous systems have not adequately evaluated these factors comprehensively and provided parents with appropriate advice and support.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes an acoustic detection device for analyzing sound to identify crying patterns and detect emotions, an environmental observation device for acquiring ambient temperature, humidity, and light intensity, and a wearable measuring device for recording physiological indicators. This enables the comprehensive identification of the cause of a baby's crying and the proposal of appropriate countermeasures that take into account the user's emotional state.

[0150] An "acoustic detection device" is a device that captures and analyzes audio signals to identify crying patterns and detect emotions.

[0151] An "environmental monitoring device" is a device used to acquire environmental data such as ambient temperature, humidity, and light intensity.

[0152] A "wearable measuring device" is a type of measuring device that is attached to the body to record physiological indicators of a baby.

[0153] An "information processing device" is a device that integrates information sent from acoustic detection devices, environmental observation devices, and wearable measurement devices to identify specific causes.

[0154] A "notification device" is a device that, based on analysis results obtained from an information processing device, conveys advice to the user that takes into account countermeasures and emotions.

[0155] "Machine learning technology" is a technique used to enable computer systems to learn from data and automatically improve at specific tasks.

[0156] This invention is a system designed to provide a comprehensive solution to the problem of a baby's crying. The system consists of a terminal, a server, and user interaction. Specifically, it aims to reduce the mental and physical burden on parents by analyzing the baby's vocalizations, environmental information, and the user's emotions.

[0157] The device is equipped with an acoustic detection device that captures the baby's cries in real time. Furthermore, it uses an environmental monitoring device to simultaneously collect environmental data such as temperature and humidity around the baby. A wearable measuring device also records the baby's physiological data. All of this data is transmitted to a server.

[0158] The server integrates the received data and uses an information processing device to identify the cause. In particular, machine learning techniques are employed to analyze the patterns of the baby's cries and environmental data to identify the cause of the crying and the baby's condition. The server also implements an algorithm for emotion analysis, evaluating the user's emotional state and, based on the analysis results, generating countermeasures and emotionally conscious advice.

[0159] Notifications are sent via the device. Through the notification device, users receive specific action suggestions based on analysis results and emotional support messages. This allows users to take quick and appropriate action. In addition, user feedback is sent to the server and used to help the system continuously learn and improve.

[0160] For example, when a baby starts crying in the middle of the night, the system sends a notification to the user saying, "The temperature is a little high. We recommend cooling it down. Also, try to relax a little." In this way, the user receives specific instructions on how to soothe the baby, while also taking into consideration the parent's own emotional state.

[0161] Examples of prompts to input into a generative AI model include: "What should I do when my baby cries? Also, please provide a message that is sensitive to the user's feelings."

[0162] In this way, this system provides a multifaceted solution to the problem of babies crying at night.

[0163] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0164] Step 1:

[0165] The device uses an acoustic detection device to capture the baby's cries. The input is ambient sound data. This data is converted into digital data using digital signal processing technology and output. This data is then sent to a server for analysis.

[0166] Step 2:

[0167] The device collects environmental data from the baby's surroundings using an environmental monitoring device. Inputs include environmental information such as temperature, humidity, and light intensity. This information is measured by sensors and output as digital data. Similarly, physiological data from the baby (e.g., heart rate, body temperature) is acquired via a wearable measuring device, output, and transmitted to a server.

[0168] Step 3:

[0169] The server integrates crying data, environmental data, and physiological data received from the terminal and supplies it to the information processing unit. Input data includes voice, environmental, and physiological information. The server utilizes machine learning algorithms to analyze crying patterns, performs data processing and calculations to identify the cause, and generates the results as output.

[0170] Step 4:

[0171] The server uses an emotion algorithm to analyze the user's voice and facial expression data, thereby evaluating the user's emotional state. The input consists of voice and image data, which are analyzed to output the user's emotion score. This emotional information is then incorporated into the assessment of the baby's condition to formulate the optimal course of action.

[0172] Step 5:

[0173] The server formulates countermeasures based on the analyzed data and generates a notification message. This message includes action suggestions that take into account both the baby's condition and the user's emotions. As output, notification information is formed and sent to the terminal.

[0174] Step 6:

[0175] The device informs the user of notification information received from the server. Specifically, it displays messages on the screen or provides suggestions using a voice assistant. This allows the user to understand and take action on specific countermeasures.

[0176] Step 7:

[0177] Users implement the suggested measures and input feedback on their effectiveness and their impressions into the terminal. This feedback is sent to the server and used to improve the machine learning model. Based on user input, the system makes self-improvements to increase the accuracy of future responses.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0180] To reduce the burden on workers and improve efficiency in the workplace, it is necessary to accurately understand the emotional state and environment of workers and implement appropriate measures. Conventional systems have struggled to quickly and accurately assess the stress levels of individual workers and provide support based on that assessment. Therefore, a new system is needed to improve productivity while reducing the psychological and physiological burden on workers.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes emotion recognition means for detecting and analyzing the emotional state of a worker, environment acquisition means for acquiring data on the work environment, and input device means for collecting detected voice data. This makes it possible to provide appropriate suggestions and rest instructions in real time according to the worker's situation.

[0183] An "emotion recognition device" is a device that analyzes a worker's psychological state from their facial expressions and voice to identify the type and intensity of their emotions.

[0184] An "environmental data acquisition device" is a device that collects data such as temperature, humidity, and illuminance of the work environment and provides environmental information tailored to the work conditions.

[0185] "Input device means" refers to an input device for detecting audio data during work and collecting it as a digital signal.

[0186] A "data processing system" is a system that integrates collected emotional state data and environmental data and performs analysis to evaluate the burden on workers.

[0187] An "information presentation means" is a display device that provides appropriate suggestions and warnings to workers based on processed data.

[0188] The system that realizes this invention monitors the worker's emotions and environmental conditions in real time and makes suggestions to improve work efficiency based on that information. The specific form of this system is described below.

[0189] The server uses emotion recognition to analyze the emotional state of workers. This is achieved by acquiring facial expression data using a camera and collecting audio data using a microphone. This data is transferred to the server in real time and used to evaluate the emotional state. For the evaluation, machine learning frameworks such as TensorFlow and PyTorch are used as emotion recognition technologies.

[0190] Environmental data acquisition methods include using temperature sensors and light meters to collect data on the work environment. This environmental information influences worker comfort and concentration, and therefore serves as an indicator for creating an appropriate work environment.

[0191] The server integrates this emotional and environmental data using data processing tools to assess the worker's current workload. Based on the assessment, it makes suggestions through information presentation tools. Display devices, such as displays and audio speakers, are used to prompt workers to take appropriate breaks or change their work procedures.

[0192] For example, if the system determines that a worker is accumulating fatigue due to prolonged, focused work, it will notify the worker via voice through a speaker, suggesting that they "take a short break."

[0193] As an example of a prompt message for a generative AI model, you can input the following:

[0194] "Worker's emotional data: 'Feeling tired', Environmental data: 'High temperature', Generate appropriate suggestions."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The device uses a camera and microphone to collect facial and voice data from the worker in real time. This data includes facial feature points, voice tone, and intonation, and is used for initial analysis of emotional state.

[0198] Step 2:

[0199] The terminal acquires data such as temperature, humidity, and illuminance of the work environment through environmental sensors. This environmental data is considered as a factor affecting worker comfort and is transmitted to a database.

[0200] Step 3:

[0201] The server integrates facial expression and voice data transmitted from the terminal and uses machine learning algorithms to analyze the worker's emotional state. This analysis uses an emotion recognition model based on TensorFlow to identify the type and intensity of emotion.

[0202] Step 4:

[0203] The server combines received environmental data with analyzed emotional states to assess the worker's burden. This assessment is performed by a data processing engine, which generates a score to determine whether the worker is comfortable.

[0204] Step 5:

[0205] Based on the evaluation results, the server generates suggestions to encourage appropriate actions from the worker. At this stage, a generative AI model is used to suggest breaks or improvements to work procedures as needed. An example of a prompt is: "Worker's emotional data: 'Feeling tired', Environmental data: 'High temperature', Please generate appropriate suggestions."

[0206] Step 6:

[0207] The server sends the generated suggestions back to the terminal and notifies the worker through an information display device. This notification is made via a display or audio speaker, prompting the worker to take action.

[0208] Step 7:

[0209] Users act on the suggestions they receive and send feedback to the server. This feedback is used to improve the accuracy of data analysis and adjust future suggestions to be more appropriate.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] This invention is a system that integrates multiple technologies to reduce the burden on parents caused by their baby's nighttime crying and to provide a more comfortable childcare environment. This system collects data on the baby's crying, physiological data, and environmental data, analyzes this data comprehensively to identify the cause of the nighttime crying, and proposes specific countermeasures to parents. Specific embodiments are described below.

[0227] This system primarily consists of three components: a terminal, a server, and a user. First, the terminal captures the baby's cries and transmits them to the server as digital data. It also acquires environmental data such as temperature, humidity, and illuminance through environmental sensors placed in the room, and collects physiological data such as body temperature, heart rate, and sleep patterns from a wearable device attached to the baby. All of this data is transferred to the server in real time.

[0228] Next, the server receives the data sent from the terminal and analyzes it using machine learning algorithms. It identifies patterns in the baby's crying and identifies possible causes of nighttime crying based on those characteristics. Environmental and physiological data are also integrated and analyzed to comprehensively evaluate the baby's condition. If the analysis determines that the crying is due to hunger, the server prepares to notify the user accordingly.

[0229] Finally, the user (the parent) receives notifications from the device. These notifications include the identified cause of nighttime crying and suggested solutions (for example, "time to feed milk" if the baby is hungry, or "need to adjust room temperature" if the temperature is too high), providing the parent with specific actions to take. Users can also submit feedback on the system's suggestions, which is then incorporated into the server's machine learning to improve the accuracy of future suggestions.

[0230] In this way, the system of the present invention provides embodiments that enable accurate identification of the cause of nighttime crying, provide appropriate support to parents, and reduce the burden of childcare. For example, if the crying is intermittent and follows a certain pattern, the server determines that "hunger is likely" and sends a notification to the user via the terminal prompting them to prepare milk. Also, if the environmental sensor detects a high temperature, it makes a suggestion to take cooling measures. This allows the user to take quick and appropriate action.

[0231] The following describes the processing flow.

[0232] Step 1:

[0233] The device captures the baby's cries with a microphone and records them as digital data. Simultaneously, it acquires environmental data such as room temperature, humidity, and illuminance via environmental sensors, and further collects data on the baby's body temperature, heart rate, and physiological state through a wearable device.

[0234] Step 2:

[0235] The device transmits collected baby crying data, physiological data, and environmental data to the server in real time.

[0236] Step 3:

[0237] The server inputs the received crying data into a machine learning algorithm to analyze the characteristics of the crying. This allows it to compare the data with pre-learned patterns and identify possible causes of nighttime crying (such as hunger, poor health, or environmental maladjustment).

[0238] Step 4:

[0239] The server simultaneously analyzes physiological and environmental data to comprehensively assess the baby's current condition. Based on the analysis results, it determines whether there are any factors hindering the baby's comfort.

[0240] Step 5:

[0241] Based on the identified cause of the baby crying at night, the server generates specific countermeasures for the parents. For example, suggestions such as "give milk" or "lower the room temperature" are decided at this stage.

