system

Abstract

We provide the system. [Solution] A means of analyzing the content of inquiries received from users using text analysis technology to identify the category of the inquiry content, A means of searching for relevant information from a database based on an identified category and evaluating the risk level of the consultation content, A means of providing the user with generated advice and information on related facilities, A means of receiving user feedback and analyzing it to improve the performance of the generative model, A system that includes this.

Description

Technical Field

[0001] The technology of this 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 steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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] Parents with children have the problem that it is difficult to immediately find an appropriate counselor for various problems they face daily. In particular, problems related to children's health, education, diet, and environment are diverse and often require specialized and individually customized advice. However, due to the limited number of people they can consult, it has become a factor increasing parents' anxiety and stress. Solving such problems and enabling parents to concentrate on raising children with peace of mind is required.

Means for Solving the Problems

[0005] This invention provides a system that analyzes consultation content received from users using text analysis technology to identify the category of the consultation content. Based on the identified category, it searches a database for relevant information and evaluates the risk level of the consultation content. Furthermore, it includes means for providing the user with generated advice and information on relevant facilities. It also receives user feedback and analyzes it to improve the performance of the AI ​​model, enabling the provision of more accurate advice. This allows for immediate and appropriate support for the various challenges faced by parents.

[0006] A "user" is the entity that uses the service and enters the details of their inquiry.

[0007] "Consultation details" refer to information that expresses the problems or questions the user has.

[0008] "Text analysis" is the process of analyzing received text using natural language processing technology and understanding its meaning.

[0009] A "category" is a classification item for the content of the consultation identified through text analysis.

[0010] A "database" is a collection of information that systematically stores related information.

[0011] "Assessing the degree of risk" means estimating the importance and urgency of the matter being discussed.

[0012] "Advice" refers to suggestions or recommended actions provided in response to a consultation.

[0013] "Facility information" refers to information about local facilities and services relevant to the user.

[0014] "Feedback" refers to the evaluation and expression of opinions that a user makes regarding the advice they have received.

[0015] A "generation model" is an AI technology used to create advice provided to users.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It 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 the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiments for Carrying Out the Invention

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a labeled 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.

[0020] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention provides an AI system for addressing various parenting-related concerns. This system is used by users through an application accessible via a smartphone or computer.

[0038] First, the user launches the application installed on their device and enters their question. For example, the user might enter a question such as, "My child is going through a rebellious phase and we're having trouble communicating."

[0039] Next, the device sends the user's input to the server. This transmission may include the user's location information and profile information.

[0040] The server analyzes the received consultation content using text analysis technology (natural language processing). Based on the analysis results, the consultation content is classified into categories such as education, health, nutrition, and environment. For example, keywords related to "rebellious phase" and "communication" are extracted and classified into the education category.

[0041] The server then searches its internal database for information related to the relevant category and assesses the level of risk involved in the consultation. This assessment determines the degree of support required.

[0042] The server uses an AI model to generate advice that should be provided to the user. This includes specific countermeasures and action suggestions based on the extracted information. For example, it can create advice on "appropriate ways to interact with children" or "communication strategies that should be shared between parents and children."

[0043] The server also collects relevant facility information based on the user's location. This includes information on local classes, counseling services, and other services accessible to the user.

[0044] The terminal displays advice and facility information sent from the server to the user. The user can then use this information to take specific actions.

[0045] Furthermore, users send feedback to the server via their device, including the results of implementing the provided advice and their opinions. This feedback is analyzed to improve the accuracy of the AI ​​model and enhance the quality of advice provided next time.

[0046] Thus, the present invention is provided as an AI system for quickly and effectively solving the challenges related to child-rearing that parents face, and can provide appropriate and personalized support to users.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] The user launches the application on their device and enters their questions about parenting. For example, they might enter a question about "my child won't eat vegetables."

[0050] Step 2:

[0051] The terminal sends the entered consultation details to the server. The user's location information may be included in the transmission.

[0052] Step 3:

[0053] The server analyzes the received inquiries using natural language processing technology and classifies the content into categories such as education, health, nutrition, and environment. For example, information related to "not eating vegetables" is extracted and classified into the nutrition category.

[0054] Step 4:

[0055] The server searches its internal database for relevant information based on the classified categories. It also assesses the severity of any problems that arise. For example, it might evaluate the degree of impact that a lack of vegetables has on children's health.

[0056] Step 5:

[0057] The server uses an AI model to generate specific advice for the user based on analysis results and search information. For example, it might suggest recipes or presentation methods for enjoying a particular vegetable.

[0058] Step 6:

[0059] The server collects information about relevant local facilities and services based on the user's location and adds this information to the advice. For example, it collects information about nearby cooking classes or nutritionist consultation services.

[0060] Step 7:

[0061] The server sends the generated advice and facility information to the terminal. This includes specific action suggestions, information on how to obtain ingredients, and details about nearby facilities.

[0062] Step 8:

[0063] The terminal displays information received from the server to the user. Based on the advice, the user can take specific actions to improve their eating habits.

[0064] Step 9:

[0065] Users input the results of the advice they received and their own opinions into their device and send them to the server as feedback. This allows them to communicate the effects obtained and any problems that arose.

[0066] Step 10:

[0067] The server analyzes the feedback sent by users and uses it as data to improve the performance of the AI ​​model. This allows for more accurate advice to be provided next time.

[0068] (Example 1)

[0069] 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."

[0070] One challenge in parenting is the difficulty in obtaining prompt and accurate advice on the various issues and concerns parents face. In particular, the diversity of these concerns makes it difficult to provide individualized support. Furthermore, efficiently obtaining and appropriately utilizing information on local services and facilities is not easy.

[0071] 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.

[0072] In this invention, the server includes means for analyzing the consultation content received from the user using natural language processing technology to identify the category of the consultation content, means for searching for relevant information from a database based on the identified category and evaluating the risk level of the consultation content, and means for providing the user with advice generated using a generative AI model and information on relevant facilities. As a result, the user can receive personalized advice and efficiently obtain and utilize information on facilities relevant to the region.

[0073] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0074] "Identifying a category" is the process of sorting received information into a specific classification.

[0075] A "data bank" is a collection of data that stores information and allows it to be searched and used as needed.

[0076] "Assessing the level of risk" means analyzing how much risk the information in question poses.

[0077] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate appropriate answers or suggestions from input information.

[0078] "Information about related facilities" refers to data about specific services or facilities that the user is interested in.

[0079] "Receiving feedback" refers to obtaining information and opinions from users.

[0080] This invention provides an AI system that effectively resolves parenting-related issues faced by users. This system, utilizing appropriate hardware and software, has the capability to provide prompt and accurate advice and relevant information.

[0081] First, the user uses an application installed on a device such as a smartphone or computer. This application has an interface that allows the user to easily input and send their consultation details. For example, the user might input a consultation request such as, "My child is going through a rebellious phase and I'm having trouble communicating with them."

[0082] The device encrypts the information entered by the user and transmits it to a central server via a secure connection. The transmitted information may include not only text data but also the user's location and profile information.

[0083] The server is equipped with a high-performance computing system to process the received data. The server uses natural language processing techniques to analyze the user's inquiry and classify it into multiple categories. A generative AI model is used at this stage. For example, if a specific keyword is extracted, the category is identified as "education" based on that keyword.

[0084] Next, the server retrieves data related to the classified category from the database and uses it to assess the risk level of the consultation. If necessary, it uses a generative AI model to generate specific advice for the user. This advice includes "appropriate ways to interact with children" and "communication strategies to share between parents and children." Location information is also used to collect information about local facilities relevant to the user.

[0085] The terminal displays advice and facility information provided by the server to the user. The user can then use this information to plan and execute specific actions. Later, the user sends feedback to the server regarding the results of these actions and their opinions. This feedback is used as data to improve the accuracy of the AI ​​model.

[0086] An example of a prompt message might be, "What specific actions should parents take when their child is rebellious and won't listen?" In this way, the present invention is a system that technically supports parents in facing various challenges in raising their children.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The user launches the application installed on their device and enters their parenting-related questions. The entered text data includes specific questions such as, "My child is going through a rebellious phase and I'm having trouble communicating with them." This input data is then formatted into the required format and prepared for transmission.

[0090] Step 2:

[0091] The terminal sends the text data entered by the user to the server. During this process, the data is encrypted using secure protocols such as SSL, and the user's location and profile information are added. The transmitted input data includes the text of the consultation, location information, and profile information.

[0092] Step 3:

[0093] The server analyzes the received data using natural language processing techniques. First, it extracts keywords from the text data of the input consultation. Based on this analysis, words such as "rebellious phase" and "communication" are classified into the education category. The output is data classified into a specific category.

[0094] Step 4:

[0095] The server searches its internal database for relevant information based on the identified category. Next, an algorithm is applied to assess the risk level of the data. At this stage, if the consultation is determined to be a family relationship issue, it may be assessed as high risk. The relevant data and its assessment are then output.

[0096] Step 5:

[0097] The server uses a generative AI model to generate specific advice. Based on the extracted information, suggestions such as "appropriate ways to interact with children" and "effective communication methods between parents and children" are created. The server also utilizes the user's location information to collect information on local extracurricular activities and counseling services. The output consists of customized advice and relevant facility information.

[0098] Step 6:

[0099] The terminal receives output from the server and presents it to the user. This includes specific advice and information about local facilities. The user can review this information and use it in their daily life. The presented information is the final output.

[0100] Step 7:

[0101] Users send feedback to the server via their device, detailing the results and opinions of implementing the provided advice. This feedback is used as training data for the AI ​​model and analyzed to improve the accuracy of the advice. The feedback sent becomes important input data for future improvements.

[0102] (Application Example 1)

[0103] 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."

[0104] In child-rearing, it is crucial for parents to receive appropriate guidance and advice regarding their children's safety. However, information regarding safety when going out is diverse, making it difficult for parents to judge each aspect individually. Therefore, there is a need for a system that is easy for users to use and provides safety information tailored to individual situations.

[0105] 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.

[0106] In this invention, the server includes means for analyzing the consultation content received from the user using text analysis technology to identify the category of the consultation content, means for searching for relevant information from a database based on the identified category and evaluating the degree of risk of the consultation content, and means for combining location information and local safety information to propose a safe action plan to the user. As a result, the user can obtain specific and safe action guidelines that take into account the current situation.

[0107] A "user" is an entity that uses the system to input information about their concerns and receives advice and safety information.

[0108] "Text analysis technology" is a technology that uses natural language processing to analyze the content of inquiries entered by users, and to understand and classify that content.

[0109] A "category" is a framework for classifying consultation content into specific fields or topics.

[0110] A "database" is an information aggregation system that stores related information and past cases, and allows for searching and referencing them as needed.

[0111] "Risk assessment" is the process of determining the level of risk based on the content of the consultation received and the current environment.

[0112] "Location information" refers to data that indicates the user's current location, and is obtained through methods such as GPS.

[0113] "Local safety information" refers to information about safety in a specific area, such as crime statistics and traffic conditions.

[0114] An "action plan" is a proposal outlining specific steps and routes for users to act safely.

[0115] A "generative model" is an algorithm that automatically creates advice and suggestions to provide to users, and it utilizes AI technology.

[0116] The system for implementing this invention mainly consists of a user terminal, a server, and a database for processing various related information. The user accesses the system using a terminal such as a smartphone or personal computer and begins by entering their inquiry details.

[0117] The application installed on the user's device sends the entered consultation content to the server. The server analyzes the consultation content using natural language processing technology and identifies the category of the consultation content based on this analysis. Specific software used may include Python or natural language processing libraries based on Tensorflow®.

[0118] Based on the results of this analysis, the server searches the database for relevant information and evaluates the level of risk in the consultation. For example, it uses the Google® Maps API to obtain local safety information and generates suggestions for how children can act safely based on that information. This generated advice and action plan are then provided to the user by the server.

[0119] User feedback is sent back to the server and used to improve the generated AI model. The AI ​​model analyzes past data and user feedback, and is adjusted based on the newly gained insights to improve the accuracy of future advice.

[0120] As a concrete example, if a user enters "I want my child to get to the park safely, but I want to know the safest route," the system will consider local crime statistics and combine them with Google Maps route information to suggest a safe course of action. An example of a prompt used in this case would be, "Please tell me the safety information for my child's destination. Current location is the inserted location information, and destination is the entered destination."

[0121] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0122] Step 1:

[0123] The user launches an application installed on a device such as a smartphone and enters their inquiry. The input data includes text such as, "I want to know a safe route for my child to the park." This input serves as the basis for subsequent analysis.

[0124] Step 2:

[0125] The terminal sends user input to the server. At the same time, the terminal also acquires the user's location information (GPS data) and sends it along with profile information. The server receives this and creates a unique request tailored to each user.

[0126] Step 3:

[0127] The server analyzes the received consultation content using natural language processing technology. It analyzes the input text and identifies categories such as education, safety, and environment. It generates prompt sentences and sends them to the AI ​​model, outputting the analysis results.

[0128] Step 4:

[0129] The server searches the database based on identified categories to retrieve local safety information. For example, it uses the Google Maps API to match data on crime statistics and traffic conditions and extract relevant information. This data forms the basis for advice given to the user.

[0130] Step 5:

[0131] Based on the acquired information, the server uses a generative AI model to create advice for the user. Specifically, it generates action plans such as safe routes and points to note. In this process, the model integrates and analyzes the information to construct the recommendations.

