system

Abstract

We provide the system. [Solution] A means for receiving parenting consultations from parents via a communication terminal and generating responses using a natural language processing model, A means for analyzing video data of children using image processing technology to evaluate their growth and health status, A means of analyzing a child's crying using an acoustic analysis module, identifying the cause of the crying, and generating appropriate advice, A means of presenting support plans tailored to the individual needs of each household based on information from administrative services, 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, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the anxiety and sense of isolation of parents in child-rearing have been increasing, and many parents need consultations and support regarding child-rearing. In response to such a situation, it is required to provide child-rearing support optimized for individual families. However, conventional child-rearing support services have problems in that it is difficult to provide 24-hour support due to time and human resource constraints, and it is impossible to quickly provide optimal support according to individual family situations.

Means for Solving the Problems

[0005] This invention is a system that receives childcare consultations from parents in real time via a communication terminal and provides immediate responses using a natural language processing model. Furthermore, it uses image processing technology to objectively evaluate a child's growth and health, and an acoustic analysis module to identify the cause of crying and propose appropriate responses. In addition, by utilizing information from administrative services to present support plans tailored to each family's needs, it realizes personalized childcare support 24 hours a day.

[0006] A "communication terminal" is an electronic device used to receive childcare consultations from parents and transmit the information to a server.

[0007] A "natural language processing model" is an AI technology that understands human language and generates responses in a natural way.

[0008] "Image processing technology" refers to analytical methods used to evaluate a child's growth and health status from transmitted video data.

[0009] An "acoustic analysis module" is a device that analyzes audio data and identifies specific audio patterns.

[0010] "Administrative services" refer to information and policies regarding childcare support and welfare services provided by government agencies.

[0011] A "support plan" is a plan for optimal childcare support created based on the specific needs of each family. [Brief explanation of the drawing]

[0012] [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]It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It 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 an 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 an emotion engine is combined. <000​​​​​​​​​​​​​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.

[0016] 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.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention provides a system that allows users to receive various forms of support related to childcare using a communication terminal. Users can send questions about childcare, records of their child's growth, and audio data of their child crying through a widely used messaging application.

[0034] The device sends this data received from the user to the server. The server uses a natural language processing model to generate responses to the user's questions and provides advice, including psychological support. The server also utilizes image processing technology to measure growth indicators such as height and weight from the transmitted video data of the child and assesses their health status. This analysis result is then fed back to the user's device.

[0035] Furthermore, the server uses an acoustic analysis module to analyze the transmitted crying audio data. It analyzes the characteristics of the crying, infers its cause, and recommends the most appropriate action for the user. This process provides parents with information to respond appropriately to their baby's crying.

[0036] In addition, because the system is linked to government services, the server references data on local government services based on each household's situation and presents users with appropriate support plans. These plans include information on available facilities and applicable childcare support programs. This allows users to conveniently access the necessary government support.

[0037] For example, if a user sends a message saying, "My baby cries a lot at night," the server will analyze the message using natural language processing and generate advice such as, "Crying at night is common during the growth process. You might want to try establishing a regular sleep schedule or playing relaxing music before your child falls asleep."

[0038] Furthermore, if a user submits an image related to their child's health, the server uses image processing technology to evaluate the child's health and provides feedback such as, "This month's height and weight growth is within the normal range." In this way, the system aims to alleviate parental anxiety and support the healthy growth of children by providing 24-hour childcare support.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device receives messages, images, and audio data related to childcare consultations that users input using messaging platforms such as the LINE application.

[0042] Step 2:

[0043] The terminal converts the received data into the appropriate format and prepares it for transmission to the server. Data from the messaging platform is compressed and formatted according to a specific protocol.

[0044] Step 3:

[0045] The server passes the text message sent from the terminal to the natural language processing unit, which then begins the analysis. This unit uses a GPT-4® level model to understand the context and generate a response.

[0046] Step 4:

[0047] The server sends the generated response back to the terminal. This response includes specific advice, including psychological support for the parent.

[0048] Step 5:

[0049] The server passes image data sent from the terminal to an image processing module for analysis of growth indicators and health status. The module uses machine learning algorithms to estimate growth data such as height and weight.

[0050] Step 6:

[0051] The server generates a report based on the image analysis results and provides feedback to the user. The user can then use this information to understand their child's growth and health status.

[0052] Step 7:

[0053] The server sends the crying audio data provided by the terminal to the acoustic analysis unit. This unit analyzes the audio data and infers the cause from the crying pattern.

[0054] Step 8:

[0055] The server generates advice for the user based on the acoustic analysis results and sends it back to the terminal. The user can then follow this advice to find ways to deal with the child's crying.

[0056] Step 9:

[0057] The server accesses a database of government services to identify appropriate support plans for each family. It organizes information on available childcare support programs and facilities based on the user's region and family circumstances.

[0058] Step 10:

[0059] The server provides support plan information to users through their terminals. This allows users to understand specific ways to effectively utilize local government services.

[0060] (Example 1)

[0061] 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."

[0062] In modern child-rearing, many of the problems parents face require prompt attention regardless of time or place. In particular, anxieties about a child's health and emotional changes are major concerns for parents. Furthermore, accessing government services often involves numerous procedures, which can be burdensome for parents. To comprehensively address these challenges, a system is needed that provides prompt and appropriate child-rearing support.

[0063] 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.

[0064] In this invention, the server includes means for receiving questions about childcare from parents via communication equipment and generating answers using a generative AI algorithm; means for analyzing image data of infants using image analysis technology and evaluating indicators of growth and health; and means for analyzing infants' cries using an acoustic analysis device, identifying the cause of the crying, and providing appropriate advice. This makes it possible for parents to receive the childcare support they need accurately and quickly, regardless of time or place.

[0065] "Communication equipment" refers to devices used by parents to send and receive information related to childcare, and which operate particularly through widely used messaging services.

[0066] A "generative AI algorithm" is a type of artificial intelligence technology that automatically generates accurate answers to questions related to childcare.

[0067] "Image analysis technology" is a technique for analyzing image data of infants to measure indicators of their growth and health.

[0068] An "acoustic analysis device" is a device that analyzes the cries of infants, identifies their characteristics, and infers the cause of the crying.

[0069] "Public service information" refers to data on childcare support programs and facilities provided by the government, which is used to develop support plans based on the individual needs of each family.

[0070] To implement this invention, it is necessary to use communication equipment with internet connectivity, a generation AI algorithm, image analysis technology, and an acoustic analysis device. Specific embodiments are described below.

[0071] Users send questions and information about childcare through communication devices equipped with widely used messaging services. For example, users might type messages such as, "My one-year-old son cries frequently at night. What should I do?" This data may include photos of the infant or audio recordings of the baby crying.

[0072] The terminal forwards data sent by the user to the server. When the server analyzes the received data, it utilizes a generative AI model to understand the user's question in natural language and generate an appropriate answer. In this process, a generative AI algorithm is used to quickly generate advice.

[0073] The server uses image analysis technology to evaluate the growth and health of infants based on the image data. For example, it can measure growth indicators such as weight and height, and use this information to generate reports similar to those from health checkups.

[0074] Similarly, acoustic data is analyzed by an acoustic analysis device to determine the cause of the crying by analyzing the frequency and tone of the cries. For example, information such as "the crying is high-pitched, so the dog may be hungry" is provided.

[0075] Furthermore, the server can refer to a public services database and present support plans tailored to each family's needs. This includes information on available childcare support programs and facilities. Based on this information, the server provides users with the most suitable support guidance. This embodiment allows for the smooth resolution of various anxieties and questions related to childcare.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] Users input and send questions and information related to childcare using communication devices. Specifically, users input text questions, photos of infants, and audio data of crying babies as messages. The input data is provided through a messaging service.

[0079] Step 2:

[0080] The terminal transfers the data received from the user to the server. Here, the terminal converts the input data into an appropriate format and sends it to the server via the internet. The output is the data that has been successfully transferred to the server.

[0081] Step 3:

[0082] The server analyzes the received data. First, it uses a generative AI model to analyze text data and generate appropriate responses to user questions. The input is the user's question data, and the output is the generated answer text. The server runs the AI ​​model to provide content that matches the user's request.

[0083] Step 4:

[0084] The server processes image data using image analysis technology to evaluate infant growth indicators. Specifically, it extracts facial and body features from the image data and compares them with existing growth data. The input is image data, and the output is the growth evaluation result. Based on this, the server provides feedback on the child's health status.

[0085] Step 5:

[0086] The server uses an acoustic analysis device to analyze the audio data of the crying sound. It analyzes the frequency and duration of the crying sound and infers the cause of the crying based on its characteristics. The input is the audio data, and the output is the inferred cause. For example, information such as "the cat may be hungry" might be generated.

[0087] Step 6:

[0088] The server retrieves support plans tailored to each household from a public services database. It obtains administrative service information that matches the user's needs and proposes appropriate support. The input is the user's registration information, and the output is the recommended support plan. The server then provides this information to the user.

[0089] (Application Example 1)

[0090] 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."

[0091] Parents who need information about childcare are required to receive appropriate advice and information on government services, as well as to have their children's health assessed and personalized recommendations for childcare products, thereby reducing anxiety and burden in childcare. Furthermore, it is necessary to overcome the time constraints faced by parents by providing support that is accessible 24 hours a day.

[0092] 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.

[0093] In this invention, the server includes means for receiving parental childcare consultations via a communication device and generating replies using natural language processing technology; means for analyzing image data of children using image analysis technology and evaluating their growth and health status; and means for analyzing children's cries using acoustic analysis technology, estimating the cause of the cries, and generating appropriate advice. This enables the provision of personalized childcare support and allows parents to receive information to further select more appropriate childcare products.

[0094] A "communication device" is a device equipped with the function of receiving consultations and data from parents regarding childcare, and has a means of exchanging information via a widely used messaging application.

[0095] "Natural language processing technology" is a technology that allows machines to understand and process human language, making it possible to automatically generate appropriate responses to childcare consultations.

[0096] "Image analysis technology" is a technique that analyzes image data to extract specific information, and is used as a means of evaluating a child's growth and health.

[0097] "Acoustic analysis technology" is a technique that analyzes audio data and extracts its characteristics. It can analyze a child's crying to estimate the cause and generate appropriate advice.

[0098] A "support plan" refers to a plan that presents support tailored to the individual needs of each family, based on information about administrative services, and includes suggestions for childcare products.

[0099] The "childcare product e-commerce function" refers to a function that provides information on childcare-related products and enables parents to purchase them.

[0100] "Personalization" refers to the process of optimizing a user's parenting experience by providing information and suggestions tailored to the individual user's characteristics and needs.

[0101] In this invention, a system that provides support related to childcare is mainly built around a server and a communication terminal. Users send questions about childcare via a messaging application using a communication terminal such as a smartphone. This communication terminal is responsible for receiving text data from users using natural language processing technology and sending it to the server.

[0102] The server processes the received text data using a natural language processing model (e.g., built using TENSORFLOW® or PyTorch) to generate personalized replies for parents. These replies include advice and information on childcare. Additionally, image analysis techniques are used to extract growth indicators from the child's image data submitted by the user. This is done using image processing libraries (e.g., OpenCV) to calculate indicators such as the child's height and weight and assess their health.

[0103] Furthermore, acoustic analysis technology is used to analyze crying data transmitted from the user. This identifies the characteristics of the crying and estimates the cause of the crying. Based on this information, the server generates appropriate advice and sends it back to the communication terminal.

[0104] In addition, the server is linked to a database of government services and has the function to present support plans tailored to the needs of each family. This includes suggestions for childcare products. This function allows parents to easily access government services and purchase necessary childcare products online.

[0105] For example, if a user sends the prompt "My child is crying a lot at night. Can you give me some advice?", the natural language processing model analyzes this and generates advice such as, "Crying at night is a part of growth, but try to create a relaxing environment for your child. These baby products may be helpful." This allows the user to reduce their anxiety about parenting.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user uses a communication terminal to send questions and requests for advice regarding childcare through a messaging application. The input is the user's text message, and the recipient is the server. The message contains specific details related to the childcare consultation. In this step, the communication terminal prepares to receive the text data and forward it to the server.

[0109] Step 2:

[0110] The server acquires text data received from the communication terminal and analyzes it using natural language processing technology. The input is the text message received in step 1, and the output is the understood consultation content. The server utilizes a generative AI model to identify the user's intentions and emotions and prepare to generate optimal advice. This analysis process is performed by the generative AI model.

[0111] Step 3:

[0112] The server processes image data of children submitted by users using image analysis techniques. The input is image data submitted by the user, and the output is evaluation data related to growth indicators. The server analyzes the image data using an image processing library (e.g., OpenCV) and extracts information necessary to assess the child's health status (e.g., height, weight).

[0113] Step 4:

[0114] The server analyzes user-submitted crying data using acoustic analysis technology. The input is audio data provided by the user, and the output is information about the cause of the crying. The server processes the audio data with an acoustic analysis module and uses a feature extraction algorithm to identify crying patterns. This allows the server to estimate the cause of the crying.