[0242] Step 6:

[0243] The device receives suggested solutions from the server and notifies the user (parent). The notification includes the cause of the baby's crying and specific recommended actions.

[0244] Step 7:

[0245] Users check the notifications on their devices and take the suggested actions. At the same time, they send feedback information, such as the results and their impressions, to the system, which is used as training data for the server to improve the accuracy of future suggestions.

[0246] (Example 1)

[0247] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0248] Parents raising young children often experience a great deal of stress due to their baby's nighttime crying. While it's crucial to quickly and accurately identify the cause of the crying and take appropriate action, this is currently difficult. To address these issues, a system is needed that comprehensively analyzes the crying, the environment, and the baby's own physiological state, and then provides concrete solutions.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0250] In this invention, the server includes an acoustic acquisition means, an environmental acquisition means for collecting ambient environmental information, and a wearable acquisition means for collecting physiological information. This makes it possible to comprehensively evaluate the baby's condition and propose quick and accurate countermeasures based on the results.

[0251] "Sound acquisition means" refers to devices or methods for detecting a baby's crying and other sound information and collecting it as digital information.

[0252] "Environmental acquisition means" refers to a device or method for measuring and collecting environmental information such as ambient temperature, humidity, and illuminance as data.

[0253] "Wearable data acquisition methods" refer to devices or methods that are attached to babies to measure and digitize physiological information (such as body temperature, heart rate, and sleep patterns).

[0254] "Information analysis means" refers to techniques for integrating and analyzing collected acoustic data, environmental data, and physiological data to extract specific causes.

[0255] "Notification means" refers to a device or method for notifying the user of countermeasures based on the analysis results.

[0256] In data analysis techniques, the "learning process" refers to a method for recognizing patterns in data and improving its accuracy based on past data.

[0257] "User" refers to an individual who operates this system and acts in accordance with the measures indicated.

[0258] Modes for carrying out the invention

[0259] This invention is a system that identifies the causes of nighttime crying in babies and proposes countermeasures to parents. This system provides rapid and effective childcare support by aggregating and analyzing acoustic data, environmental data, and physiological data. The embodiments of this invention are described in detail below.

[0260] Device configuration and functions

[0261] The device is equipped with a microphone to detect the baby's cries. The acoustic data recorded by this microphone is digitized in real time. The device also has sensors to measure the room's temperature, humidity, and illuminance. The environmental data acquired by these sensors is also transmitted to the server in digital format. Furthermore, physiological data such as body temperature, heart rate, and sleep patterns obtained from wearable devices attached to the baby are also collected on the server via the device.

[0262] Server configuration and functionality

[0263] The server receives data sent from the terminal and stores it in storage. It then uses a generative AI model to analyze the patterns in the acoustic data. Machine learning algorithms identify the characteristics of crying and pinpoint the cause of nighttime crying. For example, if the crying pattern is consistent and intermittent, it is likely to be due to hunger. Simultaneously, the server comprehensively evaluates environmental and physiological data to make an overall assessment of the baby's health.

[0264] User notifications and interface

[0265] Parents, as users, can receive analysis results from the server as notifications on their devices. These notifications specifically describe the cause of nighttime crying and recommended countermeasures. For example, instructions such as "Prepare milk" or "Lower the room temperature" may be displayed. This allows users to take quick action.

[0266] Examples of specific cases and prompt statements

[0267] For example, if a baby cries continuously and the server determines that the baby is hungry, a notification will be sent to the user via their device saying, "It's time to feed the baby." Also, if the system determines that the room temperature is too high, it will suggest, "Please adjust the air conditioner temperature."

[0268] An example of a prompt for a generative AI model would be:

[0269] "Please propose the optimal algorithm for identifying the cause of a baby's nighttime crying. If possible, please explain an integrated analysis method that also considers environmental and physiological data."

[0270] Thus, the system of the present invention has specific embodiments that analyze a baby's nighttime crying and provide effective childcare support.

[0271] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0272] Step 1:

[0273] The device detects the baby's cries using a microphone. The input is ambient sound, which is converted into digital audio data. The output is this digitized audio data, which is sent to a server to identify the cause of the nighttime crying.

[0274] Step 2:

[0275] The device measures room temperature, humidity, and illuminance using environmental sensors. The input is an analog signal from the environmental sensors, which is then processed to convert it into digital data. The output is digital environmental data, which is transmitted to a server in real time and used to evaluate the baby's comfort level.

[0276] Step 3:

[0277] The device collects body temperature, heart rate, and sleep patterns from a wearable device attached to the baby. The input consists of physiological indicators from the wearable device, which are then processed to convert them into a digital format. The output is digital physiological data, which is sent to a server to comprehensively assess the baby's health.

[0278] Step 4:

[0279] The server integratively analyzes the acoustic data, environmental data, and physiological data received from the terminal. The input is these digital data, and data analysis is performed using a machine learning algorithm. The output is a result indicating the possible cause of night crying. A generative AI model is used for the analysis to extract and evaluate the characteristics of the crying sound pattern.

[0280] Step 5:

[0281] The server generates a countermeasure based on the analysis result. The input is the analysis result indicating the cause of night crying, and a process of creating a specific action proposal is performed. The output is a notification message including the countermeasure. This notification is sent to the terminal to enable the user to take appropriate actions promptly.

[0282] Step 6:

[0283] The user receives a notification from the server via the terminal. The input is the proposed countermeasure sent from the server, and actions are taken based on it. The output is the user's action. The user ensures the comfort of the baby by giving milk or adjusting the room temperature as proposed.

[0284] Step 7:

[0285] The user provides feedback to the server. The input is the user's evaluation and opinion on the proposal, which is digitized and sent to the server. The output is data that contributes to the server's machine learning algorithm and is utilized to improve the analysis accuracy in subsequent times.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0288] In childcare, a baby's nighttime crying is a significant burden for parents, and it is crucial to quickly and accurately identify the cause and take appropriate measures. However, conventional methods make it difficult to understand the cause of nighttime crying and fail to alleviate parenting stress. Therefore, the present invention aims to provide a system that comprehensively analyzes a baby's crying, environment, and physiological data to identify the cause of nighttime crying, enabling parents to respond quickly.

[0289] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0290] In this invention, the server includes acoustic analysis means, environmental measurement means, and wearable device means. This allows for the analysis of crying patterns, environmental and physiological changes, real-time notification of countermeasures to parents, and identification of the cause of nighttime crying, enabling a rapid response.

[0291] "Acoustic analysis means" refers to a device or system that detects a baby's crying and analyzes its sound pattern.

[0292] "Environmental measurement means" refers to a device or group of sensors that acquires environmental data such as temperature, humidity, and illuminance around the baby.

[0293] A "wearable device" is a device that is attached to a baby's body to collect physiological information (such as body temperature and heart rate).

[0294] "Information processing means" refers to a computer system for receiving data from acoustic analysis means, environmental measurement means, and wearable device means, and performing integrated analysis.

[0295] "Notification provision means" refers to a communication device or software that informs the user of the identified cause of nighttime crying and the countermeasures to be taken.

[0296] "Display means" refers to a device that has a display function for visually showing countermeasure information through a mobile device.

[0297] The system for realizing this invention includes acoustic analysis means, environmental measurement means, wearable device means, information processing means, notification provision means, and display means. At the heart of the system is the information processing means that integrally acquires and analyzes the baby's crying, physiological data, and environmental data. This makes it possible to identify the cause of nighttime crying and provide parents with specific countermeasures.

[0298] The server receives baby crying data acquired from acoustic analysis equipment. For example, it analyzes this crying data using machine learning algorithms employing deep learning techniques (such as TensorFlow or PyTorch) to identify specific crying patterns. It also comprehensively processes data such as temperature and humidity acquired from environmental measurement equipment, as well as physiological information such as body temperature and heart rate from wearable devices, to perform a comprehensive evaluation.

[0299] The device receives analysis results from the server and provides information to the user (parent) via smartphone or smart glasses. This notification includes identified causes of nighttime crying and possible countermeasures, such as action suggestions like "prepare milk" or "adjust room temperature." Furthermore, the device functions as a display tool to effectively show this countermeasure information, enabling parents to respond quickly and efficiently.

[0300] Parents, as users, can take action according to the provided solutions. Furthermore, by sending user feedback to the server, the accuracy of the system's machine learning algorithms improves, resulting in more precise suggestions in the future.

[0301] As a specific example, a parent may receive a voice notification during work, recommending that "the baby's body temperature is high and the room temperature is too high, so please use the air conditioner." Also, in this case, if the crying pattern corresponds to the "hungry pattern," a notification will be sent saying, "Please prepare milk."

[0302] Examples of the prompt sentences input into the generative AI model are as follows:

[0303] "Please explain the operation of an app that analyzes the baby's crying sound data, body temperature, and environmental temperature and proposes the causes and countermeasures for crying."

[0304] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0305] Step 1:

[0306] The server activates the acoustic analysis means to detect the baby's crying sound. As input, there is voice data acquired in real time. For this voice data, voice pattern analysis is performed using a machine learning algorithm. As output, a characteristic pattern of the crying sound is obtained. As a specific operation, a deep learning model is used to analyze the amplitude and frequency patterns of the voice data.

[0307] Step 2:

[0308] The server receives data from the environmental measurement means and the wearable device means. The input is data on temperature, humidity, illuminance, and physiological data such as body temperature and heart rate. These data are converted into a unified format and stored in a database. By this process, all sensor data is integrated and comprehensive state information is output. As a specific operation, normalization and averaging of heterogeneous data are performed.

[0309] Step 3:

[0310] The server integrates crying patterns from acoustic analysis devices with environmental and physiological data to perform data analysis. It uses integrated information from various data sources as input. It utilizes a generative AI model to execute an algorithm to identify the cause of nighttime crying. The output includes the identified cause and recommended behavioral measures. The specific operation includes a cause identification inference process using machine learning libraries.

[0311] Step 4:

[0312] The device receives analysis results sent from the server and presents countermeasures to the user through a notification system. The input is the identified cause of nighttime crying and information on countermeasures. The output shows specific actions to be conveyed to the user, such as "prepare milk" or "adjust the room temperature." As a concrete action, information is quickly conveyed to the user using the smartphone's push notification system.

[0313] Step 5:

[0314] Users take action and implement countermeasures based on notifications from their devices. User feedback is sent back to the server, which then serves as input for improving the accuracy of future analyses. The feedback data is analyzed, and new training data is obtained as output, contributing to future improvements in proposal accuracy. Specifically, this process involves collecting feedback information and evaluating it using data science techniques.

[0315] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0316] This invention combines a system designed to alleviate the burden on parents regarding their baby's nighttime crying and provide a more comfortable childcare environment with an emotion engine that recognizes the user's emotions. This system collects and analyzes the baby's crying, physiological data, and environmental data, and can propose countermeasures while considering the user's emotional state. Specific embodiments are described below.

[0317] This system primarily consists of three components: a terminal, a server, and a user. First, the terminal captures the baby's cries and transmits them to the server as digital data. Furthermore, the terminal is equipped with a camera and microphone, which are used to collect the user's voice and facial expression data. This allows the terminal to provide the server with a realistic picture of the environment the user is in.