[0132] Step 6:

[0133] The server generates advice and action plans, which are then sent to the device. The device receives these and presents them to the user. The user can then use the displayed information as a reference when their child goes out.

[0134] Step 7:

[0135] Users send feedback on the advice provided to the server via their device. The server collects the feedback and analyzes it to improve the accuracy of the generated AI model. This improves the quality of advice in the future.

[0136] 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.

[0137] This invention is an AI-powered childcare support system that combines an emotion engine. In addition to analyzing the user's consultation content, it understands their emotional state to provide more precise and personalized advice. This system can take the user's emotions into consideration and adjust the content and expression of the advice accordingly.

[0138] First, the user enters their concerns via their device about an issue affecting their emotions. For example, they might enter a question like, "My child won't listen to me, and it's making me frustrated," which can be stressful and anxiety-inducing for a parent.

[0139] The terminal sends input information to the server and also invokes an emotion recognition function to capture diverse emotional data. This allows the emotion engine to analyze the emotions expressed from the user's input.

[0140] The server analyzes the received text data using natural language processing and classifies the consultation category. During this process, the emotion engine identifies the user's emotional state and selects the appropriate format and tone of advice. For example, if the emotion "irritated" is detected, gentle encouragement and advice to maintain calmness will be provided.

[0141] Furthermore, the server takes into account the user's emotional state, searches the database for relevant information, and assesses the risk level of the consultation problem. It can also determine the priority of support based on the intensity of the emotions.

[0142] The generated advice is delivered in appropriate wording, adjusted by an emotion engine. The server simultaneously provides information on nearby support facilities linked to the user's location. This may include individual counseling services or stress care classes, as needed.

[0143] The information provided is presented to the user via their device, allowing them to understand specific actions to take. After performing an action, the user reports their feedback, including any changes in their emotions.

[0144] The collected feedback and emotional change data are analyzed by the server and continuously contribute to improving the accuracy of the AI ​​model. This will enable the user to receive even more accurate support during the next consultation.

[0145] This invention makes it possible to support a more comprehensive and healthy child-rearing environment by considering not only the resolution of the issues raised but also the emotional aspects of the user.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] The user uses the device's interface to input details about emotionally charged problems or worries. For example, they might input a situation like, "My child won't sleep, and I'm worried."

[0149] Step 2:

[0150] The device sends data to the server, along with the user's consultation content, to identify their emotions. This includes emotion indicators derived from the user's text input.

[0151] Step 3:

[0152] The server analyzes the received consultation content using natural language processing technology to identify the category of the problem. For example, it might be classified as a consultation about sleep.

[0153] Step 4:

[0154] The server uses an emotion engine to analyze the user's emotional state. The emotion engine recognizes the user's anxiety and stress from the text of the consultation and adjusts the tone of the advice based on this.

[0155] Step 5:

[0156] The server searches its internal database for appropriate information based on identified categories and sentiment data. During this process, it evaluates the importance and urgency of the consultation and sets priorities according to the sentiment.

[0157] Step 6:

[0158] The server creates emotionally sensitive advice and selects appropriate language based on the content of the suggestions. For example, it might offer gentle encouragement to alleviate anxiety or suggest concrete action plans.

[0159] Step 7:

[0160] The server takes the user's location into consideration, collects information on relevant local support facilities and services, and provides this information along with the advice.

[0161] Step 8:

[0162] The terminal displays advice and facility information received from the server to the user. The user can use this information to plan specific actions.

[0163] Step 9:

[0164] Users input feedback on the results of their actions and any new emotional states into their device and send it to the server. This clarifies the effectiveness of the advice and areas for improvement.

[0165] Step 10:

[0166] The server analyzes user feedback and emotional change data, and uses this information to improve the AI ​​model's performance. This enables more accurate support during subsequent user consultations.

[0167] (Example 2)

[0168] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0169] Conventional childcare support systems struggle to provide personalized advice that fully considers the emotional aspects of users' consultations. As a result, users often fail to receive appropriate support that respects their feelings, leading to insufficient problem-solving. There is a need to improve this situation and enable the provision of appropriate advice that is sensitive to the user's emotional state.

[0170] 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.

[0171] This invention includes a server that analyzes text using natural language processing and emotion recognition technologies to analyze the content of a consultation received from a user and its emotional state, and includes means for identifying the category and emotional state of the consultation; means for retrieving relevant information from information storage based on the identified category and emotional state and evaluating the urgency of the consultation; and means for providing the user with generated advice and information on relevant support facilities. This makes it possible to provide accurate and personalized parenting advice that takes into account the user's emotional state.

[0172] A "user" refers to an individual who uses this system to seek advice and support regarding childcare.

[0173] "Consultation details" refer to text data containing information about the specific problems or situations the user is facing, including emotional aspects.

[0174] "Natural language processing technology" is an information processing technology that analyzes text data to understand and process human language.

[0175] "Emotion recognition technology" is a technology that identifies emotional states from a user's text data, determining the intensity and type of emotion.

[0176] A "category" is a classification criterion used to identify and categorize the nature and subject matter of a consultation.

[0177] "Emotional state" refers to information that represents the type and intensity of emotions expressed in the user's consultation.

[0178] "Information storage" refers to databases and information storage systems used to store and retrieve related information.

[0179] "Urgency" is a scale used to evaluate the importance and urgency of the consultation topic.

[0180] "Advice" refers to specific action suggestions and suggestions provided to the user, generated based on the content of the consultation and their emotional state.

[0181] A "support facility" is a collection of organizations and services that provide the support that users need.

[0182] This invention is an AI-powered childcare support system that combines an emotion engine to analyze the content of the user's consultation and provide personalized advice according to their emotional state. Based on the text data provided by the user, this system uses natural language processing and emotion recognition technologies to analyze the user's emotions and provide more appropriate advice.

[0183] First, the user uses their device to input a specific prompt, such as, "My child is going through a rebellious phase and won't listen to anything I do, and I'm worried about how to deal with it." The user's inquiry is then taken into the device as text data to be used with a natural language processing engine and emotion recognition technology.

[0184] The terminal sends this input data to the server and simultaneously activates the emotion recognition module. The emotion recognition technology analyzes the emotional state from the input text and provides information to more precisely understand its content. This allows for the selection of appropriate responses based on the user's emotional state.

[0185] Next, the server analyzes the received text data using natural language processing technology and categorizes the consultation content according to its content. Using an emotion engine, it identifies the user's emotional state, and based on the results, a generative AI model generates individually tailored advice.

[0186] Specifically, the software will utilize commonly used platforms and libraries for natural language processing (e.g., TensorFlow and PyTorch), and introduce sentiment analysis tools that employ machine learning algorithms for emotion recognition. These technologies will enable the system to accurately understand and respond flexibly to user emotions.

[0187] Ultimately, the server provides the user with generated advice and information on support facilities as needed, via the terminal. This process allows the user to receive guidance that takes their emotional state into consideration, enabling them to attempt appropriate actions toward problem-solving. This invention makes it possible to provide more comprehensive and accurate support for various emotional challenges in parenting.

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

[0189] Step 1:

[0190] The user enters their parenting concerns into the device. For example, they might enter a prompt such as, "I'm worried because my child's grades at school have dropped." This input is then captured by the device as text data.

[0191] Step 2:

[0192] The terminal sends the text data entered by the user to the server and simultaneously activates an emotion recognition module. The transmitted data is analyzed by emotion recognition technology, and data is prepared to visualize the user's emotional state as expressed in the text.

[0193] Step 3:

[0194] The server analyzes the received text data using natural language processing techniques. Specifically, it divides the text into tokens and detects the context and meaning. This allows it to classify the consultation content into corresponding categories and generate categorized data as output.

[0195] Step 4:

[0196] The server uses an emotion engine to identify the user's emotional state. In this step, a generative AI model is used to analyze emotional features extracted from the input data and generate output data that identifies the type and intensity of the emotion.

[0197] Step 5:

[0198] The server searches for relevant resources from information storage based on the identified category and emotional state. In this step, it retrieves advice and support facility information that matches the user's needs and generates the output.

[0199] Step 6:

[0200] The server uses an emotion engine to refine the generated advice into a format suitable for the user. The generative AI model customizes the wording and tone of the advice to fit the user's emotional state. This output becomes the final advice and related information.

[0201] Step 7:

[0202] The terminal presents the user with the final advice and information sent from the server. Based on this information, the user can then plan specific actions.

[0203] Step 8:

[0204] After the user performs the suggested action, they report the results and changes in their emotions as feedback via their device. Based on this feedback, emotion change data is generated.

[0205] Step 9:

[0206] The server analyzes the received feedback data and uses it to improve the accuracy of the generated AI model. This updates the dataset, contributing to improved support in the future.

[0207] (Application Example 2)

[0208] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0209] Conventional consultation support systems have the drawback of only being able to provide standardized responses to user inquiries, and failing to offer detailed support tailored to the emotional state of individual users. Furthermore, they lack the ability to suggest appropriate contact methods to those being contacted, resulting in a limited user experience. This makes it difficult to improve user emotional satisfaction and provide more effective support.

[0210] 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.

[0211] In this invention, the server includes means for analyzing the content of inquiries received from users using text analysis technology and identifying the category of the inquiries; means for analyzing emotional data received from users using emotion recognition technology; and means for providing suggestions on how to contact the contact person based on the results of the emotion recognition technology. This makes it possible to provide detailed responses that take into account the emotional state of the user and to suggest appropriate contact methods to employees.

[0212] A "user" is the entity that uses the system to input their consultation details and receive support.

[0213] "Text analysis technology" is a technology that analyzes the text of consultations provided by users to identify the meaning and intent of the content.

[0214] A "category" refers to a specific group classified based on the characteristics of the consultation content.

[0215] An "information recording device" refers to a database or storage system that stores necessary information and keeps it in a searchable state.

[0216] "Risk level" refers to the result of evaluating the degree of risk associated with the content of the consultation.

[0217] "Advice" refers to solutions or guidelines provided to users.

[0218] "Feedback" refers to responses or opinions that users provide based on advice or the results of a service.

[0219] A "generative model" refers to the learning algorithm of artificial intelligence used to generate advice for users.

[0220] "Emotion recognition technology" is a technology that analyzes a user's emotions from their facial expressions and behavior, and identifies the type and intensity of those emotions.

[0221] "Contact method" refers to the specific actions and approaches that a contact person should take depending on the user's emotional state.

[0222] This system is an assistance system that uses emotion recognition technology to enable users to receive personalized advice based on their individual emotions. Its primary operation is based on communication between terminals, servers, and information recording devices.

[0223] First, the user uses a device such as a smartphone or tablet to input their consultation details as text. This consultation content may express the user's emotions. The device sends the entered text data to a server, and at the same time, uses emotion recognition technology to analyze the user's emotions. In this process, for example, natural language processing (NLP) is used for voice data, while visual analysis technology is applied to facial expression data.

[0224] The server analyzes the received text data using advanced text analysis techniques and searches for relevant information from the information recording device based on the identified category of the consultation content. At the same time, it uses a generative model in the cloud, incorporating the results of emotion recognition, to generate advice and propose contact methods tailored to the user's emotional state.

[0225] For example, if a user is experiencing excessive stress, the server generates encouraging messages in a gentle tone to reduce stress, as well as recommendations for available counseling services. The generated advice is then refined by an emotion engine and delivered to the user in the most appropriate format.

[0226] The device presents the generated advice to the user, allowing the user to take action based on that advice. Furthermore, user feedback is sent back to the system to improve the accuracy of the generated AI model. This will enable the system to provide more accurate advice in similar situations in the future.

[0227] For example, if a user says, "I'm feeling down today," the server will provide advice such as, "Let's find some time to relax. I can recommend a nearby relaxation facility." An example of a prompt message would be, "Suggest ways to contact the user when they are feeling down."

[0228] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0229] Step 1:

[0230] The terminal receives the user's inquiry as text input. It constructs the input text data and sends it to the server. In this process, the user's terminal secures the text data and structures it.

[0231] Step 2:

[0232] The server analyzes the received text data. Here, natural language processing (NLP) techniques are used to extract intent and categorize the text. The input is text data, and the output is information about the consultation category and emotions. This allows the server to understand the context of the text and prepare it for further processing.

[0233] Step 3:

[0234] The server retrieves relevant information from the information recording device based on the consultation category obtained from text analysis. The input is category information, and the output is the corresponding relevant data. The main operation of this step is to quickly retrieve the appropriate information using database queries.

[0235] Step 4:

[0236] The device uses emotion recognition technology to understand the user's emotional state. Input is sensor data obtained from the user's voice and facial expressions, while output is the type and intensity of the emotion. This allows the device to identify the user's emotions in real time and transmit the information to the server with the required level of accuracy.

[0237] Step 5:

[0238] The server uses acquired sentiment information and related data to generate advice using a generative AI model. The input is sentiment data and categorical information, and the output is personalized advice. The generative model makes predictions using appropriate prompts to create advice optimized for the user.

[0239] Step 6:

[0240] The terminal presents the user with advice received from the server. The input is the generated advice, and the output is the feedback reflected in the user's response. The goal is to ensure that the user fully understands the information provided and can take follow-up actions.

[0241] Step 7:

[0242] The user takes action based on the advice provided and reports the results and feedback to the server via their device. The input is the user's actions, and the output is feedback data used for improvement. This feedback is used to tune the generated AI model, promoting overall system accuracy improvement.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] [Second Embodiment]

[0247] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0248] 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.

