System

Abstract

A system is provided.SOLUTION: A system equipped with a voice interactive artificial intelligence and including means for collecting information on a lifestyle and an eating habit of a user by interacting with the user, means for transmitting the collected information to a server, analyzing the collected information, and generating advice on a health condition and a nutrition balance of the user, means for providing the generated advice to the user by voice and visually and displaying a goal and progress for maintaining a motivation of the user, and means for collecting feedback from the user and adjusting an artificial intelligence model based on the feedback.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Improving healthy life expectancy through improvements to daily lifestyle and dietary habits is an extremely important issue for both individuals and society. However, it is difficult to collect information, formulate a specific improvement plan, and continue it on one's own. Furthermore, methods using cameras and monitoring devices are fraught with resistance to privacy violations. Furthermore, people often find it difficult to accept advice from family and parents, making it difficult to obtain effective advice. Therefore, there is a need for a system that allows people to passively receive health advice while maintaining ongoing motivation. [Means for solving the problem]

[0005] This invention provides a system that uses a home device equipped with voice-interactive AI to interact with users on a daily basis and collect information on their lifestyle and dietary habits. The system transmits the collected information to a server, which analyzes the data and generates optimal advice on the user's health and nutritional balance. The generated advice is provided to the user both audibly and visually, and keeps the user motivated by displaying goals, progress, and rewards for achievement. Furthermore, by collecting user feedback and adjusting the AI ​​model based on that feedback, the system continues to provide optimal advice. By allowing users to customize the gender, tone, and conversational style of the voice, the system is more receptive to advice than that of family or parents, and is privacy-conscious.

[0006] "Voice-activated interactive artificial intelligence" refers to artificial intelligence that can converse with users through voice and provide or collect information in response to questions or instructions.

[0007] "Home device" refers to an electronic device or system used in the home that is equipped with voice-interactive artificial intelligence.

[0008] "User" means an individual who uses the system to receive health advice.

[0009] "Lifestyle habits" refers to a user's daily actions and habits, such as patterns of exercise, sleep, stress management, etc.

[0010] "Diet" refers to the content and pattern of the meals a user eats on a daily basis, as well as the nutritional balance.

[0011] A "server" is a computer system that analyzes collected data and generates recommendations for users.

[0012] "Analysis" refers to the process of evaluating the user's health condition and nutritional balance based on collected data and deriving optimal advice.

[0013] "Advice" refers to specific recommendations for action or precautions provided to users based on the analysis results.

[0014] "Visually presenting" refers to displaying information in a visually perceptible form, such as through a display or screen.

[0015] Maintaining "motivation" refers to psychological support that encourages users to continue taking actions to improve their health.

[0016] "Goals and progress" refers to the goals set by the user for improving their health and the progress of achieving them.

[0017] "Achievement rewards" are motivational rewards or incentives given to users when they achieve a specific goal.

[0018] "Feedback" refers to the opinions and reactions that users provide to advice.

[0019] An "artificial intelligence model" is a learning algorithm or statistical model that analyzes and provides advice based on data about a user's lifestyle and diet.

[0020] "Customizable" refers to the ability to change system settings and functionality according to user preferences.

[0021] "Privacy" refers to the right or state in which a user's personal information is protected from being leaked to third parties. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0023] 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.

[0024] First, the terms used in the following description will be explained.

[0025] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0026] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0027] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0028] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0029] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0030] [First embodiment]

[0031] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0032] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0033] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0034] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0035] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0036] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0037] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0038] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0040] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0041] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0042] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0043] The "your personal 'meddling' health advisor" of this invention is a system that supports the user's daily life using a home device equipped with voice-interactive AI. This system consists of the following steps:

[0044] System Configuration

[0045] This system is broadly composed of three elements: terminals, servers, and users.

[0046] 1. Terminal (home device)

[0047] The device is equipped with voice-activated interactive AI, enabling real-time dialogue with the user. The device asks questions about the user's lifestyle and dietary habits and records the responses. It also provides settings that allow users to customize the gender, tone, and conversational style of the voice. Furthermore, it sends the collected data to a server, displays the advice returned from the server, and relays it to the user via voice.

[0048] 2. Server

[0049] The server analyzes data about the user's lifestyle and dietary habits sent from the device and generates optimal health advice for the user. The server also performs a comprehensive analysis, including past data, to evaluate the user's health status and nutritional balance. The generated advice is sent to the device, and the artificial intelligence model is adjusted based on the feedback to improve the performance of the entire system.

[0050] 3. Users

[0051] Users interact with the device to provide information about their lifestyle and dietary habits and receive advice from the server. Not only can they improve their lifestyle based on the advice they receive, but they can also provide feedback that will be reflected in the next advice.

[0052] Example

[0053] Example 1: Customization settings according to usage

[0054] 1. Voice settings: During initial setup, the device asks the user for the gender and tone of their voice. For example, the device might say, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other." The user responds, "I prefer a female voice." The device records this and uses it for future interactions.

[0055] 2. Setting the conversation tone: The device then asks, "Next, please choose the conversation tone. You can choose from casual, formal, or friendly." The user requests "I prefer friendly." The device also records this and reflects it in future conversations.

[0056] Example 2: Data collection and advice provision

[0057] 1. Daily Questions: To understand daily habits and eating habits, the device asks, "What did you eat today?" The user answers, "I had toast and coffee for breakfast, pasta for lunch, and salad for dinner." The device records this information and sends it to the server.

[0058] 2. Data analysis: The server analyzes the collected data and evaluates the user's nutritional balance. For example, the server determines that the user is deficient in vitamin D based on their dietary data.

[0059] 3. Advice generation and provision: The server generates advice such as "You're lacking in vitamin D, so why not include fish in your dinner tonight?" and sends it to the device. The device then conveys this advice to the user via voice and also displays visual information (e.g., recipes using fish).

[0060] Example 3: User feedback and model adjustment

[0061] 1. Obtaining feedback: After providing advice, the device asks the user, "What did you think of today's advice? Is there anything else we can improve?" The user gives feedback such as, "I like fish dishes, so please tell me more recipes."

[0062] 2. Feedback analysis and model adjustment: The server analyzes this feedback and adjusts the AI ​​model to reflect it in the next recommendation. For example, a variety of fish-based recipes will be prioritized in the next recommendation.

[0063] As described above, the present invention provides an effective system that allows users to passively obtain health advice by combining voice dialogue-based artificial intelligence and data analysis technology.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The device will enter initial setup mode and ask the user to customize the gender, tone, and conversational style of the voice. For example, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other."

[0067] Step 2:

[0068] The user selects the gender of the voice and responds, "I prefer a female voice." The device then asks, "Next, please select the conversation tone. You can choose from casual, formal, or friendly." The user requests, "I prefer friendly."

[0069] Step 3:

[0070] The device records the settings and prepares to reflect them in subsequent interactions. It notifies the user that "the settings have been recorded," completing the initial setup.

[0071] Step 4:

[0072] The device periodically asks the user questions about their lifestyle and diet. For example, it asks, "What did you eat today?" The user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[0073] Step 5:

[0074] The device saves the collected data in its internal storage and then sends it to the server. It displays "Information recorded" and sends the data.

[0075] Step 6:

[0076] The server receives the data sent from the device and performs a comprehensive analysis, including the user's past data. It evaluates nutritional balance and health status and determines that the user has recently been lacking in vitamin C.

[0077] Step 7:

[0078] The server generates optimal advice for the user, for example, "We recommend eating oranges and kiwi fruits, which are rich in vitamin C," and sends it to the device.

[0079] Step 8:

[0080] The device receives advice from the server and provides it to the user both audibly and visually. It says, "You're lacking in vitamin C, so we recommend eating oranges and kiwis," and shows images of oranges and kiwis and nutritional information on the screen.

[0081] Step 9:

[0082] The device asks the user for feedback: "What did you think of today's advice?", and the user responds, "I'd like to know more meat recipes."

[0083] Step 10:

[0084] The device sends the collected feedback to a server, which analyzes it and adjusts the AI ​​model. From the next time onward, the settings are changed to prioritize meat recipes based on the user's preferences.

[0085] Example 1

[0086] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0087] Conventional health management systems often collect data about users' lifestyles and dietary habits and provide advice in a fragmented and inconsistent manner. They also lacked mechanisms for fully incorporating user preferences and feedback, making it difficult to provide personalized health advice. Furthermore, they were limited in their ability to customize the gender, tone, and style of the voice, limiting the user experience. There is a need to resolve these issues and provide a health management system that users will want to use on an ongoing basis.