[0318] The server receives data from the terminal and uses machine learning algorithms to analyze the baby's crying patterns. This analysis identifies the characteristics of the crying and pinpoints the cause of nighttime crying. It also comprehensively analyzes environmental and physiological data to evaluate the baby's condition. Furthermore, the server has an emotion engine implemented that analyzes the user's voice and facial expression data to recognize their emotional state. This emotional information is considered along with the baby's condition to help suggest the most appropriate countermeasures.

[0319] The device receives suggested solutions from the server and sends notifications to the user. These notifications include not only the suspected cause and recommended actions, but also supportive messages for parents based on the user's current emotional state. For example, messages such as "Take a break when you're tired" or "Don't worry, we'll fix it soon" may be displayed, showing emotional consideration.

[0320] Users review the notification and take the suggested actions. They can also provide feedback to the system through this experience. This feedback is used in the server's further learning process to improve the system's performance.

[0321] For example, when a baby starts crying in the middle of the night, the system detects the user's fatigue and suggests solutions such as, "The temperature is a little high. I recommend adjusting it. Also, try to relax a little." This gives the user concrete ways to soothe the baby, while also addressing their own emotional needs, thus reducing their mental burden.

[0322] The following describes the processing flow.

[0323] Step 1:

[0324] The device captures the baby's cries with a microphone and saves them as audio data. Simultaneously, it uses a camera and microphone to capture the user's facial expressions and voice, collecting data to measure their emotions. In addition, it measures the room's temperature, humidity, and illuminance via environmental sensors, and obtains the baby's body temperature and heart rate from a wearable device.

[0325] Step 2:

[0326] The device sends the collected data to the server. The data sent includes crying data, user voice and facial expression data, environmental data, and physiological data.

[0327] Step 3:

[0328] The server applies machine learning algorithms to analyze the received crying data and identify crying patterns. Based on this analysis, it identifies factors that are likely to be causing nighttime crying.

[0329] Step 4:

[0330] The server analyzes the received user voice and facial expression data using an emotion engine to recognize the user's emotional state. For example, if patterns suggesting fatigue or stress are detected, the server evaluates the situation based on that.

[0331] Step 5:

[0332] The server integrates the analysis results and generates the optimal response based on the baby's condition and the user's emotions. This response includes specific actions related to the cause of the baby's crying (e.g., "It's time for milk," "Let's lower the room temperature") and messages that take the user's emotions into consideration (e.g., "Let's relax a little").

[0333] Step 6:

[0334] The device receives a response generated from the server and notifies the user. The notification includes the cause, recommended actions, and mental support.

[0335] Step 7:

[0336] Users check notifications on their devices and take action according to the suggested measures. Simultaneously, they provide feedback on the results and experience, sending their thoughts and suggestions for improvement to the system. This feedback is sent to the server and used as learning data for future analysis and suggestions.

[0337] (Example 2)

[0338] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0339] A baby's crying is often a major source of stress for parents, especially at night, leading to sleep deprivation and emotional distress. Furthermore, identifying the cause of a baby's crying and addressing it appropriately requires not only sound analysis but also consideration of environmental data, the baby's physiological state, and even the parents' emotional state. However, previous systems have not adequately evaluated these factors comprehensively and provided parents with appropriate advice and support.

[0340] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0341] In this invention, the server includes an acoustic detection device for analyzing sound to identify crying patterns and detect emotions, an environmental observation device for acquiring ambient temperature, humidity, and light intensity, and a wearable measuring device for recording physiological indicators. This enables the comprehensive identification of the cause of a baby's crying and the proposal of appropriate countermeasures that take into account the user's emotional state.

[0342] An "acoustic detection device" is a device that captures and analyzes audio signals to identify crying patterns and detect emotions.

[0343] An "environmental monitoring device" is a device used to acquire environmental data such as ambient temperature, humidity, and light intensity.

[0344] A "wearable measuring device" is a type of measuring device that is attached to the body to record physiological indicators of a baby.

[0345] An "information processing device" is a device that integrates information sent from acoustic detection devices, environmental observation devices, and wearable measurement devices to identify specific causes.

[0346] A "notification device" is a device that, based on analysis results obtained from an information processing device, conveys advice to the user that takes into account countermeasures and emotions.

[0347] "Machine learning technology" is a technique used to enable computer systems to learn from data and automatically improve at specific tasks.

[0348] This invention is a system designed to provide a comprehensive solution to the problem of a baby's crying. The system consists of a terminal, a server, and user interaction. Specifically, it aims to reduce the mental and physical burden on parents by analyzing the baby's vocalizations, environmental information, and the user's emotions.

[0349] The device is equipped with an acoustic detection device that captures the baby's cries in real time. Furthermore, it uses an environmental monitoring device to simultaneously collect environmental data such as temperature and humidity around the baby. A wearable measuring device also records the baby's physiological data. All of this data is transmitted to a server.

[0350] The server integrates the received data and uses an information processing device to identify the cause. In particular, machine learning techniques are employed to analyze the patterns of the baby's cries and environmental data to identify the cause of the crying and the baby's condition. The server also implements an algorithm for emotion analysis, evaluating the user's emotional state and, based on the analysis results, generating countermeasures and emotionally conscious advice.

[0351] Notifications are sent via the device. Through the notification device, users receive specific action suggestions based on analysis results and emotional support messages. This allows users to take quick and appropriate action. In addition, user feedback is sent to the server and used to help the system continuously learn and improve.

[0352] For example, when a baby starts crying in the middle of the night, the system sends a notification to the user saying, "The temperature is a little high. We recommend cooling it down. Also, try to relax a little." In this way, the user receives specific instructions on how to soothe the baby, while also taking into consideration the parent's own emotional state.

[0353] Examples of prompts to input into a generative AI model include: "What should I do when my baby cries? Also, please provide a message that is sensitive to the user's feelings."

[0354] In this way, this system provides a multifaceted solution to the problem of babies crying at night.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The device uses an acoustic detection device to capture the baby's cries. The input is ambient sound data. This data is converted into digital data using digital signal processing technology and output. This data is then sent to a server for analysis.

[0358] Step 2:

[0359] The device collects environmental data from the baby's surroundings using an environmental monitoring device. Inputs include environmental information such as temperature, humidity, and light intensity. This information is measured by sensors and output as digital data. Similarly, physiological data from the baby (e.g., heart rate, body temperature) is acquired via a wearable measuring device, output, and transmitted to a server.

[0360] Step 3:

[0361] The server integrates crying data, environmental data, and physiological data received from the terminal and supplies it to the information processing unit. Input data includes voice, environmental, and physiological information. The server utilizes machine learning algorithms to analyze crying patterns, performs data processing and calculations to identify the cause, and generates the results as output.

[0362] Step 4:

[0363] The server uses an emotion algorithm to analyze the user's voice and facial expression data, thereby evaluating the user's emotional state. The input consists of voice and image data, which are analyzed to output the user's emotion score. This emotional information is then incorporated into the assessment of the baby's condition to formulate the optimal course of action.

[0364] Step 5:

[0365] The server formulates countermeasures based on the analyzed data and generates a notification message. This message includes action suggestions that take into account both the baby's condition and the user's emotions. As output, notification information is formed and sent to the terminal.

[0366] Step 6:

[0367] The device informs the user of notification information received from the server. Specifically, it displays messages on the screen or provides suggestions using a voice assistant. This allows the user to understand and take action on specific countermeasures.

[0368] Step 7:

[0369] Users implement the suggested measures and input feedback on their effectiveness and their impressions into the terminal. This feedback is sent to the server and used to improve the machine learning model. Based on user input, the system makes self-improvements to increase the accuracy of future responses.

[0370] (Application Example 2)

[0371] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0372] To reduce the burden on workers and improve efficiency in the workplace, it is necessary to accurately understand the emotional state and environment of workers and implement appropriate measures. Conventional systems have struggled to quickly and accurately assess the stress levels of individual workers and provide support based on that assessment. Therefore, a new system is needed to improve productivity while reducing the psychological and physiological burden on workers.

[0373] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0374] In this invention, the server includes emotion recognition means for detecting and analyzing the emotional state of a worker, environment acquisition means for acquiring data on the work environment, and input device means for collecting detected voice data. This makes it possible to provide appropriate suggestions and rest instructions in real time according to the worker's situation.

[0375] An "emotion recognition device" is a device that analyzes a worker's psychological state from their facial expressions and voice to identify the type and intensity of their emotions.

[0376] An "environmental data acquisition device" is a device that collects data such as temperature, humidity, and illuminance of the work environment and provides environmental information tailored to the work conditions.

[0377] "Input device means" refers to an input device for detecting audio data during work and collecting it as a digital signal.

[0378] A "data processing system" is a system that integrates collected emotional state data and environmental data and performs analysis to evaluate the burden on workers.

[0379] An "information presentation means" is a display device that provides appropriate suggestions and warnings to workers based on processed data.

[0380] The system that realizes this invention monitors the worker's emotions and environmental conditions in real time and makes suggestions to improve work efficiency based on that information. The specific form of this system is described below.

[0381] The server uses emotion recognition to analyze the emotional state of workers. This is achieved by acquiring facial expression data using a camera and collecting audio data using a microphone. This data is transferred to the server in real time and used to evaluate the emotional state. For the evaluation, machine learning frameworks such as TensorFlow and PyTorch are used as emotion recognition technologies.

[0382] Environmental data acquisition methods include using temperature sensors and light meters to collect data on the work environment. This environmental information influences worker comfort and concentration, and therefore serves as an indicator for creating an appropriate work environment.

[0383] The server integrates this emotional and environmental data using data processing tools to assess the worker's current workload. Based on the assessment, it makes suggestions through information presentation tools. Display devices, such as displays and audio speakers, are used to prompt workers to take appropriate breaks or change their work procedures.

[0384] For example, if the system determines that a worker is accumulating fatigue due to prolonged, focused work, it will notify the worker via voice through a speaker, suggesting that they "take a short break."

[0385] As an example of a prompt message for a generative AI model, you can input the following:

[0386] "Worker's emotional data: 'Feeling tired', Environmental data: 'High temperature', Generate appropriate suggestions."

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] The device uses a camera and microphone to collect facial and voice data from the worker in real time. This data includes facial feature points, voice tone, and intonation, and is used for initial analysis of emotional state.

[0390] Step 2:

[0391] The terminal acquires data such as temperature, humidity, and illuminance of the work environment through environmental sensors. This environmental data is considered as a factor affecting worker comfort and is transmitted to a database.

[0392] Step 3:

[0393] The server integrates facial expression and voice data transmitted from the terminal and uses machine learning algorithms to analyze the worker's emotional state. This analysis uses an emotion recognition model based on TensorFlow to identify the type and intensity of emotion.

[0394] Step 4:

[0395] The server combines received environmental data with analyzed emotional states to assess the worker's burden. This assessment is performed by a data processing engine, which generates a score to determine whether the worker is comfortable.

[0396] Step 5:

[0397] Based on the evaluation results, the server generates suggestions to encourage appropriate actions from the worker. At this stage, a generative AI model is used to suggest breaks or improvements to work procedures as needed. An example of a prompt is: "Worker's emotional data: 'Feeling tired', Environmental data: 'High temperature', Please generate appropriate suggestions."

[0398] Step 6:

[0399] The server sends the generated suggestions back to the terminal and notifies the worker through an information display device. This notification is made via a display or audio speaker, prompting the worker to take action.