[0249] 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).

[0250] 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.

[0251] 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.

[0252] 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).

[0253] 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.

[0254] 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.

[0255] 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.

[0256] 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.

[0257] 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.

[0258] 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".

[0259] This invention provides an AI system for addressing various parenting-related concerns. This system is used by users through an application accessible via a smartphone or computer.

[0260] First, the user launches the application installed on their device and enters their question. For example, the user might enter a question such as, "My child is going through a rebellious phase and we're having trouble communicating."

[0261] Next, the device sends the user's input to the server. This transmission may include the user's location information and profile information.

[0262] The server analyzes the received consultation content using text analysis technology (natural language processing). Based on the analysis results, the consultation content is classified into categories such as education, health, nutrition, and environment. For example, keywords related to "rebellious phase" and "communication" are extracted and classified into the education category.

[0263] The server then searches its internal database for information related to the relevant category and assesses the level of risk involved in the consultation. This assessment determines the degree of support required.

[0264] The server uses an AI model to generate advice that should be provided to the user. This includes specific countermeasures and action suggestions based on the extracted information. For example, it can create advice on "appropriate ways to interact with children" or "communication strategies that should be shared between parents and children."

[0265] The server also collects relevant facility information based on the user's location. This includes information on local classes, counseling services, and other services accessible to the user.

[0266] The terminal displays advice and facility information sent from the server to the user. The user can then use this information to take specific actions.

[0267] Furthermore, users send feedback to the server via their device, including the results of implementing the provided advice and their opinions. This feedback is analyzed to improve the accuracy of the AI ​​model and enhance the quality of advice provided next time.

[0268] Thus, the present invention is provided as an AI system for quickly and effectively solving the challenges related to child-rearing that parents face, and can provide appropriate and personalized support to users.

[0269] The following describes the processing flow.

[0270] Step 1:

[0271] The user launches the application on their device and enters their questions about parenting. For example, they might enter a question about "my child won't eat vegetables."

[0272] Step 2:

[0273] The terminal sends the entered consultation details to the server. The user's location information may be included in the transmission.

[0274] Step 3:

[0275] The server analyzes the received inquiries using natural language processing technology and classifies the content into categories such as education, health, nutrition, and environment. For example, information related to "not eating vegetables" is extracted and classified into the nutrition category.

[0276] Step 4:

[0277] The server searches its internal database for relevant information based on the classified categories. It also assesses the severity of any problems that arise. For example, it might evaluate the degree of impact that a lack of vegetables has on children's health.

[0278] Step 5:

[0279] The server uses an AI model to generate specific advice for the user based on analysis results and search information. For example, it might suggest recipes or presentation methods for enjoying a particular vegetable.

[0280] Step 6:

[0281] Based on the user's location information, the server collects facility and service information in the relevant area and adds that information to the advice. For example, it collects information on nearby cooking classes and dietitian consultation services.

[0282] Step 7:

[0283] The server sends the generated advice and facility information to the terminal. This includes specific action proposals, ways to obtain ingredients, and details of nearby facilities.

[0284] Step 8:

[0285] The terminal displays the information received from the server to the user. Based on the advice, the user can take specific actions to improve their eating habits.

[0286] Step 9:

[0287] The user inputs the results of the implemented advice and their own opinions into the terminal and sends them to the server as feedback. This allows the obtained effects and problems to be communicated.

[0288] Step 10:

[0289] The server analyzes the feedback sent by the user and uses it as data to improve the performance of the AI model. This makes the next advice more accurate.

[0290] (Example 1)

[0291] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] One challenge in parenting is the difficulty in obtaining prompt and accurate advice on the various issues and concerns parents face. In particular, the diversity of these concerns makes it difficult to provide individualized support. Furthermore, efficiently obtaining and appropriately utilizing information on local services and facilities is not easy.

[0293] 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.

[0294] In this invention, the server includes means for analyzing the consultation content received from the user using natural language processing technology to identify the category of the consultation content, means for searching for relevant information from a database based on the identified category and evaluating the risk level of the consultation content, and means for providing the user with advice generated using a generative AI model and information on relevant facilities. As a result, the user can receive personalized advice and efficiently obtain and utilize information on facilities relevant to the region.

[0295] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0296] "Identifying a category" is the process of sorting received information into a specific classification.

[0297] A "data bank" is a collection of data that stores information and allows it to be searched and used as needed.

[0298] "Assessing the level of risk" means analyzing how much risk the information in question poses.

[0299] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate appropriate answers or suggestions from input information.

[0300] "Information about related facilities" refers to data about specific services or facilities that the user is interested in.

[0301] "Receiving feedback" refers to obtaining information and opinions from users.

[0302] This invention provides an AI system that effectively resolves parenting-related issues faced by users. This system, utilizing appropriate hardware and software, has the capability to provide prompt and accurate advice and relevant information.

[0303] First, the user uses an application installed on a device such as a smartphone or computer. This application has an interface that allows the user to easily input and send their consultation details. For example, the user might input a consultation request such as, "My child is going through a rebellious phase and I'm having trouble communicating with them."

[0304] The device encrypts the information entered by the user and transmits it to a central server via a secure connection. The transmitted information may include not only text data but also the user's location and profile information.

[0305] The server is equipped with a high-performance computing system to process the received data. The server uses natural language processing techniques to analyze the user's inquiry and classify it into multiple categories. A generative AI model is used at this stage. For example, if a specific keyword is extracted, the category is identified as "education" based on that keyword.

[0306] Next, the server retrieves data related to the classified category from the database and uses it to assess the risk level of the consultation. If necessary, it uses a generative AI model to generate specific advice for the user. This advice includes "appropriate ways to interact with children" and "communication strategies to share between parents and children." Location information is also used to collect information about local facilities relevant to the user.

[0307] The terminal presents advice and facility information provided by the server to the user. The user can plan and execute specific actions by referring to this. Later, the user sends the results and opinions of these actions to the server as feedback. This feedback is utilized as data for improving the accuracy of the AI model.

[0308] Examples of prompt sentences include ones like "What are the specific actions that parents should take when their children are in the rebellious stage and don't listen to them?" In this way, the present invention is a system that technically supports various issues faced by parents in raising children.

[0309] The flow of the specific process in Example 1 will be described using FIG. 11.

[0310] Step 1:

[0311] The user launches the application installed on the terminal and enters the consultation content regarding child-rearing. The input text data includes specific consultation content such as "The child is in the rebellious stage and communication is not going well." This input data is formatted into the required form and prepared for transmission.

[0312] Step 2:

[0313] The terminal sends the text data input by the user to the server. In this process, the data is encrypted using a secure protocol such as SSL, and the user's location information and profile information are added. The transmitted input data includes the text of the consultation content, location information, and profile information.

[0314] Step 3:

[0315] The server analyzes the received data using natural language processing techniques. First, it extracts keywords from the text data of the input consultation. Based on this analysis, words such as "rebellious phase" and "communication" are classified into the education category. The output is data classified into a specific category.

[0316] Step 4:

[0317] The server searches its internal database for relevant information based on the identified category. Next, an algorithm is applied to assess the risk level of the data. At this stage, if the consultation is determined to be a family relationship issue, it may be assessed as high risk. The relevant data and its assessment are then output.

[0318] Step 5:

[0319] The server uses a generative AI model to generate specific advice. Based on the extracted information, suggestions such as "appropriate ways to interact with children" and "effective communication methods between parents and children" are created. The server also utilizes the user's location information to collect information on local extracurricular activities and counseling services. The output consists of customized advice and relevant facility information.

[0320] Step 6:

[0321] The terminal receives output from the server and presents it to the user. This includes specific advice and information about local facilities. The user can review this information and use it in their daily life. The presented information is the final output.

[0322] Step 7:

[0323] Users send feedback to the server via their device, detailing the results and opinions of implementing the provided advice. This feedback is used as training data for the AI ​​model and analyzed to improve the accuracy of the advice. The feedback sent becomes important input data for future improvements.

[0324] (Application Example 1)

[0325] 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."

[0326] In child-rearing, it is crucial for parents to receive appropriate guidance and advice regarding their children's safety. However, information regarding safety when going out is diverse, making it difficult for parents to judge each aspect individually. Therefore, there is a need for a system that is easy for users to use and provides safety information tailored to individual situations.

[0327] 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.

[0328] In this invention, the server includes means for analyzing the consultation content received from the user using text analysis technology to identify the category of the consultation content, means for searching for relevant information from a database based on the identified category and evaluating the degree of risk of the consultation content, and means for combining location information and local safety information to propose a safe action plan to the user. As a result, the user can obtain specific and safe action guidelines that take into account the current situation.

[0329] A "user" is an entity that uses the system to input information about their concerns and receives advice and safety information.

[0330] "Text analysis technology" is a technology that uses natural language processing to analyze the content of inquiries entered by users, and to understand and classify that content.

[0331] A "category" is a framework for classifying consultation content into specific fields or topics.

[0332] A "database" is an information aggregation system that stores related information and past cases, and allows for searching and referencing them as needed.

[0333] "Risk assessment" is the process of determining the level of risk based on the content of the consultation received and the current environment.

[0334] "Location information" refers to data that indicates the user's current location, and is obtained through methods such as GPS.

[0335] "Local safety information" refers to information about safety in a specific area, such as crime statistics and traffic conditions.

[0336] An "action plan" is a proposal outlining specific steps and routes for users to act safely.

[0337] A "generative model" is an algorithm that automatically creates advice and suggestions to provide to users, and it utilizes AI technology.

[0338] The system for implementing this invention mainly consists of a user terminal, a server, and a database for processing various related information. The user accesses the system using a terminal such as a smartphone or personal computer and begins by entering their inquiry details.

[0339] The application installed on the user's device sends the entered consultation content to the server. The server analyzes the consultation content using natural language processing technology and identifies the category of the consultation content based on this analysis. Specific software used may include natural language processing libraries based on Python or TensorFlow.

[0340] Based on the results of this analysis, the server searches the database for relevant information and assesses the level of risk in the consultation. For example, it uses the Google Maps API to obtain local safety information and generates suggestions for how children can act safely based on that information. This generated advice and action plan are then provided to the user by the server.

[0341] User feedback is sent back to the server and used to improve the generated AI model. The AI ​​model analyzes past data and user feedback, and is adjusted based on the newly gained insights to improve the accuracy of future advice.

[0342] As a concrete example, if a user enters "I want my child to get to the park safely, but I want to know the safest route," the system will consider local crime statistics and combine them with Google Maps route information to suggest a safe course of action. An example of a prompt used in this case would be, "Please tell me the safety information for my child's destination. Current location is the inserted location information, and destination is the entered destination."

[0343] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0344] Step 1:

[0345] The user launches an application installed on a device such as a smartphone and enters their inquiry. The input data includes text such as, "I want to know a safe route for my child to the park." This input serves as the basis for subsequent analysis.

[0346] Step 2:

[0347] The terminal sends user input to the server. At the same time, the terminal also acquires the user's location information (GPS data) and sends it along with profile information. The server receives this and creates a unique request tailored to each user.

[0348] Step 3:

[0349] The server analyzes the received consultation content using natural language processing technology. It analyzes the input text and identifies categories such as education, safety, and environment. It generates prompt sentences and sends them to the AI ​​model, outputting the analysis results.

[0350] Step 4:

[0351] The server searches the database based on identified categories to retrieve local safety information. For example, it uses the Google Maps API to match data on crime statistics and traffic conditions and extract relevant information. This data forms the basis for advice given to the user.

[0352] Step 5:

[0353] Based on the acquired information, the server uses a generative AI model to create advice for the user. Specifically, it generates action plans such as safe routes and points to note. In this process, the model integrates and analyzes the information to construct the recommendations.

[0354] Step 6:

[0355] The server generates advice and action plans, which are then sent to the device. The device receives these and presents them to the user. The user can then use the displayed information as a reference when their child goes out.

[0356] Step 7:

[0357] Users send feedback on the advice provided to the server via their device. The server collects the feedback and analyzes it to improve the accuracy of the generated AI model. This improves the quality of advice in the future.

[0358] 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.

[0359] This invention is an AI-powered childcare support system that combines an emotion engine. In addition to analyzing the user's consultation content, it understands their emotional state to provide more precise and personalized advice. This system can take the user's emotions into consideration and adjust the content and expression of the advice accordingly.

[0360] First, the user enters their concerns via their device about an issue affecting their emotions. For example, they might enter a question like, "My child won't listen to me, and it's making me frustrated," which can be stressful and anxiety-inducing for a parent.

[0361] The terminal sends input information to the server and also invokes an emotion recognition function to capture diverse emotional data. This allows the emotion engine to analyze the emotions expressed from the user's input.

[0362] The server analyzes the received text data using natural language processing and classifies the consultation category. During this process, the emotion engine identifies the user's emotional state and selects the appropriate format and tone of advice. For example, if the emotion "irritated" is detected, gentle encouragement and advice to maintain calmness will be provided.

[0363] Furthermore, the server takes into account the user's emotional state, searches the database for relevant information, and assesses the risk level of the consultation problem. It can also determine the priority of support based on the intensity of the emotions.

[0364] The generated advice is delivered in appropriate wording, adjusted by an emotion engine. The server simultaneously provides information on nearby support facilities linked to the user's location. This may include individual counseling services or stress care classes, as needed.