[0088] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0089] In this invention, the server includes: a means for collecting information about a user's lifestyle and diet through real-time dialogue with the user, a means for converting the collected information into a data format and sending it to the server, which then analyzes the information to evaluate the user's health condition and nutritional balance and generate optimal health advice; a means for providing the generated advice to the user audibly and visually and notifying the advice in a set gender and tone; and a means for collecting feedback from the user after providing the advice, adjusting the AI ​​model based on the feedback, and reflecting the feedback in subsequent advice. This makes it possible to provide personalized health care optimized for the user's lifestyle and preferences, thereby continuously increasing the user's motivation.

[0090] "Voice-based interactive AI" refers to an AI technology that interacts with users via voice and collects and provides information in response to their instructions and questions.

[0091] "Real-time interaction" refers to a communication method in which interaction with a user occurs immediately without delay.

[0092] "Lifestyle habits" refers to the user's daily actions and patterns of behavior, including, for example, activities such as eating, exercise, and sleep.

[0093] "Diet" refers to the content and pattern of the food a user eats on a daily basis.

[0094] "Converting to data format" refers to the process of converting collected information into a format that can be analyzed by the server.

[0095] A "server" refers to a computer system that receives information, analyzes it, and returns the results.

[0096] "Health status" refers to the user's physical and mental condition, and is assessed based on medical indicators and lifestyle habits.

[0097] "Nutritional balance" refers to the balance of nutrients that the user consumes, and aims to ensure that the appropriate amount of nutrition is consumed without excess or deficiency.

[0098] "Health Advice" means specific suggestions or instructions provided to a User to improve or maintain their health.

[0099] "Audio and visual presentation" refers to the means by which generated information is conveyed to the user through audio and visual displays.

[0100] "Feedback" refers to reactions such as ratings and opinions provided by users.

[0101] "Artificial intelligence model" refers to machine learning algorithms and data analysis methods that automatically generate advice based on data.

[0102] "Customization" refers to the ability to change system settings based on user preferences and requirements.

[0103] "Conversational style" refers to the tone or style of a conversation, such as casual, formal, or friendly.

[0104] This invention is a system called "your personal 'meddling' health advisor," which uses a home device equipped with voice-interactive AI to support the user's daily life. This system is mainly composed of three elements: a terminal, a server, and a user.

[0105] Terminal (home device)

[0106] The device is equipped with voice interactive AI, which enables real-time voice interaction with the user. The device collects information from the user by asking questions such as:

[0107] "What did you eat today?"

[0108] "How is it going?"

[0109] The user's responses are recorded, converted into data, and sent to a server. The device also offers settings that allow users to customize the gender, tone, and conversational style of the voice.

[0110] server

[0111] The server analyzes the information sent from the device and generates appropriate health advice. Specifically, the server analyzes the user's dietary and lifestyle data to evaluate their nutritional balance and health status. If the server determines that the user is deficient in vitamin D, for example, it generates the following advice:

[0112] "You're lacking vitamin D, so why not try incorporating some fish into your dinner tonight?"

[0113] The analysis is performed using data analysis software such as Python or R, and optimal advice is provided using generative AI models, which are then sent to the device and notified to the user via audio and visual means.

[0114] User

[0115] Through interaction with the device, the user provides information about their lifestyle and dietary habits and receives advice from the server. Based on this advice, the user not only improves their daily life but also provides feedback. For example, if the device asks, "What did you think of today's advice? Is there anything else I can improve?" the user might respond, "I like fish dishes, so please tell me more recipes."

[0116] Prompt Sentence Examples

[0117] To actually use this system, users would enter prompts like the following to receive specific advice or information:

[0118] Please record what you ate today.

[0119] "What ingredients should I include in my next meal?"

[0120] "Please give me some advice on how to improve my lifestyle."

[0121] "I'll provide feedback on yesterday's advice."

[0122] These prompts allow users to receive specific health advice and feedback, ultimately helping them optimize their lifestyle and nutritional balance.

[0123] Specific examples of hardware and software used

[0124] The following hardware and software are important for the implementation of the system:

[0125] Terminals (home devices): Smart speakers and smart displays equipped with voice-activated AI

[0126] Server: A server for high-performance data analysis (e.g., an environment for running scripts in Python or R)

[0127] Software: Machine learning frameworks for building generative AI models (TENSORFLOW®, PyTorch, etc.)

[0128] Combining these elements makes it possible to provide users with ongoing personalized health advice.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1: Reset audio settings

[0131] The device initializes the voice settings when the system starts up. The device asks the user, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other." The user responds, "I prefer a female voice." The input to this dialogue is the user's preference information, which the device converts into a data format and records. As an output, the device completes the voice settings to be used for subsequent interactions.

[0132] Step 2: Set the tone of the conversation

[0133] After setting the voice, the device sets the tone of the conversation. The device asks, "Next, please select the tone of the conversation. You can choose from casual, formal, or friendly," and the user requests, "I prefer friendly." This input information is also converted into data format and recorded. As an output, the device reflects the set tone in subsequent conversations.

[0134] Step 3: Daily Questioning and Data Collection

[0135] The device starts the user's daily assistance by asking, "What did you eat today?" The user answers, "I had toast and coffee for breakfast, pasta for lunch, and salad for dinner." This input information is recorded by the device and converted into a data format. As an output, the collected data is sent to the server.

[0136] Step 4: Data transmission and analysis

[0137] The server receives the data sent from the device. The input information is the user's dietary details, and the server analyzes this information using a data analysis script (e.g., Python). The data is processed by analyzing the dietary details and evaluating whether there are any nutrient deficiencies or excesses. The output is an evaluation of the user's nutritional balance.

[0138] Step 5: Advice Generation

[0139] The server generates specific health advice for the user based on the analysis results. For example, if it determines that the user is deficient in vitamin D, it generates the advice, "You're deficient in vitamin D, so why not try including some fish in your dinner tonight?" This advice is generated using a generative AI model. The generated advice is sent to the device as output.

[0140] Step 6: Providing advice

[0141] The device communicates the advice received from the server to the user via voice. The device notifies the user, saying, "You're lacking in vitamin D, so why not include fish in your dinner tonight?" and also displays a fish recipe as visual information. The input to this dialogue is the advice from the server, and the output is a voice notification and a visual presentation.

[0142] Step 7: Get user feedback

[0143] After providing the advice, the device asks the user for feedback, asking, "What did you think of today's advice? Is there anything else I can improve?" The user might reply, "I like fish dishes, so please tell me more recipes." This feedback information is recorded on the device, converted into a data format, and sent to the server. The feedback data is collected as output.

[0144] Step 8: Feedback analysis and model adjustment

[0145] The server analyzes the feedback sent. The input information is feedback from the user, and the server uses natural language processing technology to understand the user's wishes. As a data calculation, the AI ​​model is adjusted and reflected in the next advice. As an output, the adjusted AI model optimizes the advice for the next time and beyond.

[0146] (Application example 1)

[0147] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0148] In modern industrial facilities, managing the health status of workers is important, but traditionally workers have been expected to manage their health independently, resulting in a lack of effective support. Furthermore, the lack of personalized health advice tailored to the work environment has led to a decline in worker efficiency and motivation. The present invention aims to solve these problems and provide a system that supports worker health management.

[0149] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0150] In this invention, the server includes: [a means equipped with voice interactive artificial intelligence and interacting with a user to collect information about the user's lifestyle and dietary habits;] [a means for sending the collected information to the server and analyzing it to generate advice about the user's health condition and nutritional balance;] [a means for providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation;] [a means for collecting feedback from the user and adjusting the artificial intelligence model based thereon;] [a means for interacting with workers at industrial facilities and collecting information about the workers' health conditions; and] [a means for analyzing the collected information and providing the workers with advice about their health conditions audibly and visually. This makes it possible to grasp the health conditions of workers in real time and provide appropriate advice.

[0151] "Voice-based interactive AI" is AI that can naturally converse with users through voice, gather information, understand instructions, and respond accordingly.

[0152] "Worker" refers to an employee who performs various tasks in an industrial facility.

[0153] "health condition" means the state of a worker's physical and mental health;

[0154] "Nutritional balance" refers to the balance of nutrients that workers are properly consuming, and evaluates the balance of vitamins and minerals necessary to maintain good health.