[0400] Step 7:

[0401] Users act on the suggestions they receive and send feedback to the server. This feedback is used to improve the accuracy of data analysis and adjust future suggestions to be more appropriate.

[0402] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0403] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0404] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0408] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0409] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0410] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0411] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0412] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0413] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0414] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0415] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0416] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0417] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0418] This invention is a system that integrates multiple technologies to reduce the burden on parents caused by their baby's nighttime crying and to provide a more comfortable childcare environment. This system collects data on the baby's crying, physiological data, and environmental data, analyzes this data comprehensively to identify the cause of the nighttime crying, and proposes specific countermeasures to parents. Specific embodiments are described below.

[0419] This system primarily consists of three components: a terminal, a server, and a user. First, the terminal captures the baby's cries and transmits them to the server as digital data. It also acquires environmental data such as temperature, humidity, and illuminance through environmental sensors placed in the room, and collects physiological data such as body temperature, heart rate, and sleep patterns from a wearable device attached to the baby. All of this data is transferred to the server in real time.

[0420] Next, the server receives the data sent from the terminal and analyzes it using machine learning algorithms. It identifies patterns in the baby's crying and identifies possible causes of nighttime crying based on those characteristics. Environmental and physiological data are also integrated and analyzed to comprehensively evaluate the baby's condition. If the analysis determines that the crying is due to hunger, the server prepares to notify the user accordingly.

[0421] Finally, the user (the parent) receives notifications from the device. These notifications include the identified cause of nighttime crying and suggested solutions (for example, "time to feed milk" if the baby is hungry, or "need to adjust room temperature" if the temperature is too high), providing the parent with specific actions to take. Users can also submit feedback on the system's suggestions, which is then incorporated into the server's machine learning to improve the accuracy of future suggestions.

[0422] In this way, the system of the present invention provides embodiments that enable accurate identification of the cause of nighttime crying, provide appropriate support to parents, and reduce the burden of childcare. For example, if the crying is intermittent and follows a certain pattern, the server determines that "hunger is likely" and sends a notification to the user via the terminal prompting them to prepare milk. Also, if the environmental sensor detects a high temperature, it makes a suggestion to take cooling measures. This allows the user to take quick and appropriate action.

[0423] The following describes the processing flow.

[0424] Step 1:

[0425] The device captures the baby's cries with a microphone and records them as digital data. Simultaneously, it acquires environmental data such as room temperature, humidity, and illuminance via environmental sensors, and further collects data on the baby's body temperature, heart rate, and physiological state through a wearable device.

[0426] Step 2:

[0427] The device transmits collected baby crying data, physiological data, and environmental data to the server in real time.

[0428] Step 3:

[0429] The server inputs the received crying data into a machine learning algorithm to analyze the characteristics of the crying. This allows it to compare the data with pre-learned patterns and identify possible causes of nighttime crying (such as hunger, poor health, or environmental maladjustment).

[0430] Step 4:

[0431] The server simultaneously analyzes physiological and environmental data to comprehensively assess the baby's current condition. Based on the analysis results, it determines whether there are any factors hindering the baby's comfort.

[0432] Step 5:

[0433] Based on the identified cause of the baby crying at night, the server generates specific countermeasures for the parents. For example, suggestions such as "give milk" or "lower the room temperature" are decided at this stage.

[0434] Step 6:

[0435] The device receives suggested solutions from the server and notifies the user (parent). The notification includes the cause of the baby's crying and specific recommended actions.

[0436] Step 7:

[0437] Users check the notifications on their devices and take the suggested actions. At the same time, they send feedback information, such as the results and their impressions, to the system, which is used as training data for the server to improve the accuracy of future suggestions.

[0438] (Example 1)

[0439] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0440] Parents raising young children often experience a great deal of stress due to their baby's nighttime crying. While it's crucial to quickly and accurately identify the cause of the crying and take appropriate action, this is currently difficult. To address these issues, a system is needed that comprehensively analyzes the crying, the environment, and the baby's own physiological state, and then provides concrete solutions.

[0441] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0442] In this invention, the server includes an acoustic acquisition means, an environmental acquisition means for collecting ambient environmental information, and a wearable acquisition means for collecting physiological information. This makes it possible to comprehensively evaluate the baby's condition and propose quick and accurate countermeasures based on the results.

[0443] "Sound acquisition means" refers to devices or methods for detecting a baby's crying and other sound information and collecting it as digital information.

[0444] "Environmental acquisition means" refers to a device or method for measuring and collecting environmental information such as ambient temperature, humidity, and illuminance as data.

[0445] "Wearable data acquisition methods" refer to devices or methods that are attached to babies to measure and digitize physiological information (such as body temperature, heart rate, and sleep patterns).

[0446] "Information analysis means" refers to techniques for integrating and analyzing collected acoustic data, environmental data, and physiological data to extract specific causes.

[0447] "Notification means" refers to a device or method for notifying the user of countermeasures based on the analysis results.

[0448] In data analysis techniques, the "learning process" refers to a method for recognizing patterns in data and improving its accuracy based on past data.

[0449] "User" refers to an individual who operates this system and acts in accordance with the measures indicated.

[0450] Modes for carrying out the invention

[0451] This invention is a system that identifies the causes of nighttime crying in babies and proposes countermeasures to parents. This system provides rapid and effective childcare support by aggregating and analyzing acoustic data, environmental data, and physiological data. The embodiments of this invention are described in detail below.

[0452] Device configuration and functions

[0453] The device is equipped with a microphone to detect the baby's cries. The acoustic data recorded by this microphone is digitized in real time. The device also has sensors to measure the room's temperature, humidity, and illuminance. The environmental data acquired by these sensors is also transmitted to the server in digital format. Furthermore, physiological data such as body temperature, heart rate, and sleep patterns obtained from wearable devices attached to the baby are also collected on the server via the device.

[0454] Server configuration and functionality

[0455] The server receives data sent from the terminal and stores it in storage. It then uses a generative AI model to analyze the patterns in the acoustic data. Machine learning algorithms identify the characteristics of crying and pinpoint the cause of nighttime crying. For example, if the crying pattern is consistent and intermittent, it is likely to be due to hunger. Simultaneously, the server comprehensively evaluates environmental and physiological data to make an overall assessment of the baby's health.

[0456] User notifications and interface

[0457] Parents, as users, can receive analysis results from the server as notifications on their devices. These notifications specifically describe the cause of nighttime crying and recommended countermeasures. For example, instructions such as "Prepare milk" or "Lower the room temperature" may be displayed. This allows users to take quick action.

[0458] Examples of specific cases and prompt statements

[0459] For example, if a baby cries continuously and the server determines that the baby is hungry, a notification will be sent to the user via their device saying, "It's time to feed the baby." Also, if the system determines that the room temperature is too high, it will suggest, "Please adjust the air conditioner temperature."

[0460] An example of a prompt for a generative AI model would be:

[0461] "Please propose the optimal algorithm for identifying the cause of a baby's nighttime crying. If possible, please explain an integrated analysis method that also considers environmental and physiological data."

[0462] Thus, the system of the present invention has specific embodiments that analyze a baby's nighttime crying and provide effective childcare support.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] The device detects the baby's cries using a microphone. The input is ambient sound, which is converted into digital audio data. The output is this digitized audio data, which is sent to a server to identify the cause of the nighttime crying.

[0466] Step 2:

[0467] The device measures room temperature, humidity, and illuminance using environmental sensors. The input is an analog signal from the environmental sensors, which is then processed to convert it into digital data. The output is digital environmental data, which is transmitted to a server in real time and used to evaluate the baby's comfort level.

[0468] Step 3:

[0469] The device collects body temperature, heart rate, and sleep patterns from a wearable device attached to the baby. The input consists of physiological indicators from the wearable device, which are then processed to convert them into a digital format. The output is digital physiological data, which is sent to a server to comprehensively assess the baby's health.

[0470] Step 4:

[0471] The server integrates and analyzes acoustic, environmental, and physiological data received from the terminal. The input consists of this digital data, and machine learning algorithms are used for data analysis. The output is a result indicating possible causes of nighttime crying. A generative AI model is used for the analysis to extract and evaluate features of crying patterns.

[0472] Step 5:

[0473] The server generates countermeasures based on the analysis results. The input is the analysis results indicating the cause of the baby's crying at night, and the server processes this to create specific action suggestions. The output is a notification message containing the countermeasures. This notification is sent to the terminal so that the user can take appropriate action quickly.

[0474] Step 6:

[0475] The user receives notifications from the server via their device. The input is the suggested course of action sent from the server, and the user takes action based on this. The output is the user's action. The user ensures the baby's comfort by giving milk or adjusting the room temperature as suggested.

[0476] Step 7:

[0477] Users provide feedback to the server. Input consists of user evaluations and opinions on suggestions, which are digitized and sent to the server. Output is data that contributes to the server's machine learning algorithms, used to improve the accuracy of future analyses.

[0478] (Application Example 1)

[0479] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0480] In childcare, a baby's nighttime crying is a significant burden for parents, and it is crucial to quickly and accurately identify the cause and take appropriate measures. However, conventional methods make it difficult to understand the cause of nighttime crying and fail to alleviate parenting stress. Therefore, the present invention aims to provide a system that comprehensively analyzes a baby's crying, environment, and physiological data to identify the cause of nighttime crying, enabling parents to respond quickly.

[0481] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0482] In this invention, the server includes acoustic analysis means, environmental measurement means, and wearable device means. This allows for the analysis of crying patterns, environmental and physiological changes, real-time notification of countermeasures to parents, and identification of the cause of nighttime crying, enabling a rapid response.

[0483] "Acoustic analysis means" refers to a device or system that detects a baby's crying and analyzes its sound pattern.

[0484] "Environmental measurement means" refers to a device or group of sensors that acquires environmental data such as temperature, humidity, and illuminance around the baby.

[0485] A "wearable device" is a device that is attached to a baby's body to collect physiological information (such as body temperature and heart rate).

[0486] "Information processing means" refers to a computer system for receiving data from acoustic analysis means, environmental measurement means, and wearable device means, and performing integrated analysis.

[0487] "Notification provision means" refers to a communication device or software that informs the user of the identified cause of nighttime crying and the countermeasures to be taken.

[0488] "Display means" refers to a device that has a display function for visually showing countermeasure information through a mobile device.

[0489] The system for realizing this invention includes acoustic analysis means, environmental measurement means, wearable device means, information processing means, notification provision means, and display means. At the heart of the system is the information processing means that integrally acquires and analyzes the baby's crying, physiological data, and environmental data. This makes it possible to identify the cause of nighttime crying and provide parents with specific countermeasures.

[0490] The server receives baby crying data acquired from acoustic analysis equipment. For example, it analyzes this crying data using machine learning algorithms employing deep learning techniques (such as TensorFlow or PyTorch) to identify specific crying patterns. It also comprehensively processes data such as temperature and humidity acquired from environmental measurement equipment, as well as physiological information such as body temperature and heart rate from wearable devices, to perform a comprehensive evaluation.

[0491] The device receives analysis results from the server and provides information to the user (parent) via smartphone or smart glasses. This notification includes identified causes of nighttime crying and possible countermeasures, such as action suggestions like "prepare milk" or "adjust room temperature." Furthermore, the device functions as a display tool to effectively show this countermeasure information, enabling parents to respond quickly and efficiently.