[0115] Step 5:

[0116] The server generates and sends advice to the user based on the analysis results. The input is the analysis results from steps 2, 3, and 4, and the output is personalized advice and suggestions for childcare products for the user. The generated advice is sent to the user's communication terminal.

[0117] Step 6:

[0118] The user reviews the advice and suggestions received from the server via a communication terminal. The input is the advice and suggestions received from the server, and the output is the user's understanding and actions. In this step, the user can decide on the next steps in childcare based on the support information received.

[0119] 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.

[0120] This invention provides optimal support to users through a system that combines a communication terminal with an emotion engine, allowing users to seek childcare advice. Users can send daily childcare questions and concerns as text or voice data via a messaging application.

[0121] The terminal forwards data received from the user to the server. The server uses a natural language processing model to analyze the user's inquiry from the text data and generate appropriate advice. This includes understanding the parent's emotions and providing psychological support tailored to their state.

[0122] Furthermore, image processing technology is used to analyze the photos of children submitted by users to check their growth, development, and health status. Based on this data, specific growth indicators and health recommendations are generated and provided as feedback to the user.

[0123] The server uses an acoustic analysis module to analyze the crying audio data provided by the user. Based on the analysis results, it identifies the type and cause of the crying and provides parents with coping strategies and advice.

[0124] Furthermore, the emotion engine recognizes the user's emotional state from text and voice. This engine identifies emotions such as stress, anxiety, and joy from the user's speech and tone, and optimizes the communication method based on the results.

[0125] For example, if a user sends a message saying, "I'm worried because my baby has been crying a lot at night lately," the server uses natural language processing to analyze the message and responds with advice such as, "Night crying is common, but creating a safe and secure environment can also help." The emotion engine determines that this message is accompanied by anxiety and provides additional psychological support messages such as, "I understand your anxiety. Please feel free to talk to me, and I'm here to support you anytime."

[0126] Thus, the system aims to provide 24-hour childcare support and support the healthy development of children while being attentive to the parents' emotions.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] Users use messaging applications to type and send text or voice inquiries about childcare on their devices.

[0130] Step 2:

[0131] The terminal sends received text and audio data to the server. The data is formatted appropriately and transferred quickly over the network.

[0132] Step 3:

[0133] The server passes the transmitted text data to a natural language processing unit. This unit uses GPT-4 level AI to analyze the context of the text and generate an appropriate response.

[0134] Step 4:

[0135] The server passes the generated text response to the sentiment engine. The sentiment engine estimates the user's emotional state and adjusts the emotional tone of the response. For example, it might modify the message to use a reassuring tone.

[0136] Step 5:

[0137] Finally, the server sends a refined response to the terminal and displays it to the user. The user reviews the generated feedback and proceeds to the next step if necessary.

[0138] Step 6:

[0139] If the user provides image data, the device sends that data to the server.

[0140] Step 7:

[0141] The server inputs image data into an image processing module to analyze the child's growth indicators and health status. It then generates a report to send back to the user.

[0142] Step 8:

[0143] When a user sends audio data of a crying baby, the server processes the audio data using an acoustic analysis module. It analyzes the cause of the crying and generates advice for the user based on the results.

[0144] Step 9:

[0145] The server references a database of government services to configure the most suitable support plan for the user. This includes region-specific support programs and services.

[0146] Step 10:

[0147] The server notifies the user of the created support plan via the terminal and supports the user in making appropriate decisions based on the available information.

[0148] (Example 2)

[0149] 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 will be referred to as the "terminal."

[0150] In modern society, worries and anxieties about childcare are a significant burden for parents. In particular, the sheer volume of information available on childcare makes it difficult to determine which information is reliable and useful. Furthermore, receiving real-time expert advice and support is not easy. Therefore, there is a need for a system that allows parents to receive real-time and effective childcare support.

[0151] 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.

[0152] In this invention, the server includes means for receiving parental childcare consultations via a communication device and generating responses using natural language processing technology; means for analyzing video data of children using video analysis technology and evaluating growth and health indicators; and means for analyzing children's voice data using acoustic analysis functionality, identifying the cause of the voice, and generating appropriate advice. This enables parents to receive professional childcare support 24 hours a day.

[0153] A "communication device" is an electronic device used to receive data related to childcare consultations from parents and transfer it to a server.

[0154] "Natural language processing technology" is an information processing technology that analyzes received text data to understand the parents' intentions and the content of their consultations.

[0155] "Video analysis technology" is a technology that analyzes video data of children to evaluate indicators related to their growth and health.

[0156] The "acoustic analysis function" is a feature that analyzes children's voice data, understands the voice patterns, and identifies the cause.

[0157] The "emotion recognition function" is a function that evaluates the emotional state of parents from text and audio data and provides psychological support.

[0158] "Advice" refers to guidelines and suggestions regarding childcare provided to parents based on the analysis results.

[0159] "24-hour operation" refers to a state where the system operates continuously throughout the day and is available to respond to parent inquiries at any time.

[0160] As an embodiment of this invention, a childcare support system will be specifically described.

[0161] Users use communication devices to input questions and concerns about childcare. This input is done in text or voice format through popular messaging applications. The data sent from the communication device is transferred to the server via the internet.

[0162] The server performs various analyses based on the received data and generates appropriate support information. Specifically, it uses natural language processing technology to analyze text data and implements a natural language processing model to understand the content of the user's inquiry. The model used includes an advanced generative AI model that tokenizes raw text data and understands the context.

[0163] Next, the server uses video analysis technology to analyze the video data of the child provided by the user. This includes utilizing image processing libraries to evaluate growth, development, and health indicators in detail. For example, a face recognition algorithm is used to detect the child's facial features and estimate their growth rate and health status.

[0164] Furthermore, the server uses acoustic analysis capabilities to analyze audio data, including children's crying. In this process, an acoustic analysis library is used to extract and classify audio features, making it possible to clearly identify the cause of the crying.

[0165] Finally, analysis using emotion recognition is also performed. The server identifies the user's emotional state from text or voice and provides psychological support for emotions such as anxiety and stress. This support is achieved by generating messages that are adapted to the emotions the user is feeling.

[0166] For example, if a user sends a message saying, "I'm worried because my baby has been crying a lot at night lately," the server will use this information to provide advice such as, "Night crying is common, but creating a safe and secure environment can also help." Furthermore, if the system detects that the user is experiencing anxiety, an additional psychological support message will be sent, such as, "I understand your anxiety. Please feel free to talk to me; I'm here to support you anytime."

[0167] An example of a prompt would be, "Please tell me how to respond to inquiries about concerns regarding nighttime crying in infants." This would enable the continuous provision of 24-hour childcare support to parents.

[0168] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0169] Step 1:

[0170] Users use a communication device to input inquiries about childcare. The input data is in text or voice format and is sent through popular messaging applications. Specifically, the user uses the in-app chat box to type a message such as, "I'm worried because my baby has been crying a lot at night lately," and presses the send button. Based on this input data, the communication device formats the information as a digital message and transfers it to the server.

[0171] Step 2:

[0172] The terminal transfers data received from the user to the server. Specifically, messages are sent to the server via the internet as encrypted data packets. Input is text or voice data from the user, and output becomes data for analysis usable on the server. This process ensures the secure transfer of data.

[0173] Step 3:

[0174] The server analyzes the received data using natural language processing techniques. Specifically, it first tokenizes the received text data, then uses a generative AI model to analyze the context and understand the intent of the inquiry. The input is the transmitted text data, and the output is structured data interpreted from the user's intent. This analysis clarifies the problem the user is facing.

[0175] Step 4:

[0176] The server uses image analysis technology to analyze video data of children received from users. Specifically, the server uses an image processing library to extract facial recognition and physical features from the video data and estimate growth, development, and health status. The input is video data provided by the user, and the output is indicator information regarding growth, development, and health status. This makes it possible to evaluate a child's health and growth in real time.

[0177] Step 5:

[0178] The server uses acoustic analysis capabilities to analyze audio data, such as children's cries. Using an acoustic analysis library, it extracts frequency characteristics from the audio data and analyzes the resulting audio patterns. The input is audio data, and the output is classification information indicating the cause of the crying. Based on this output, appropriate countermeasures and advice are generated.

[0179] Step 6:

[0180] The server uses emotion recognition to understand the user's emotional state. Specifically, it combines natural language processing and speech analysis to evaluate emotions from the user's text or voice tone. The input is the user's text or voice data, and the output is information indicating the user's emotional state. Based on this information, support messages are customized, enabling more helpful responses.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0183] In modern parenting, parents often face numerous anxieties, questions, and emotional burdens. Concerns about newborn crying and developmental progress are particularly serious, and there is a strong need for appropriate advice and information. However, parents often lack access to 24-hour support. Furthermore, personalized support tailored to the emotions and needs of individual families is insufficient. A system is needed to address these issues.

[0184] 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.

[0185] In this invention, the server includes means for receiving parenting consultations from parents via communication equipment and generating responses using natural language processing technology; means for analyzing the child's visual data using image analysis technology and evaluating their growth and health status; and means for analyzing the child's voice data using an acoustic analysis system, identifying the cause of the voice, and generating appropriate support advice. As a result, parents can not only receive personalized parenting support 24 hours a day, but also receive emotional support through messages that recognize their emotional state and provide a sense of security.

[0186] "Communication equipment" refers to electronic devices used to receive, transmit, and process digital data.

[0187] "Guardian" refers to an adult who has the responsibility to care for and educate a child.

[0188] "Childcare consultation" refers to the act of asking others questions or expressing concerns about childcare.

[0189] "Natural language processing technology" is the technology that allows computers to understand, interpret, and generate human language.

[0190] "Image analysis technology" is a technique that processes digital images and extracts meaningful information.

[0191] "Visual data" refers to information that is represented visually, such as images and videos.

[0192] "Growth and health status" refers to the state of a child's physical development and health.

[0193] An "acoustic analysis system" is a set of technologies that analyze audio data and extract its features.

[0194] "Audio data" refers to information recorded in digital format as sound pressure waves.

[0195] "Supportive advice" refers to information or suggestions or guidance provided to address a specific problem.

[0196] An "emotional analysis engine" is a technology used to identify emotional states from text and audio.

[0197] A "reassuring message" is a message intended to make the recipient feel safe and at ease.

[0198] "Administrative agencies" refer to public organizations such as the government and local authorities.

[0199] A "support plan" is a set of action guidelines or strategies formulated to achieve a specific objective.

[0200] The system for implementing this invention is initiated when a parent uses their personal communication device (e.g., a smartphone) to send a consultation or message regarding childcare. The device sends the message, image data, and audio data entered by the parent to the server. The server then performs the following processing:

[0201] The server first uses natural language processing technology on the text data received from the communication device. Specifically, it uses a generative AI model to analyze the content of the parent's inquiry. In this process, it uses an emotion analysis engine to recognize the parent's emotional state and generate a message that provides reassurance. An example of such a prompt would be, "Generate a reassuring response to the parent's message saying, 'I'm worried because my baby cries a lot at night.'"

[0202] Next, the server utilizes image analysis technology. This technology uses Google® Cloud Vision API and other tools to analyze the child's visual data transmitted from the communication device. Based on this analysis, the server assesses the child's growth and health status and provides feedback to the parents.

[0203] Furthermore, the server uses an acoustic analysis system to analyze the child's crying from the audio data. Based on the analysis, it identifies the cause of the crying and generates appropriate support advice.

[0204] Ultimately, the server uses information from government agencies to present a support plan tailored to each family's needs. This support plan enables more specific and effective childcare support. Through this system, parents will have access to 24-hour childcare consultation and support that meets their individual needs.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] Users input text messages, images, and audio data related to childcare using their personal communication devices. This input includes questions and concerns related to childcare consultations. The user's device then sends this data to the server.

[0208] Step 2:

[0209] The server analyzes text data received from the terminal using natural language processing technology. Input: Text data. The processing uses a generative AI model to analyze the user's question and generate an appropriate response based on that analysis. Output: Generated response message. This response includes specific advice addressing the user's concerns and questions.

[0210] Step 3:

[0211] The server uses an emotion analysis engine to analyze the user's emotions from the received text. Input: User's text data. Processing involves evaluating the emotional state and generating additional messages to provide reassurance. Output: A message providing supplementary psychological support. This message is returned taking the user's emotions into consideration.

[0212] Step 4:

[0213] The server analyzes image data of children sent from the device using image analysis technologies such as the Google Cloud Vision API. Input: Image data. During processing, growth indicators and health status are automatically evaluated from the images, and advice is generated based on the results. Output: Growth and health evaluation results and recommendations.

[0214] Step 5:

[0215] The server analyzes audio data using an acoustic analysis system. Input: Audio data. Processing involves analyzing audio patterns and performing data calculations based on an algorithm that identifies the cause of the crying. Output: The cause of the crying and related advice.

[0216] Step 6:

[0217] The server develops support plans tailored to the specific needs of each household, based on information obtained from government agencies. Input: Information from government agencies and data on household needs. Processing: Combining this information to create a customized support plan. Output: Support plan for each household.