[0155] "Advice" refers to advice and suggestions regarding health management provided to workers based on collected information and analysis results.

[0156] "Goals" refer to specific targets that are set for workers to achieve when managing their health.

[0157] "Progress" refers to data that indicates the current progress toward a set goal.

[0158] "Feedback" refers to the opinions, thoughts, and further requests that workers provide in response to advice.

[0159] "Artificial intelligence model" refers to an algorithm or system that generates advice based on collected data.

[0160] "Work environment" refers to the total physical and psychological environment in which workers perform their duties within an industrial establishment.

[0161] "Visual information" means advice or instructions provided through a display or other visual means.

[0162] This invention is a system for supporting the health management of workers in industrial facilities. This system consists of three main components: a robot equipped with voice-interactive AI, a server for analyzing data, and workers.

[0163] System Configuration

[0164] 1. Robot (voice-based AI-equipped terminal)

[0165] The robots can be placed anywhere in an industrial facility and interact with workers in real time. They have the following capabilities:

[0166] The voice dialogue function asks questions about the worker's health status and records their responses.

[0167] You can customize the gender, tone, and conversational style of the voice.

[0168] The collected data is sent to a server, and advice returned from the server is provided to the worker in the form of audio and visual information.

[0169] 2. Server

[0170] The server has the following features:

[0171] The robot analyzes data on the worker's health status and generates appropriate health advice. The server performs a comprehensive evaluation, including past data.

[0172] Collect worker feedback and adjust artificial intelligence models.

[0173] The specific technologies used by the server include data analysis algorithms (e.g., Python's Pandas, NumPy), voice dialogue AI engines (e.g., Google® Dialogflow), and cloud databases (e.g., AWS® DynamoDB).

[0174] 3. Workers

[0175] Workers use the system in the following steps:

[0176] Through dialogue with the robot, users can provide feedback on their health status.

[0177] Receive and act on advice provided by the robot and server.

[0178] They will provide feedback on how the advice provided was implemented and this will be reflected in the next advice.

[0179] Specific examples

[0180] Example 1: Everyday questions and advice

[0181] The robot asks, "How are you feeling today?" and the worker replies, "I'm a little tired." The robot sends this information to a server, which uses past data to generate advice such as, "You may not be hydrated enough today. Please drink some water," and provides this to the worker via the robot.

[0182] Example 2: Obtaining feedback and adjusting advice

[0183] After providing the advice, the robot asks, "What did you think of today's advice?", to which the worker responds, "It was helpful, but I'd like to know more about how to take breaks." Based on this feedback, the server adjusts the content of the next advice to include more detailed information about how to take breaks.

[0184] Prompt Sentence Examples

[0185] If a user reports "I have a headache today":

[0186] Robot: "You've reported a headache. Based on past data, it may be due to dehydration. Please try drinking some water now."

[0187] As shown in this example of a prompt, the robot will hold a brief dialogue, and the server will provide specific advice based on the analysis, allowing workers to understand their own health condition in real time and take appropriate measures.

[0188] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0189] Step 1:

[0190] The robot asks the worker, "How are you feeling today?" The input is the worker's verbal response, and the output is the voice data of that response. The robot converts this voice data into text data using speech recognition software (e.g., Google Dialogflow).

[0191] Step 2:

[0192] The robot collects the converted text data and sends it to a server via the Internet. The input is the text data, and the output is the data received by the server.

[0193] Step 3:

[0194] The server analyzes the received text data and performs a comprehensive evaluation, including past data. The input is the current text data and past data, and the output is the analysis result. The server uses a data analysis algorithm (e.g., Python's Pandas or NumPy) to derive a conclusion, such as "insufficient water."

[0195] Step 4:

[0196] The server generates health advice based on the analysis results. The input is the analysis results, and the output is text data of the advice. The server uses a generative AI model (e.g., GPT-3 (registered trademark)) to generate appropriate advice text. Take the prompt text "You are dehydrated, so please drink water" as an example.

[0197] Step 5:

[0198] The server sends the generated advice to the robot via the Internet. The input is the text data of the advice, and the output is the data received by the robot.

[0199] Step 6:

[0200] The robot receives advice and provides it to the worker as audio and visual information. The input is the text data of the advice, and the output is an audio message and visual information displayed on a display. The robot uses speech synthesis software to convert the text to audio, saying, "Drink some water."

[0201] Step 7:

[0202] The robot collects feedback from the worker. The input is the worker's verbal feedback, and the output is the voice data of that feedback. The robot converts this voice data into text data using speech recognition software (e.g., Google Dialogflow).

[0203] Step 8:

[0204] The robot sends text data as feedback to the server. The input is the text data, and the output is the feedback data received by the server.

[0205] Step 9:

[0206] The server analyzes the received feedback and adjusts the AI ​​model to reflect it in the next advice. The input is the text data of the feedback, and the output is the adjusted AI model. The server analyzes the feedback data and draws a conclusion, for example, that "detailed resting guidance is needed," and updates the model.

[0207] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0208] The "your personal 'meddling' health advisor" of this invention is a system that provides more precise support for the user's daily life by combining a home device equipped with voice-interactive AI with an emotion engine. This system consists of the following steps:

[0209] System Configuration

[0210] This system is broadly composed of three elements: terminals, servers, and users.

[0211] 1. Terminal (home device)

[0212] The device is equipped with voice-interactive AI, enabling real-time dialogue with the user. The device asks questions about the user's lifestyle and dietary habits and records the responses. In addition, by combining it with an emotion engine, it recognizes the user's emotional state from their speech and behavior and adjusts advice based on that information. It also provides settings that allow users to customize the gender, tone, and conversational style of the voice. The collected data is sent to a server, which generates advice and provides it to the user.

[0213] 2. Server

[0214] The server analyzes data about the user's lifestyle and dietary habits sent from the device and generates optimal health advice for the user. The server performs a comprehensive analysis, including past data, to evaluate the user's health condition and nutritional balance. The server also takes into account the user's emotional state as recognized by the emotion engine and adjusts the tone and content of the advice. The generated advice is sent to the device, and the artificial intelligence model is adjusted based on the feedback to improve the performance of the entire system.

[0215] 3. Users

[0216] Users interact with the device to provide information about their lifestyle and dietary habits and receive advice from the server. Based on the advice, users can improve their lifestyle habits and provide feedback, which will be reflected in future advice. The system also uses an emotion engine to understand the user's emotional state, allowing for more personalized service.

[0217] Example

[0218] Example 1: Customization settings according to usage

[0219] 1. Voice settings: During initial setup, the device asks the user for the gender and tone of the voice. For example, it says, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other." The user responds, "I prefer a female voice."

[0220] 2. Setting the conversation tone: The device then asks, "Next, please choose your conversation tone. You can choose from casual, formal, or friendly." The user requests "Friendly."

[0221] Example 2: Data collection and advice provision

[0222] 1. Daily Questions: To understand daily habits and eating habits, the device asks, "What did you eat today?" The user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[0223] 2. Emotion recognition: The device recognizes the user's emotional state from their tone of voice and choice of words, and provides feedback such as, "You seem happy today" or "You seem a little tired today."

[0224] 3. Data storage: The device stores the collected data and emotional state and sends it to the server. It displays "Information recorded."

[0225] 4. Data analysis and advice generation: The server analyzes the data and evaluates the user's health condition and nutritional balance. If the user is tired, the server recommends relaxing meals and exercise.

[0226] 5. Providing advice: The server generates a message saying, "You are lacking in vitamin C, so we recommend eating oranges and kiwis," and sends it to the device. The device then verbally conveys this advice to the user and displays images of oranges and kiwis and their nutritional information on the display.

[0227] Example 3: User feedback and model adjustment

[0228] 1. Feedback acquisition: After providing advice, the device asks the user, "What did you think of today's advice?" The user gives feedback such as, "I'd like to know more meat recipes."

[0229] 2. Feedback analysis and model adjustment: The server analyzes this feedback and adjusts the AI ​​model to reflect it in the next recommendation. For example, meat recipes will be prioritized in the next recommendation.

[0230] As described above, the present invention combines voice-based interactive AI with an emotion recognition engine to provide an effective system that allows users to passively receive health advice, thereby supporting users in maintaining and improving their health through a more personalized experience.

[0231] The processing flow will be explained below.

[0232] Step 1:

[0233] The device will enter initial setup mode and ask the user to customize the gender, tone, and conversational style of the voice. For example, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other."