[0365] The information provided is presented to the user via their device, allowing them to understand specific actions to take. After performing an action, the user reports their feedback, including any changes in their emotions.

[0366] The collected feedback and emotional change data are analyzed by the server and continuously contribute to improving the accuracy of the AI ​​model. This will enable the user to receive even more accurate support during the next consultation.

[0367] This invention makes it possible to support a more comprehensive and healthy child-rearing environment by considering not only the resolution of the issues raised but also the emotional aspects of the user.

[0368] The following describes the processing flow.

[0369] Step 1:

[0370] The user uses the device's interface to input details about emotionally charged problems or worries. For example, they might input a situation like, "My child won't sleep, and I'm worried."

[0371] Step 2:

[0372] The device sends data to the server, along with the user's consultation content, to identify their emotions. This includes emotion indicators derived from the user's text input.

[0373] Step 3:

[0374] The server analyzes the received consultation content using natural language processing technology to identify the category of the problem. For example, it might be classified as a consultation about sleep.

[0375] Step 4:

[0376] The server uses an emotion engine to analyze the user's emotional state. The emotion engine recognizes the user's anxiety and stress from the text of the consultation and adjusts the tone of the advice based on this.

[0377] Step 5:

[0378] The server searches its internal database for appropriate information based on identified categories and sentiment data. During this process, it evaluates the importance and urgency of the consultation and sets priorities according to the sentiment.

[0379] Step 6:

[0380] The server creates emotionally sensitive advice and selects appropriate language based on the content of the suggestions. For example, it might offer gentle encouragement to alleviate anxiety or suggest concrete action plans.

[0381] Step 7:

[0382] The server takes the user's location into consideration, collects information on relevant local support facilities and services, and provides this information along with the advice.

[0383] Step 8:

[0384] The terminal displays advice and facility information received from the server to the user. The user can use this information to plan specific actions.

[0385] Step 9:

[0386] Users input feedback on the results of their actions and any new emotional states into their device and send it to the server. This clarifies the effectiveness of the advice and areas for improvement.

[0387] Step 10:

[0388] The server analyzes user feedback and emotional change data, and uses this information to improve the AI ​​model's performance. This enables more accurate support during subsequent user consultations.

[0389] (Example 2)

[0390] 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".

[0391] Conventional childcare support systems struggle to provide personalized advice that fully considers the emotional aspects of users' consultations. As a result, users often fail to receive appropriate support that respects their feelings, leading to insufficient problem-solving. There is a need to improve this situation and enable the provision of appropriate advice that is sensitive to the user's emotional state.

[0392] 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.

[0393] This invention includes a server that analyzes text using natural language processing and emotion recognition technologies to analyze the content of a consultation received from a user and its emotional state, and includes means for identifying the category and emotional state of the consultation; means for retrieving relevant information from information storage based on the identified category and emotional state and evaluating the urgency of the consultation; and means for providing the user with generated advice and information on relevant support facilities. This makes it possible to provide accurate and personalized parenting advice that takes into account the user's emotional state.

[0394] A "user" refers to an individual who uses this system to seek advice and support regarding childcare.

[0395] "Consultation details" refer to text data containing information about the specific problems or situations the user is facing, including emotional aspects.

[0396] "Natural language processing technology" is an information processing technology that analyzes text data to understand and process human language.

[0397] "Emotion recognition technology" is a technology that identifies emotional states from a user's text data, determining the intensity and type of emotion.

[0398] A "category" is a classification criterion used to identify and categorize the nature and subject matter of a consultation.

[0399] "Emotional state" refers to information that represents the type and intensity of emotions expressed in the user's consultation.

[0400] "Information storage" refers to databases and information storage systems used to store and retrieve related information.

[0401] "Urgency" is a scale used to evaluate the importance and urgency of the consultation topic.

[0402] "Advice" refers to specific action suggestions and suggestions provided to the user, generated based on the content of the consultation and their emotional state.

[0403] A "support facility" is a collection of organizations and services that provide the support that users need.

[0404] This invention is an AI-powered childcare support system that combines an emotion engine to analyze the content of the user's consultation and provide personalized advice according to their emotional state. Based on the text data provided by the user, this system uses natural language processing and emotion recognition technologies to analyze the user's emotions and provide more appropriate advice.

[0405] First, the user uses their device to input a specific prompt, such as, "My child is going through a rebellious phase and won't listen to anything I do, and I'm worried about how to deal with it." The user's inquiry is then taken into the device as text data to be used with a natural language processing engine and emotion recognition technology.

[0406] The terminal sends this input data to the server and simultaneously activates the emotion recognition module. The emotion recognition technology analyzes the emotional state from the input text and provides information to more precisely understand its content. This allows for the selection of appropriate responses based on the user's emotional state.

[0407] Next, the server analyzes the received text data using natural language processing technology and categorizes the consultation content according to its content. Using an emotion engine, it identifies the user's emotional state, and based on the results, a generative AI model generates individually tailored advice.

[0408] Specifically, the software will utilize commonly used platforms and libraries for natural language processing (e.g., TensorFlow and PyTorch), and introduce sentiment analysis tools that employ machine learning algorithms for emotion recognition. These technologies will enable the system to accurately understand and respond flexibly to user emotions.

[0409] Ultimately, the server provides the user with generated advice and information on support facilities as needed, via the terminal. This process allows the user to receive guidance that takes their emotional state into consideration, enabling them to attempt appropriate actions toward problem-solving. This invention makes it possible to provide more comprehensive and accurate support for various emotional challenges in parenting.

[0410] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0411] Step 1:

[0412] The user enters their parenting concerns into the device. For example, they might enter a prompt such as, "I'm worried because my child's grades at school have dropped." This input is then captured by the device as text data.

[0413] Step 2:

[0414] The terminal sends the text data entered by the user to the server and simultaneously activates an emotion recognition module. The transmitted data is analyzed by emotion recognition technology, and data is prepared to visualize the user's emotional state as expressed in the text.

[0415] Step 3:

[0416] The server analyzes the received text data using natural language processing techniques. Specifically, it divides the text into tokens and detects the context and meaning. This allows it to classify the consultation content into corresponding categories and generate categorized data as output.

[0417] Step 4:

[0418] The server uses an emotion engine to identify the user's emotional state. In this step, a generative AI model is used to analyze emotional features extracted from the input data and generate output data that identifies the type and intensity of the emotion.

[0419] Step 5:

[0420] The server searches for relevant resources from information storage based on the identified category and emotional state. In this step, it retrieves advice and support facility information that matches the user's needs and generates the output.

[0421] Step 6:

[0422] The server uses an emotion engine to refine the generated advice into a format suitable for the user. The generative AI model customizes the wording and tone of the advice to fit the user's emotional state. This output becomes the final advice and related information.

[0423] Step 7:

[0424] The terminal presents the user with the final advice and information sent from the server. Based on this information, the user can then plan specific actions.

[0425] Step 8:

[0426] After the user performs the suggested action, they report the results and changes in their emotions as feedback via their device. Based on this feedback, emotion change data is generated.

[0427] Step 9:

[0428] The server analyzes the received feedback data and uses it to improve the accuracy of the generated AI model. This updates the dataset, contributing to improved support in the future.

[0429] (Application Example 2)

[0430] 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."

[0431] Conventional consultation support systems have the drawback of only being able to provide standardized responses to user inquiries, and failing to offer detailed support tailored to the emotional state of individual users. Furthermore, they lack the ability to suggest appropriate contact methods to those being contacted, resulting in a limited user experience. This makes it difficult to improve user emotional satisfaction and provide more effective support.

[0432] 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.

[0433] In this invention, the server includes means for analyzing the content of inquiries received from users using text analysis technology and identifying the category of the inquiries; means for analyzing emotional data received from users using emotion recognition technology; and means for providing suggestions on how to contact the contact person based on the results of the emotion recognition technology. This makes it possible to provide detailed responses that take into account the emotional state of the user and to suggest appropriate contact methods to employees.

[0434] A "user" is the entity that uses the system to input their consultation details and receive support.

[0435] "Text analysis technology" is a technology that analyzes the text of consultations provided by users to identify the meaning and intent of the content.

[0436] A "category" refers to a specific group classified based on the characteristics of the consultation content.

[0437] An "information recording device" refers to a database or storage system that stores necessary information and keeps it in a searchable state.

[0438] "Risk level" refers to the result of evaluating the degree of risk associated with the content of the consultation.

[0439] "Advice" refers to solutions or guidelines provided to users.

[0440] "Feedback" refers to responses or opinions that users provide based on advice or the results of a service.

[0441] A "generative model" refers to the learning algorithm of artificial intelligence used to generate advice for users.

[0442] "Emotion recognition technology" is a technology that analyzes a user's emotions from their facial expressions and behavior, and identifies the type and intensity of those emotions.

[0443] "Contact method" refers to the specific actions and approaches that a contact person should take depending on the user's emotional state.

[0444] This system is an assistance system that uses emotion recognition technology to enable users to receive personalized advice based on their individual emotions. Its primary operation is based on communication between terminals, servers, and information recording devices.

[0445] First, the user uses a device such as a smartphone or tablet to input their consultation details as text. This consultation content may express the user's emotions. The device sends the entered text data to a server, and at the same time, uses emotion recognition technology to analyze the user's emotions. In this process, for example, natural language processing (NLP) is used for voice data, while visual analysis technology is applied to facial expression data.

[0446] The server analyzes the received text data using advanced text analysis techniques and searches for relevant information from the information recording device based on the identified category of the consultation content. At the same time, it uses a generative model in the cloud, incorporating the results of emotion recognition, to generate advice and propose contact methods tailored to the user's emotional state.

[0447] For example, if a user is experiencing excessive stress, the server generates encouraging messages in a gentle tone to reduce stress, as well as recommendations for available counseling services. The generated advice is then refined by an emotion engine and delivered to the user in the most appropriate format.

[0448] The device presents the generated advice to the user, allowing the user to take action based on that advice. Furthermore, user feedback is sent back to the system to improve the accuracy of the generated AI model. This will enable the system to provide more accurate advice in similar situations in the future.

[0449] For example, if a user says, "I'm feeling down today," the server will provide advice such as, "Let's find some time to relax. I can recommend a nearby relaxation facility." An example of a prompt message would be, "Suggest ways to contact the user when they are feeling down."

[0450] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0451] Step 1:

[0452] The terminal receives the user's inquiry as text input. It constructs the input text data and sends it to the server. In this process, the user's terminal secures the text data and structures it.

[0453] Step 2:

[0454] The server analyzes the received text data. Here, natural language processing (NLP) techniques are used to extract intent and categorize the text. The input is text data, and the output is information about the consultation category and emotions. This allows the server to understand the context of the text and prepare it for further processing.

[0455] Step 3:

[0456] The server retrieves relevant information from the information recording device based on the consultation category obtained from text analysis. The input is category information, and the output is the corresponding relevant data. The main operation of this step is to quickly retrieve the appropriate information using database queries.

[0457] Step 4:

[0458] The device uses emotion recognition technology to understand the user's emotional state. Input is sensor data obtained from the user's voice and facial expressions, while output is the type and intensity of the emotion. This allows the device to identify the user's emotions in real time and transmit the information to the server with the required level of accuracy.

[0459] Step 5:

[0460] The server uses acquired sentiment information and related data to generate advice using a generative AI model. The input is sentiment data and categorical information, and the output is personalized advice. The generative model makes predictions using appropriate prompts to create advice optimized for the user.

[0461] Step 6:

[0462] The terminal presents the user with advice received from the server. The input is the generated advice, and the output is the feedback reflected in the user's response. The goal is to ensure that the user fully understands the information provided and can take follow-up actions.

[0463] Step 7:

[0464] The user takes action based on the advice provided and reports the results and feedback to the server via their device. The input is the user's actions, and the output is feedback data used for improvement. This feedback is used to tune the generated AI model, promoting overall system accuracy improvement.

[0465] 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.

[0466] 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.

[0467] 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.

[0468] [Third Embodiment]

[0469] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0470] 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.

[0471] 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).

[0472] 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.

[0473] 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.

[0474] 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).

[0475] 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.

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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".

[0481] This invention provides an AI system for addressing various parenting-related concerns. This system is used by users through an application accessible via a smartphone or computer.

[0482] First, the user launches the application installed on their device and enters their question. For example, the user might enter a question such as, "My child is going through a rebellious phase and we're having trouble communicating."

[0483] Next, the device sends the user's input to the server. This transmission may include the user's location information and profile information.

[0484] The server analyzes the received consultation content using text analysis technology (natural language processing). Based on the analysis results, the consultation content is classified into categories such as education, health, nutrition, and environment. For example, keywords related to "rebellious phase" and "communication" are extracted and classified into the education category.

[0485] The server then searches its internal database for information related to the relevant category and assesses the level of risk involved in the consultation. This assessment determines the degree of support required.

[0486] The server uses an AI model to generate advice that should be provided to the user. This includes specific countermeasures and action suggestions based on the extracted information. For example, it can create advice on "appropriate ways to interact with children" or "communication strategies that should be shared between parents and children."

[0487] The server also collects relevant facility information based on the user's location. This includes information on local classes, counseling services, and other services accessible to the user.

[0488] The terminal displays advice and facility information sent from the server to the user. The user can then use this information to take specific actions.

[0489] Furthermore, users send feedback to the server via their device, including the results of implementing the provided advice and their opinions. This feedback is analyzed to improve the accuracy of the AI ​​model and enhance the quality of advice provided next time.