[0218] This entire process allows users to receive personalized support to address their concerns and anxieties about childcare.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Second Embodiment]

[0223] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0224] 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.

[0225] 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).

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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".

[0235] This invention provides a system that allows users to receive various forms of support related to childcare using a communication terminal. Users can send questions about childcare, records of their child's growth, and audio data of their child crying through a widely used messaging application.

[0236] The device sends this data received from the user to the server. The server uses a natural language processing model to generate responses to the user's questions and provides advice, including psychological support. The server also utilizes image processing technology to measure growth indicators such as height and weight from the transmitted video data of the child and assesses their health status. This analysis result is then fed back to the user's device.

[0237] Furthermore, the server uses an acoustic analysis module to analyze the transmitted crying audio data. It analyzes the characteristics of the crying, infers its cause, and recommends the most appropriate action for the user. This process provides parents with information to respond appropriately to their baby's crying.

[0238] In addition, because the system is linked to government services, the server references data on local government services based on each household's situation and presents users with appropriate support plans. These plans include information on available facilities and applicable childcare support programs. This allows users to conveniently access the necessary government support.

[0239] For example, if a user sends a message saying, "My baby cries a lot at night," the server will analyze the message using natural language processing and generate advice such as, "Crying at night is common during the growth process. You might want to try establishing a regular sleep schedule or playing relaxing music before your child falls asleep."

[0240] Furthermore, if a user submits an image related to their child's health, the server uses image processing technology to evaluate the child's health and provides feedback such as, "This month's height and weight growth is within the normal range." In this way, the system aims to alleviate parental anxiety and support the healthy growth of children by providing 24-hour childcare support.

[0241] The following describes the processing flow.

[0242] Step 1:

[0243] The device receives messages, images, and audio data related to childcare consultations that users input using messaging platforms such as the LINE application.

[0244] Step 2:

[0245] The terminal converts the received data into the appropriate format and prepares it for transmission to the server. Data from the messaging platform is compressed and formatted according to a specific protocol.

[0246] Step 3:

[0247] The server passes the text message sent from the terminal to the natural language processing unit, which then begins the analysis. This unit uses a GPT-4 level model to understand the context and generate a response.

[0248] Step 4:

[0249] The server sends the generated response back to the terminal. This response includes specific advice, including psychological support for the parent.

[0250] Step 5:

[0251] The server passes image data sent from the terminal to an image processing module for analysis of growth indicators and health status. The module uses machine learning algorithms to estimate growth data such as height and weight.

[0252] Step 6:

[0253] The server generates a report based on the image analysis results and provides feedback to the user. The user can then use this information to understand their child's growth and health status.

[0254] Step 7:

[0255] The server sends the crying audio data provided by the terminal to the acoustic analysis unit. This unit analyzes the audio data and infers the cause from the crying pattern.

[0256] Step 8:

[0257] The server generates advice for the user based on the acoustic analysis results and sends it back to the terminal. The user can then follow this advice to find ways to deal with the child's crying.

[0258] Step 9:

[0259] The server accesses a database of government services to identify appropriate support plans for each family. It organizes information on available childcare support programs and facilities based on the user's region and family circumstances.

[0260] Step 10:

[0261] The server provides support plan information to users through their terminals. This allows users to understand specific ways to effectively utilize local government services.

[0262] (Example 1)

[0263] 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."

[0264] In modern child-rearing, many of the problems parents face require prompt attention regardless of time or place. In particular, anxieties about a child's health and emotional changes are major concerns for parents. Furthermore, accessing government services often involves numerous procedures, which can be burdensome for parents. To comprehensively address these challenges, a system is needed that provides prompt and appropriate child-rearing support.

[0265] 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.

[0266] In this invention, the server includes means for receiving questions about childcare from parents via communication equipment and generating answers using a generative AI algorithm; means for analyzing image data of infants using image analysis technology and evaluating indicators of growth and health; and means for analyzing infants' cries using an acoustic analysis device, identifying the cause of the crying, and providing appropriate advice. This makes it possible for parents to receive the childcare support they need accurately and quickly, regardless of time or place.

[0267] "Communication equipment" refers to devices used by parents to send and receive information related to childcare, and which operate particularly through widely used messaging services.

[0268] A "generative AI algorithm" is a type of artificial intelligence technology that automatically generates accurate answers to questions related to childcare.

[0269] "Image analysis technology" is a technique for analyzing image data of infants to measure indicators of their growth and health.

[0270] An "acoustic analysis device" is a device that analyzes the cries of infants, identifies their characteristics, and infers the cause of the crying.

[0271] "Public service information" refers to data on childcare support programs and facilities provided by the government, which is used to develop support plans based on the individual needs of each family.

[0272] To implement this invention, it is necessary to use communication equipment with internet connectivity, a generation AI algorithm, image analysis technology, and an acoustic analysis device. Specific embodiments are described below.

[0273] Users send questions and information about childcare through communication devices equipped with widely used messaging services. For example, users might type messages such as, "My one-year-old son cries frequently at night. What should I do?" This data may include photos of the infant or audio recordings of the baby crying.

[0274] The terminal forwards data sent by the user to the server. When the server analyzes the received data, it utilizes a generative AI model to understand the user's question in natural language and generate an appropriate answer. In this process, a generative AI algorithm is used to quickly generate advice.

[0275] The server uses image analysis technology to evaluate the growth and health of infants based on the image data. For example, it can measure growth indicators such as weight and height, and use this information to generate reports similar to those from health checkups.

[0276] Similarly, acoustic data is analyzed by an acoustic analysis device to determine the cause of the crying by analyzing the frequency and tone of the cries. For example, information such as "the crying is high-pitched, so the dog may be hungry" is provided.

[0277] Furthermore, the server can refer to a public services database and present support plans tailored to each family's needs. This includes information on available childcare support programs and facilities. Based on this information, the server provides users with the most suitable support guidance. This embodiment allows for the smooth resolution of various anxieties and questions related to childcare.

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The user inputs and transmits questions and information regarding child-rearing using a communication device. Specifically, the user inputs the text of the question, a photo of the infant, and voice data of the crying as a message. The input data is provided through a messaging service.

[0281] Step 2:

[0282] The terminal transfers the data received from the user to the server. Here, the terminal converts the input data into an appropriate format and transmits it to the server via the Internet. The output is the data successfully transferred to the server.

[0283] Step 3:

[0284] The server analyzes the data it receives. First, it uses a generative AI model to analyze the text data and generate an appropriate response to the user's question. The input is the question data from the user, and the output is the generated answer text. The server operates the AI model to provide content that meets the user's requirements.

[0285] Step 4:

[0286] The server processes the image data with image analysis technology to evaluate the growth indicators of the infant. As a specific operation, it extracts features of the face and body from the image data and compares them with existing growth data. The input is the image data, and the output is the evaluation result of the growth. The server provides feedback on the health status based on this.

[0287] Step 5:

[0288] The server analyzes the voice data of the crying using an acoustic analysis device. It analyzes the frequency and length of the crying sound, etc., and infers the cause of the crying based on its characteristics. The input is the voice data, and the output is the inferred result of the cause. For example, information such as "There may be a possibility of hunger" is generated.

[0289] Step 6:

[0290] The server retrieves support plans tailored to each household from a public services database. It obtains administrative service information that matches the user's needs and proposes appropriate support. The input is the user's registration information, and the output is the recommended support plan. The server then provides this information to the user.

[0291] (Application Example 1)

[0292] 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 glasses 214 will be referred to as the "terminal."

[0293] Parents who need information about childcare are required to receive appropriate advice and information on government services, as well as to have their children's health assessed and personalized recommendations for childcare products, thereby reducing anxiety and burden in childcare. Furthermore, it is necessary to overcome the time constraints faced by parents by providing support that is accessible 24 hours a day.

[0294] 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.

[0295] In this invention, the server includes means for receiving parental childcare consultations via a communication device and generating replies using natural language processing technology; means for analyzing image data of children using image analysis technology and evaluating their growth and health status; and means for analyzing children's cries using acoustic analysis technology, estimating the cause of the cries, and generating appropriate advice. This enables the provision of personalized childcare support and allows parents to receive information to further select more appropriate childcare products.

[0296] A "communication device" is a device equipped with the function of receiving consultations and data from parents regarding childcare, and has a means of exchanging information via a widely used messaging application.

[0297] "Natural language processing technology" is a technology that allows machines to understand and process human language, making it possible to automatically generate appropriate responses to childcare consultations.

[0298] "Image analysis technology" is a technique that analyzes image data to extract specific information, and is used as a means of evaluating a child's growth and health.

[0299] "Acoustic analysis technology" is a technique that analyzes audio data and extracts its characteristics. It can analyze a child's crying to estimate the cause and generate appropriate advice.

[0300] A "support plan" refers to a plan that presents support tailored to the individual needs of each family, based on information about administrative services, and includes suggestions for childcare products.

[0301] The "childcare product e-commerce function" refers to a function that provides information on childcare-related products and enables parents to purchase them.

[0302] "Personalization" refers to the process of optimizing a user's parenting experience by providing information and suggestions tailored to the individual user's characteristics and needs.

[0303] In this invention, a system that provides support related to childcare is mainly built around a server and a communication terminal. Users send questions about childcare via a messaging application using a communication terminal such as a smartphone. This communication terminal is responsible for receiving text data from users using natural language processing technology and sending it to the server.

[0304] The server applies the received text data to a natural language processing model (constructed using, for example, TensorFlow or PyTorch) to generate a personalized reply to the parent. The reply includes advice and information related to childcare. Also, using image analysis technology, growth indicators are extracted from the image data of the child sent by the user. This is done using an image processing library (such as OpenCV, etc.) to calculate indicators such as the child's height and weight and evaluate the health status.

[0305] Furthermore, using acoustic analysis technology, the crying voice data sent from the user is analyzed. Thereby, the characteristics of the crying voice are identified and the cause of the crying voice is estimated. Based on this information, the server generates appropriate advice and replies to the communication terminal.

[0306] In addition, the server is also equipped with a function to cooperate with the database of administrative services and present a support plan according to the needs of each family. This includes suggestions for childcare supplies. With this function, parents can easily access administrative services and purchase the required childcare supplies online.

[0307] As a specific example, when the user sends a prompt sentence such as "My child's night crying continues. Can I get some advice?", the natural language processing model analyzes this and generates advice such as "Night crying is part of growth, but try to create a relaxing environment. These childcare supplies may be helpful". Thereby, it is possible for the user to reduce the anxiety of childcare.

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1: [[ID=二十一]]

[0310] [[ID=二十三]] The user uses a communication terminal to send questions and requests for advice regarding childcare through a messaging application. The input is the user's text message, and the recipient is the server. The message contains specific details related to the childcare consultation. In this step, the communication terminal prepares to receive the text data and forward it to the server.

[0311] Step 2:

[0312] The server acquires text data received from the communication terminal and analyzes it using natural language processing technology. The input is the text message received in step 1, and the output is the understood consultation content. The server utilizes a generative AI model to identify the user's intentions and emotions and prepare to generate optimal advice. This analysis process is performed by the generative AI model.

[0313] Step 3:

[0314] The server processes image data of children submitted by users using image analysis techniques. The input is image data submitted by the user, and the output is evaluation data related to growth indicators. The server analyzes the image data using an image processing library (e.g., OpenCV) and extracts information necessary to assess the child's health status (e.g., height, weight).

[0315] Step 4:

[0316] The server analyzes user-submitted crying data using acoustic analysis technology. The input is audio data provided by the user, and the output is information about the cause of the crying. The server processes the audio data with an acoustic analysis module and uses a feature extraction algorithm to identify crying patterns. This allows the server to estimate the cause of the crying.

[0317] Step 5:

[0318] The server generates and sends advice to the user based on the analysis results. The input is the analysis results from steps 2, 3, and 4, and the output is personalized advice and suggestions for childcare products for the user. The generated advice is sent to the user's communication terminal.

[0319] Step 6:

[0320] The user reviews the advice and suggestions received from the server via a communication terminal. The input is the advice and suggestions received from the server, and the output is the user's understanding and actions. In this step, the user can decide on the next steps in childcare based on the support information received.

[0321] 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.

[0322] This invention provides optimal support to users through a system that combines a communication terminal with an emotion engine, allowing users to seek childcare advice. Users can send daily childcare questions and concerns as text or voice data via a messaging application.

[0323] The terminal forwards data received from the user to the server. The server uses a natural language processing model to analyze the user's inquiry from the text data and generate appropriate advice. This includes understanding the parent's emotions and providing psychological support tailored to their state.

[0324] Furthermore, image processing technology is used to analyze the photos of children submitted by users to check their growth, development, and health status. Based on this data, specific growth indicators and health recommendations are generated and provided as feedback to the user.

[0325] The server uses an acoustic analysis module to analyze the crying audio data provided by the user. Based on the analysis results, it identifies the type and cause of the crying and provides parents with coping strategies and advice.

[0326] Furthermore, the emotion engine recognizes the user's emotional state from text and voice. This engine identifies emotions such as stress, anxiety, and joy from the user's speech and tone, and optimizes the communication method based on the results.