[0492] Parents, as users, can take action according to the provided solutions. Furthermore, by sending user feedback to the server, the accuracy of the system's machine learning algorithms improves, resulting in more precise suggestions in the future.

[0493] For example, a parent might receive an audio notification while at work, advising them to "use the air conditioner because the baby's temperature is high and the room temperature is too high." Furthermore, if the crying pattern matches the "hungry pattern," the notification might instruct them to "prepare milk."

[0494] Examples of prompts to input into a generative AI model are as follows:

[0495] "Please explain how an app works by analyzing a baby's crying data, body temperature, and ambient temperature to suggest the cause of the crying and how to address it."

[0496] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0497] Step 1:

[0498] The server activates an acoustic analysis system to detect a baby's crying. The input is real-time audio data. This audio data is then analyzed using a machine learning algorithm to perform speech pattern analysis. The output is a characteristic pattern of the crying. Specifically, a deep learning model is used to analyze the amplitude and frequency patterns of the audio data.

[0499] Step 2:

[0500] The server receives data from environmental measurement devices and wearable devices. Inputs include temperature, humidity, illuminance data, and physiological data such as body temperature and heart rate. This data is converted to a unified format and stored in a database. This process integrates all sensor data, outputting comprehensive state information. Specifically, it performs normalization and averaging of disparate data.

[0501] Step 3:

[0502] The server integrates crying patterns from acoustic analysis devices with environmental and physiological data to perform data analysis. It uses integrated information from various data sources as input. It utilizes a generative AI model to execute an algorithm to identify the cause of nighttime crying. The output includes the identified cause and recommended behavioral measures. The specific operation includes a cause identification inference process using machine learning libraries.

[0503] Step 4:

[0504] The device receives analysis results sent from the server and presents countermeasures to the user through a notification system. The input is the identified cause of nighttime crying and information on countermeasures. The output shows specific actions to be conveyed to the user, such as "prepare milk" or "adjust the room temperature." As a concrete action, information is quickly conveyed to the user using the smartphone's push notification system.

[0505] Step 5:

[0506] Users take action and implement countermeasures based on notifications from their devices. User feedback is sent back to the server, which then serves as input for improving the accuracy of future analyses. The feedback data is analyzed, and new training data is obtained as output, contributing to future improvements in proposal accuracy. Specifically, this process involves collecting feedback information and evaluating it using data science techniques.

[0507] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0508] This invention combines a system designed to alleviate the burden on parents regarding their baby's nighttime crying and provide a more comfortable childcare environment with an emotion engine that recognizes the user's emotions. This system collects and analyzes the baby's crying, physiological data, and environmental data, and can propose countermeasures while considering the user's emotional state. Specific embodiments are described below.

[0509] This system primarily consists of three components: a terminal, a server, and a user. First, the terminal captures the baby's cries and transmits them to the server as digital data. Furthermore, the terminal is equipped with a camera and microphone, which are used to collect the user's voice and facial expression data. This allows the terminal to provide the server with a realistic picture of the environment the user is in.

[0510] The server receives data from the terminal and uses machine learning algorithms to analyze the baby's crying patterns. This analysis identifies the characteristics of the crying and pinpoints the cause of nighttime crying. It also comprehensively analyzes environmental and physiological data to evaluate the baby's condition. Furthermore, the server has an emotion engine implemented that analyzes the user's voice and facial expression data to recognize their emotional state. This emotional information is considered along with the baby's condition to help suggest the most appropriate countermeasures.

[0511] The device receives suggested solutions from the server and sends notifications to the user. These notifications include not only the suspected cause and recommended actions, but also supportive messages for parents based on the user's current emotional state. For example, messages such as "Take a break when you're tired" or "Don't worry, we'll fix it soon" may be displayed, showing emotional consideration.

[0512] Users review the notification and take the suggested actions. They can also provide feedback to the system through this experience. This feedback is used in the server's further learning process to improve the system's performance.

[0513] For example, when a baby starts crying in the middle of the night, the system detects the user's fatigue and suggests solutions such as, "The temperature is a little high. I recommend adjusting it. Also, try to relax a little." This gives the user concrete ways to soothe the baby, while also addressing their own emotional needs, thus reducing their mental burden.

[0514] The following describes the processing flow.

[0515] Step 1:

[0516] The device captures the baby's cries with a microphone and saves them as audio data. Simultaneously, it uses a camera and microphone to capture the user's facial expressions and voice, collecting data to measure their emotions. In addition, it measures the room's temperature, humidity, and illuminance via environmental sensors, and obtains the baby's body temperature and heart rate from a wearable device.

[0517] Step 2:

[0518] The device sends the collected data to the server. The data sent includes crying data, user voice and facial expression data, environmental data, and physiological data.

[0519] Step 3:

[0520] The server applies machine learning algorithms to analyze the received crying data and identify crying patterns. Based on this analysis, it identifies factors that are likely to be causing nighttime crying.

[0521] Step 4:

[0522] The server analyzes the received user voice and facial expression data using an emotion engine to recognize the user's emotional state. For example, if patterns suggesting fatigue or stress are detected, the server evaluates the situation based on that.

[0523] Step 5:

[0524] The server integrates the analysis results and generates the optimal response based on the baby's condition and the user's emotions. This response includes specific actions related to the cause of the baby's crying (e.g., "It's time for milk," "Let's lower the room temperature") and messages that take the user's emotions into consideration (e.g., "Let's relax a little").

[0525] Step 6:

[0526] The device receives a response generated from the server and notifies the user. The notification includes the cause, recommended actions, and mental support.

[0527] Step 7:

[0528] Users check notifications on their devices and take action according to the suggested measures. Simultaneously, they provide feedback on the results and experience, sending their thoughts and suggestions for improvement to the system. This feedback is sent to the server and used as learning data for future analysis and suggestions.

[0529] (Example 2)

[0530] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0531] A baby's crying is often a major source of stress for parents, especially at night, leading to sleep deprivation and emotional distress. Furthermore, identifying the cause of a baby's crying and addressing it appropriately requires not only sound analysis but also consideration of environmental data, the baby's physiological state, and even the parents' emotional state. However, previous systems have not adequately evaluated these factors comprehensively and provided parents with appropriate advice and support.

[0532] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0533] In this invention, the server includes an acoustic detection device for analyzing sound to identify crying patterns and detect emotions, an environmental observation device for acquiring ambient temperature, humidity, and light intensity, and a wearable measuring device for recording physiological indicators. This enables the comprehensive identification of the cause of a baby's crying and the proposal of appropriate countermeasures that take into account the user's emotional state.

[0534] An "acoustic detection device" is a device that captures and analyzes audio signals to identify crying patterns and detect emotions.

[0535] An "environmental monitoring device" is a device used to acquire environmental data such as ambient temperature, humidity, and light intensity.

[0536] A "wearable measuring device" is a type of measuring device that is attached to the body to record physiological indicators of a baby.

[0537] An "information processing device" is a device that integrates information sent from acoustic detection devices, environmental observation devices, and wearable measurement devices to identify specific causes.

[0538] A "notification device" is a device that, based on analysis results obtained from an information processing device, conveys advice to the user that takes into account countermeasures and emotions.

[0539] "Machine learning technology" is a technique used to enable computer systems to learn from data and automatically improve at specific tasks.

[0540] This invention is a system designed to provide a comprehensive solution to the problem of a baby's crying. The system consists of a terminal, a server, and user interaction. Specifically, it aims to reduce the mental and physical burden on parents by analyzing the baby's vocalizations, environmental information, and the user's emotions.

[0541] The device is equipped with an acoustic detection device that captures the baby's cries in real time. Furthermore, it uses an environmental monitoring device to simultaneously collect environmental data such as temperature and humidity around the baby. A wearable measuring device also records the baby's physiological data. All of this data is transmitted to a server.

[0542] The server integrates the received data and uses an information processing device to identify the cause. In particular, machine learning techniques are employed to analyze the patterns of the baby's cries and environmental data to identify the cause of the crying and the baby's condition. The server also implements an algorithm for emotion analysis, evaluating the user's emotional state and, based on the analysis results, generating countermeasures and emotionally conscious advice.

[0543] Notifications are sent via the device. Through the notification device, users receive specific action suggestions based on analysis results and emotional support messages. This allows users to take quick and appropriate action. In addition, user feedback is sent to the server and used to help the system continuously learn and improve.

[0544] For example, when a baby starts crying in the middle of the night, the system sends a notification to the user saying, "The temperature is a little high. We recommend cooling it down. Also, try to relax a little." In this way, the user receives specific instructions on how to soothe the baby, while also taking into consideration the parent's own emotional state.

[0545] Examples of prompts to input into a generative AI model include: "What should I do when my baby cries? Also, please provide a message that is sensitive to the user's feelings."

[0546] In this way, this system provides a multifaceted solution to the problem of babies crying at night.

[0547] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0548] Step 1:

[0549] The device uses an acoustic detection device to capture the baby's cries. The input is ambient sound data. This data is converted into digital data using digital signal processing technology and output. This data is then sent to a server for analysis.

[0550] Step 2:

[0551] The device collects environmental data from the baby's surroundings using an environmental monitoring device. Inputs include environmental information such as temperature, humidity, and light intensity. This information is measured by sensors and output as digital data. Similarly, physiological data from the baby (e.g., heart rate, body temperature) is acquired via a wearable measuring device, output, and transmitted to a server.

[0552] Step 3:

[0553] The server integrates crying data, environmental data, and physiological data received from the terminal and supplies it to the information processing unit. Input data includes voice, environmental, and physiological information. The server utilizes machine learning algorithms to analyze crying patterns, performs data processing and calculations to identify the cause, and generates the results as output.

[0554] Step 4:

[0555] The server uses an emotion algorithm to analyze the user's voice and facial expression data, thereby evaluating the user's emotional state. The input consists of voice and image data, which are analyzed to output the user's emotion score. This emotional information is then incorporated into the assessment of the baby's condition to formulate the optimal course of action.

[0556] Step 5:

[0557] The server formulates countermeasures based on the analyzed data and generates a notification message. This message includes action suggestions that take into account both the baby's condition and the user's emotions. As output, the notification information is formed and sent to the terminal.

[0558] Step 6:

[0559] The device informs the user of notification information received from the server. Specifically, it displays messages on the screen or provides suggestions using a voice assistant. This allows the user to understand and take action on specific countermeasures.

[0560] Step 7:

[0561] Users implement the suggested measures and input feedback on their effectiveness and their own impressions into the terminal. This feedback is sent to the server and used to improve the machine learning model. Based on the user's input, the system makes self-improvements to increase the accuracy of future responses.

[0562] (Application Example 2)

[0563] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0564] To reduce the burden on workers and improve efficiency in the workplace, it is necessary to accurately understand the emotional state and environment of workers and implement appropriate measures. Conventional systems have struggled to quickly and accurately assess the stress levels of individual workers and provide support based on that assessment. Therefore, a new system is needed to improve productivity while reducing the psychological and physiological burden on workers.

[0565] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0566] In this invention, the server includes emotion recognition means for detecting and analyzing the emotional state of a worker, environment acquisition means for acquiring data on the work environment, and input device means for collecting detected voice data. This makes it possible to provide appropriate suggestions and rest instructions in real time according to the worker's situation.