[0490] Thus, the present invention is provided as an AI system for quickly and effectively solving the challenges related to child-rearing that parents face, and can provide appropriate and personalized support to users.

[0491] The following describes the processing flow.

[0492] Step 1:

[0493] The user launches the application on their device and enters their questions about parenting. For example, they might enter a question about "my child won't eat vegetables."

[0494] Step 2:

[0495] The terminal sends the entered consultation details to the server. The user's location information may be included in the transmission.

[0496] Step 3:

[0497] The server analyzes the received inquiries using natural language processing technology and classifies the content into categories such as education, health, nutrition, and environment. For example, information related to "not eating vegetables" is extracted and classified into the nutrition category.

[0498] Step 4:

[0499] The server searches its internal database for relevant information based on the classified categories. It also assesses the severity of any problems that arise. For example, it might evaluate the degree of impact that a lack of vegetables has on children's health.

[0500] Step 5:

[0501] The server uses an AI model to generate specific advice for the user based on analysis results and search information. For example, it might suggest recipes or presentation methods for enjoying a particular vegetable.

[0502] Step 6:

[0503] The server collects information about relevant local facilities and services based on the user's location and adds this information to the advice. For example, it collects information about nearby cooking classes or nutritionist consultation services.

[0504] Step 7:

[0505] The server sends the generated advice and facility information to the terminal. This includes specific action suggestions, information on how to obtain ingredients, and details about nearby facilities.

[0506] Step 8:

[0507] The terminal displays information received from the server to the user. Based on the advice, the user can take specific actions to improve their eating habits.

[0508] Step 9:

[0509] Users input the results of the advice they received and their own opinions into their device and send them to the server as feedback. This allows them to communicate the effects obtained and any problems that arose.

[0510] Step 10:

[0511] The server analyzes the feedback sent by users and uses it as data to improve the performance of the AI ​​model. This allows for more accurate advice to be provided next time.

[0512] (Example 1)

[0513] 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."

[0514] One challenge in parenting is the difficulty in obtaining prompt and accurate advice on the various issues and concerns parents face. In particular, the diversity of these concerns makes it difficult to provide individualized support. Furthermore, efficiently obtaining and appropriately utilizing information on local services and facilities is not easy.

[0515] 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.

[0516] In this invention, the server includes means for analyzing the consultation content received from the user using natural language processing technology to identify the category of the consultation content, means for searching for relevant information from a database based on the identified category and evaluating the risk level of the consultation content, and means for providing the user with advice generated using a generative AI model and information on relevant facilities. As a result, the user can receive personalized advice and efficiently obtain and utilize information on facilities relevant to the region.

[0517] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0518] "Identifying a category" is the process of sorting received information into a specific classification.

[0519] A "data bank" is a collection of data that stores information and allows it to be searched and used as needed.

[0520] "Assessing the level of risk" means analyzing how much risk the information in question poses.

[0521] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate appropriate answers or suggestions from input information.

[0522] "Information about related facilities" refers to data about specific services or facilities that the user is interested in.

[0523] "Receiving feedback" refers to obtaining information and opinions from users.

[0524] This invention provides an AI system that effectively resolves parenting-related issues faced by users. This system, utilizing appropriate hardware and software, has the capability to provide prompt and accurate advice and relevant information.

[0525] First, the user uses an application installed on a device such as a smartphone or computer. This application has an interface that allows the user to easily input and send their consultation details. For example, the user might input a consultation request such as, "My child is going through a rebellious phase and I'm having trouble communicating with them."

[0526] The device encrypts the information entered by the user and transmits it to a central server via a secure connection. The transmitted information may include not only text data but also the user's location and profile information.

[0527] The server is equipped with a high-performance computing system to process the received data. The server uses natural language processing techniques to analyze the user's inquiry and classify it into multiple categories. A generative AI model is used at this stage. For example, if a specific keyword is extracted, the category is identified as "education" based on that keyword.

[0528] Next, the server retrieves data related to the classified category from the database and uses it to assess the risk level of the consultation. If necessary, it uses a generative AI model to generate specific advice for the user. This advice includes "appropriate ways to interact with children" and "communication strategies to share between parents and children." Location information is also used to collect information about local facilities relevant to the user.

[0529] The terminal displays advice and facility information provided by the server to the user. The user can then use this information to plan and execute specific actions. Later, the user sends feedback to the server regarding the results of these actions and their opinions. This feedback is used as data to improve the accuracy of the AI ​​model.

[0530] An example of a prompt message might be, "What specific actions should parents take when their child is rebellious and won't listen?" In this way, the present invention is a system that technically supports parents in facing various challenges in raising their children.

[0531] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0532] Step 1:

[0533] The user launches the application installed on their device and enters their parenting-related questions. The entered text data includes specific questions such as, "My child is going through a rebellious phase and I'm having trouble communicating with them." This input data is then formatted into the required format and prepared for transmission.

[0534] Step 2:

[0535] The terminal sends the text data entered by the user to the server. During this process, the data is encrypted using secure protocols such as SSL, and the user's location and profile information are added. The transmitted input data includes the text of the consultation, location information, and profile information.

[0536] Step 3:

[0537] The server analyzes the received data using natural language processing techniques. First, it extracts keywords from the text data of the input consultation. Based on this analysis, words such as "rebellious phase" and "communication" are classified into the education category. The output is data classified into a specific category.

[0538] Step 4:

[0539] The server searches its internal database for relevant information based on the identified category. Next, an algorithm is applied to assess the risk level of the data. At this stage, if the consultation is determined to be a family relationship issue, it may be assessed as high risk. The relevant data and its assessment are then output.

[0540] Step 5:

[0541] The server uses a generative AI model to generate specific advice. Based on the extracted information, suggestions such as "appropriate ways to interact with children" and "effective communication methods between parents and children" are created. The server also utilizes the user's location information to collect information on local extracurricular activities and counseling services. The output consists of customized advice and relevant facility information.

[0542] Step 6:

[0543] The terminal receives output from the server and presents it to the user. This includes specific advice and information about local facilities. The user can review this information and use it in their daily life. The presented information is the final output.

[0544] Step 7:

[0545] Users send feedback to the server via their device, detailing the results and opinions of implementing the provided advice. This feedback is used as training data for the AI ​​model and analyzed to improve the accuracy of the advice. The feedback sent becomes important input data for future improvements.

[0546] (Application Example 1)

[0547] 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."

[0548] In child-rearing, it is crucial for parents to receive appropriate guidance and advice regarding their children's safety. However, information regarding safety when going out is diverse, making it difficult for parents to judge each aspect individually. Therefore, there is a need for a system that is easy for users to use and provides safety information tailored to individual situations.

[0549] 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.

[0550] In this invention, the server includes means for analyzing the consultation content received from the user using text analysis technology to identify the category of the consultation content, means for searching for relevant information from a database based on the identified category and evaluating the degree of risk of the consultation content, and means for combining location information and local safety information to propose a safe action plan to the user. As a result, the user can obtain specific and safe action guidelines that take into account the current situation.

[0551] A "user" is an entity that uses the system to input information about their concerns and receives advice and safety information.

[0552] "Text analysis technology" is a technology that uses natural language processing to analyze the content of inquiries entered by users, and to understand and classify that content.

[0553] A "category" is a framework for classifying consultation content into specific fields or topics.

[0554] A "database" is an information aggregation system that stores related information and past cases, and allows for searching and referencing them as needed.

[0555] "Risk assessment" is the process of determining the level of risk based on the content of the consultation received and the current environment.

[0556] "Location information" refers to data that indicates the user's current location, and is obtained through methods such as GPS.

[0557] "Local safety information" refers to information about safety in a specific area, such as crime statistics and traffic conditions.

[0558] An "action plan" is a proposal outlining specific steps and routes for users to act safely.

[0559] A "generative model" is an algorithm that automatically creates advice and suggestions to provide to users, and it utilizes AI technology.

[0560] The system for implementing this invention mainly consists of a user terminal, a server, and a database for processing various related information. The user accesses the system using a terminal such as a smartphone or personal computer and begins by entering their inquiry details.

[0561] The application installed on the user's device sends the entered consultation content to the server. The server analyzes the consultation content using natural language processing technology and identifies the category of the consultation content based on this analysis. Specific software used may include natural language processing libraries based on Python or TensorFlow.

[0562] Based on the results of this analysis, the server searches the database for relevant information and assesses the level of risk in the consultation. For example, it uses the Google Maps API to obtain local safety information and generates suggestions for how children can act safely based on that information. This generated advice and action plan are then provided to the user by the server.

[0563] User feedback is sent back to the server and used to improve the generated AI model. The AI ​​model analyzes past data and user feedback, and is adjusted based on the newly gained insights to improve the accuracy of future advice.

[0564] As a concrete example, if a user enters "I want my child to get to the park safely, but I want to know the safest route," the system will consider local crime statistics and combine them with Google Maps route information to suggest a safe course of action. An example of a prompt used in this case would be, "Please tell me the safety information for my child's destination. Current location is the inserted location information, and destination is the entered destination."

[0565] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0566] Step 1:

[0567] The user launches an application installed on a device such as a smartphone and enters their inquiry. The input data includes text such as, "I want to know a safe route for my child to the park." This input serves as the basis for subsequent analysis.

[0568] Step 2:

[0569] The terminal sends user input to the server. At the same time, the terminal also acquires the user's location information (GPS data) and sends it along with profile information. The server receives this and creates a unique request tailored to each user.

[0570] Step 3:

[0571] The server analyzes the received consultation content using natural language processing technology. It analyzes the input text and identifies categories such as education, safety, and environment. It generates prompt sentences and sends them to the AI ​​model, outputting the analysis results.

[0572] Step 4:

[0573] The server searches the database based on identified categories to retrieve local safety information. For example, it uses the Google Maps API to match data on crime statistics and traffic conditions and extract relevant information. This data forms the basis for advice given to the user.

[0574] Step 5:

[0575] Based on the acquired information, the server uses a generative AI model to create advice for the user. Specifically, it generates action plans such as safe routes and points to note. In this process, the model integrates and analyzes the information to construct the recommendations.

[0576] Step 6:

[0577] The server generates advice and action plans, which are then sent to the device. The device receives these and presents them to the user. The user can then use the displayed information as a reference when their child goes out.

[0578] Step 7:

[0579] Users send feedback on the advice provided to the server via their device. The server collects the feedback and analyzes it to improve the accuracy of the generated AI model. This improves the quality of advice in the future.

[0580] 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.

[0581] This invention is an AI-powered childcare support system that combines an emotion engine. In addition to analyzing the user's consultation content, it understands their emotional state to provide more precise and personalized advice. This system can take the user's emotions into consideration and adjust the content and expression of the advice accordingly.

[0582] First, the user enters their concerns via their device about an issue affecting their emotions. For example, they might enter a question like, "My child won't listen to me, and it's making me frustrated," which can be stressful and anxiety-inducing for a parent.

[0583] The terminal sends input information to the server and also invokes an emotion recognition function to capture diverse emotional data. This allows the emotion engine to analyze the emotions expressed from the user's input.

[0584] The server analyzes the received text data using natural language processing and classifies the consultation category. During this process, the emotion engine identifies the user's emotional state and selects the appropriate format and tone of advice. For example, if the emotion "irritated" is detected, gentle encouragement and advice to maintain calmness will be provided.

[0585] Furthermore, the server takes into account the user's emotional state, searches the database for relevant information, and assesses the risk level of the consultation problem. It can also determine the priority of support based on the intensity of the emotions.

[0586] The generated advice is delivered in appropriate wording, adjusted by an emotion engine. The server simultaneously provides information on nearby support facilities linked to the user's location. This may include individual counseling services or stress care classes, as needed.

[0587] The information provided is presented to the user via their device, allowing them to understand specific actions to take. After performing an action, the user reports their feedback, including any changes in their emotions.

[0588] The collected feedback and emotional change data are analyzed by the server and continuously contribute to improving the accuracy of the AI ​​model. This will enable the user to receive even more accurate support during the next consultation.

[0589] This invention makes it possible to support a more comprehensive and healthy child-rearing environment by considering not only the resolution of the issues raised but also the emotional aspects of the user.

[0590] The following describes the processing flow.

[0591] Step 1:

[0592] The user uses the device's interface to input details about emotionally charged problems or worries. For example, they might input a situation like, "My child won't sleep, and I'm worried."

[0593] Step 2:

[0594] The device sends data to the server, along with the user's consultation content, to identify their emotions. This includes emotion indicators derived from the user's text input.

[0595] Step 3:

[0596] The server analyzes the received consultation content using natural language processing technology to identify the category of the problem. For example, it might be classified as a consultation about sleep.

[0597] Step 4:

[0598] The server uses an emotion engine to analyze the user's emotional state. The emotion engine recognizes the user's anxiety and stress from the text of the consultation and adjusts the tone of the advice based on this.

[0599] Step 5:

[0600] The server searches its internal database for appropriate information based on identified categories and sentiment data. During this process, it evaluates the importance and urgency of the consultation and sets priorities according to the sentiment.

[0601] Step 6:

[0602] The server creates emotionally sensitive advice and selects appropriate language based on the content of the suggestions. For example, it might offer gentle encouragement to alleviate anxiety or suggest concrete action plans.