[0327] For example, if a user sends a message saying, "I'm worried because my baby has been crying a lot at night lately," the server uses natural language processing to analyze the message and responds with advice such as, "Night crying is common, but creating a safe and secure environment can also help." The emotion engine determines that this message is accompanied by anxiety and provides additional psychological support messages such as, "I understand your anxiety. Please feel free to talk to me, and I'm here to support you anytime."

[0328] Thus, the system aims to provide 24-hour childcare support and support the healthy development of children while being attentive to the parents' emotions.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] Users use messaging applications to type and send text or voice inquiries about childcare on their devices.

[0332] Step 2:

[0333] The terminal sends received text and audio data to the server. The data is formatted appropriately and transferred quickly over the network.

[0334] Step 3:

[0335] The server passes the transmitted text data to a natural language processing unit. This unit uses GPT-4 level AI to analyze the context of the text and generate an appropriate response.

[0336] Step 4:

[0337] The server passes the generated text response to the sentiment engine. The sentiment engine estimates the user's emotional state and adjusts the emotional tone of the response. For example, it might modify the message to use a reassuring tone.

[0338] Step 5:

[0339] Finally, the server sends a refined response to the terminal and displays it to the user. The user reviews the generated feedback and proceeds to the next step if necessary.

[0340] Step 6:

[0341] If the user provides image data, the device sends that data to the server.

[0342] Step 7:

[0343] The server inputs image data into an image processing module to analyze the child's growth indicators and health status. It then generates a report to send back to the user.

[0344] Step 8:

[0345] When a user sends audio data of a crying baby, the server processes the audio data using an acoustic analysis module. It analyzes the cause of the crying and generates advice for the user based on the results.

[0346] Step 9:

[0347] The server references a database of government services to configure the most suitable support plan for the user. This includes region-specific support programs and services.

[0348] Step 10:

[0349] The server notifies the user of the created support plan via the terminal and supports the user in making appropriate decisions based on the available information.

[0350] (Example 2)

[0351] 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".

[0352] In modern society, worries and anxieties about childcare are a significant burden for parents. In particular, the sheer volume of information available on childcare makes it difficult to determine which information is reliable and useful. Furthermore, receiving real-time expert advice and support is not easy. Therefore, there is a need for a system that allows parents to receive real-time and effective childcare support.

[0353] 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.

[0354] In this invention, the server includes means for receiving parental childcare consultations via a communication device and generating responses using natural language processing technology; means for analyzing video data of children using video analysis technology and evaluating growth and health indicators; and means for analyzing children's voice data using acoustic analysis functionality, identifying the cause of the voice, and generating appropriate advice. This enables parents to receive professional childcare support 24 hours a day.

[0355] A "communication device" is an electronic device used to receive data related to childcare consultations from parents and transfer it to a server.

[0356] "Natural language processing technology" is an information processing technology that analyzes received text data to understand the parents' intentions and the content of their consultations.

[0357] "Video analysis technology" is a technology that analyzes video data of children to evaluate indicators related to their growth and health.

[0358] The "acoustic analysis function" is a feature that analyzes children's voice data, understands the voice patterns, and identifies the cause.

[0359] The "emotion recognition function" is a function that evaluates the emotional state of parents from text and audio data and provides psychological support.

[0360] "Advice" refers to guidelines and suggestions regarding childcare provided to parents based on the analysis results.

[0361] "24-hour operation" refers to a state where the system operates continuously throughout the day and is available to respond to parent inquiries at any time.

[0362] As an embodiment of this invention, a childcare support system will be specifically described.

[0363] Users use communication devices to input questions and concerns about childcare. This input is done in text or voice format through popular messaging applications. The data sent from the communication device is transferred to the server via the internet.

[0364] The server performs various analyses based on the received data and generates appropriate support information. Specifically, it uses natural language processing technology to analyze text data and implements a natural language processing model to understand the content of the user's inquiry. The model used includes an advanced generative AI model that tokenizes raw text data and understands the context.

[0365] Next, the server uses video analysis technology to analyze the video data of the child provided by the user. This includes utilizing image processing libraries to evaluate growth, development, and health indicators in detail. For example, a face recognition algorithm is used to detect the child's facial features and estimate their growth rate and health status.

[0366] Furthermore, the server uses acoustic analysis capabilities to analyze audio data, including children's crying. In this process, an acoustic analysis library is used to extract and classify audio features, making it possible to clearly identify the cause of the crying.

[0367] Finally, analysis using emotion recognition is also performed. The server identifies the user's emotional state from text or voice and provides psychological support for emotions such as anxiety and stress. This support is achieved by generating messages that are adapted to the emotions the user is feeling.

[0368] For example, if a user sends a message saying, "I'm worried because my baby has been crying a lot at night lately," the server will use this information to provide advice such as, "Night crying is common, but creating a safe and secure environment can also help." Furthermore, if the system detects that the user is experiencing anxiety, an additional psychological support message will be sent, such as, "I understand your anxiety. Please feel free to talk to me; I'm here to support you anytime."

[0369] An example of a prompt would be, "Please tell me how to respond to inquiries about concerns regarding nighttime crying in infants." This would enable the continuous provision of 24-hour childcare support to parents.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] Users use a communication device to input inquiries about childcare. The input data is in text or voice format and is sent through popular messaging applications. Specifically, the user uses the in-app chat box to type a message such as, "I'm worried because my baby has been crying a lot at night lately," and presses the send button. Based on this input data, the communication device formats the information as a digital message and transfers it to the server.

[0373] Step 2:

[0374] The terminal transfers data received from the user to the server. Specifically, messages are sent to the server via the internet as encrypted data packets. Input is text or voice data from the user, and output becomes data for analysis usable on the server. This process ensures the secure transfer of data.

[0375] Step 3:

[0376] The server analyzes the received data using natural language processing techniques. Specifically, it first tokenizes the received text data, then uses a generative AI model to analyze the context and understand the intent of the inquiry. The input is the transmitted text data, and the output is structured data interpreted from the user's intent. This analysis clarifies the problem the user is facing.

[0377] Step 4:

[0378] The server uses image analysis technology to analyze video data of children received from users. Specifically, the server uses an image processing library to extract facial recognition and physical features from the video data and estimate growth, development, and health status. The input is video data provided by the user, and the output is indicator information regarding growth, development, and health status. This makes it possible to evaluate a child's health and growth in real time.

[0379] Step 5:

[0380] The server uses acoustic analysis capabilities to analyze audio data, such as children's cries. Using an acoustic analysis library, it extracts frequency characteristics from the audio data and analyzes the resulting audio patterns. The input is audio data, and the output is classification information indicating the cause of the crying. Based on this output, appropriate countermeasures and advice are generated.

[0381] Step 6:

[0382] The server uses emotion recognition to understand the user's emotional state. Specifically, it combines natural language processing and speech analysis to evaluate emotions from the user's text or voice tone. The input is the user's text or voice data, and the output is information indicating the user's emotional state. Based on this information, support messages are customized, enabling more helpful responses.

[0383] (Application Example 2)

[0384] 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."

[0385] In modern parenting, parents often face numerous anxieties, questions, and emotional burdens. Concerns about newborn crying and developmental progress are particularly serious, and there is a strong need for appropriate advice and information. However, parents often lack access to 24-hour support. Furthermore, personalized support tailored to the emotions and needs of individual families is insufficient. A system is needed to address these issues.

[0386] 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.

[0387] In this invention, the server includes means for receiving parenting consultations from parents via communication equipment and generating responses using natural language processing technology; means for analyzing the child's visual data using image analysis technology and evaluating their growth and health status; and means for analyzing the child's voice data using an acoustic analysis system, identifying the cause of the voice, and generating appropriate support advice. As a result, parents can not only receive personalized parenting support 24 hours a day, but also receive emotional support through messages that recognize their emotional state and provide a sense of security.

[0388] "Communication equipment" refers to electronic devices used to receive, transmit, and process digital data.

[0389] "Guardian" refers to an adult who has the responsibility to care for and educate a child.

[0390] "Childcare consultation" refers to the act of asking others questions or expressing concerns about childcare.

[0391] "Natural language processing technology" is the technology that allows computers to understand, interpret, and generate human language.

[0392] "Image analysis technology" is a technique that processes digital images and extracts meaningful information.

[0393] "Visual data" refers to information that is represented visually, such as images and videos.

[0394] "Growth and health status" refers to the state of a child's physical development and health.

[0395] An "acoustic analysis system" is a set of technologies that analyze audio data and extract its features.

[0396] "Audio data" refers to information recorded in digital format as sound pressure waves.

[0397] "Supportive advice" refers to information or suggestions or guidance provided to address a specific problem.

[0398] An "emotional analysis engine" is a technology used to identify emotional states from text and audio.

[0399] A "reassuring message" is a message intended to make the recipient feel safe and at ease.

[0400] "Administrative agencies" refer to public organizations such as the government and local authorities.

[0401] A "support plan" is a set of action guidelines or strategies formulated to achieve a specific objective.

[0402] The system for implementing this invention is initiated when a parent uses their personal communication device (e.g., a smartphone) to send a consultation or message regarding childcare. The device sends the message, image data, and audio data entered by the parent to the server. The server then performs the following processing:

[0403] The server first uses natural language processing technology on the text data received from the communication device. Specifically, it uses a generative AI model to analyze the content of the parent's inquiry. In this process, it uses an emotion analysis engine to recognize the parent's emotional state and generate a message that provides reassurance. An example of such a prompt would be, "Generate a reassuring response to the parent's message saying, 'I'm worried because my baby cries a lot at night.'"

[0404] Next, the server utilizes image analysis technology. This technology uses tools such as the Google Cloud Vision API to analyze the child's visual data transmitted from the communication device. Based on this analysis, the server assesses the child's growth and health status and provides feedback to the parents.

[0405] Furthermore, the server uses an acoustic analysis system to analyze the child's crying from the audio data. Based on the analysis, it identifies the cause of the crying and generates appropriate support advice.

[0406] Ultimately, the server uses information from government agencies to present a support plan tailored to each family's needs. This support plan enables more specific and effective childcare support. Through this system, parents will have access to 24-hour childcare consultation and support that meets their individual needs.

[0407] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0408] Step 1:

[0409] Users input text messages, images, and audio data related to childcare using their personal communication devices. This input includes questions and concerns related to childcare consultations. The user's device then sends this data to the server.

[0410] Step 2:

[0411] The server analyzes text data received from the terminal using natural language processing technology. Input: Text data. The processing uses a generative AI model to analyze the user's question and generate an appropriate response based on that analysis. Output: Generated response message. This response includes specific advice addressing the user's concerns and questions.

[0412] Step 3:

[0413] The server uses an emotion analysis engine to analyze the user's emotions from the received text. Input: User's text data. Processing involves evaluating the emotional state and generating additional messages to provide reassurance. Output: A message providing supplementary psychological support. This message is returned taking the user's emotions into consideration.

[0414] Step 4:

[0415] The server analyzes image data of children sent from the device using image analysis technologies such as the Google Cloud Vision API. Input: Image data. During processing, growth indicators and health status are automatically evaluated from the images, and advice is generated based on the results. Output: Growth and health evaluation results and recommendations.

[0416] Step 5:

[0417] The server analyzes audio data using an acoustic analysis system. Input: Audio data. Processing involves analyzing audio patterns and performing data calculations based on an algorithm that identifies the cause of the crying. Output: The cause of the crying and related advice.

[0418] Step 6:

[0419] The server develops support plans tailored to the specific needs of each household, based on information obtained from government agencies. Input: Information from government agencies and data on household needs. Processing: Combining this information to create a customized support plan. Output: Support plan for each household.

[0420] This entire process allows users to receive personalized support to address their concerns and anxieties about childcare.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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".

[0437] This invention provides a system that allows users to receive various forms of support related to childcare using a communication terminal. Users can send questions about childcare, records of their child's growth, and audio data of their child crying through a widely used messaging application.

[0438] The device sends this data received from the user to the server. The server uses a natural language processing model to generate responses to the user's questions and provides advice, including psychological support. The server also utilizes image processing technology to measure growth indicators such as height and weight from the transmitted video data of the child and assesses their health status. This analysis result is then fed back to the user's device.

[0439] Furthermore, the server uses an acoustic analysis module to analyze the transmitted crying audio data. It analyzes the characteristics of the crying, infers its cause, and recommends the most appropriate action for the user. This process provides parents with information to respond appropriately to their baby's crying.

[0440] In addition, because the system is linked to government services, the server references data on local government services based on each household's situation and presents users with appropriate support plans. These plans include information on available facilities and applicable childcare support programs. This allows users to conveniently access the necessary government support.

[0441] For example, if a user sends a message saying, "My baby cries a lot at night," the server will analyze the message using natural language processing and generate advice such as, "Crying at night is common during the growth process. You might want to try establishing a regular sleep schedule or playing relaxing music before your child falls asleep."