[0567] An "emotion recognition device" is a device that analyzes a worker's psychological state from their facial expressions and voice to identify the type and intensity of their emotions.

[0568] An "environmental data acquisition device" is a device that collects data such as temperature, humidity, and illuminance of the work environment and provides environmental information tailored to the work conditions.

[0569] "Input device means" refers to an input device for detecting audio data during work and collecting it as a digital signal.

[0570] A "data processing system" is a system that integrates collected emotional state data and environmental data and performs analysis to evaluate the burden on workers.

[0571] An "information presentation means" is a display device that provides appropriate suggestions and warnings to workers based on processed data.

[0572] The system that realizes this invention monitors the worker's emotions and environmental conditions in real time and makes suggestions to improve work efficiency based on that information. The specific form of this system is described below.

[0573] The server uses emotion recognition to analyze the emotional state of workers. This is achieved by acquiring facial expression data using a camera and collecting audio data using a microphone. This data is transferred to the server in real time and used to evaluate the emotional state. For the evaluation, machine learning frameworks such as TensorFlow and PyTorch are used as emotion recognition technologies.

[0574] Environmental data acquisition methods include using temperature sensors and light meters to collect data on the work environment. This environmental information influences worker comfort and concentration, and therefore serves as an indicator for creating an appropriate work environment.

[0575] The server integrates this emotional and environmental data using data processing tools to assess the worker's current workload. Based on the assessment, it makes suggestions through information presentation tools. Display devices, such as displays and audio speakers, are used to prompt workers to take appropriate breaks or change their work procedures.

[0576] For example, if the system determines that a worker is accumulating fatigue due to prolonged, focused work, it will notify the worker via voice through a speaker, suggesting that they "take a short break."

[0577] As an example of a prompt message for a generative AI model, you can input the following:

[0578] "Worker's emotional data: 'Feeling tired', Environmental data: 'High temperature', Generate appropriate suggestions."

[0579] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0580] Step 1:

[0581] The device uses a camera and microphone to collect facial and voice data from the worker in real time. This data includes facial feature points, voice tone, and intonation, and is used for initial analysis of emotional state.

[0582] Step 2:

[0583] The terminal acquires data such as temperature, humidity, and illuminance of the work environment through environmental sensors. This environmental data is considered as a factor affecting worker comfort and is transmitted to a database.

[0584] Step 3:

[0585] The server integrates facial expression and voice data transmitted from the terminal and uses machine learning algorithms to analyze the worker's emotional state. This analysis uses an emotion recognition model based on TensorFlow to identify the type and intensity of emotion.

[0586] Step 4:

[0587] The server combines received environmental data with analyzed emotional states to assess the worker's burden. This assessment is performed by a data processing engine, which generates a score to determine whether the worker is comfortable or not.

[0588] Step 5:

[0589] Based on the evaluation results, the server generates suggestions to encourage appropriate actions from the worker. At this stage, a generative AI model is used to suggest breaks or improvements to work procedures as needed. An example of a prompt is: "Worker's emotional data: 'Feeling tired', Environmental data: 'High temperature', Please generate appropriate suggestions."

[0590] Step 6:

[0591] The server sends the generated suggestions back to the terminal and notifies the worker through an information display device. This notification is made via a display or audio speaker, prompting the worker to take action.

[0592] Step 7:

[0593] Users act on the suggestions they receive and send feedback to the server. This feedback is used to improve the accuracy of data analysis and adjust future suggestions to be more appropriate.

[0594] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0595] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0596] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0600] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0601] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0602] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0603] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0604] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0605] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0606] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0607] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0608] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0609] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0610] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] This invention is a system that integrates multiple technologies to reduce the burden on parents caused by their baby's nighttime crying and to provide a more comfortable childcare environment. This system collects data on the baby's crying, physiological data, and environmental data, analyzes this data comprehensively to identify the cause of the nighttime crying, and proposes specific countermeasures to parents. Specific embodiments are described below.

[0612] This system primarily consists of three components: a terminal, a server, and a user. First, the terminal captures the baby's cries and transmits them to the server as digital data. It also acquires environmental data such as temperature, humidity, and illuminance through environmental sensors placed in the room, and collects physiological data such as body temperature, heart rate, and sleep patterns from a wearable device attached to the baby. All of this data is transferred to the server in real time.

[0613] Next, the server receives the data sent from the terminal and analyzes it using machine learning algorithms. It identifies patterns in the baby's crying and identifies possible causes of nighttime crying based on those characteristics. Environmental and physiological data are also integrated and analyzed to comprehensively evaluate the baby's condition. If the analysis determines that the crying is due to hunger, the server prepares to notify the user accordingly.

[0614] Finally, the user (the parent) receives notifications from the device. These notifications include the identified cause of nighttime crying and suggested solutions (for example, "time to feed milk" if the baby is hungry, or "need to adjust room temperature" if the temperature is too high), providing the parent with specific actions to take. Users can also submit feedback on the system's suggestions, which is then incorporated into the server's machine learning to improve the accuracy of future suggestions.

[0615] In this way, the system of the present invention provides embodiments that enable accurate identification of the cause of nighttime crying, provide appropriate support to parents, and reduce the burden of childcare. For example, if the crying is intermittent and follows a certain pattern, the server determines that "hunger is likely" and sends a notification to the user via the terminal prompting them to prepare milk. Also, if the environmental sensor detects a high temperature, it makes a suggestion to take cooling measures. This allows the user to take quick and appropriate action.

[0616] The following describes the processing flow.

[0617] Step 1:

[0618] The device captures the baby's cries with a microphone and records them as digital data. Simultaneously, it acquires environmental data such as room temperature, humidity, and illuminance via environmental sensors, and further collects data on the baby's body temperature, heart rate, and physiological state through a wearable device.

[0619] Step 2:

[0620] The device transmits collected baby crying data, physiological data, and environmental data to the server in real time.

[0621] Step 3:

[0622] The server inputs the received crying data into a machine learning algorithm to analyze the characteristics of the crying. This allows it to compare the data with pre-learned patterns and identify possible causes of nighttime crying (such as hunger, poor health, or environmental maladjustment).

[0623] Step 4:

[0624] The server simultaneously analyzes physiological and environmental data to comprehensively assess the baby's current condition. Based on the analysis results, it determines whether there are any factors hindering the baby's comfort.

[0625] Step 5:

[0626] Based on the identified cause of the baby crying at night, the server generates specific countermeasures for the parents. For example, suggestions such as "give milk" or "lower the room temperature" are decided at this stage.

[0627] Step 6:

[0628] The device receives suggested solutions from the server and notifies the user (parent). The notification includes the cause of the baby's crying and specific recommended actions.

[0629] Step 7:

[0630] Users check the notifications on their devices and take the suggested actions. At the same time, they send feedback information, such as the results and their impressions, to the system, which is used as training data for the server to improve the accuracy of future suggestions.

[0631] (Example 1)

[0632] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0633] Parents raising young children often experience a great deal of stress due to their baby's nighttime crying. While it's crucial to quickly and accurately identify the cause of the crying and take appropriate action, this is currently difficult. To address these issues, a system is needed that comprehensively analyzes the crying, the environment, and the baby's own physiological state, and then provides concrete solutions.

[0634] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0635] In this invention, the server includes an acoustic acquisition means, an environmental acquisition means for collecting ambient environmental information, and a wearable acquisition means for collecting physiological information. This makes it possible to comprehensively evaluate the baby's condition and propose quick and accurate countermeasures based on the results.

[0636] "Sound acquisition means" refers to devices or methods for detecting a baby's crying and other sound information and collecting it as digital information.

[0637] "Environmental acquisition means" refers to a device or method for measuring and collecting environmental information such as ambient temperature, humidity, and illuminance as data.

[0638] "Wearable data acquisition methods" refer to devices or methods that are attached to babies to measure and digitize physiological information (such as body temperature, heart rate, and sleep patterns).

[0639] "Information analysis means" refers to techniques for integrating and analyzing collected acoustic data, environmental data, and physiological data to extract specific causes.

[0640] "Notification means" refers to a device or method for notifying the user of countermeasures based on the analysis results.

[0641] In data analysis techniques, the "learning process" refers to a method for recognizing patterns in data and improving its accuracy based on past data.

[0642] "User" refers to an individual who operates this system and acts in accordance with the measures indicated.

[0643] Modes for carrying out the invention

[0644] This invention is a system that identifies the causes of nighttime crying in babies and proposes countermeasures to parents. This system provides rapid and effective childcare support by aggregating and analyzing acoustic data, environmental data, and physiological data. The embodiments of this invention are described in detail below.

[0645] Device configuration and functions

[0646] The device is equipped with a microphone to detect the baby's cries. The acoustic data recorded by this microphone is digitized in real time. The device also has sensors to measure the room's temperature, humidity, and illuminance. The environmental data acquired by these sensors is also transmitted to the server in digital format. Furthermore, physiological data such as body temperature, heart rate, and sleep patterns obtained from wearable devices attached to the baby are also collected on the server via the device.

[0647] Server configuration and functionality

[0648] The server receives data sent from the terminal and stores it in storage. It then uses a generative AI model to analyze the patterns in the acoustic data. Machine learning algorithms identify the characteristics of crying and pinpoint the cause of nighttime crying. For example, if the crying pattern is consistent and intermittent, it is likely to be due to hunger. Simultaneously, the server comprehensively evaluates environmental and physiological data to make an overall assessment of the baby's health.

[0649] User notifications and interface

[0650] Parents, as users, can receive analysis results from the server as notifications on their devices. These notifications specifically describe the cause of nighttime crying and recommended countermeasures. For example, instructions such as "Prepare milk" or "Lower the room temperature" may be displayed. This allows users to take quick action.

[0651] Examples of specific cases and prompt statements

[0652] For example, if a baby cries continuously and the server determines that the baby is hungry, a notification will be sent to the user via their device saying, "It's time to feed the baby." Also, if the system determines that the room temperature is too high, it will suggest, "Please adjust the air conditioner temperature."

[0653] An example of a prompt for a generative AI model would be:

[0654] "Please propose the optimal algorithm for identifying the cause of a baby's nighttime crying. If possible, please explain an integrated analysis method that also considers environmental and physiological data."

[0655] Thus, the system of the present invention has specific embodiments that analyze a baby's nighttime crying and provide effective childcare support.

[0656] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0657] Step 1:

[0658] The device detects the baby's cries using a microphone. The input is ambient sound, which is converted into digital audio data. The output is this digitized audio data, which is sent to a server to identify the cause of the nighttime crying.

[0659] Step 2:

[0660] The device measures room temperature, humidity, and illuminance using environmental sensors. The input is an analog signal from the environmental sensors, which is then processed to convert it into digital data. The output is digital environmental data, which is transmitted to a server in real time and used to evaluate the baby's comfort level.

[0661] Step 3:

[0662] The device collects body temperature, heart rate, and sleep patterns from a wearable device attached to the baby. The input consists of physiological indicators from the wearable device, which are then processed to convert them into a digital format. The output is digital physiological data, which is sent to a server to comprehensively assess the baby's health.

[0663] Step 4:

[0664] The server integrates and analyzes acoustic, environmental, and physiological data received from the terminal. The input consists of this digital data, and machine learning algorithms are used for data analysis. The output is a result indicating possible causes of nighttime crying. A generative AI model is used for the analysis to extract and evaluate features of crying patterns.