[0603] Step 7:

[0604] The server takes the user's location into consideration, collects information on relevant local support facilities and services, and provides this information along with the advice.

[0605] Step 8:

[0606] The terminal displays advice and facility information received from the server to the user. The user can use this information to plan specific actions.

[0607] Step 9:

[0608] Users input feedback on the results of their actions and any new emotional states into their device and send it to the server. This clarifies the effectiveness of the advice and areas for improvement.

[0609] Step 10:

[0610] The server analyzes user feedback and emotional change data, and uses this information to improve the AI ​​model's performance. This enables more accurate support during subsequent user consultations.

[0611] (Example 2)

[0612] 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."

[0613] Conventional childcare support systems struggle to provide personalized advice that fully considers the emotional aspects of users' consultations. As a result, users often fail to receive appropriate support that respects their feelings, leading to insufficient problem-solving. There is a need to improve this situation and enable the provision of appropriate advice that is sensitive to the user's emotional state.

[0614] 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.

[0615] This invention includes a server that analyzes text using natural language processing and emotion recognition technologies to analyze the content of a consultation received from a user and its emotional state, and includes means for identifying the category and emotional state of the consultation; means for retrieving relevant information from information storage based on the identified category and emotional state and evaluating the urgency of the consultation; and means for providing the user with generated advice and information on relevant support facilities. This makes it possible to provide accurate and personalized parenting advice that takes into account the user's emotional state.

[0616] A "user" refers to an individual who uses this system to seek advice and support regarding childcare.

[0617] "Consultation details" refer to text data containing information about the specific problems or situations the user is facing, including emotional aspects.

[0618] "Natural language processing technology" is an information processing technology that analyzes text data to understand and process human language.

[0619] "Emotion recognition technology" is a technology that identifies emotional states from a user's text data, determining the intensity and type of emotion.

[0620] A "category" is a classification criterion used to identify and categorize the nature and subject matter of a consultation.

[0621] "Emotional state" refers to information that represents the type and intensity of emotions expressed in the user's consultation.

[0622] "Information storage" refers to databases and information storage systems used to store and retrieve related information.

[0623] "Urgency" is a scale used to evaluate the importance and urgency of the consultation topic.

[0624] "Advice" refers to specific action suggestions and suggestions provided to the user, generated based on the content of the consultation and their emotional state.

[0625] A "support facility" is a collection of organizations and services that provide the support that users need.

[0626] This invention is an AI-powered childcare support system that combines an emotion engine to analyze the content of the user's consultation and provide personalized advice according to their emotional state. Based on the text data provided by the user, this system uses natural language processing and emotion recognition technologies to analyze the user's emotions and provide more appropriate advice.

[0627] First, the user uses their device to input a specific prompt, such as, "My child is going through a rebellious phase and won't listen to anything I do, and I'm worried about how to deal with it." The user's inquiry is then taken into the device as text data to be used with a natural language processing engine and emotion recognition technology.

[0628] The terminal sends this input data to the server and simultaneously activates the emotion recognition module. The emotion recognition technology analyzes the emotional state from the input text and provides information to more precisely understand its content. This allows for the selection of appropriate responses based on the user's emotional state.

[0629] Next, the server analyzes the received text data using natural language processing technology and categorizes the consultation content according to its content. Using an emotion engine, it identifies the user's emotional state, and based on the results, a generative AI model generates individually tailored advice.

[0630] Specifically, the software will utilize commonly used platforms and libraries for natural language processing (e.g., TensorFlow and PyTorch), and introduce sentiment analysis tools that employ machine learning algorithms for emotion recognition. These technologies will enable the system to accurately understand and respond flexibly to user emotions.

[0631] Ultimately, the server provides the user with generated advice and information on support facilities as needed, via the terminal. This process allows the user to receive guidance that takes their emotional state into consideration, enabling them to attempt appropriate actions toward problem-solving. This invention makes it possible to provide more comprehensive and accurate support for various emotional challenges in parenting.

[0632] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0633] Step 1:

[0634] The user enters their parenting concerns into the device. For example, they might enter a prompt such as, "I'm worried because my child's grades at school have dropped." This input is then captured by the device as text data.

[0635] Step 2:

[0636] The terminal sends the text data entered by the user to the server and simultaneously activates an emotion recognition module. The transmitted data is analyzed by emotion recognition technology, and data is prepared to visualize the user's emotional state as expressed in the text.

[0637] Step 3:

[0638] The server analyzes the received text data using natural language processing techniques. Specifically, it divides the text into tokens and detects the context and meaning. This allows it to classify the consultation content into corresponding categories and generate categorized data as output.

[0639] Step 4:

[0640] The server uses an emotion engine to identify the user's emotional state. In this step, a generative AI model is used to analyze emotional features extracted from the input data and generate output data that identifies the type and intensity of the emotion.

[0641] Step 5:

[0642] The server searches for relevant resources from information storage based on the identified category and emotional state. In this step, it retrieves advice and support facility information that matches the user's needs and generates the output.

[0643] Step 6:

[0644] The server uses an emotion engine to refine the generated advice into a format suitable for the user. The generative AI model customizes the wording and tone of the advice to fit the user's emotional state. This output becomes the final advice and related information.

[0645] Step 7:

[0646] The terminal presents the user with the final advice and information sent from the server. Based on this information, the user can then plan specific actions.

[0647] Step 8:

[0648] After the user performs the suggested action, they report the results and changes in their emotions as feedback via their device. Based on this feedback, emotion change data is generated.

[0649] Step 9:

[0650] The server analyzes the received feedback data and uses it to improve the accuracy of the generated AI model. This updates the dataset, contributing to improved support in the future.

[0651] (Application Example 2)

[0652] 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."

[0653] Conventional consultation support systems have the drawback of only being able to provide standardized responses to user inquiries, and failing to offer detailed support tailored to the emotional state of individual users. Furthermore, they lack the ability to suggest appropriate contact methods to those being contacted, resulting in a limited user experience. This makes it difficult to improve user emotional satisfaction and provide more effective support.

[0654] 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.

[0655] In this invention, the server includes means for analyzing the content of inquiries received from users using text analysis technology and identifying the category of the inquiries; means for analyzing emotional data received from users using emotion recognition technology; and means for providing suggestions on how to contact the contact person based on the results of the emotion recognition technology. This makes it possible to provide detailed responses that take into account the emotional state of the user and to suggest appropriate contact methods to employees.

[0656] A "user" is the entity that uses the system to input their consultation details and receive support.

[0657] "Text analysis technology" is a technology that analyzes the text of consultations provided by users to identify the meaning and intent of the content.

[0658] A "category" refers to a specific group classified based on the characteristics of the consultation content.

[0659] An "information recording device" refers to a database or storage system that stores necessary information and keeps it in a searchable state.

[0660] "Risk level" refers to the result of evaluating the degree of risk associated with the content of the consultation.

[0661] "Advice" refers to solutions or guidelines provided to users.

[0662] "Feedback" refers to responses or opinions that users provide based on advice or the results of a service.

[0663] A "generative model" refers to the learning algorithm of artificial intelligence used to generate advice for users.

[0664] "Emotion recognition technology" is a technology that analyzes a user's emotions from their facial expressions and behavior, and identifies the type and intensity of those emotions.

[0665] "Contact method" refers to the specific actions and approaches that a contact person should take depending on the user's emotional state.

[0666] This system is an assistance system that uses emotion recognition technology to enable users to receive personalized advice based on their individual emotions. Its primary operation is based on communication between terminals, servers, and information recording devices.

[0667] First, the user uses a device such as a smartphone or tablet to input their consultation details as text. This consultation content may express the user's emotions. The device sends the entered text data to a server, and at the same time, uses emotion recognition technology to analyze the user's emotions. In this process, for example, natural language processing (NLP) is used for voice data, while visual analysis technology is applied to facial expression data.

[0668] The server analyzes the received text data using advanced text analysis techniques and searches for relevant information from the information recording device based on the identified category of the consultation content. At the same time, it uses a generative model in the cloud, incorporating the results of emotion recognition, to generate advice and propose contact methods tailored to the user's emotional state.

[0669] For example, if a user is experiencing excessive stress, the server generates encouraging messages in a gentle tone to reduce stress, as well as recommendations for available counseling services. The generated advice is then refined by an emotion engine and delivered to the user in the most appropriate format.

[0670] The device presents the generated advice to the user, allowing the user to take action based on that advice. Furthermore, user feedback is sent back to the system to improve the accuracy of the generated AI model. This will enable the system to provide more accurate advice in similar situations in the future.

[0671] For example, if a user says, "I'm feeling down today," the server will provide advice such as, "Let's find some time to relax. I can recommend a nearby relaxation facility." An example of a prompt message would be, "Suggest ways to contact the user when they are feeling down."

[0672] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0673] Step 1:

[0674] The terminal receives the user's inquiry as text input. It constructs the input text data and sends it to the server. In this process, the user's terminal secures the text data and structures it.

[0675] Step 2:

[0676] The server analyzes the received text data. Here, natural language processing (NLP) techniques are used to extract intent and categorize the text. The input is text data, and the output is information about the consultation category and emotions. This allows the server to understand the context of the text and prepare it for further processing.

[0677] Step 3:

[0678] The server retrieves relevant information from the information recording device based on the consultation category obtained from text analysis. The input is category information, and the output is the corresponding relevant data. The main operation of this step is to quickly retrieve the appropriate information using database queries.

[0679] Step 4:

[0680] The device uses emotion recognition technology to understand the user's emotional state. Input is sensor data obtained from the user's voice and facial expressions, while output is the type and intensity of the emotion. This allows the device to identify the user's emotions in real time and transmit the information to the server with the required level of accuracy.

[0681] Step 5:

[0682] The server uses acquired sentiment information and related data to generate advice using a generative AI model. The input is sentiment data and categorical information, and the output is personalized advice. The generative model makes predictions using appropriate prompts to create advice optimized for the user.

[0683] Step 6:

[0684] The terminal presents the user with advice received from the server. The input is the generated advice, and the output is the feedback reflected in the user's response. The goal is to ensure that the user fully understands the information provided and can take follow-up actions.

[0685] Step 7:

[0686] The user takes action based on the advice provided and reports the results and feedback to the server via their device. The input is the user's actions, and the output is feedback data used for improvement. This feedback is used to tune the generated AI model, promoting overall system accuracy improvement.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] [Fourth Embodiment]

[0691] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0692] 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.

[0693] 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).

[0694] 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.

[0695] 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.

[0696] 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).

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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.

[0702] 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.

[0703] 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".

[0704] This invention provides an AI system for addressing various parenting-related concerns. This system is used by users through an application accessible via a smartphone or computer.

[0705] First, the user launches the application installed on their device and enters their question. For example, the user might enter a question such as, "My child is going through a rebellious phase and we're having trouble communicating."

[0706] Next, the device sends the user's input to the server. This transmission may include the user's location information and profile information.

[0707] The server analyzes the received consultation content using text analysis technology (natural language processing). Based on the analysis results, the consultation content is classified into categories such as education, health, nutrition, and environment. For example, keywords related to "rebellious phase" and "communication" are extracted and classified into the education category.

[0708] The server then searches its internal database for information related to the relevant category and assesses the level of risk involved in the consultation. This assessment determines the degree of support required.

[0709] The server uses an AI model to generate advice that should be provided to the user. This includes specific countermeasures and action suggestions based on the extracted information. For example, it can create advice on "appropriate ways to interact with children" or "communication strategies that should be shared between parents and children."

[0710] The server also collects relevant facility information based on the user's location. This includes information on local classes, counseling services, and other services accessible to the user.

[0711] The terminal displays advice and facility information sent from the server to the user. The user can then use this information to take specific actions.

[0712] Furthermore, users send feedback to the server via their device, including the results of implementing the provided advice and their opinions. This feedback is analyzed to improve the accuracy of the AI ​​model and enhance the quality of advice provided next time.

[0713] Thus, the present invention is provided as an AI system for quickly and effectively solving the challenges related to child-rearing that parents face, and can provide appropriate and personalized support to users.

[0714] The following describes the processing flow.

[0715] Step 1:

[0716] The user launches the application on their device and enters their questions about parenting. For example, they might enter a question about "my child won't eat vegetables."

[0717] Step 2:

[0718] The terminal sends the entered consultation details to the server. The user's location information may be included in the transmission.

[0719] Step 3:

[0720] The server analyzes the received inquiries using natural language processing technology and classifies the content into categories such as education, health, nutrition, and environment. For example, information related to "not eating vegetables" is extracted and classified into the nutrition category.

[0721] Step 4:

[0722] The server searches its internal database for relevant information based on the classified categories. It also assesses the severity of any problems that arise. For example, it might evaluate the degree of impact that a lack of vegetables has on children's health.

[0723] Step 5:

[0724] The server uses an AI model to generate specific advice for the user based on analysis results and search information. For example, it might suggest recipes or presentation methods for enjoying a particular vegetable.

[0725] Step 6:

[0726] The server collects information about relevant local facilities and services based on the user's location and adds this information to the advice. For example, it collects information about nearby cooking classes or nutritionist consultation services.

[0727] Step 7:

[0728] The server sends the generated advice and facility information to the terminal. This includes specific action suggestions, information on how to obtain ingredients, and details about nearby facilities.

[0729] Step 8:

[0730] The terminal displays information received from the server to the user. Based on the advice, the user can take specific actions to improve their eating habits.