[0442] Furthermore, if a user submits an image related to their child's health, the server uses image processing technology to evaluate the child's health and provides feedback such as, "This month's height and weight growth is within the normal range." In this way, the system aims to alleviate parental anxiety and support the healthy growth of children by providing 24-hour childcare support.

[0443] The following describes the processing flow.

[0444] Step 1:

[0445] The device receives messages, images, and audio data related to childcare consultations that users input using messaging platforms such as the LINE application.

[0446] Step 2:

[0447] The terminal converts the received data into the appropriate format and prepares it for transmission to the server. Data from the messaging platform is compressed and formatted according to a specific protocol.

[0448] Step 3:

[0449] The server passes the text message sent from the terminal to the natural language processing unit, which then begins the analysis. This unit uses a GPT-4 level model to understand the context and generate a response.

[0450] Step 4:

[0451] The server sends the generated response back to the terminal. This response includes specific advice, including psychological support for the parent.

[0452] Step 5:

[0453] The server passes image data sent from the terminal to an image processing module for analysis of growth indicators and health status. The module uses machine learning algorithms to estimate growth data such as height and weight.

[0454] Step 6:

[0455] The server generates a report based on the image analysis results and provides feedback to the user. The user can then use this information to understand their child's growth and health status.

[0456] Step 7:

[0457] The server sends the crying audio data provided by the terminal to the acoustic analysis unit. This unit analyzes the audio data and infers the cause from the crying pattern.

[0458] Step 8:

[0459] The server generates advice for the user based on the acoustic analysis results and sends it back to the terminal. The user can then follow this advice to find ways to deal with the child's crying.

[0460] Step 9:

[0461] The server accesses a database of government services to identify appropriate support plans for each family. It organizes information on available childcare support programs and facilities based on the user's region and family circumstances.

[0462] Step 10:

[0463] The server provides support plan information to users through their terminals. This allows users to understand specific ways to effectively utilize local government services.

[0464] (Example 1)

[0465] 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."

[0466] In modern child-rearing, many of the problems parents face require prompt attention regardless of time or place. In particular, anxieties about a child's health and emotional changes are major concerns for parents. Furthermore, accessing government services often involves numerous procedures, which can be burdensome for parents. To comprehensively address these challenges, a system is needed that provides prompt and appropriate child-rearing support.

[0467] 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.

[0468] In this invention, the server includes means for receiving questions about childcare from parents via communication equipment and generating answers using a generative AI algorithm; means for analyzing image data of infants using image analysis technology and evaluating indicators of growth and health; and means for analyzing infants' cries using an acoustic analysis device, identifying the cause of the crying, and providing appropriate advice. This makes it possible for parents to receive the childcare support they need accurately and quickly, regardless of time or place.

[0469] "Communication equipment" refers to devices used by parents to send and receive information related to childcare, and which operate particularly through widely used messaging services.

[0470] A "generative AI algorithm" is a type of artificial intelligence technology that automatically generates accurate answers to questions related to childcare.

[0471] "Image analysis technology" is a technique for analyzing image data of infants to measure indicators of their growth and health.

[0472] An "acoustic analysis device" is a device that analyzes the cries of infants, identifies their characteristics, and infers the cause of the crying.

[0473] "Public service information" refers to data on childcare support programs and facilities provided by the government, which is used to develop support plans based on the individual needs of each family.

[0474] To implement this invention, it is necessary to use communication equipment with internet connectivity, a generation AI algorithm, image analysis technology, and an acoustic analysis device. Specific embodiments are described below.

[0475] Users send questions and information about childcare through communication devices equipped with widely used messaging services. For example, users might type messages such as, "My one-year-old son cries frequently at night. What should I do?" This data may include photos of the infant or audio recordings of the baby crying.

[0476] The terminal forwards data sent by the user to the server. When the server analyzes the received data, it utilizes a generative AI model to understand the user's question in natural language and generate an appropriate answer. In this process, a generative AI algorithm is used to quickly generate advice.

[0477] The server uses image analysis technology to evaluate the growth and health of infants based on the image data. For example, it can measure growth indicators such as weight and height, and use this information to generate reports similar to those from health checkups.

[0478] Similarly, acoustic data is analyzed by an acoustic analysis device to determine the cause of the crying by analyzing the frequency and tone of the cries. For example, information such as "the crying is high-pitched, so the dog may be hungry" is provided.

[0479] Furthermore, the server can refer to a public services database and present support plans tailored to each family's needs. This includes information on available childcare support programs and facilities. Based on this information, the server provides users with the most suitable support guidance. This embodiment allows for the smooth resolution of various anxieties and questions related to childcare.

[0480] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0481] Step 1:

[0482] Users input and send questions and information related to childcare using communication devices. Specifically, users input text questions, photos of infants, and audio data of crying babies as messages. The input data is provided through a messaging service.

[0483] Step 2:

[0484] The terminal transfers the data received from the user to the server. Here, the terminal converts the input data into an appropriate format and sends it to the server via the internet. The output is the data that has been successfully transferred to the server.

[0485] Step 3:

[0486] The server analyzes the received data. First, it uses a generative AI model to analyze text data and generate appropriate responses to user questions. The input is the user's question data, and the output is the generated answer text. The server runs the AI ​​model to provide content that matches the user's request.

[0487] Step 4:

[0488] The server processes image data using image analysis technology to evaluate infant growth indicators. Specifically, it extracts facial and body features from the image data and compares them with existing growth data. The input is image data, and the output is the growth evaluation result. Based on this, the server provides feedback on the child's health status.

[0489] Step 5:

[0490] The server uses an acoustic analysis device to analyze the audio data of the crying sound. It analyzes the frequency and duration of the crying sound and infers the cause of the crying based on its characteristics. The input is the audio data, and the output is the inferred cause. For example, information such as "the cat may be hungry" might be generated.

[0491] Step 6:

[0492] The server retrieves support plans tailored to each household from a public services database. It obtains administrative service information that matches the user's needs and proposes appropriate support. The input is the user's registration information, and the output is the recommended support plan. The server then provides this information to the user.

[0493] (Application Example 1)

[0494] 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."

[0495] Parents who need information about childcare are required to receive appropriate advice and information on government services, as well as to have their children's health assessed and personalized recommendations for childcare products, thereby reducing anxiety and burden in childcare. Furthermore, it is necessary to overcome the time constraints faced by parents by providing support that is accessible 24 hours a day.

[0496] 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.

[0497] In this invention, the server includes means for receiving parental childcare consultations via a communication device and generating replies using natural language processing technology; means for analyzing image data of children using image analysis technology and evaluating their growth and health status; and means for analyzing children's cries using acoustic analysis technology, estimating the cause of the cries, and generating appropriate advice. This enables the provision of personalized childcare support and allows parents to receive information to further select more appropriate childcare products.

[0498] A "communication device" is a device equipped with the function of receiving consultations and data from parents regarding childcare, and has a means of exchanging information via a widely used messaging application.

[0499] "Natural language processing technology" is a technology that allows machines to understand and process human language, making it possible to automatically generate appropriate responses to childcare consultations.

[0500] "Image analysis technology" is a technique that analyzes image data to extract specific information, and is used as a means of evaluating a child's growth and health.

[0501] "Acoustic analysis technology" is a technique that analyzes audio data and extracts its characteristics. It can analyze a child's crying to estimate the cause and generate appropriate advice.

[0502] A "support plan" refers to a plan that presents support tailored to the individual needs of each family, based on information about administrative services, and includes suggestions for childcare products.

[0503] The "childcare product e-commerce function" refers to a function that provides information on childcare-related products and enables parents to purchase them.

[0504] "Personalization" refers to the process of optimizing a user's parenting experience by providing information and suggestions tailored to the individual user's characteristics and needs.

[0505] In this invention, a system that provides support related to childcare is mainly built around a server and a communication terminal. Users send questions about childcare via a messaging application using a communication terminal such as a smartphone. This communication terminal is responsible for receiving text data from users using natural language processing technology and sending it to the server.

[0506] The server processes the received text data using a natural language processing model (e.g., built using TensorFlow or PyTorch) to generate personalized replies for parents. These replies include advice and information on childcare. Additionally, image analysis techniques are used to extract growth indicators from the child's image data submitted by the user. This is done using image processing libraries (e.g., OpenCV) to calculate indicators such as the child's height and weight and assess their health.

[0507] Furthermore, acoustic analysis technology is used to analyze crying data transmitted from the user. This identifies the characteristics of the crying and estimates the cause of the crying. Based on this information, the server generates appropriate advice and sends it back to the communication terminal.

[0508] In addition, the server is linked to a database of government services and has the function to present support plans tailored to the needs of each family. This includes suggestions for childcare products. This function allows parents to easily access government services and purchase necessary childcare products online.

[0509] For example, if a user sends the prompt "My child is crying a lot at night. Can you give me some advice?", the natural language processing model analyzes this and generates advice such as, "Crying at night is a part of growth, but try to create a relaxing environment for your child. These baby products may be helpful." This allows the user to reduce their anxiety about parenting.

[0510] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0511] Step 1:

[0512] The user uses a communication terminal to send questions and requests for advice regarding childcare through a messaging application. The input is the user's text message, and the recipient is the server. The message contains specific details related to the childcare consultation. In this step, the communication terminal prepares to receive the text data and forward it to the server.

[0513] Step 2:

[0514] The server acquires text data received from the communication terminal and analyzes it using natural language processing technology. The input is the text message received in step 1, and the output is the understood consultation content. The server utilizes a generative AI model to identify the user's intentions and emotions and prepare to generate optimal advice. This analysis process is performed by the generative AI model.

[0515] Step 3:

[0516] The server processes image data of children submitted by users using image analysis techniques. The input is image data submitted by the user, and the output is evaluation data related to growth indicators. The server analyzes the image data using an image processing library (e.g., OpenCV) and extracts information necessary to assess the child's health status (e.g., height, weight).

[0517] Step 4:

[0518] The server analyzes user-submitted crying data using acoustic analysis technology. The input is audio data provided by the user, and the output is information about the cause of the crying. The server processes the audio data with an acoustic analysis module and uses a feature extraction algorithm to identify crying patterns. This allows the server to estimate the cause of the crying.

[0519] Step 5:

[0520] The server generates and sends advice to the user based on the analysis results. The input is the analysis results from steps 2, 3, and 4, and the output is personalized advice and suggestions for childcare products for the user. The generated advice is sent to the user's communication terminal.

[0521] Step 6:

[0522] The user reviews the advice and suggestions received from the server via a communication terminal. The input is the advice and suggestions received from the server, and the output is the user's understanding and actions. In this step, the user can decide on the next steps in childcare based on the support information received.

[0523] 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.

[0524] This invention provides optimal support to users through a system that combines a communication terminal with an emotion engine, allowing users to seek childcare advice. Users can send daily childcare questions and concerns as text or voice data via a messaging application.

[0525] The terminal forwards data received from the user to the server. The server uses a natural language processing model to analyze the user's inquiry from the text data and generate appropriate advice. This includes understanding the parent's emotions and providing psychological support tailored to their state.

[0526] Furthermore, image processing technology is used to analyze the photos of children submitted by users to check their growth, development, and health status. Based on this data, specific growth indicators and health recommendations are generated and provided as feedback to the user.

[0527] The server uses an acoustic analysis module to analyze the crying audio data provided by the user. Based on the analysis results, it identifies the type and cause of the crying and provides parents with coping strategies and advice.

[0528] Furthermore, the emotion engine recognizes the user's emotional state from text and voice. This engine identifies emotions such as stress, anxiety, and joy from the user's speech and tone, and optimizes the communication method based on the results.

[0529] For example, if a user sends a message saying, "I'm worried because my baby has been crying a lot at night lately," the server uses natural language processing to analyze the message and responds with advice such as, "Night crying is common, but creating a safe and secure environment can also help." The emotion engine determines that this message is accompanied by anxiety and provides additional psychological support messages such as, "I understand your anxiety. Please feel free to talk to me, and I'm here to support you anytime."

[0530] Thus, the system aims to provide 24-hour childcare support and support the healthy development of children while being attentive to the parents' emotions.

[0531] The following describes the processing flow.

[0532] Step 1:

[0533] Users use messaging applications to type and send text or voice inquiries about childcare on their devices.

[0534] Step 2:

[0535] The terminal sends received text and audio data to the server. The data is formatted appropriately and transferred quickly over the network.

[0536] Step 3:

[0537] The server passes the transmitted text data to a natural language processing unit. This unit uses GPT-4 level AI to analyze the context of the text and generate an appropriate response.

[0538] Step 4:

[0539] The server passes the generated text response to the sentiment engine. The sentiment engine estimates the user's emotional state and adjusts the emotional tone of the response. For example, it might modify the message to use a reassuring tone.

[0540] Step 5:

[0541] Finally, the server sends a refined response to the terminal and displays it to the user. The user reviews the generated feedback and proceeds to the next step if necessary.

[0542] Step 6:

[0543] If the user provides image data, the device sends that data to the server.

[0544] Step 7:

[0545] The server inputs image data into an image processing module to analyze the child's growth indicators and health status. It then generates a report to send back to the user.