[0665] Step 5:

[0666] The server generates countermeasures based on the analysis results. The input is the analysis results indicating the cause of the baby's crying at night, and the server processes this to create specific action suggestions. The output is a notification message containing the countermeasures. This notification is sent to the terminal so that the user can take appropriate action quickly.

[0667] Step 6:

[0668] The user receives notifications from the server via their device. The input is the suggested course of action sent from the server, and the user takes action based on this. The output is the user's action. The user ensures the baby's comfort by giving milk or adjusting the room temperature as suggested.

[0669] Step 7:

[0670] Users provide feedback to the server. Input consists of user evaluations and opinions on suggestions, which are digitized and sent to the server. Output is data that contributes to the server's machine learning algorithms, used to improve the accuracy of future analyses.

[0671] (Application Example 1)

[0672] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0673] In childcare, a baby's nighttime crying is a significant burden for parents, and it is crucial to quickly and accurately identify the cause and take appropriate measures. However, conventional methods make it difficult to understand the cause of nighttime crying and fail to alleviate parenting stress. Therefore, the present invention aims to provide a system that comprehensively analyzes a baby's crying, environment, and physiological data to identify the cause of nighttime crying, enabling parents to respond quickly.

[0674] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0675] In this invention, the server includes acoustic analysis means, environmental measurement means, and wearable device means. This allows for the analysis of crying patterns, environmental and physiological changes, real-time notification of countermeasures to parents, and identification of the cause of nighttime crying, enabling a rapid response.

[0676] "Acoustic analysis means" refers to a device or system that detects a baby's crying and analyzes its sound pattern.

[0677] "Environmental measurement means" refers to a device or group of sensors that acquires environmental data such as temperature, humidity, and illuminance around the baby.

[0678] A "wearable device" is a device that is attached to a baby's body to collect physiological information (such as body temperature and heart rate).

[0679] "Information processing means" refers to a computer system for receiving data from acoustic analysis means, environmental measurement means, and wearable device means, and performing integrated analysis.

[0680] "Notification provision means" refers to a communication device or software that informs the user of the identified cause of nighttime crying and the countermeasures to be taken.

[0681] "Display means" refers to a device that has a display function for visually showing countermeasure information through a mobile device.

[0682] The system for realizing this invention includes acoustic analysis means, environmental measurement means, wearable device means, information processing means, notification provision means, and display means. At the heart of the system is the information processing means that integrally acquires and analyzes the baby's crying, physiological data, and environmental data. This makes it possible to identify the cause of nighttime crying and provide parents with specific countermeasures.

[0683] The server receives baby crying data acquired from acoustic analysis equipment. For example, it analyzes this crying data using machine learning algorithms employing deep learning techniques (such as TensorFlow or PyTorch) to identify specific crying patterns. It also comprehensively processes data such as temperature and humidity acquired from environmental measurement equipment, as well as physiological information such as body temperature and heart rate from wearable devices, to perform a comprehensive evaluation.

[0684] The device receives analysis results from the server and provides information to the user (parent) via smartphone or smart glasses. This notification includes identified causes of nighttime crying and possible countermeasures, such as action suggestions like "prepare milk" or "adjust room temperature." Furthermore, the device functions as a display tool to effectively show this countermeasure information, enabling parents to respond quickly and efficiently.

[0685] Parents, as users, can take action according to the provided solutions. Furthermore, by sending user feedback to the server, the accuracy of the system's machine learning algorithms improves, resulting in more precise suggestions in the future.

[0686] For example, a parent might receive an audio notification while at work, advising them to "use the air conditioner because the baby's temperature is high and the room temperature is too high." Furthermore, if the crying pattern matches the "hungry pattern," the notification might instruct them to "prepare milk."

[0687] Examples of prompts to input into a generative AI model are as follows:

[0688] "Please explain how an app works by analyzing a baby's crying data, body temperature, and ambient temperature to suggest the cause of the crying and how to address it."

[0689] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0690] Step 1:

[0691] The server activates an acoustic analysis system to detect a baby's crying. The input is real-time audio data. This audio data is then analyzed using a machine learning algorithm to perform speech pattern analysis. The output is a characteristic pattern of the crying. Specifically, a deep learning model is used to analyze the amplitude and frequency patterns of the audio data.

[0692] Step 2:

[0693] The server receives data from environmental measurement devices and wearable devices. Inputs include temperature, humidity, illuminance data, and physiological data such as body temperature and heart rate. This data is converted to a unified format and stored in a database. This process integrates all sensor data, outputting comprehensive state information. Specifically, it performs normalization and averaging of disparate data.

[0694] Step 3:

[0695] The server integrates crying patterns from acoustic analysis devices with environmental and physiological data to perform data analysis. It uses integrated information from various data sources as input. It utilizes a generative AI model to execute an algorithm to identify the cause of nighttime crying. The output includes the identified cause and recommended behavioral measures. The specific operation includes a cause identification inference process using machine learning libraries.

[0696] Step 4:

[0697] The device receives analysis results sent from the server and presents countermeasures to the user through a notification system. The input is the identified cause of nighttime crying and information on countermeasures. The output shows specific actions to be conveyed to the user, such as "prepare milk" or "adjust the room temperature." As a concrete action, information is quickly conveyed to the user using the smartphone's push notification system.

[0698] Step 5:

[0699] Users take action and implement countermeasures based on notifications from their devices. User feedback is sent back to the server, which then serves as input for improving the accuracy of future analyses. The feedback data is analyzed, and new training data is obtained as output, contributing to future improvements in proposal accuracy. Specifically, this process involves collecting feedback information and evaluating it using data science techniques.

[0700] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0701] This invention combines a system designed to alleviate the burden on parents regarding their baby's nighttime crying and provide a more comfortable childcare environment with an emotion engine that recognizes the user's emotions. This system collects and analyzes the baby's crying, physiological data, and environmental data, and can propose countermeasures while considering the user's emotional state. Specific embodiments are described below.

[0702] This system primarily consists of three components: a terminal, a server, and a user. First, the terminal captures the baby's cries and transmits them to the server as digital data. Furthermore, the terminal is equipped with a camera and microphone, which are used to collect the user's voice and facial expression data. This allows the terminal to provide the server with a realistic picture of the environment the user is in.

[0703] The server receives data from the terminal and uses machine learning algorithms to analyze the baby's crying patterns. This analysis identifies the characteristics of the crying and pinpoints the cause of nighttime crying. It also comprehensively analyzes environmental and physiological data to evaluate the baby's condition. Furthermore, the server has an emotion engine implemented that analyzes the user's voice and facial expression data to recognize their emotional state. This emotional information is considered along with the baby's condition to help suggest the most appropriate countermeasures.

[0704] The device receives suggested solutions from the server and sends notifications to the user. These notifications include not only the suspected cause and recommended actions, but also supportive messages for parents based on the user's current emotional state. For example, messages such as "Take a break when you're tired" or "Don't worry, we'll fix it soon" may be displayed, showing emotional consideration.

[0705] Users review the notification and take the suggested actions. They can also provide feedback to the system through this experience. This feedback is used in the server's further learning process to improve the system's performance.

[0706] For example, when a baby starts crying in the middle of the night, the system detects the user's fatigue and suggests solutions such as, "The temperature is a little high. I recommend adjusting it. Also, try to relax a little." This gives the user concrete ways to soothe the baby, while also addressing their own emotional needs, thus reducing their mental burden.

[0707] The following describes the processing flow.

[0708] Step 1:

[0709] The device captures the baby's cries with a microphone and saves them as audio data. Simultaneously, it uses a camera and microphone to capture the user's facial expressions and voice, collecting data to measure their emotions. In addition, it measures the room's temperature, humidity, and illuminance via environmental sensors, and obtains the baby's body temperature and heart rate from a wearable device.

[0710] Step 2:

[0711] The device sends the collected data to the server. The data sent includes crying data, user voice and facial expression data, environmental data, and physiological data.

[0712] Step 3:

[0713] The server applies machine learning algorithms to analyze the received crying data and identify crying patterns. Based on this analysis, it identifies factors that are likely to be causing nighttime crying.

[0714] Step 4:

[0715] The server analyzes the received user voice and facial expression data using an emotion engine to recognize the user's emotional state. For example, if patterns suggesting fatigue or stress are detected, the server evaluates the situation based on that.

[0716] Step 5:

[0717] The server integrates the analysis results and generates the optimal response based on the baby's condition and the user's emotions. This response includes specific actions related to the cause of the baby's crying (e.g., "It's time for milk," "Let's lower the room temperature") and messages that take the user's emotions into consideration (e.g., "Let's relax a little").

[0718] Step 6:

[0719] The device receives a response generated from the server and notifies the user. The notification includes the cause, recommended actions, and mental support.

[0720] Step 7:

[0721] Users check notifications on their devices and take action according to the suggested measures. Simultaneously, they provide feedback on the results and experience, sending their thoughts and suggestions for improvement to the system. This feedback is sent to the server and used as learning data for future analysis and suggestions.

[0722] (Example 2)

[0723] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0724] A baby's crying is often a major source of stress for parents, especially at night, leading to sleep deprivation and emotional distress. Furthermore, identifying the cause of a baby's crying and addressing it appropriately requires not only sound analysis but also consideration of environmental data, the baby's physiological state, and even the parents' emotional state. However, previous systems have not adequately evaluated these factors comprehensively and provided parents with appropriate advice and support.

[0725] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0726] In this invention, the server includes an acoustic detection device for analyzing sound to identify crying patterns and detect emotions, an environmental observation device for acquiring ambient temperature, humidity, and light intensity, and a wearable measuring device for recording physiological indicators. This enables the comprehensive identification of the cause of a baby's crying and the proposal of appropriate countermeasures that take into account the user's emotional state.

[0727] An "acoustic detection device" is a device that captures and analyzes audio signals to identify crying patterns and detect emotions.

[0728] An "environmental monitoring device" is a device used to acquire environmental data such as ambient temperature, humidity, and light intensity.

[0729] A "wearable measuring device" is a type of measuring device that is attached to the body to record physiological indicators of a baby.

[0730] An "information processing device" is a device that integrates information sent from acoustic detection devices, environmental observation devices, and wearable measurement devices to identify specific causes.

[0731] A "notification device" is a device that, based on analysis results obtained from an information processing device, conveys advice to the user that takes into account countermeasures and emotions.

[0732] "Machine learning technology" is a technique used to enable computer systems to learn from data and automatically improve at specific tasks.

[0733] This invention is a system designed to provide a comprehensive solution to the problem of a baby's crying. The system consists of a terminal, a server, and user interaction. Specifically, it aims to reduce the mental and physical burden on parents by analyzing the baby's vocalizations, environmental information, and the user's emotions.

[0734] The device is equipped with an acoustic detection device that captures the baby's cries in real time. Furthermore, it uses an environmental monitoring device to simultaneously collect environmental data such as temperature and humidity around the baby. A wearable measuring device also records the baby's physiological data. All of this data is transmitted to a server.

[0735] The server integrates the received data and uses an information processing device to identify the cause. In particular, machine learning techniques are employed to analyze the patterns of the baby's cries and environmental data to identify the cause of the crying and the baby's condition. The server also implements an algorithm for emotion analysis, evaluating the user's emotional state and, based on the analysis results, generating countermeasures and emotionally conscious advice.