[0234] Step 2:

[0235] The user selects the gender of the voice and responds, "I prefer a female voice." The device then asks, "Next, please select the conversation tone. You can choose from casual, formal, or friendly." The user requests, "I prefer friendly."

[0236] Step 3:

[0237] The device records the settings and prepares to reflect them in subsequent interactions. It notifies the user that "the settings have been recorded," completing the initial setup.

[0238] Step 4:

[0239] The device periodically asks the user questions about their lifestyle and diet. For example, it asks, "What did you eat today?" The user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[0240] Step 5:

[0241] At the same time as listening to what the user is saying, the device uses an emotion engine to analyze the user's emotional state and provides feedback such as "You look happy today" or "You seem a little tired."

[0242] Step 6:

[0243] The device stores the collected data and emotional state in its internal storage, then transmits the data to the server. The device notifies the user that the information has been recorded, and transmits the data.

[0244] Step 7:

[0245] The server receives the data and performs a comprehensive analysis, including past data, to evaluate the user's nutritional balance and determine that the user has recently been lacking in vitamin C.

[0246] Step 8:

[0247] The server takes into account the user's current emotional state and generates appropriate advice. For example, if the user feels tired, the server generates advice recommending a relaxing meal or light exercise.

[0248] Step 9:

[0249] The server sends the generated advice to the device, which then provides it to the user both audibly and visually: "You're lacking in vitamin C, so we recommend eating oranges and kiwis," the device says, and the display shows images of oranges and kiwis and their nutritional information.

[0250] Step 10:

[0251] The user takes food in response to the advice and provides feedback to the device. When the device asks, "What did you think of today's advice?", the user responds, "I'd like to know more meat recipes."

[0252] Step 11:

[0253] The device collects the feedback and sends it to the server, which analyzes it and adjusts the AI ​​model to reflect the feedback in the next recommendation. For example, it may change the settings so that meat recipes are prioritized in future recommendations.

[0254] Example 2

[0255] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0256] In recent years, health problems such as lifestyle-related diseases and nutritional imbalances have been increasing. Many people want to take measures to address these problems, but self-management is difficult, and maintaining motivation is also challenging. Existing health management systems are unable to provide advice that takes into account the user's emotional state, tend to provide uniform information, and lack truly personalized support. This makes it difficult for users to realize the effects of self-management, making it difficult to continue.

[0257] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0258] In this invention, the server includes: [means equipped with voice interactive artificial intelligence and interacting with the user to collect information about the user's lifestyle and dietary habits; [means sending the collected information to the server for analysis and generating advice about the user's health condition and nutritional balance; [means providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation;] [means equipped with an emotion recognition engine that recognizes the user's emotional state from their speech and actions and adjusts the advice based on that information; and] [means collecting feedback from the user and adjusting the artificial intelligence model based on that. This makes it possible [to provide personalized health advice based on each user's emotional state and lifestyle habits and maintain motivation].

[0259] "Voice-activated interactive artificial intelligence" refers to artificial intelligence that can have natural conversations with users, and includes voice recognition, natural language processing, and voice synthesis technologies.

[0260] "User" means an individual who uses the system and receives health advice.

[0261] "Lifestyle habits" refers to the patterns of activities and behavior that a user engages in on a daily basis, including diet, exercise, sleep, and the like.

[0262] "Diet" refers to the food and eating habits that a user regularly consumes.

[0263] A "server" is a computer that performs the central calculations and data processing of a system.

[0264] "Advice" refers to suggestions and instructions generated by the system based on the user's health status and nutritional balance.

[0265] An "emotion recognition engine" is software that recognizes a user's emotional state from their speech and behavior, including analyzing voice tone and vocabulary.

[0266] "Feedback" refers to the thoughts and opinions that a user provides about advice, and is information that will be reflected in the next piece of advice.

[0267] An "artificial intelligence model" is a trained model for analyzing user data and generating health advice.

[0268] "Personalized" means optimized for the individual user.

[0269] "Motivation" refers to the will and enthusiasm a user has to achieve a goal.

[0270] A "goal" is a specific health-related standard or outcome that a user aims to achieve.

[0271] "Progress" refers to the progress or steps achieved towards a goal.

[0272] This invention relates to a "personal, nosy health advisor" that combines voice-activated interactive AI and an emotion recognition engine. This system collects and analyzes information about the user's lifestyle and dietary habits, and provides optimal health advice.

[0273] Hardware and software used

[0274] Hardware

[0275] Home devices: These are devices equipped with voice interactive artificial intelligence and enable real-time interaction with users.

[0276] software

[0277] Speech dialogue engine: Google Dialogflow

[0278] Emotion recognition engine: Affectiva SDK

[0279] Data analysis engine: Apache Spark (registered trademark)

[0280] Database system: MySQL (registered trademark)

[0281] Program processing description

[0282] 1. Initial device setup

[0283] During initial setup, a terminal (home device) asks the user for voice and conversation tone settings. First, the voice dialogue engine sets the voice gender and tone based on the user's preferences. For example, the terminal might say, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose from male, female, or other," and the user might respond, "I prefer a female voice."

[0284] 2. Daily interactions with users

[0285] The device routinely asks the user questions about their lifestyle and diet. For example, if the device asks, "What did you eat today?" and the user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner," the device collects and records that information. It also uses an emotion recognition engine to analyze the user's emotional state from their tone of voice and choice of words, providing feedback such as, "You seem happy today" or "You seem a little tired today."

[0286] 3. Data storage and transmission

[0287] The device stores the collected lifestyle and emotional state data in a MySQL database and sends it to the server. The device displays "Information recorded."

[0288] 4. Data analysis by the server

[0289] The server uses Apache Spark to analyze the data it receives, including evaluating lifestyle and emotional state data. For example, the server might determine that the user is deficient in vitamin C.

[0290] 5. Advice Generation

[0291] The server uses a generative AI model based on the analysis results to generate optimal health advice for the user, such as "You are lacking in vitamin C, so we recommend eating oranges and kiwis," and sends the advice to the device.

[0292] 6. Providing advice

[0293] The device provides the user with advice from the server both audibly and visually. The device will say, "You are lacking in vitamin C, so we recommend eating oranges and kiwis," and will show images of oranges and kiwis and their nutritional information on the display.

[0294] 7. Gathering User Feedback

[0295] After providing the advice, the device asks the user, "What did you think of today's advice?" If the user provides feedback such as "I'd like to know more meat recipes," that information is recorded by the device.

[0296] 8. Feedback analysis and model adjustment

[0297] The server analyzes user feedback and adjusts the generative AI model, so that future advice suggestions will be tailored to the user's preferences. For example, meat recipes will be prioritized from the next time.

[0298] Prompt Sentence Examples

[0299] "What did you eat today?"

[0300] "How are you feeling today?"

[0301] "What did you think of today's advice?"

[0302] As described above, the "personal, nosy health advisor" of the present invention is a system that supports users in improving their lifestyle habits and maintaining their health by effectively combining voice-interactive artificial intelligence and an emotion recognition engine.

[0303] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0304] Processing Step Description

[0305] Step 1: Initial device setup

[0306] Specific behavior:

[0307] 1. The terminal presents the user with an initial setup start prompt.

[0308] 2. Device: "Hello! I'm your personal health advisor. First, please select the gender of my voice. You can choose male, female, or other."

[0309] 3. User: "Female voices are nice."

[0310] 4. Device: "Got it. I'll be speaking with a female voice from now on."

[0311] 5. Next, the device: "Next, please choose your conversational tone. You can choose casual, formal, or friendly."

[0312] 6. User: "Friendly."

[0313] 7. Terminal: "I'll speak to you in a friendly tone!"

[0314] Input: User voice preference, conversational tone preference

[0315] Data manipulation: Apply voice and speech tone settings based on user preferences

[0316] Output: Configured voice interaction environment

[0317] Step 2: Daily interactions with users

[0318] Specific behavior:

[0319] 1. At a set time each day, the device begins asking questions to the user.

[0320] 2. Terminal: "What did you eat today?"

[0321] 3. User: "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[0322] 4. Terminal: "Information recorded."

[0323] 5. Next, the device performs emotion recognition and provides feedback such as, "You seem happy today" or "You seem a little tired today."