[0731] Step 9:

[0732] Users input the results of the advice they received and their own opinions into their device and send them to the server as feedback. This allows them to communicate the effects obtained and any problems that arose.

[0733] Step 10:

[0734] The server analyzes the feedback sent by users and uses it as data to improve the performance of the AI ​​model. This allows for more accurate advice to be provided next time.

[0735] (Example 1)

[0736] 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".

[0737] One challenge in parenting is the difficulty in obtaining prompt and accurate advice on the various issues and concerns parents face. In particular, the diversity of these concerns makes it difficult to provide individualized support. Furthermore, efficiently obtaining and appropriately utilizing information on local services and facilities is not easy.

[0738] 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.

[0739] In this invention, the server includes means for analyzing the consultation content received from the user using natural language processing technology to identify the category of the consultation content, means for searching for relevant information from a database based on the identified category and evaluating the risk level of the consultation content, and means for providing the user with advice generated using a generative AI model and information on relevant facilities. As a result, the user can receive personalized advice and efficiently obtain and utilize information on facilities relevant to the region.

[0740] "Natural language processing technology" is a technology that enables computers to understand and analyze human language.

[0741] "Identifying a category" is the process of sorting received information into a specific classification.

[0742] A "data bank" is a collection of data that stores information and allows it to be searched and used as needed.

[0743] "Assessing the level of risk" means analyzing how much risk the information in question poses.

[0744] A "generative AI model" is an algorithm that uses artificial intelligence technology to generate appropriate answers or suggestions from input information.

[0745] "Information about related facilities" refers to data about specific services or facilities that the user is interested in.

[0746] "Receiving feedback" refers to obtaining information and opinions from users.

[0747] This invention provides an AI system that effectively resolves parenting-related issues faced by users. This system, utilizing appropriate hardware and software, has the capability to provide prompt and accurate advice and relevant information.

[0748] First, the user uses an application installed on a device such as a smartphone or computer. This application has an interface that allows the user to easily input and send their consultation details. For example, the user might input a consultation request such as, "My child is going through a rebellious phase and I'm having trouble communicating with them."

[0749] The device encrypts the information entered by the user and transmits it to a central server via a secure connection. The transmitted information may include not only text data but also the user's location and profile information.

[0750] The server is equipped with a high-performance computing system to process the received data. The server uses natural language processing techniques to analyze the user's inquiry and classify it into multiple categories. A generative AI model is used at this stage. For example, if a specific keyword is extracted, the category is identified as "education" based on that keyword.

[0751] Next, the server retrieves data related to the classified category from the database and uses it to assess the risk level of the consultation. If necessary, it uses a generative AI model to generate specific advice for the user. This advice includes "appropriate ways to interact with children" and "communication strategies to share between parents and children." Location information is also used to collect information about local facilities relevant to the user.

[0752] The terminal displays advice and facility information provided by the server to the user. The user can then use this information to plan and execute specific actions. Later, the user sends feedback to the server regarding the results of these actions and their opinions. This feedback is used as data to improve the accuracy of the AI ​​model.

[0753] An example of a prompt message might be, "What specific actions should parents take when their child is rebellious and won't listen?" In this way, the present invention is a system that technically supports parents in facing various challenges in raising their children.

[0754] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0755] Step 1:

[0756] The user launches the application installed on their device and enters their parenting-related questions. The entered text data includes specific questions such as, "My child is going through a rebellious phase and I'm having trouble communicating with them." This input data is then formatted into the required format and prepared for transmission.

[0757] Step 2:

[0758] The terminal sends the text data entered by the user to the server. During this process, the data is encrypted using secure protocols such as SSL, and the user's location and profile information are added. The transmitted input data includes the text of the consultation, location information, and profile information.

[0759] Step 3:

[0760] The server analyzes the received data using natural language processing techniques. First, it extracts keywords from the text data of the input consultation. Based on this analysis, words such as "rebellious phase" and "communication" are classified into the education category. The output is data classified into a specific category.

[0761] Step 4:

[0762] The server searches its internal database for relevant information based on the identified category. Next, an algorithm is applied to assess the risk level of the data. At this stage, if the consultation is determined to be a family relationship issue, it may be assessed as high risk. The relevant data and its assessment are then output.

[0763] Step 5:

[0764] The server uses a generative AI model to generate specific advice. Based on the extracted information, suggestions such as "appropriate ways to interact with children" and "effective communication methods between parents and children" are created. The server also utilizes the user's location information to collect information on local extracurricular activities and counseling services. The output consists of customized advice and relevant facility information.

[0765] Step 6:

[0766] The terminal receives output from the server and presents it to the user. This includes specific advice and information about local facilities. The user can review this information and use it in their daily life. The presented information is the final output.

[0767] Step 7:

[0768] Users send feedback to the server via their device, detailing the results and opinions of implementing the provided advice. This feedback is used as training data for the AI ​​model and analyzed to improve the accuracy of the advice. The feedback sent becomes important input data for future improvements.

[0769] (Application Example 1)

[0770] 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".

[0771] In child-rearing, it is crucial for parents to receive appropriate guidance and advice regarding their children's safety. However, information regarding safety when going out is diverse, making it difficult for parents to judge each aspect individually. Therefore, there is a need for a system that is easy for users to use and provides safety information tailored to individual situations.

[0772] 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.

[0773] In this invention, the server includes means for analyzing the consultation content received from the user using text analysis technology to identify the category of the consultation content, means for searching for relevant information from a database based on the identified category and evaluating the degree of risk of the consultation content, and means for combining location information and local safety information to propose a safe action plan to the user. As a result, the user can obtain specific and safe action guidelines that take into account the current situation.

[0774] A "user" is an entity that uses the system to input information about their concerns and receives advice and safety information.

[0775] "Text analysis technology" is a technology that uses natural language processing to analyze the content of inquiries entered by users, and to understand and classify that content.

[0776] A "category" is a framework for classifying consultation content into specific fields or topics.

[0777] A "database" is an information aggregation system that stores related information and past cases, and allows for searching and referencing them as needed.

[0778] "Risk assessment" is the process of determining the level of risk based on the content of the consultation received and the current environment.

[0779] "Location information" refers to data that indicates the user's current location, and is obtained through methods such as GPS.

[0780] "Local safety information" refers to information about safety in a specific area, such as crime statistics and traffic conditions.

[0781] An "action plan" is a proposal outlining specific steps and routes for users to act safely.

[0782] A "generative model" is an algorithm that automatically creates advice and suggestions to provide to users, and it utilizes AI technology.

[0783] The system for implementing this invention mainly consists of a user terminal, a server, and a database for processing various related information. The user accesses the system using a terminal such as a smartphone or personal computer and begins by entering their inquiry details.

[0784] The application installed on the user's device sends the entered consultation content to the server. The server analyzes the consultation content using natural language processing technology and identifies the category of the consultation content based on this analysis. Specific software used may include natural language processing libraries based on Python or TensorFlow.

[0785] Based on the results of this analysis, the server searches the database for relevant information and assesses the level of risk in the consultation. For example, it uses the Google Maps API to obtain local safety information and generates suggestions for how children can act safely based on that information. This generated advice and action plan are then provided to the user by the server.

[0786] User feedback is sent back to the server and used to improve the generated AI model. The AI ​​model analyzes past data and user feedback, and is adjusted based on the newly gained insights to improve the accuracy of future advice.

[0787] As a concrete example, if a user enters "I want my child to get to the park safely, but I want to know the safest route," the system will consider local crime statistics and combine them with Google Maps route information to suggest a safe course of action. An example of a prompt used in this case would be, "Please tell me the safety information for my child's destination. Current location is the inserted location information, and destination is the entered destination."

[0788] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0789] Step 1:

[0790] The user launches an application installed on a device such as a smartphone and enters their inquiry. The input data includes text such as, "I want to know a safe route for my child to the park." This input serves as the basis for subsequent analysis.

[0791] Step 2:

[0792] The terminal sends user input to the server. At the same time, the terminal also acquires the user's location information (GPS data) and sends it along with profile information. The server receives this and creates a unique request tailored to each user.

[0793] Step 3:

[0794] The server analyzes the received consultation content using natural language processing technology. It analyzes the input text and identifies categories such as education, safety, and environment. It generates prompt sentences and sends them to the AI ​​model, outputting the analysis results.

[0795] Step 4:

[0796] The server searches the database based on identified categories to retrieve local safety information. For example, it uses the Google Maps API to match data on crime statistics and traffic conditions and extract relevant information. This data forms the basis for advice given to the user.

[0797] Step 5:

[0798] Based on the acquired information, the server uses a generative AI model to create advice for the user. Specifically, it generates action plans such as safe routes and points to note. In this process, the model integrates and analyzes the information to construct the recommendations.

[0799] Step 6:

[0800] The server generates advice and action plans, which are then sent to the device. The device receives these and presents them to the user. The user can then use the displayed information as a reference when their child goes out.

[0801] Step 7:

[0802] Users send feedback on the advice provided to the server via their device. The server collects the feedback and analyzes it to improve the accuracy of the generated AI model. This improves the quality of advice in the future.

[0803] 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.

[0804] This invention is an AI-powered childcare support system that combines an emotion engine. In addition to analyzing the user's consultation content, it understands their emotional state to provide more precise and personalized advice. This system can take the user's emotions into consideration and adjust the content and expression of the advice accordingly.

[0805] First, the user enters their concerns via their device about an issue affecting their emotions. For example, they might enter a question like, "My child won't listen to me, and it's making me frustrated," which can be stressful and anxiety-inducing for a parent.

[0806] The terminal sends input information to the server and also invokes an emotion recognition function to capture diverse emotional data. This allows the emotion engine to analyze the emotions expressed from the user's input.

[0807] The server analyzes the received text data using natural language processing and classifies the consultation category. During this process, the emotion engine identifies the user's emotional state and selects the appropriate format and tone of advice. For example, if the emotion "irritated" is detected, gentle encouragement and advice to maintain calmness will be provided.

[0808] Furthermore, the server takes into account the user's emotional state, searches the database for relevant information, and assesses the risk level of the consultation problem. It can also determine the priority of support based on the intensity of the emotions.

[0809] The generated advice is delivered in appropriate wording, adjusted by an emotion engine. The server simultaneously provides information on nearby support facilities linked to the user's location. This may include individual counseling services or stress care classes, as needed.

[0810] The information provided is presented to the user via their device, allowing them to understand specific actions to take. After performing an action, the user reports their feedback, including any changes in their emotions.

[0811] The collected feedback and emotional change data are analyzed by the server and continuously contribute to improving the accuracy of the AI ​​model. This will enable the user to receive even more accurate support during the next consultation.

[0812] This invention makes it possible to support a more comprehensive and healthy child-rearing environment by considering not only the resolution of the issues raised but also the emotional aspects of the user.

[0813] The following describes the processing flow.

[0814] Step 1:

[0815] The user uses the device's interface to input details about emotionally charged problems or worries. For example, they might input a situation like, "My child won't sleep, and I'm worried."

[0816] Step 2:

[0817] The device sends data to the server, along with the user's consultation content, to identify their emotions. This includes emotion indicators derived from the user's text input.

[0818] Step 3:

[0819] The server analyzes the received consultation content using natural language processing technology to identify the category of the problem. For example, it might be classified as a consultation about sleep.

[0820] Step 4:

[0821] The server uses an emotion engine to analyze the user's emotional state. The emotion engine recognizes the user's anxiety and stress from the text of the consultation and adjusts the tone of the advice based on this.

[0822] Step 5:

[0823] The server searches its internal database for appropriate information based on identified categories and sentiment data. During this process, it evaluates the importance and urgency of the consultation and sets priorities according to the sentiment.

[0824] Step 6:

[0825] The server creates emotionally sensitive advice and selects appropriate language based on the content of the suggestions. For example, it might offer gentle encouragement to alleviate anxiety or suggest concrete action plans.

[0826] Step 7:

[0827] The server takes the user's location into consideration, collects information on relevant local support facilities and services, and provides this information along with the advice.

[0828] Step 8:

[0829] The terminal displays advice and facility information received from the server to the user. The user can use this information to plan specific actions.

[0830] Step 9:

[0831] Users input feedback on the results of their actions and any new emotional states into their device and send it to the server. This clarifies the effectiveness of the advice and areas for improvement.

[0832] Step 10:

[0833] The server analyzes user feedback and emotional change data, and uses this information to improve the AI ​​model's performance. This enables more accurate support during subsequent user consultations.

[0834] (Example 2)

[0835] 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".

[0836] Conventional childcare support systems struggle to provide personalized advice that fully considers the emotional aspects of users' consultations. As a result, users often fail to receive appropriate support that respects their feelings, leading to insufficient problem-solving. There is a need to improve this situation and enable the provision of appropriate advice that is sensitive to the user's emotional state.

[0837] 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.

[0838] This invention includes a server that analyzes text using natural language processing and emotion recognition technologies to analyze the content of a consultation received from a user and its emotional state, and includes means for identifying the category and emotional state of the consultation; means for retrieving relevant information from information storage based on the identified category and emotional state and evaluating the urgency of the consultation; and means for providing the user with generated advice and information on relevant support facilities. This makes it possible to provide accurate and personalized parenting advice that takes into account the user's emotional state.

[0839] A "user" refers to an individual who uses this system to seek advice and support regarding childcare.

[0840] "Consultation details" refer to text data containing information about the specific problems or situations the user is facing, including emotional aspects.