[0546] Step 8:

[0547] When a user sends audio data of a crying baby, the server processes the audio data using an acoustic analysis module. It analyzes the cause of the crying and generates advice for the user based on the results.

[0548] Step 9:

[0549] The server references a database of government services to configure the most suitable support plan for the user. This includes region-specific support programs and services.

[0550] Step 10:

[0551] The server notifies the user of the created support plan via the terminal and supports the user in making appropriate decisions based on the available information.

[0552] (Example 2)

[0553] 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."

[0554] In modern society, worries and anxieties about childcare are a significant burden for parents. In particular, the sheer volume of information available on childcare makes it difficult to determine which information is reliable and useful. Furthermore, receiving real-time expert advice and support is not easy. Therefore, there is a need for a system that allows parents to receive real-time and effective childcare support.

[0555] 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.

[0556] In this invention, the server includes means for receiving parental childcare consultations via a communication device and generating responses using natural language processing technology; means for analyzing video data of children using video analysis technology and evaluating growth and health indicators; and means for analyzing children's voice data using acoustic analysis functionality, identifying the cause of the voice, and generating appropriate advice. This enables parents to receive professional childcare support 24 hours a day.

[0557] A "communication device" is an electronic device used to receive data related to childcare consultations from parents and transfer it to a server.

[0558] "Natural language processing technology" is an information processing technology that analyzes received text data to understand the parents' intentions and the content of their consultations.

[0559] "Video analysis technology" is a technology that analyzes video data of children to evaluate indicators related to their growth and health.

[0560] The "acoustic analysis function" is a feature that analyzes children's voice data, understands the voice patterns, and identifies the cause.

[0561] The "emotion recognition function" is a function that evaluates the emotional state of parents from text and audio data and provides psychological support.

[0562] "Advice" refers to guidelines and suggestions regarding childcare provided to parents based on the analysis results.

[0563] "24-hour operation" refers to a state where the system operates continuously throughout the day and is available to respond to parent inquiries at any time.

[0564] As an embodiment of this invention, a childcare support system will be specifically described.

[0565] Users use communication devices to input questions and concerns about childcare. This input is done in text or voice format through popular messaging applications. The data sent from the communication device is transferred to the server via the internet.

[0566] The server performs various analyses based on the received data and generates appropriate support information. Specifically, it uses natural language processing technology to analyze text data and implements a natural language processing model to understand the content of the user's inquiry. The model used includes an advanced generative AI model that tokenizes raw text data and understands the context.

[0567] Next, the server uses video analysis technology to analyze the video data of the child provided by the user. This includes utilizing image processing libraries to evaluate growth, development, and health indicators in detail. For example, a face recognition algorithm is used to detect the child's facial features and estimate their growth rate and health status.

[0568] Furthermore, the server uses acoustic analysis capabilities to analyze audio data, including children's crying. In this process, an acoustic analysis library is used to extract and classify audio features, making it possible to clearly identify the cause of the crying.

[0569] Finally, analysis using emotion recognition is also performed. The server identifies the user's emotional state from text or voice and provides psychological support for emotions such as anxiety and stress. This support is achieved by generating messages that are adapted to the emotions the user is feeling.

[0570] For example, if a user sends a message saying, "I'm worried because my baby has been crying a lot at night lately," the server will use this information to provide advice such as, "Night crying is common, but creating a safe and secure environment can also help." Furthermore, if the system detects that the user is experiencing anxiety, an additional psychological support message will be sent, such as, "I understand your anxiety. Please feel free to talk to me; I'm here to support you anytime."

[0571] An example of a prompt would be, "Please tell me how to respond to inquiries about concerns regarding nighttime crying in infants." This would enable the continuous provision of 24-hour childcare support to parents.

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] Users use a communication device to input inquiries about childcare. The input data is in text or voice format and is sent through popular messaging applications. Specifically, the user uses the in-app chat box to type a message such as, "I'm worried because my baby has been crying a lot at night lately," and presses the send button. Based on this input data, the communication device formats the information as a digital message and transfers it to the server.

[0575] Step 2:

[0576] The terminal transfers data received from the user to the server. Specifically, messages are sent to the server via the internet as encrypted data packets. Input is text or voice data from the user, and output becomes data for analysis usable on the server. This process ensures the secure transfer of data.

[0577] Step 3:

[0578] The server analyzes the received data using natural language processing techniques. Specifically, it first tokenizes the received text data, then uses a generative AI model to analyze the context and understand the intent of the inquiry. The input is the transmitted text data, and the output is structured data interpreted from the user's intent. This analysis clarifies the problem the user is facing.

[0579] Step 4:

[0580] The server uses image analysis technology to analyze video data of children received from users. Specifically, the server uses an image processing library to extract facial recognition and physical features from the video data and estimate growth, development, and health status. The input is video data provided by the user, and the output is indicator information regarding growth, development, and health status. This makes it possible to evaluate a child's health and growth in real time.

[0581] Step 5:

[0582] The server uses acoustic analysis capabilities to analyze audio data, such as children's cries. Using an acoustic analysis library, it extracts frequency characteristics from the audio data and analyzes the resulting audio patterns. The input is audio data, and the output is classification information indicating the cause of the crying. Based on this output, appropriate countermeasures and advice are generated.

[0583] Step 6:

[0584] The server uses emotion recognition to understand the user's emotional state. Specifically, it combines natural language processing and speech analysis to evaluate emotions from the user's text or voice tone. The input is the user's text or voice data, and the output is information indicating the user's emotional state. Based on this information, support messages are customized, enabling more helpful responses.

[0585] (Application Example 2)

[0586] 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."

[0587] In modern parenting, parents often face numerous anxieties, questions, and emotional burdens. Concerns about newborn crying and developmental progress are particularly serious, and there is a strong need for appropriate advice and information. However, parents often lack access to 24-hour support. Furthermore, personalized support tailored to the emotions and needs of individual families is insufficient. A system is needed to address these issues.

[0588] 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.

[0589] In this invention, the server includes means for receiving parenting consultations from parents via communication equipment and generating responses using natural language processing technology; means for analyzing the child's visual data using image analysis technology and evaluating their growth and health status; and means for analyzing the child's voice data using an acoustic analysis system, identifying the cause of the voice, and generating appropriate support advice. As a result, parents can not only receive personalized parenting support 24 hours a day, but also receive emotional support through messages that recognize their emotional state and provide a sense of security.

[0590] "Communication equipment" refers to electronic devices used to receive, transmit, and process digital data.

[0591] "Guardian" refers to an adult who has the responsibility to care for and educate a child.

[0592] "Childcare consultation" refers to the act of asking others questions or expressing concerns about childcare.

[0593] "Natural language processing technology" is the technology that allows computers to understand, interpret, and generate human language.

[0594] "Image analysis technology" is a technique that processes digital images and extracts meaningful information.

[0595] "Visual data" refers to information that is represented visually, such as images and videos.

[0596] "Growth and health status" refers to the state of a child's physical development and health.

[0597] An "acoustic analysis system" is a set of technologies that analyze audio data and extract its features.

[0598] "Audio data" refers to information recorded in digital format as sound pressure waves.

[0599] "Supportive advice" refers to information or suggestions or guidance provided to address a specific problem.

[0600] An "emotional analysis engine" is a technology used to identify emotional states from text and audio.

[0601] A "reassuring message" is a message intended to make the recipient feel safe and at ease.

[0602] "Administrative agencies" refer to public organizations such as the government and local authorities.

[0603] A "support plan" is a set of action guidelines or strategies formulated to achieve a specific objective.

[0604] The system for implementing this invention is initiated when a parent uses their personal communication device (e.g., a smartphone) to send a consultation or message regarding childcare. The device sends the message, image data, and audio data entered by the parent to the server. The server then performs the following processing:

[0605] The server first uses natural language processing technology on the text data received from the communication device. Specifically, it uses a generative AI model to analyze the content of the parent's inquiry. In this process, it uses an emotion analysis engine to recognize the parent's emotional state and generate a message that provides reassurance. An example of such a prompt would be, "Generate a reassuring response to the parent's message saying, 'I'm worried because my baby cries a lot at night.'"

[0606] Next, the server utilizes image analysis technology. This technology uses tools such as the Google Cloud Vision API to analyze the child's visual data transmitted from the communication device. Based on this analysis, the server assesses the child's growth and health status and provides feedback to the parents.

[0607] Furthermore, the server uses an acoustic analysis system to analyze the child's crying from the audio data. Based on the analysis, it identifies the cause of the crying and generates appropriate support advice.

[0608] Ultimately, the server uses information from government agencies to present a support plan tailored to each family's needs. This support plan enables more specific and effective childcare support. Through this system, parents will have access to 24-hour childcare consultation and support that meets their individual needs.

[0609] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0610] Step 1:

[0611] Users input text messages, images, and audio data related to childcare using their personal communication devices. This input includes questions and concerns related to childcare consultations. The user's device then sends this data to the server.

[0612] Step 2:

[0613] The server analyzes text data received from the terminal using natural language processing technology. Input: Text data. The processing uses a generative AI model to analyze the user's question and generate an appropriate response based on that analysis. Output: Generated response message. This response includes specific advice addressing the user's concerns and questions.

[0614] Step 3:

[0615] The server uses an emotion analysis engine to analyze the user's emotions from the received text. Input: User's text data. Processing involves evaluating the emotional state and generating additional messages to provide reassurance. Output: A message providing supplementary psychological support. This message is returned taking the user's emotions into consideration.

[0616] Step 4:

[0617] The server analyzes image data of children sent from the device using image analysis technologies such as the Google Cloud Vision API. Input: Image data. During processing, growth indicators and health status are automatically evaluated from the images, and advice is generated based on the results. Output: Growth and health evaluation results and recommendations.

[0618] Step 5:

[0619] The server analyzes audio data using an acoustic analysis system. Input: Audio data. Processing involves analyzing audio patterns and performing data calculations based on an algorithm that identifies the cause of the crying. Output: The cause of the crying and related advice.

[0620] Step 6:

[0621] The server develops support plans tailored to the specific needs of each household, based on information obtained from government agencies. Input: Information from government agencies and data on household needs. Processing: Combining this information to create a customized support plan. Output: Support plan for each household.

[0622] This entire process allows users to receive personalized support to address their concerns and anxieties about childcare.

[0623] 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.

[0624] 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.

[0625] 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.

[0626] [Fourth Embodiment]

[0627] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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).

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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".

[0640] This invention provides a system that allows users to receive various forms of support related to childcare using a communication terminal. Users can send questions about childcare, records of their child's growth, and audio data of their child crying through a widely used messaging application.

[0641] The device sends this data received from the user to the server. The server uses a natural language processing model to generate responses to the user's questions and provides advice, including psychological support. The server also utilizes image processing technology to measure growth indicators such as height and weight from the transmitted video data of the child and assesses their health status. This analysis result is then fed back to the user's device.

[0642] Furthermore, the server uses an acoustic analysis module to analyze the transmitted crying audio data. It analyzes the characteristics of the crying, infers its cause, and recommends the most appropriate action for the user. This process provides parents with information to respond appropriately to their baby's crying.

[0643] In addition, because the system is linked to government services, the server references data on local government services based on each household's situation and presents users with appropriate support plans. These plans include information on available facilities and applicable childcare support programs. This allows users to conveniently access the necessary government support.

[0644] For example, if a user sends a message saying, "My baby cries a lot at night," the server will analyze the message using natural language processing and generate advice such as, "Crying at night is common during the growth process. You might want to try establishing a regular sleep schedule or playing relaxing music before your child falls asleep."

[0645] Furthermore, if a user submits an image related to their child's health, the server uses image processing technology to evaluate the child's health and provides feedback such as, "This month's height and weight growth is within the normal range." In this way, the system aims to alleviate parental anxiety and support the healthy growth of children by providing 24-hour childcare support.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The device receives messages, images, and audio data related to childcare consultations that users input using messaging platforms such as the LINE application.

[0649] Step 2:

[0650] The terminal converts the received data into the appropriate format and prepares it for transmission to the server. Data from the messaging platform is compressed and formatted according to a specific protocol.

[0651] Step 3:

[0652] The server passes the text message sent from the terminal to the natural language processing unit, which then begins the analysis. This unit uses a GPT-4 level model to understand the context and generate a response.

[0653] Step 4:

[0654] The server sends the generated response back to the terminal. This response includes specific advice, including psychological support for the parent.

[0655] Step 5:

[0656] The server passes image data sent from the terminal to an image processing module for analysis of growth indicators and health status. The module uses machine learning algorithms to estimate growth data such as height and weight.

[0657] Step 6:

[0658] The server generates a report based on the image analysis results and provides feedback to the user. The user can then use this information to understand their child's growth and health status.

[0659] Step 7:

[0660] The server sends the crying audio data provided by the terminal to the acoustic analysis unit. This unit analyzes the audio data and infers the cause from the crying pattern.

[0661] Step 8:

[0662] The server generates advice for the user based on the acoustic analysis results and sends it back to the terminal. The user can then follow this advice to find ways to deal with the child's crying.