[0324] Input: User's diet, voice tone, and language

[0325] Data processing: Record meal contents as text data and analyze emotions from voice tone and vocabulary

[0326] Output: Recorded food data, emotional state recognition

[0327] Step 3: Saving and sending data

[0328] Specific behavior:

[0329] 1. The device stores the collected data (lifestyle habits and emotional state) in an internal database.

[0330] 2. Terminal: Displays "Information sent to server."

[0331] 3. The data is sent over the internet to a server.

[0332] Input: User's lifestyle data, emotional state data

[0333] Data processing: storing data in a database and sending data to a server

[0334] Output: Records stored in the database, data sent to the server

[0335] Step 4: Data analysis by the server

[0336] Specific behavior:

[0337] 1. The server receives the transmitted data and stores it in a database.

[0338] 2. Analyze lifestyle data and emotional state data using Apache Spark.

[0339] 3. Evaluate the nutritional balance based on the meal content and also determine the user's psychological state based on their emotional state.

[0340] Input: Lifestyle data and emotional state data sent from the device

[0341] Data processing: Analyze data in the database and evaluate nutritional balance and emotional state

[0342] Output: Health status assessment report

[0343] Step 5: Advice generation

[0344] Specific behavior:

[0345] 1. The server uses a generative AI model to create health advice based on the analysis results.

[0346] 2. For example, if the nutritional balance is insufficient, we might suggest, "You are lacking vitamin C, so we recommend eating oranges or kiwis."

[0347] Input: Health Assessment Report

[0348] Data processing: Generate advice using AI models generated from analysis results

[0349] Output: Health advice to provide to the user

[0350] Step 6: Providing advice

[0351] Specific behavior:

[0352] 1. The server sends the generated advice to the device.

[0353] 2. The device will say, "You are lacking in vitamin C. We recommend eating oranges or kiwis."

[0354] 3. Display images and nutritional information of oranges and kiwis on the display.

[0355] Input: Generated health advice

[0356] Data processing: Sending advice data from the server to the device

[0357] Output: Advice and visual information provided to the user

[0358] Step 7: Gather user feedback

[0359] Specific behavior:

[0360] 1. Device: Ask the user, "What did you think about today's advice?"

[0361] 2. User: Provides feedback saying, "I'd like to see more meat recipes."

[0362] 3. Device: Display "Feedback recorded."

[0363] Input: User feedback

[0364] Data processing: recording and storing feedback data

[0365] Output: Recorded feedback data

[0366] Step 8: Feedback analysis and model adjustment

[0367] Specific behavior:

[0368] 1. The server analyzes the collected feedback.

[0369] 2. The generative AI model is adjusted based on the analysis results and reflected in the next advice generation.

[0370] 3. Update the model so that, for example, meat recipes are prioritized from the next time onwards.

[0371] Input: User feedback data

[0372] Data processing: feedback analysis, updating generative AI models

[0373] Output: The new model settings reflected in the next advice generation.

[0374] (Application example 2)

[0375] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0376] Conventional health advice systems collect information about a user's lifestyle and dietary habits and provide basic advice, but they struggle to provide personalized advice that takes into account the user's emotional state. It is also difficult to maintain consistency between health advice and dietary suggestions, which can lead to a continuous decline in user motivation. Furthermore, few systems effectively incorporate user feedback on the advice provided, making it difficult for users to continue receiving effective advice.

[0377] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0378] In this invention, the server includes: [means equipped with voice interactive artificial intelligence and interacting with the user to collect information about the user's lifestyle and dietary habits;] means for sending the collected information to the server and analyzing it to generate advice about the user's health condition and nutritional balance; [means for providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation;] means for collecting feedback from the user and adjusting the artificial intelligence model based thereon; [means for recognizing the user's emotional state using an emotion engine and adjusting the tone and content of health advice and dietary suggestions; and [means for providing a food delivery function that makes customized dietary suggestions based on the user's emotional state and dietary habits.] As a result, health advice and dietary suggestions are consistently provided taking the user's emotional state into consideration, thereby continuously motivating the user and enabling effective health maintenance and revitalization.

[0379] "Voice-based interactive AI" is an AI technology that interacts with users through voice input, understands their intentions and requests, and generates appropriate responses.

[0380] "Lifestyle" refers to behaviors and habits in daily life, such as exercise, sleep, and eating patterns.

[0381] "Diet" refers to habits related to eating behavior, such as the type of food consumed on a daily basis, the time of eating, and frequency of eating.

[0382] A "server" refers to a computer system that provides data and services to other computers and devices over a network.

[0383] "Health status" refers to information that indicates an individual's physical condition and level of health, including physical and mental status.

[0384] "Nutritional balance" refers to the appropriate intake of each nutrient (e.g., vitamins, minerals, proteins, carbohydrates, lipids, etc.) required by the body.

[0385] "Advice" refers to suggestions or advice that can help improve a user's health or diet.

[0386] An "emotion engine" refers to technology that analyzes a user's emotional state from their speech and behavior and recognizes their emotions.

[0387] "Feedback" refers to a user's evaluation or opinion of the advice or service provided.

[0388] "Personalized advice" refers to advice and suggestions that are customized to suit the individual characteristics and circumstances of a user.

[0389] "Meal suggestions" refers to proposing specific meal plans and menus to the user.

[0390] "Food delivery function" refers to a function that provides suggested foods and dishes to users as a delivery service.

[0391] "Customization" refers to adjusting settings and content to suit the user's preferences and needs.

[0392] This invention uses a system that combines voice-interactive AI and an emotion recognition engine to provide health advice based on a user's lifestyle and dietary habits. The system of this invention is mainly composed of three elements: a terminal (home device), a server, and a user.

[0393] 1. Terminal (home device)

[0394] The device is equipped with voice-activated interactive AI, enabling real-time dialogue with the user. When the user speaks to the device about their lifestyle and dietary habits, the device records the information and uses an emotion engine to identify the user's emotional state. This information and emotion data is sent to the server. The device's voice is customizable, allowing users to set gender, tone, and conversational style.

[0395] 2. Server

[0396] The server receives and analyzes the data sent from the device. Generative AI models such as the Transformer model (Hugging Face Transformers) are used for data analysis. The server evaluates the user's health and nutritional balance and generates optimal health advice based on that. It also adjusts the tone and content of the advice based on the user's emotional state, based on data from the emotion engine.

[0397] The generated advice is sent to the device as audio and visual information and provided to the user. Feedback data from the user is also sent to the server, and this is used to continuously optimize the generative AI model. For example, if a user provides feedback such as "I'd like to know more meat dish recipes," information about meat dishes will be prioritized in future advice suggestions.

[0398] 3. Users

[0399] Users can provide information about their lifestyle and dietary habits through interactions with the device. They can also receive health advice and meal suggestions from the server and improve their lifestyle based on those advice. The device recognizes the user's emotional state and provides appropriate advice based on their emotions, allowing users to enjoy a more personalized experience.

[0400] For example, if a user says, "I was full this morning, so I had just soup for lunch. It was delicious!", the emotion engine will recognize that the user is satisfied, and the server will generate the following advice: "I can see that you enjoyed a light lunch. However, you may be lacking in vitamins and minerals. Why not try a salad with lots of vegetables and some fruit for dinner?"

[0401] In addition, users can order the suggested meal menu as a delivery service, making it easy for them to get healthy meals. At this time, feedback on the advice provided is also collected and reflected in future advice.

[0402] The present invention consistently provides personalized health advice and dietary suggestions that are useful in daily life while taking into account the user's emotional state, thereby helping the user maintain their health and improve their motivation.

[0403] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0404] Step 1: Collecting user data on the device

[0405] The device collects information about the user's lifestyle and eating habits through dialogue with the user. If the user says, "I've been full since this morning, so I had just soup for lunch," the device uses voice recognition to record that information as text data. The input is the user's voice data, and the output is text data. This text data is temporarily stored in the device and later sent to the server.

[0406] Step 2: Recognizing your emotional state

[0407] The device uses an emotion engine to recognize the user's emotional state from the collected user speech data. From the tone and expression of the voice, the device determines the user's emotions, such as "happy" or "tired." In this case, the user is recognized as satisfied. The input is the user's voice data, and the output is emotional data.

[0408] Step 3: Sending data

[0409] The device sends the collected text data and emotion data to a server. The input is text data and emotion data, and the output is data transfer to the server. Specifically, the data is sent to the server via the Internet.