[0736] Notifications are sent via the device. Through the notification device, users receive specific action suggestions based on analysis results and emotional support messages. This allows users to take quick and appropriate action. In addition, user feedback is sent to the server and used to help the system continuously learn and improve.

[0737] For example, when a baby starts crying in the middle of the night, the system sends a notification to the user saying, "The temperature is a little high. We recommend cooling it down. Also, try to relax a little." In this way, the user receives specific instructions on how to soothe the baby, while also taking into consideration the parent's own emotional state.

[0738] Examples of prompts to input into a generative AI model include: "What should I do when my baby cries? Also, please provide a message that is sensitive to the user's feelings."

[0739] In this way, this system provides a multifaceted solution to the problem of babies crying at night.

[0740] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0741] Step 1:

[0742] The device uses an acoustic detection device to capture the baby's cries. The input is ambient sound data. This data is converted into digital data using digital signal processing technology and output. This data is then sent to a server for analysis.

[0743] Step 2:

[0744] The device collects environmental data from the baby's surroundings using an environmental monitoring device. Inputs include environmental information such as temperature, humidity, and light intensity. This information is measured by sensors and output as digital data. Similarly, physiological data from the baby (e.g., heart rate, body temperature) is acquired via a wearable measuring device, output, and transmitted to a server.

[0745] Step 3:

[0746] The server integrates crying data, environmental data, and physiological data received from the terminal and supplies it to the information processing unit. Input data includes voice, environmental, and physiological information. The server utilizes machine learning algorithms to analyze crying patterns, performs data processing and calculations to identify the cause, and generates the results as output.

[0747] Step 4:

[0748] The server uses an emotion algorithm to analyze the user's voice and facial expression data, thereby evaluating the user's emotional state. The input consists of voice and image data, which are analyzed to output the user's emotion score. This emotional information is then incorporated into the assessment of the baby's condition to formulate the optimal course of action.

[0749] Step 5:

[0750] The server formulates countermeasures based on the analyzed data and generates a notification message. This message includes action suggestions that take into account both the baby's condition and the user's emotions. As output, the notification information is formed and sent to the terminal.

[0751] Step 6:

[0752] The device informs the user of notification information received from the server. Specifically, it displays messages on the screen or provides suggestions using a voice assistant. This allows the user to understand and take action on specific countermeasures.

[0753] Step 7:

[0754] Users implement the suggested measures and input feedback on their effectiveness and their own impressions into the terminal. This feedback is sent to the server and used to improve the machine learning model. Based on the user's input, the system makes self-improvements to increase the accuracy of future responses.

[0755] (Application Example 2)

[0756] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0757] To reduce the burden on workers and improve efficiency in the workplace, it is necessary to accurately understand the emotional state and environment of workers and implement appropriate measures. Conventional systems have struggled to quickly and accurately assess the stress levels of individual workers and provide support based on that assessment. Therefore, a new system is needed to improve productivity while reducing the psychological and physiological burden on workers.

[0758] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0759] In this invention, the server includes emotion recognition means for detecting and analyzing the emotional state of a worker, environment acquisition means for acquiring data on the work environment, and input device means for collecting detected voice data. This makes it possible to provide appropriate suggestions and rest instructions in real time according to the worker's situation.

[0760] An "emotion recognition device" is a device that analyzes a worker's psychological state from their facial expressions and voice to identify the type and intensity of their emotions.

[0761] An "environmental data acquisition device" is a device that collects data such as temperature, humidity, and illuminance of the work environment and provides environmental information tailored to the work conditions.

[0762] "Input device means" refers to an input device for detecting audio data during work and collecting it as a digital signal.

[0763] A "data processing system" is a system that integrates collected emotional state data and environmental data and performs analysis to evaluate the burden on workers.

[0764] An "information presentation means" is a display device that provides appropriate suggestions and warnings to workers based on processed data.

[0765] The system that realizes this invention monitors the worker's emotions and environmental conditions in real time and makes suggestions to improve work efficiency based on that information. The specific form of this system is described below.

[0766] The server uses emotion recognition to analyze the emotional state of workers. This is achieved by acquiring facial expression data using a camera and collecting audio data using a microphone. This data is transferred to the server in real time and used to evaluate the emotional state. For the evaluation, machine learning frameworks such as TensorFlow and PyTorch are used as emotion recognition technologies.

[0767] Environmental data acquisition methods include using temperature sensors and light meters to collect data on the work environment. This environmental information influences worker comfort and concentration, and therefore serves as an indicator for creating an appropriate work environment.

[0768] The server integrates this emotional and environmental data using data processing tools to assess the worker's current workload. Based on the assessment, it makes suggestions through information presentation tools. Display devices, such as displays and audio speakers, are used to prompt workers to take appropriate breaks or change their work procedures.

[0769] For example, if the system determines that a worker is accumulating fatigue due to prolonged, focused work, it will notify the worker via voice through a speaker, suggesting that they "take a short break."

[0770] As an example of a prompt message for a generative AI model, you can input the following:

[0771] "Worker's emotional data: 'Feeling tired', Environmental data: 'High temperature', Generate appropriate suggestions."

[0772] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0773] Step 1:

[0774] The device uses a camera and microphone to collect facial and voice data from the worker in real time. This data includes facial feature points, voice tone, and intonation, and is used for initial analysis of emotional state.

[0775] Step 2:

[0776] The terminal acquires data such as temperature, humidity, and illuminance of the work environment through environmental sensors. This environmental data is considered as a factor affecting worker comfort and is transmitted to a database.

[0777] Step 3:

[0778] The server integrates facial expression and voice data transmitted from the terminal and uses machine learning algorithms to analyze the worker's emotional state. This analysis uses an emotion recognition model based on TensorFlow to identify the type and intensity of emotion.

[0779] Step 4:

[0780] The server combines received environmental data with analyzed emotional states to assess the worker's burden. This assessment is performed by a data processing engine, which generates a score to determine whether the worker is comfortable or not.

[0781] Step 5:

[0782] Based on the evaluation results, the server generates suggestions to encourage appropriate actions from the worker. At this stage, a generative AI model is used to suggest breaks or improvements to work procedures as needed. An example of a prompt is: "Worker's emotional data: 'Feeling tired', Environmental data: 'High temperature', Please generate appropriate suggestions."

[0783] Step 6:

[0784] The server sends the generated suggestions back to the terminal and notifies the worker through an information display device. This notification is made via a display or audio speaker, prompting the worker to take action.

[0785] Step 7:

[0786] Users act on the suggestions they receive and send feedback to the server. This feedback is used to improve the accuracy of data analysis and adjust future suggestions to be more appropriate.

[0787] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0788] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0789] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0790] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0791] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0792] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0793] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0794] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0795] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0796] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0797] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0798] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0799] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0800] 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.

[0801] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0802] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0803] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0804] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0805] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0806] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0807] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0808] The following is further disclosed regarding the embodiments described above.

[0809] (Claim 1)

[0810] An acoustic detection means that detects a baby's crying and analyzes the sound pattern,

[0811] An environmental sensor means for acquiring environmental data around a baby,

[0812] A wearable device for collecting physiological data from babies,

[0813] A data analysis means that integrates data transmitted from the aforementioned acoustic detection means, environmental sensor means, and wearable device means to identify the cause,

[0814] A notification means that proposes countermeasures based on the causes identified by the data analysis means,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, characterized in that the data analysis means identifies crying patterns using a machine learning algorithm.

[0818] (Claim 3)

[0819] The system according to claim 1, characterized in that the notification means receives user feedback and contributes to the learning of the data analysis means.

[0820] "Example 1"

[0821] (Claim 1)

[0822] Acoustic acquisition means for acquiring and analyzing acoustic information,

[0823] An environmental acquisition method for collecting surrounding environmental information,

[0824] A wearable acquisition method for collecting physiological information,

[0825] Information analysis means for integrating information transmitted from the aforementioned sound acquisition means, environment acquisition means, and wearable acquisition means to identify the cause,

[0826] A notification means that proposes countermeasures based on the cause identified by the information analysis means,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, characterized in that the information analysis means identifies acoustic patterns using a learning process.

[0830] (Claim 3)

[0831] The system according to claim 1, characterized in that the notification means receives evaluations from users and contributes to the improvement of the information analysis means.

[0832] "Application Example 1"

[0833] (Claim 1)

[0834] An acoustic analysis means that detects a baby's crying and analyzes the sound pattern,

[0835] An environmental measurement method that detects the baby's surroundings,

[0836] A wearable device means for collecting physiological information from a baby,

[0837] An information processing means for integrating and analyzing data transmitted from the acoustic analysis means, environmental measurement means, and wearable device means to identify the cause,

[0838] A notification provision means that presents countermeasures based on the cause identified by the aforementioned information processing means,

[0839] A display means that visualizes countermeasure information through a mobile device,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, characterized in that the information processing means uses machine learning technology to identify voice patterns and provides countermeasures in real time.

[0843] (Claim 3)

[0844] The system according to claim 1, characterized in that the notification provision means receives a response from a user and reflects it in the learning process of the information processing means.

[0845] "Example 2 of combining an emotion engine"

[0846] (Claim 1)

[0847] An acoustic detection device that analyzes sound to identify crying patterns and detect emotions,

[0848] An environmental observation device that acquires ambient temperature, humidity, and light intensity,

[0849] A wearable measuring device for recording physiological indicators,

[0850] An information processing device that integrates information from the aforementioned acoustic detection device, environmental observation device, and wearable measurement device to identify the cause,

[0851] A notification device that provides advice considering countermeasures and emotions based on information from the aforementioned information processing device,

[0852] A system that includes this.

[0853] (Claim 2)

[0854] The system according to claim 1, characterized in that the information processing device uses machine learning technology to identify speech patterns and perform sentiment analysis.

[0855] (Claim 3)

[0856] The system according to claim 1, characterized in that the notification device receives opinions from the user and contributes to the learning of the information processing device.

[0857] "Application example 2 when combining with an emotional engine"

[0858] (Claim 1)

[0859] An emotion recognition means that detects and analyzes the emotional state of a worker,

[0860] A means of acquiring data about the work environment,

[0861] An input device means for collecting detected audio data,

[0862] A data processing means for evaluating workload by integrating data transmitted from the emotion recognition means, environment acquisition means, and input device means,

[0863] Information presentation means that makes a proposal based on the workload evaluated by the data processing means,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, characterized in that the data processing means uses a machine learning algorithm to identify an emotional state.

[0867] (Claim 3)

[0868] The system according to claim 1, characterized in that the information presentation means receives feedback from users and contributes to improving the capabilities of the data processing means. [Explanation of symbols]

[0869] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. An acoustic detection means that detects a baby's crying and analyzes the sound pattern, An environmental sensor means for acquiring environmental data around a baby, A wearable device for collecting physiological data from babies, A data analysis means that integrates data transmitted from the aforementioned acoustic detection means, environmental sensor means, and wearable device means to identify the cause, A notification means that proposes countermeasures based on the causes identified by the data analysis means, A system that includes this.

2. The system according to claim 1, characterized in that the data analysis means identifies crying patterns using a machine learning algorithm.

3. The system according to claim 1, characterized in that the notification means receives user feedback and contributes to the learning of the data analysis means.

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