[0663] Step 9:

[0664] The server accesses a database of government services to identify appropriate support plans for each family. It organizes information on available childcare support programs and facilities based on the user's region and family circumstances.

[0665] Step 10:

[0666] The server provides support plan information to users through their terminals. This allows users to understand specific ways to effectively utilize local government services.

[0667] (Example 1)

[0668] 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".

[0669] In modern child-rearing, many of the problems parents face require prompt attention regardless of time or place. In particular, anxieties about a child's health and emotional changes are major concerns for parents. Furthermore, accessing government services often involves numerous procedures, which can be burdensome for parents. To comprehensively address these challenges, a system is needed that provides prompt and appropriate child-rearing support.

[0670] 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.

[0671] In this invention, the server includes means for receiving questions about childcare from parents via communication equipment and generating answers using a generative AI algorithm; means for analyzing image data of infants using image analysis technology and evaluating indicators of growth and health; and means for analyzing infants' cries using an acoustic analysis device, identifying the cause of the crying, and providing appropriate advice. This makes it possible for parents to receive the childcare support they need accurately and quickly, regardless of time or place.

[0672] "Communication equipment" refers to devices used by parents to send and receive information related to childcare, and which operate particularly through widely used messaging services.

[0673] A "generative AI algorithm" is a type of artificial intelligence technology that automatically generates accurate answers to questions related to childcare.

[0674] "Image analysis technology" is a technique for analyzing image data of infants to measure indicators of their growth and health.

[0675] An "acoustic analysis device" is a device that analyzes the cries of infants, identifies their characteristics, and infers the cause of the crying.

[0676] "Public service information" refers to data on childcare support programs and facilities provided by the government, which is used to develop support plans based on the individual needs of each family.

[0677] To implement this invention, it is necessary to use communication equipment with internet connectivity, a generation AI algorithm, image analysis technology, and an acoustic analysis device. Specific embodiments are described below.

[0678] Users send questions and information about childcare through communication devices equipped with widely used messaging services. For example, users might type messages such as, "My one-year-old son cries frequently at night. What should I do?" This data may include photos of the infant or audio recordings of the baby crying.

[0679] The terminal forwards data sent by the user to the server. When the server analyzes the received data, it utilizes a generative AI model to understand the user's question in natural language and generate an appropriate answer. In this process, a generative AI algorithm is used to quickly generate advice.

[0680] The server uses image analysis technology to evaluate the growth and health of infants based on the image data. For example, it can measure growth indicators such as weight and height, and use this information to generate reports similar to those from health checkups.

[0681] Similarly, acoustic data is analyzed by an acoustic analysis device to determine the cause of the crying by analyzing the frequency and tone of the cries. For example, information such as "the crying is high-pitched, so the dog may be hungry" is provided.

[0682] Furthermore, the server can refer to a public services database and present support plans tailored to each family's needs. This includes information on available childcare support programs and facilities. Based on this information, the server provides users with the most suitable support guidance. This embodiment allows for the smooth resolution of various anxieties and questions related to childcare.

[0683] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0684] Step 1:

[0685] Users input and send questions and information related to childcare using communication devices. Specifically, users input text questions, photos of infants, and audio data of crying babies as messages. The input data is provided through a messaging service.

[0686] Step 2:

[0687] The terminal transfers the data received from the user to the server. Here, the terminal converts the input data into an appropriate format and sends it to the server via the internet. The output is the data that has been successfully transferred to the server.

[0688] Step 3:

[0689] The server analyzes the received data. First, it uses a generative AI model to analyze text data and generate appropriate responses to user questions. The input is the user's question data, and the output is the generated answer text. The server runs the AI ​​model to provide content that matches the user's request.

[0690] Step 4:

[0691] The server processes image data using image analysis technology to evaluate infant growth indicators. Specifically, it extracts facial and body features from the image data and compares them with existing growth data. The input is image data, and the output is the growth evaluation result. Based on this, the server provides feedback on the child's health status.

[0692] Step 5:

[0693] The server uses an acoustic analysis device to analyze the audio data of the crying sound. It analyzes the frequency and duration of the crying sound and infers the cause of the crying based on its characteristics. The input is the audio data, and the output is the inferred cause. For example, information such as "the cat may be hungry" might be generated.

[0694] Step 6:

[0695] The server retrieves support plans tailored to each household from a public services database. It obtains administrative service information that matches the user's needs and proposes appropriate support. The input is the user's registration information, and the output is the recommended support plan. The server then provides this information to the user.

[0696] (Application Example 1)

[0697] 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".

[0698] Parents who need information about childcare are required to receive appropriate advice and information on government services, as well as to have their children's health assessed and personalized recommendations for childcare products, thereby reducing anxiety and burden in childcare. Furthermore, it is necessary to overcome the time constraints faced by parents by providing support that is accessible 24 hours a day.

[0699] 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.

[0700] In this invention, the server includes means for receiving parental childcare consultations via a communication device and generating replies using natural language processing technology; means for analyzing image data of children using image analysis technology and evaluating their growth and health status; and means for analyzing children's cries using acoustic analysis technology, estimating the cause of the cries, and generating appropriate advice. This enables the provision of personalized childcare support and allows parents to receive information to further select more appropriate childcare products.

[0701] A "communication device" is a device equipped with the function of receiving consultations and data from parents regarding childcare, and has a means of exchanging information via a widely used messaging application.

[0702] "Natural language processing technology" is a technology that allows machines to understand and process human language, making it possible to automatically generate appropriate responses to childcare consultations.

[0703] "Image analysis technology" is a technique that analyzes image data to extract specific information, and is used as a means of evaluating a child's growth and health.

[0704] "Acoustic analysis technology" is a technique that analyzes audio data and extracts its characteristics. It can analyze a child's crying to estimate the cause and generate appropriate advice.

[0705] A "support plan" refers to a plan that presents support tailored to the individual needs of each family, based on information about administrative services, and includes suggestions for childcare products.

[0706] The "childcare product e-commerce function" refers to a function that provides information on childcare-related products and enables parents to purchase them.

[0707] "Personalization" refers to the process of optimizing a user's parenting experience by providing information and suggestions tailored to the individual user's characteristics and needs.

[0708] In this invention, a system that provides support related to childcare is mainly built around a server and a communication terminal. Users send questions about childcare via a messaging application using a communication terminal such as a smartphone. This communication terminal is responsible for receiving text data from users using natural language processing technology and sending it to the server.

[0709] The server processes the received text data using a natural language processing model (e.g., built using TensorFlow or PyTorch) to generate personalized replies for parents. These replies include advice and information on childcare. Additionally, image analysis techniques are used to extract growth indicators from the child's image data submitted by the user. This is done using image processing libraries (e.g., OpenCV) to calculate indicators such as the child's height and weight and assess their health.

[0710] Furthermore, acoustic analysis technology is used to analyze crying data transmitted from the user. This identifies the characteristics of the crying and estimates the cause of the crying. Based on this information, the server generates appropriate advice and sends it back to the communication terminal.

[0711] In addition, the server is linked to a database of government services and has the function to present support plans tailored to the needs of each family. This includes suggestions for childcare products. This function allows parents to easily access government services and purchase necessary childcare products online.

[0712] For example, if a user sends the prompt "My child is crying a lot at night. Can you give me some advice?", the natural language processing model analyzes this and generates advice such as, "Crying at night is a part of growth, but try to create a relaxing environment for your child. These baby products may be helpful." This allows the user to reduce their anxiety about parenting.

[0713] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0714] Step 1:

[0715] The user uses a communication terminal to send questions and requests for advice regarding childcare through a messaging application. The input is the user's text message, and the recipient is the server. The message contains specific details related to the childcare consultation. In this step, the communication terminal prepares to receive the text data and forward it to the server.

[0716] Step 2:

[0717] The server acquires text data received from the communication terminal and analyzes it using natural language processing technology. The input is the text message received in step 1, and the output is the understood consultation content. The server utilizes a generative AI model to identify the user's intentions and emotions and prepare to generate optimal advice. This analysis process is performed by the generative AI model.

[0718] Step 3:

[0719] The server processes image data of children submitted by users using image analysis techniques. The input is image data submitted by the user, and the output is evaluation data related to growth indicators. The server analyzes the image data using an image processing library (e.g., OpenCV) and extracts information necessary to assess the child's health status (e.g., height, weight).

[0720] Step 4:

[0721] The server analyzes user-submitted crying data using acoustic analysis technology. The input is audio data provided by the user, and the output is information about the cause of the crying. The server processes the audio data with an acoustic analysis module and uses a feature extraction algorithm to identify crying patterns. This allows the server to estimate the cause of the crying.

[0722] Step 5:

[0723] The server generates and sends advice to the user based on the analysis results. The input is the analysis results from steps 2, 3, and 4, and the output is personalized advice and suggestions for childcare products for the user. The generated advice is sent to the user's communication terminal.

[0724] Step 6:

[0725] The user reviews the advice and suggestions received from the server via a communication terminal. The input is the advice and suggestions received from the server, and the output is the user's understanding and actions. In this step, the user can decide on the next steps in childcare based on the support information received.

[0726] 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.

[0727] This invention provides optimal support to users through a system that combines a communication terminal with an emotion engine, allowing users to seek childcare advice. Users can send daily childcare questions and concerns as text or voice data via a messaging application.

[0728] The terminal forwards data received from the user to the server. The server uses a natural language processing model to analyze the user's inquiry from the text data and generate appropriate advice. This includes understanding the parent's emotions and providing psychological support tailored to their state.

[0729] Furthermore, image processing technology is used to analyze the photos of children submitted by users to check their growth, development, and health status. Based on this data, specific growth indicators and health recommendations are generated and provided as feedback to the user.

[0730] The server uses an acoustic analysis module to analyze the crying audio data provided by the user. Based on the analysis results, it identifies the type and cause of the crying and provides parents with coping strategies and advice.

[0731] Furthermore, the emotion engine recognizes the user's emotional state from text and voice. This engine identifies emotions such as stress, anxiety, and joy from the user's speech and tone, and optimizes the communication method based on the results.

[0732] For example, if a user sends a message saying, "I'm worried because my baby has been crying a lot at night lately," the server uses natural language processing to analyze the message and responds with advice such as, "Night crying is common, but creating a safe and secure environment can also help." The emotion engine determines that this message is accompanied by anxiety and provides additional psychological support messages such as, "I understand your anxiety. Please feel free to talk to me, and I'm here to support you anytime."

[0733] Thus, the system aims to provide 24-hour childcare support and support the healthy development of children while being attentive to the parents' emotions.

[0734] The following describes the processing flow.

[0735] Step 1:

[0736] Users use messaging applications to type and send text or voice inquiries about childcare on their devices.

[0737] Step 2:

[0738] The terminal sends received text and audio data to the server. The data is formatted appropriately and transferred quickly over the network.

[0739] Step 3:

[0740] The server passes the transmitted text data to a natural language processing unit. This unit uses GPT-4 level AI to analyze the context of the text and generate an appropriate response.

[0741] Step 4:

[0742] The server passes the generated text response to the sentiment engine. The sentiment engine estimates the user's emotional state and adjusts the emotional tone of the response. For example, it might modify the message to use a reassuring tone.

[0743] Step 5:

[0744] Finally, the server sends a refined response to the terminal and displays it to the user. The user reviews the generated feedback and proceeds to the next step if necessary.

[0745] Step 6:

[0746] If the user provides image data, the device sends that data to the server.

[0747] Step 7:

[0748] The server inputs image data into an image processing module to analyze the child's growth indicators and health status. It then generates a report to send back to the user.

[0749] Step 8:

[0750] When a user sends audio data of a crying baby, the server processes the audio data using an acoustic analysis module. It analyzes the cause of the crying and generates advice for the user based on the results.

[0751] Step 9:

[0752] The server references a database of government services to configure the most suitable support plan for the user. This includes region-specific support programs and services.

[0753] Step 10:

[0754] The server notifies the user of the created support plan via the terminal and supports the user in making appropriate decisions based on the available information.

[0755] (Example 2)

[0756] 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".

[0757] In modern society, worries and anxieties about childcare are a significant burden for parents. In particular, the sheer volume of information available on childcare makes it difficult to determine which information is reliable and useful. Furthermore, receiving real-time expert advice and support is not easy. Therefore, there is a need for a system that allows parents to receive real-time and effective childcare support.

[0758] 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.

[0759] In this invention, the server includes means for receiving parental childcare consultations via a communication device and generating responses using natural language processing technology; means for analyzing video data of children using video analysis technology and evaluating growth and health indicators; and means for analyzing children's voice data using acoustic analysis functionality, identifying the cause of the voice, and generating appropriate advice. This enables parents to receive professional childcare support 24 hours a day.

[0760] A "communication device" is an electronic device used to receive data related to childcare consultations from parents and transfer it to a server.

[0761] "Natural language processing technology" is an information processing technology that analyzes received text data to understand the parents' intentions and the content of their consultations.

[0762] "Video analysis technology" is a technology that analyzes video data of children to evaluate indicators related to their growth and health.