[0410] Step 4: Data analysis by the server

[0411] The server receives the data sent from the device and analyzes it using a generative AI model. The analysis uses a Transformer model (Hugging Face Transformers) to evaluate the user's diet and health status. The input is the user's text data and emotion data, and the output is the analysis results and health advice.

[0412] Step 5: Generate health advice

[0413] The server generates optimal health advice for the user based on the analysis results. For example, it generates advice such as, "We can see that you enjoyed a light lunch. However, you may be lacking in vitamins and minerals, so why not try a salad with plenty of vegetables and some fruit for dinner?" The input is the analysis results, and the output is specific advice statements.

[0414] Step 6: Providing advice

[0415] The server sends the generated advice to the terminal, which then provides it to the user as audio and visual information. The terminal will say aloud, "Today's lunch was good. For dinner, I recommend a salad and fruit, which will provide you with vitamins," and show specific meal examples on the display. The input is the advice text, and the output is the audio and visual information received by the user.

[0416] Step 7: Gather feedback

[0417] The user provides feedback on the advice provided. For example, the user might respond, "This advice is good, but I'd like to know more specific recipes." The device records this feedback and sends it to the server. The input is the user's feedback data, and the output is the transfer of the feedback data to the server.

[0418] Step 8: Tuning the generative AI model

[0419] The server analyzes the user's feedback and optimizes the generative AI model. It makes necessary adjustments to reflect the results in future advice. For example, it adjusts the model so that specific recipes are prioritized in future advice. The input is the user's feedback data, and the output is the adjusted generative AI model.

[0420] Example prompt sentence:

[0421] User input: "I was busy this morning and forgot to eat lunch. But I had some fruit as a snack in the evening."

[0422] Response prompt: "It's been a busy day, but I'd recommend including a protein-rich meal for dinner to ensure a balanced diet."

[0423] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0424] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0425] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0426] [Second embodiment]

[0427] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0428] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0429] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0430] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0431] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0432] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0433] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0434] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0435] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0436] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0437] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0438] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0439] The "Your Personal Nosy Health Advisor" of this invention is a system that supports the user's daily life using a home device equipped with voice-interactive AI. This system consists of the following steps:

[0440] System Configuration

[0441] This system is broadly composed of three elements: terminals, servers, and users.

[0442] 1. Terminal (home device)

[0443] The device is equipped with voice-activated interactive AI, enabling real-time dialogue with the user. The device asks questions about the user's lifestyle and dietary habits and records the responses. It also provides settings that allow users to customize the gender, tone, and conversational style of the voice. Furthermore, it sends the collected data to a server, displays the advice returned from the server, and relays it to the user via voice.

[0444] 2. Server

[0445] The server analyzes data about the user's lifestyle and dietary habits sent from the device and generates optimal health advice for the user. The server also performs a comprehensive analysis, including past data, to evaluate the user's health status and nutritional balance. The generated advice is sent to the device, and the artificial intelligence model is adjusted based on the feedback to improve the performance of the entire system.

[0446] 3. Users

[0447] Users interact with the device to provide information about their lifestyle and dietary habits and receive advice from the server. Not only can they improve their lifestyle based on the advice they receive, but they can also provide feedback that will be reflected in the next advice.

[0448] Example

[0449] Example 1: Customization settings according to usage

[0450] 1. Voice settings: During initial setup, the device asks the user for the gender and tone of their voice. For example, the device might say, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other." The user responds, "I prefer a female voice." The device records this and uses it for future interactions.

[0451] 2. Setting the conversation tone: The device then asks, "Next, please choose the conversation tone. You can choose from casual, formal, or friendly." The user requests "I prefer friendly." The device also records this and reflects it in future conversations.

[0452] Example 2: Data collection and advice provision

[0453] 1. Daily Questions: To understand daily habits and eating habits, the device asks, "What did you eat today?" The user answers, "I had toast and coffee for breakfast, pasta for lunch, and salad for dinner." The device records this information and sends it to the server.

[0454] 2. Data analysis: The server analyzes the collected data and evaluates the user's nutritional balance. For example, the server determines that the user is deficient in vitamin D based on their dietary data.

[0455] 3. Advice generation and provision: The server generates advice such as "You're lacking in vitamin D, so why not include fish in your dinner tonight?" and sends it to the device. The device then conveys this advice to the user via voice and also displays visual information (e.g., recipes using fish).

[0456] Example 3: User feedback and model adjustment

[0457] 1. Obtaining feedback: After providing advice, the device asks the user, "What did you think of today's advice? Is there anything else we can improve?" The user gives feedback such as, "I like fish dishes, so please tell me more recipes."

[0458] 2. Feedback analysis and model adjustment: The server analyzes this feedback and adjusts the AI ​​model to reflect it in the next recommendation. For example, a variety of fish-based recipes will be prioritized in the next recommendation.

[0459] As described above, the present invention provides an effective system that allows users to passively obtain health advice by combining voice dialogue-based artificial intelligence and data analysis technology.

[0460] The processing flow will be explained below.

[0461] Step 1:

[0462] The device will enter initial setup mode and ask the user to customize the gender, tone, and conversational style of the voice. For example, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other."

[0463] Step 2:

[0464] The user selects the gender of the voice and responds, "I prefer a female voice." The device then asks, "Next, please select the conversation tone. You can choose from casual, formal, or friendly." The user requests, "I prefer friendly."

[0465] Step 3:

[0466] The device records the settings and prepares to reflect them in subsequent interactions. It notifies the user that "the settings have been recorded," completing the initial setup.

[0467] Step 4:

[0468] The device periodically asks the user questions about their lifestyle and diet. For example, it asks, "What did you eat today?" The user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[0469] Step 5:

[0470] The device saves the collected data in its internal storage and then sends it to the server. It displays "Information recorded" and sends the data.

[0471] Step 6:

[0472] The server receives the data sent from the device and performs a comprehensive analysis, including the user's past data. It evaluates nutritional balance and health status and determines that the user has recently been lacking in vitamin C.

[0473] Step 7:

[0474] The server generates optimal advice for the user, for example, "We recommend eating oranges and kiwi fruits, which are rich in vitamin C," and sends it to the device.

[0475] Step 8:

[0476] The device receives advice from the server and provides it to the user both audibly and visually. It says, "You're lacking in vitamin C, so we recommend eating oranges and kiwis," and shows images of oranges and kiwis and nutritional information on the screen.

[0477] Step 9:

[0478] The device asks the user for feedback: "What did you think of today's advice?", and the user responds, "I'd like to know more meat recipes."

[0479] Step 10:

[0480] The device sends the collected feedback to a server, which analyzes it and adjusts the AI ​​model. From the next time onward, the settings are changed to prioritize meat recipes based on the user's preferences.

[0481] Example 1

[0482] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0483] Conventional health management systems often collect data about users' lifestyles and dietary habits and provide advice in a fragmented and inconsistent manner. They also lacked mechanisms for fully incorporating user preferences and feedback, making it difficult to provide personalized health advice. Furthermore, they were limited in their ability to customize the gender, tone, and style of the voice, limiting the user experience. There is a need to resolve these issues and provide a health management system that users will want to use on an ongoing basis.

[0484] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0485] In this invention, the server includes: a means for collecting information about a user's lifestyle and diet through real-time dialogue with the user, a means for converting the collected information into a data format and sending it to the server, which then analyzes the information to evaluate the user's health condition and nutritional balance and generate optimal health advice; a means for providing the generated advice to the user audibly and visually and notifying the advice in a set gender and tone; and a means for collecting feedback from the user after providing the advice, adjusting the AI ​​model based on the feedback, and reflecting the feedback in subsequent advice. This makes it possible to provide personalized health care optimized for the user's lifestyle and preferences, thereby continuously increasing the user's motivation.

[0486] "Voice-based interactive AI" refers to an AI technology that interacts with users via voice and collects and provides information in response to their instructions and questions.

[0487] "Real-time interaction" refers to a communication method in which interaction with a user occurs immediately without delay.

[0488] "Lifestyle habits" refers to the user's daily actions and patterns of behavior, including, for example, activities such as eating, exercise, and sleep.

[0489] "Diet" refers to the content and pattern of the food a user eats on a daily basis.

[0490] "Converting to data format" refers to the process of converting collected information into a format that can be analyzed by the server.

[0491] A "server" refers to a computer system that receives information, analyzes it, and returns the results.

[0492] "Health status" refers to the user's physical and mental condition, and is assessed based on medical indicators and lifestyle habits.