[0841] "Natural language processing technology" is an information processing technology that analyzes text data to understand and process human language.

[0842] "Emotion recognition technology" is a technology that identifies emotional states from a user's text data, determining the intensity and type of emotion.

[0843] A "category" is a classification criterion used to identify and categorize the nature and subject matter of a consultation.

[0844] "Emotional state" refers to information that represents the type and intensity of emotions expressed in the user's consultation.

[0845] "Information storage" refers to databases and information storage systems used to store and retrieve related information.

[0846] "Urgency" is a scale used to evaluate the importance and urgency of the consultation topic.

[0847] "Advice" refers to specific action suggestions and suggestions provided to the user, generated based on the content of the consultation and their emotional state.

[0848] A "support facility" is a collection of organizations and services that provide the support that users need.

[0849] This invention is an AI-powered childcare support system that combines an emotion engine to analyze the content of the user's consultation and provide personalized advice according to their emotional state. Based on the text data provided by the user, this system uses natural language processing and emotion recognition technologies to analyze the user's emotions and provide more appropriate advice.

[0850] First, the user uses their device to input a specific prompt, such as, "My child is going through a rebellious phase and won't listen to anything I do, and I'm worried about how to deal with it." The user's inquiry is then taken into the device as text data to be used with a natural language processing engine and emotion recognition technology.

[0851] The terminal sends this input data to the server and simultaneously activates the emotion recognition module. The emotion recognition technology analyzes the emotional state from the input text and provides information to more precisely understand its content. This allows for the selection of appropriate responses based on the user's emotional state.

[0852] Next, the server analyzes the received text data using natural language processing technology and categorizes the consultation content according to its content. Using an emotion engine, it identifies the user's emotional state, and based on the results, a generative AI model generates individually tailored advice.

[0853] Specifically, the software will utilize commonly used platforms and libraries for natural language processing (e.g., TensorFlow and PyTorch), and introduce sentiment analysis tools that employ machine learning algorithms for emotion recognition. These technologies will enable the system to accurately understand and respond flexibly to user emotions.

[0854] Ultimately, the server provides the user with generated advice and information on support facilities as needed, via the terminal. This process allows the user to receive guidance that takes their emotional state into consideration, enabling them to attempt appropriate actions toward problem-solving. This invention makes it possible to provide more comprehensive and accurate support for various emotional challenges in parenting.

[0855] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0856] Step 1:

[0857] The user enters their parenting concerns into the device. For example, they might enter a prompt such as, "I'm worried because my child's grades at school have dropped." This input is then captured by the device as text data.

[0858] Step 2:

[0859] The terminal sends the text data entered by the user to the server and simultaneously activates an emotion recognition module. The transmitted data is analyzed by emotion recognition technology, and data is prepared to visualize the user's emotional state as expressed in the text.

[0860] Step 3:

[0861] The server analyzes the received text data using natural language processing techniques. Specifically, it divides the text into tokens and detects the context and meaning. This allows it to classify the consultation content into corresponding categories and generate categorized data as output.

[0862] Step 4:

[0863] The server uses an emotion engine to identify the user's emotional state. In this step, a generative AI model is used to analyze emotional features extracted from the input data and generate output data that identifies the type and intensity of the emotion.

[0864] Step 5:

[0865] The server searches for relevant resources from information storage based on the identified category and emotional state. In this step, it retrieves advice and support facility information that matches the user's needs and generates the output.

[0866] Step 6:

[0867] The server uses an emotion engine to refine the generated advice into a format suitable for the user. The generative AI model customizes the wording and tone of the advice to fit the user's emotional state. This output becomes the final advice and related information.

[0868] Step 7:

[0869] The terminal presents the user with the final advice and information sent from the server. Based on this information, the user can then plan specific actions.

[0870] Step 8:

[0871] After the user performs the suggested action, they report the results and changes in their emotions as feedback via their device. Based on this feedback, emotion change data is generated.

[0872] Step 9:

[0873] The server analyzes the received feedback data and uses it to improve the accuracy of the generated AI model. This updates the dataset, contributing to improved support in the future.

[0874] (Application Example 2)

[0875] 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".

[0876] Conventional consultation support systems have the drawback of only being able to provide standardized responses to user inquiries, and failing to offer detailed support tailored to the emotional state of individual users. Furthermore, they lack the ability to suggest appropriate contact methods to those being contacted, resulting in a limited user experience. This makes it difficult to improve user emotional satisfaction and provide more effective support.

[0877] 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.

[0878] In this invention, the server includes means for analyzing the content of inquiries received from users using text analysis technology and identifying the category of the inquiries; means for analyzing emotional data received from users using emotion recognition technology; and means for providing suggestions on how to contact the contact person based on the results of the emotion recognition technology. This makes it possible to provide detailed responses that take into account the emotional state of the user and to suggest appropriate contact methods to employees.

[0879] A "user" is the entity that uses the system to input their consultation details and receive support.

[0880] "Text analysis technology" is a technology that analyzes the text of consultations provided by users to identify the meaning and intent of the content.

[0881] A "category" refers to a specific group classified based on the characteristics of the consultation content.

[0882] An "information recording device" refers to a database or storage system that stores necessary information and keeps it in a searchable state.

[0883] "Risk level" refers to the result of evaluating the degree of risk associated with the content of the consultation.

[0884] "Advice" refers to solutions or guidelines provided to users.

[0885] "Feedback" refers to responses or opinions that users provide based on advice or the results of a service.

[0886] A "generative model" refers to the learning algorithm of artificial intelligence used to generate advice for users.

[0887] "Emotion recognition technology" is a technology that analyzes a user's emotions from their facial expressions and behavior, and identifies the type and intensity of those emotions.

[0888] "Contact method" refers to the specific actions and approaches that a contact person should take depending on the user's emotional state.

[0889] This system is an assistance system that uses emotion recognition technology to enable users to receive personalized advice based on their individual emotions. Its primary operation is based on communication between terminals, servers, and information recording devices.

[0890] First, the user uses a device such as a smartphone or tablet to input their consultation details as text. This consultation content may express the user's emotions. The device sends the entered text data to a server, and at the same time, uses emotion recognition technology to analyze the user's emotions. In this process, for example, natural language processing (NLP) is used for voice data, while visual analysis technology is applied to facial expression data.

[0891] The server analyzes the received text data using advanced text analysis techniques and searches for relevant information from the information recording device based on the identified category of the consultation content. At the same time, it uses a generative model in the cloud, incorporating the results of emotion recognition, to generate advice and propose contact methods tailored to the user's emotional state.

[0892] For example, if a user is experiencing excessive stress, the server generates encouraging messages in a gentle tone to reduce stress, as well as recommendations for available counseling services. The generated advice is then refined by an emotion engine and delivered to the user in the most appropriate format.

[0893] The device presents the generated advice to the user, allowing the user to take action based on that advice. Furthermore, user feedback is sent back to the system to improve the accuracy of the generated AI model. This will enable the system to provide more accurate advice in similar situations in the future.

[0894] For example, if a user says, "I'm feeling down today," the server will provide advice such as, "Let's find some time to relax. I can recommend a nearby relaxation facility." An example of a prompt message would be, "Suggest ways to contact the user when they are feeling down."

[0895] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0896] Step 1:

[0897] The terminal receives the user's inquiry as text input. It constructs the input text data and sends it to the server. In this process, the user's terminal secures the text data and structures it.

[0898] Step 2:

[0899] The server analyzes the received text data. Here, natural language processing (NLP) techniques are used to extract intent and categorize the text. The input is text data, and the output is information about the consultation category and emotions. This allows the server to understand the context of the text and prepare it for further processing.

[0900] Step 3:

[0901] The server retrieves relevant information from the information recording device based on the consultation category obtained from text analysis. The input is category information, and the output is the corresponding relevant data. The main operation of this step is to quickly retrieve the appropriate information using database queries.

[0902] Step 4:

[0903] The device uses emotion recognition technology to understand the user's emotional state. Input is sensor data obtained from the user's voice and facial expressions, while output is the type and intensity of the emotion. This allows the device to identify the user's emotions in real time and transmit the information to the server with the required level of accuracy.

[0904] Step 5:

[0905] The server uses acquired sentiment information and related data to generate advice using a generative AI model. The input is sentiment data and categorical information, and the output is personalized advice. The generative model makes predictions using appropriate prompts to create advice optimized for the user.

[0906] Step 6:

[0907] The terminal presents the user with advice received from the server. The input is the generated advice, and the output is the feedback reflected in the user's response. The goal is to ensure that the user fully understands the information provided and can take follow-up actions.

[0908] Step 7:

[0909] The user takes action based on the advice provided and reports the results and feedback to the server via their device. The input is the user's actions, and the output is feedback data used for improvement. This feedback is used to tune the generated AI model, promoting overall system accuracy improvement.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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.

[0918] 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."

[0919] 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.

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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.

[0926] 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.

[0927] 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.

[0928] 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.

[0929] 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.

[0930] 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.

[0931] The following is further disclosed regarding the embodiments described above.

[0932] (Claim 1)

[0933] A means of analyzing the content of inquiries received from users using text analysis technology to identify the category of the inquiry content,

[0934] A means of searching for relevant information from a database based on an identified category and evaluating the risk level of the consultation content,

[0935] A means of providing the user with generated advice and information on related facilities,

[0936] A means of receiving user feedback and analyzing it to improve the performance of the generative model,

[0937] A system that includes this.

[0938] (Claim 2)

[0939] The system according to claim 1, further comprising means for collecting and presenting information on related facilities based on the user's location information.

[0940] (Claim 3)

[0941] The system according to claim 1, further comprising means for individually customizing the generated advice to match the user's consultation content.

[0942] "Example 1"

[0943] (Claim 1)

[0944] A means of analyzing the content of inquiries received from users using natural language processing technology to identify the category of the inquiry content,

[0945] A means of retrieving relevant information from a database based on a specified category and evaluating the risk level of the consultation content,

[0946] A means of providing users with advice generated using a generative AI model and information on related facilities,

[0947] A means of receiving user feedback and analyzing it to improve the performance of the generated AI model,

[0948] A system that includes this.

[0949] (Claim 2)

[0950] The system according to claim 1, further comprising means for collecting and presenting information on related facilities based on the user's location information.

[0951] (Claim 3)

[0952] The system according to claim 1, further comprising means for individually customizing the generated advice to match the user's consultation content.

[0953] "Application Example 1"

[0954] (Claim 1)

[0955] A means of analyzing the content of inquiries received from users using text analysis technology to identify the category of the inquiry content,

[0956] A means of searching for relevant information from a database based on an identified category and evaluating the risk level of the consultation content,

[0957] A means of providing the user with generated advice and information on related facilities,

[0958] A means of receiving user feedback and analyzing it to improve the performance of the generative model,

[0959] A means of proposing a safe action plan to the user by combining location information and local safety information,

[0960] A system that includes this.

[0961] (Claim 2)

[0962] The system according to claim 1, further comprising means for collecting and presenting information on related facilities and local safety data based on the user's location information.

[0963] (Claim 3)

[0964] The system according to claim 1, further comprising means for individually customizing the generated advice to match the user's consultation content and location information.

[0965] "Example 2 of combining an emotion engine"

[0966] (Claim 1)

[0967] To analyze the content of consultations received from users and their emotional states, natural language processing and emotion recognition technologies are used to analyze the text and identify the category of the consultation content and its emotional state.

[0968] A means for retrieving relevant information from information storage based on identified categories and emotional states, and for evaluating the urgency of the consultation content,

[0969] A means of providing the user with generated advice and information on related support facilities,

[0970] A means for receiving user feedback and emotional changes and analyzing them to contribute to improving the accuracy of the generative model,

[0971] A system that includes this.

[0972] (Claim 2)

[0973] The system according to claim 1, further comprising collecting and presenting information on relevant support facilities based on the user's geographical location.

[0974] (Claim 3)

[0975] The system according to claim 1, further comprising means for individually adjusting the generated advice to suit the user's consultation content and emotional state.

[0976] "Application example 2 when combining with an emotional engine"

[0977] (Claim 1)

[0978] A means of analyzing the content of inquiries received from users using text analysis technology to identify the category of the inquiry content,

[0979] A means for retrieving relevant information from an information recording device based on an identified category and for evaluating the risk level of the consultation content,

[0980] A means of providing the user with generated advice and information on related facilities,

[0981] A means of receiving user feedback and analyzing it to improve the performance of the generative model,

[0982] A means of analyzing emotional data received from users using emotion recognition technology,

[0983] A means for providing suggestions on how to contact individuals based on the results of emotion recognition technology,

[0984] A system that includes this.

[0985] (Claim 2)

[0986] The system according to claim 1, further comprising means for collecting and presenting information on related facilities based on the user's location information.

[0987] (Claim 3)

[0988] The system according to claim 1, further comprising means for individually presenting the generated advice to the user according to the content of their consultation. [Explanation of Symbols]

[0989] 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 of analyzing the content of inquiries received from users using text analysis technology to identify the category of the inquiry content, A means of searching for relevant information from a database based on an identified category and evaluating the risk level of the consultation content, A means of providing the user with generated advice and information on related facilities, A means of receiving user feedback and analyzing it to improve the performance of the generative model, A system that includes this.

2. The system according to claim 1, further comprising means for collecting and presenting information on related facilities based on the user's location information.

3. The system according to claim 1, further comprising means for individually customizing the generated advice to match the user's consultation content.

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