[0763] The "acoustic analysis function" is a feature that analyzes children's voice data, understands the voice patterns, and identifies the cause.

[0764] The "emotion recognition function" is a function that evaluates the emotional state of parents from text and audio data and provides psychological support.

[0765] "Advice" refers to guidelines and suggestions regarding childcare provided to parents based on the analysis results.

[0766] "24-hour operation" refers to a state where the system operates continuously throughout the day and is available to respond to parent inquiries at any time.

[0767] As an embodiment of this invention, a childcare support system will be specifically described.

[0768] Users use communication devices to input questions and concerns about childcare. This input is done in text or voice format through popular messaging applications. The data sent from the communication device is transferred to the server via the internet.

[0769] The server performs various analyses based on the received data and generates appropriate support information. Specifically, it uses natural language processing technology to analyze text data and implements a natural language processing model to understand the content of the user's inquiry. The model used includes an advanced generative AI model that tokenizes raw text data and understands the context.

[0770] Next, the server uses video analysis technology to analyze the video data of the child provided by the user. This includes utilizing image processing libraries to evaluate growth, development, and health indicators in detail. For example, a face recognition algorithm is used to detect the child's facial features and estimate their growth rate and health status.

[0771] Furthermore, the server uses acoustic analysis capabilities to analyze audio data, including children's crying. In this process, an acoustic analysis library is used to extract and classify audio features, making it possible to clearly identify the cause of the crying.

[0772] Finally, analysis using emotion recognition is also performed. The server identifies the user's emotional state from text or voice and provides psychological support for emotions such as anxiety and stress. This support is achieved by generating messages that are adapted to the emotions the user is feeling.

[0773] For example, if a user sends a message saying, "I'm worried because my baby has been crying a lot at night lately," the server will use this information to provide advice such as, "Night crying is common, but creating a safe and secure environment can also help." Furthermore, if the system detects that the user is experiencing anxiety, an additional psychological support message will be sent, such as, "I understand your anxiety. Please feel free to talk to me; I'm here to support you anytime."

[0774] An example of a prompt would be, "Please tell me how to respond to inquiries about concerns regarding nighttime crying in infants." This would enable the continuous provision of 24-hour childcare support to parents.

[0775] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0776] Step 1:

[0777] Users use a communication device to input inquiries about childcare. The input data is in text or voice format and is sent through popular messaging applications. Specifically, the user uses the in-app chat box to type a message such as, "I'm worried because my baby has been crying a lot at night lately," and presses the send button. Based on this input data, the communication device formats the information as a digital message and transfers it to the server.

[0778] Step 2:

[0779] The terminal transfers data received from the user to the server. Specifically, messages are sent to the server via the internet as encrypted data packets. Input is text or voice data from the user, and output becomes data for analysis usable on the server. This process ensures the secure transfer of data.

[0780] Step 3:

[0781] The server analyzes the received data using natural language processing techniques. Specifically, it first tokenizes the received text data, then uses a generative AI model to analyze the context and understand the intent of the inquiry. The input is the transmitted text data, and the output is structured data interpreted from the user's intent. This analysis clarifies the problem the user is facing.

[0782] Step 4:

[0783] The server uses image analysis technology to analyze video data of children received from users. Specifically, the server uses an image processing library to extract facial recognition and physical features from the video data and estimate growth, development, and health status. The input is video data provided by the user, and the output is indicator information regarding growth, development, and health status. This makes it possible to evaluate a child's health and growth in real time.

[0784] Step 5:

[0785] The server uses acoustic analysis capabilities to analyze audio data, such as children's cries. Using an acoustic analysis library, it extracts frequency characteristics from the audio data and analyzes the resulting audio patterns. The input is audio data, and the output is classification information indicating the cause of the crying. Based on this output, appropriate countermeasures and advice are generated.

[0786] Step 6:

[0787] The server uses emotion recognition to understand the user's emotional state. Specifically, it combines natural language processing and speech analysis to evaluate emotions from the user's text or voice tone. The input is the user's text or voice data, and the output is information indicating the user's emotional state. Based on this information, support messages are customized, enabling more helpful responses.

[0788] (Application Example 2)

[0789] 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".

[0790] In modern parenting, parents often face numerous anxieties, questions, and emotional burdens. Concerns about newborn crying and developmental progress are particularly serious, and there is a strong need for appropriate advice and information. However, parents often lack access to 24-hour support. Furthermore, personalized support tailored to the emotions and needs of individual families is insufficient. A system is needed to address these issues.

[0791] 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.

[0792] In this invention, the server includes means for receiving parenting consultations from parents via communication equipment and generating responses using natural language processing technology; means for analyzing the child's visual data using image analysis technology and evaluating their growth and health status; and means for analyzing the child's voice data using an acoustic analysis system, identifying the cause of the voice, and generating appropriate support advice. As a result, parents can not only receive personalized parenting support 24 hours a day, but also receive emotional support through messages that recognize their emotional state and provide a sense of security.

[0793] "Communication equipment" refers to electronic devices used to receive, transmit, and process digital data.

[0794] "Guardian" refers to an adult who has the responsibility to care for and educate a child.

[0795] "Childcare consultation" refers to the act of asking others questions or expressing concerns about childcare.

[0796] "Natural language processing technology" is the technology that allows computers to understand, interpret, and generate human language.

[0797] "Image analysis technology" is a technique that processes digital images and extracts meaningful information.

[0798] "Visual data" refers to information that is represented visually, such as images and videos.

[0799] "Growth and health status" refers to the state of a child's physical development and health.

[0800] An "acoustic analysis system" is a set of technologies that analyze audio data and extract its features.

[0801] "Audio data" refers to information recorded in digital format as sound pressure waves.

[0802] "Supportive advice" refers to information or suggestions or guidance provided to address a specific problem.

[0803] An "emotional analysis engine" is a technology used to identify emotional states from text and audio.

[0804] A "reassuring message" is a message intended to make the recipient feel safe and at ease.

[0805] "Administrative agencies" refer to public organizations such as the government and local authorities.

[0806] A "support plan" is a set of action guidelines or strategies formulated to achieve a specific objective.

[0807] The system for implementing this invention is initiated when a parent uses their personal communication device (e.g., a smartphone) to send a consultation or message regarding childcare. The device sends the message, image data, and audio data entered by the parent to the server. The server then performs the following processing:

[0808] The server first uses natural language processing technology on the text data received from the communication device. Specifically, it uses a generative AI model to analyze the content of the parent's inquiry. In this process, it uses an emotion analysis engine to recognize the parent's emotional state and generate a message that provides reassurance. An example of such a prompt would be, "Generate a reassuring response to the parent's message saying, 'I'm worried because my baby cries a lot at night.'"

[0809] Next, the server utilizes image analysis technology. This technology uses tools such as the Google Cloud Vision API to analyze the child's visual data transmitted from the communication device. Based on this analysis, the server assesses the child's growth and health status and provides feedback to the parents.

[0810] Furthermore, the server uses an acoustic analysis system to analyze the child's crying from the audio data. Based on the analysis, it identifies the cause of the crying and generates appropriate support advice.

[0811] Ultimately, the server uses information from government agencies to present a support plan tailored to each family's needs. This support plan enables more specific and effective childcare support. Through this system, parents will have access to 24-hour childcare consultation and support that meets their individual needs.

[0812] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0813] Step 1:

[0814] Users input text messages, images, and audio data related to childcare using their personal communication devices. This input includes questions and concerns related to childcare consultations. The user's device then sends this data to the server.

[0815] Step 2:

[0816] The server analyzes text data received from the terminal using natural language processing technology. Input: Text data. The processing uses a generative AI model to analyze the user's question and generate an appropriate response based on that analysis. Output: Generated response message. This response includes specific advice addressing the user's concerns and questions.

[0817] Step 3:

[0818] The server uses an emotion analysis engine to analyze the user's emotions from the received text. Input: User's text data. Processing involves evaluating the emotional state and generating additional messages to provide reassurance. Output: A message providing supplementary psychological support. This message is returned taking the user's emotions into consideration.

[0819] Step 4:

[0820] The server analyzes image data of children sent from the device using image analysis technologies such as the Google Cloud Vision API. Input: Image data. During processing, growth indicators and health status are automatically evaluated from the images, and advice is generated based on the results. Output: Growth and health evaluation results and recommendations.

[0821] Step 5:

[0822] The server analyzes audio data using an acoustic analysis system. Input: Audio data. Processing involves analyzing audio patterns and performing data calculations based on an algorithm that identifies the cause of the crying. Output: The cause of the crying and related advice.

[0823] Step 6:

[0824] The server develops support plans tailored to the specific needs of each household, based on information obtained from government agencies. Input: Information from government agencies and data on household needs. Processing: Combining this information to create a customized support plan. Output: Support plan for each household.

[0825] This entire process allows users to receive personalized support to address their concerns and anxieties about childcare.

[0826] 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.

[0827] 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.

[0828] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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."

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0847] The following is further disclosed regarding the embodiments described above.

[0848] (Claim 1)

[0849] A means for receiving parenting consultations from parents via a communication terminal and generating responses using a natural language processing model,

[0850] A means for analyzing video data of children using image processing technology to evaluate their growth and health status,

[0851] A means of analyzing a child's crying using an acoustic analysis module, identifying the cause of the crying, and generating appropriate advice,

[0852] A means of presenting support plans tailored to the individual needs of each household based on information from administrative services,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, wherein the communication terminal exchanges information via a widely used messaging application.

[0856] (Claim 3)

[0857] The system according to claim 1, wherein the information analysis and response generation processes are provided continuously for 24 hours.

[0858] "Example 1"

[0859] (Claim 1)

[0860] A means of receiving questions about childcare from parents via communication devices and generating answers using a generative AI algorithm,

[0861] A means of analyzing image data of infants using image analysis technology to evaluate indicators of growth and health,

[0862] A means of analyzing infant cries using an acoustic analysis device, identifying the cause of the crying, and providing appropriate advice,

[0863] A means of presenting support plans tailored to each family's needs based on information from public services,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, wherein the communication device transmits and receives data through a widely used messaging service.

[0867] (Claim 3)

[0868] The data analysis and response generation process is provided at all times in the system according to claim 1.

[0869] "Application Example 1"

[0870] (Claim 1)

[0871] A means for receiving parenting consultations from parents via a communication device and generating replies using natural language processing technology,

[0872] A means for analyzing children's image data using image analysis technology to evaluate their growth and health status,

[0873] A means for analyzing a child's crying using acoustic analysis technology, estimating the cause of the crying, and generating appropriate advice,

[0874] A means of presenting support plans tailored to the needs of each family based on information on administrative services, and further suggesting childcare products,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The communication device exchanges information via a widely used messaging application and has a childcare product mail-order function, according to claim 1.

[0878] (Claim 3)

[0879] The information analysis and response generation process is provided continuously for 24 hours, and the system according to claim 1 further includes personalized product suggestions.

[0880] "Example 2 of combining an emotion engine"

[0881] (Claim 1)

[0882] A means for receiving parental childcare consultations via a communication device and generating responses using natural language processing technology,

[0883] A means of analyzing children's video data using video analysis technology to evaluate growth and health indicators,

[0884] A means of analyzing children's voice data using acoustic analysis functions, identifying the cause of the voice, and generating appropriate advice,

[0885] A means of evaluating the user's emotional state using emotion recognition functionality and providing psychological support messages,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The communication device is a system according to claim 1 that exchanges information via widely used message-related technologies.

[0889] (Claim 3)

[0890] The system according to claim 1, wherein the information analysis and response generation steps are provided continuously for 24 hours.

[0891] "Application example 2 when combining with an emotional engine"

[0892] (Claim 1)

[0893] A means for receiving parenting consultations from guardians via communication devices and generating responses using natural language processing technology,

[0894] A means of analyzing children's visual data using image analysis technology to evaluate their growth and health status,

[0895] A means of analyzing children's voice data using an acoustic analysis system, identifying the cause of the voice, and generating appropriate support advice,

[0896] A means of recognizing the emotional state of a parent using an emotion analysis engine and generating messages that provide reassurance,

[0897] A means of presenting support plans tailored to the individual needs of each family based on information from administrative agencies,

[0898] A system that includes this.

[0899] (Claim 2)

[0900] The system according to claim 1, wherein the communication device exchanges information via a widely used message sending application.

[0901] (Claim 3)

[0902] The system according to claim 1, wherein the information analysis and response generation processes are provided continuously day and night. [Explanation of symbols]

[0903] 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. A means for receiving parenting consultations from parents via a communication terminal and generating responses using a natural language processing model, A means for analyzing video data of children using image processing technology to evaluate their growth and health status, A means of analyzing a child's crying using an acoustic analysis module, identifying the cause of the crying, and generating appropriate advice, A means of presenting support plans tailored to the individual needs of each household based on information from administrative services, A system that includes this.

2. The system according to claim 1, wherein the communication terminal exchanges information via a widely used messaging application.

3. The system according to claim 1, wherein the information analysis and response generation processes are provided continuously for 24 hours.

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