[0493] "Nutritional balance" refers to the balance of nutrients that the user consumes, and aims to ensure that the appropriate amount of nutrition is consumed without excess or deficiency.

[0494] "Health Advice" means specific suggestions or instructions provided to a User to improve or maintain their health.

[0495] "Audio and visual presentation" refers to the means by which generated information is conveyed to the user through audio and visual displays.

[0496] "Feedback" refers to reactions such as ratings and opinions provided by users.

[0497] "Artificial intelligence model" refers to machine learning algorithms and data analysis methods that automatically generate advice based on data.

[0498] "Customization" refers to the ability to change system settings based on user preferences and requirements.

[0499] "Conversational style" refers to the tone or style of a conversation, such as casual, formal, or friendly.

[0500] This invention is a system called "Your Personal Nosy Health Advisor," which uses a home device equipped with voice-interactive AI to support the user's daily life. This system is mainly composed of three elements: a terminal, a server, and a user.

[0501] Terminal (home device)

[0502] The device is equipped with voice interactive AI, which enables real-time voice interaction with the user. The device collects information from the user by asking questions such as:

[0503] "What did you eat today?"

[0504] "How is it going?"

[0505] The user's responses are recorded, converted into data, and sent to a server. The device also offers settings that allow users to customize the gender, tone, and conversational style of the voice.

[0506] server

[0507] The server analyzes the information sent from the device and generates appropriate health advice. Specifically, the server analyzes the user's dietary and lifestyle data to evaluate their nutritional balance and health status. If the server determines that the user is deficient in vitamin D, for example, it generates the following advice:

[0508] "You're lacking vitamin D, so why not try incorporating some fish into your dinner tonight?"

[0509] The analysis is performed using data analysis software such as Python or R, and optimal advice is provided using generative AI models, which are then sent to the device and notified to the user via audio and visual means.

[0510] User

[0511] Through interaction with the device, the user provides information about their lifestyle and dietary habits and receives advice from the server. Based on this advice, the user not only improves their daily life but also provides feedback. For example, if the device asks, "What did you think of today's advice? Is there anything else I can improve?" the user might respond, "I like fish dishes, so please tell me more recipes."

[0512] Prompt Sentence Examples

[0513] To actually use this system, users would enter prompts like the following to receive specific advice or information:

[0514] Please record what you ate today.

[0515] "What ingredients should I include in my next meal?"

[0516] "Please give me some advice on how to improve my lifestyle."

[0517] "I'll provide feedback on yesterday's advice."

[0518] These prompts allow users to receive specific health advice and feedback, ultimately helping them optimize their lifestyle and nutritional balance.

[0519] Specific examples of hardware and software used

[0520] The following hardware and software are important for the implementation of the system:

[0521] Terminals (home devices): Smart speakers and smart displays equipped with voice-activated AI

[0522] Server: A server for high-performance data analysis (e.g., an environment for running scripts in Python or R)

[0523] Software: Machine learning frameworks (e.g., TensorFlow, PyTorch) for building generative AI models

[0524] Combining these elements makes it possible to provide users with ongoing personalized health advice.

[0525] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0526] Step 1: Reset audio settings

[0527] The device initializes the voice settings when the system starts up. The device asks the user, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other." The user responds, "I prefer a female voice." The input to this dialogue is the user's preference information, which the device converts into a data format and records. As an output, the device completes the voice settings to be used for subsequent interactions.

[0528] Step 2: Set the tone of the conversation

[0529] After setting the voice, the device sets the tone of the conversation. The device asks, "Next, please select the tone of the conversation. You can choose from casual, formal, or friendly," and the user requests, "I prefer friendly." This input information is also converted into data format and recorded. As an output, the device reflects the set tone in subsequent conversations.

[0530] Step 3: Daily Questioning and Data Collection

[0531] The device starts the user's daily assistance by asking, "What did you eat today?" The user answers, "I had toast and coffee for breakfast, pasta for lunch, and salad for dinner." This input information is recorded by the device and converted into a data format. As an output, the collected data is sent to the server.

[0532] Step 4: Data transmission and analysis

[0533] The server receives the data sent from the device. The input information is the user's dietary details, and the server analyzes this information using a data analysis script (e.g., Python). The data is processed by analyzing the dietary details and evaluating whether there are any nutrient deficiencies or excesses. The output is an evaluation of the user's nutritional balance.

[0534] Step 5: Advice Generation

[0535] The server generates specific health advice for the user based on the analysis results. For example, if it determines that the user is deficient in vitamin D, it generates the advice, "You're deficient in vitamin D, so why not try including some fish in your dinner tonight?" This advice is generated using a generative AI model. The generated advice is sent to the device as output.

[0536] Step 6: Providing advice

[0537] The device communicates the advice received from the server to the user via voice. The device notifies the user, saying, "You're lacking in vitamin D, so why not include fish in your dinner tonight?" and also displays a fish recipe as visual information. The input to this dialogue is the advice from the server, and the output is a voice notification and a visual presentation.

[0538] Step 7: Get user feedback

[0539] After providing the advice, the device asks the user for feedback, asking, "What did you think of today's advice? Is there anything else I can improve?" The user might reply, "I like fish dishes, so please tell me more recipes." This feedback information is recorded on the device, converted into a data format, and sent to the server. The feedback data is collected as output.

[0540] Step 8: Feedback analysis and model adjustment

[0541] The server analyzes the feedback sent. The input information is feedback from the user, and the server uses natural language processing technology to understand the user's wishes. As a data calculation, the AI ​​model is adjusted and reflected in the next advice. As an output, the adjusted AI model optimizes the advice for the next time and beyond.

[0542] (Application example 1)

[0543] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0544] In modern industrial facilities, managing the health status of workers is important, but traditionally workers have been expected to manage their health independently, resulting in a lack of effective support. Furthermore, the lack of personalized health advice tailored to the work environment has led to a decline in worker efficiency and motivation. The present invention aims to solve these problems and provide a system that supports worker health management.

[0545] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0546] In this invention, the server includes: [a means equipped with voice interactive artificial intelligence and interacting with a user to collect information about the user's lifestyle and dietary habits;] [a means for sending the collected information to the server and analyzing it to generate advice about the user's health condition and nutritional balance;] [a means for providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation;] [a means for collecting feedback from the user and adjusting the artificial intelligence model based thereon;] [a means for interacting with workers at industrial facilities and collecting information about the workers' health conditions; and] [a means for analyzing the collected information and providing the workers with advice about their health conditions audibly and visually. This makes it possible to grasp the health conditions of workers in real time and provide appropriate advice.

[0547] "Voice-based interactive AI" is AI that can naturally converse with users through voice, gather information, understand instructions, and respond accordingly.

[0548] "Worker" refers to an employee who performs various tasks in an industrial facility.

[0549] "health condition" means the state of a worker's physical and mental health;

[0550] "Nutritional balance" refers to the balance of nutrients that workers are properly consuming, and evaluates the balance of vitamins and minerals necessary to maintain good health.

[0551] "Advice" refers to advice and suggestions regarding health management provided to workers based on collected information and analysis results.

[0552] "Goals" refer to specific targets that are set for workers to achieve when managing their health.

[0553] "Progress" refers to data that indicates the current progress toward a set goal.

[0554] "Feedback" refers to the opinions, thoughts, and further requests that workers provide in response to advice.

[0555] "Artificial intelligence model" refers to an algorithm or system that generates advice based on collected data.

[0556] "Work environment" refers to the total physical and psychological environment in which workers perform their duties within an industrial establishment.

[0557] "Visual information" means advice or instructions provided through a display or other visual means.

[0558] This invention is a system for supporting the health management of workers in industrial facilities. This system consists of three main components: a robot equipped with voice-interactive AI, a server for analyzing data, and workers.

[0559] System Configuration

[0560] 1. Robot (voice-based AI-equipped terminal)

[0561] The robots can be placed anywhere in an industrial facility and interact with workers in real time. They have the following capabilities:

[0562] The voice dialogue function asks questions about the worker's health status and records their responses.

[0563] You can customize the gender, tone, and conversational style of the voice.

[0564] The collected data is sent to a server, and advice returned from the server is provided to the worker in the form of audio and visual information.

[0565] 2. Server

[0566] The server has the following features:

[0567] The robot analyzes data on the worker's health status and generates appropriate health advice. The server performs a comprehensive evaluation, including past data.

[0568] Collect worker feedback and adjust artificial intelligence models.

[0569] The specific technologies used by the server include data analysis algorithms (e.g., Python's Pandas, NumPy), voice dialogue AI engines (e.g., Google Dialogflow), and cloud databases (e.g., AWS DynamoDB).

[0570] 3. Workers

[0571] Workers use the system in the following steps:

[0572] Through dialogue with the robot, users can provide feedback on their health status.

[0573] Receive and act on advice provided by the robot and server.

[0574] They will provide feedback on how the advice provided was implemented and this will be reflected in the next advice.

[0575] Specific examples

[0576] Example 1: Everyday questions and advice

[0577] The robot asks, "How are you feeling today?" and the worker replies, "I'm a little tired." The robot sends this information to a server, which uses past data to generate advice such as, "You may not be hydrated enough today. Please drink some water," and provides this to the worker via the robot.

[0578] Example 2: Obtaining feedback and adjusting advice

[0579] After providing the advice, the robot asks, "What did you think of today's advice?", to which the worker responds, "It was helpful, but I'd like to know more about how to take breaks." Based on this feedback, the server adjusts the content of the next advice to include more detailed information about how to take breaks.

[0580] Prompt Sentence Examples

[0581] If a user reports "I have a headache today":

[0582] Robot: "You've reported a headache. Based on past data, it may be due to dehydration. Please try drinking some water now."

[0583] As shown in this example of a prompt, the robot will hold a brief dialogue, and the server will provide specific advice based on the analysis, allowing workers to understand their own health condition in real time and take appropriate measures.

[0584] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0585] Step 1:

[0586] The robot asks the worker, "How are you feeling today?" The input is the worker's verbal response, and the output is the voice data of that response. The robot converts this voice data into text data using speech recognition software (e.g., Google Dialogflow).

[0587] Step 2:

[0588] The robot collects the converted text data and sends it to a server via the Internet. The input is the text data, and the output is the data received by the server.

[0589] Step 3:

[0590] The server analyzes the received text data and performs a comprehensive evaluation, including past data. The input is the current text data and past data, and the output is the analysis result. The server uses a data analysis algorithm (e.g., Python's Pandas or NumPy) to derive a conclusion, such as "insufficient water."

[0591] Step 4:

[0592] The server generates health advice based on the analysis results. The input is the analysis results, and the output is text data of the advice. The server uses a generative AI model (e.g., GPT-3) to generate appropriate advice text. Take the prompt text "You are dehydrated, so please drink water" as an example.

[0593] Step 5:

[0594] The server sends the generated advice to the robot via the Internet. The input is the text data of the advice, and the output is the data received by the robot.

[0595] Step 6:

[0596] The robot receives advice and provides it to the worker as audio and visual information. The input is the text data of the advice, and the output is an audio message and visual information displayed on a display. The robot uses speech synthesis software to convert the text to audio, saying, "Drink some water."

[0597] Step 7:

[0598] The robot collects feedback from the worker. The input is the worker's verbal feedback, and the output is the voice data of that feedback. The robot converts this voice data into text data using speech recognition software (e.g., Google Dialogflow).

[0599] Step 8:

[0600] The robot sends text data as feedback to the server. The input is the text data, and the output is the feedback data received by the server.

[0601] Step 9:

[0602] The server analyzes the received feedback and adjusts the AI ​​model to reflect it in the next advice. The input is the text data of the feedback, and the output is the adjusted AI model. The server analyzes the feedback data and draws a conclusion, for example, that "detailed resting guidance is needed," and updates the model.

[0603] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0604] The "your personal 'meddling' health advisor" of this invention is a system that provides more precise support for the user's daily life by combining a home device equipped with voice-interactive AI and an emotion engine. This system consists of the following steps:

[0605] System Configuration

[0606] This system is broadly composed of three elements: terminals, servers, and users.

[0607] 1. Terminal (home device)

[0608] The device is equipped with voice-interactive AI, enabling real-time dialogue with the user. The device asks questions about the user's lifestyle and dietary habits and records the responses. In addition, by combining it with an emotion engine, it recognizes the user's emotional state from their speech and behavior and adjusts advice based on that information. It also provides settings that allow users to customize the gender, tone, and conversational style of the voice. The collected data is sent to a server, which generates advice and provides it to the user.

[0609] 2. Server

[0610] The server analyzes data about the user's lifestyle and dietary habits sent from the device and generates optimal health advice for the user. The server performs a comprehensive analysis, including past data, to evaluate the user's health condition and nutritional balance. The server also takes into account the user's emotional state as recognized by the emotion engine and adjusts the tone and content of the advice. The generated advice is sent to the device, and the artificial intelligence model is adjusted based on the feedback to improve the performance of the entire system.

[0611] 3. Users

[0612] Users interact with the device to provide information about their lifestyle and dietary habits and receive advice from the server. Based on the advice, users can improve their lifestyle habits and provide feedback, which will be reflected in future advice. The system also uses an emotion engine to understand the user's emotional state, allowing for more personalized service.

[0613] Example

[0614] Example 1: Customization settings according to usage

[0615] 1. Voice settings: During initial setup, the device asks the user for the gender and tone of the voice. For example, it says, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other." The user responds, "I prefer a female voice."

[0616] 2. Setting the conversation tone: The device then asks, "Next, please choose your conversation tone. You can choose from casual, formal, or friendly." The user requests "Friendly."

[0617] Example 2: Data collection and advice provision

[0618] 1. Daily Questions: To understand daily habits and eating habits, the device asks, "What did you eat today?" The user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[0619] 2. Emotion recognition: The device recognizes the user's emotional state from their tone of voice and choice of words, and provides feedback such as, "You seem happy today" or "You seem a little tired today."

[0620] 3. Data storage: The device stores the collected data and emotional state and sends it to the server. It displays "Information recorded."

[0621] 4. Data analysis and advice generation: The server analyzes the data and evaluates the user's health condition and nutritional balance. If the user is tired, the server recommends relaxing meals and exercise.

[0622] 5. Providing advice: The server generates a message saying, "You are lacking in vitamin C, so we recommend eating oranges and kiwis," and sends it to the device. The device then verbally conveys this advice to the user and displays images of oranges and kiwis and their nutritional information on the display.

[0623] Example 3: User feedback and model adjustment

[0624] 1. Feedback acquisition: After providing advice, the device asks the user, "What did you think of today's advice?" The user gives feedback such as, "I'd like to know more meat recipes."

[0625] 2. Feedback analysis and model adjustment: The server analyzes this feedback and adjusts the AI ​​model to reflect it in the next recommendation. For example, meat recipes will be prioritized in the next recommendation.

[0626] As described above, the present invention combines voice-based interactive AI with an emotion recognition engine to provide an effective system that allows users to passively receive health advice, thereby supporting users in maintaining and improving their health through a more personalized experience.

[0627] The processing flow will be explained below.

[0628] Step 1:

[0629] The device will enter initial setup mode and ask the user to customize the gender, tone, and conversational style of the voice. For example, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other."

[0630] Step 2:

[0631] The user selects the gender of the voice and responds, "I prefer a female voice." The device then asks, "Next, please select the conversation tone. You can choose from casual, formal, or friendly." The user requests, "I prefer friendly."

[0632] Step 3:

[0633] The device records the settings and prepares to reflect them in subsequent interactions. It notifies the user that "the settings have been recorded," completing the initial setup.

[0634] Step 4:

[0635] The device periodically asks the user questions about their lifestyle and diet. For example, it asks, "What did you eat today?" The user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[0636] Step 5:

[0637] At the same time as listening to what the user is saying, the device uses an emotion engine to analyze the user's emotional state and provides feedback such as "You look happy today" or "You seem a little tired."

[0638] Step 6:

[0639] The device stores the collected data and emotional state in its internal storage, then transmits the data to the server. The device notifies the user that the information has been recorded, and transmits the data.

[0640] Step 7:

[0641] The server receives the data and performs a comprehensive analysis, including past data, to evaluate the user's nutritional balance and determine that the user has recently been lacking in vitamin C.

[0642] Step 8:

[0643] The server takes into account the user's current emotional state and generates appropriate advice. For example, if the user feels tired, the server generates advice recommending a relaxing meal or light exercise.

[0644] Step 9:

[0645] The server sends the generated advice to the device, which then provides it to the user both audibly and visually: "You're lacking in vitamin C, so we recommend eating oranges and kiwis," the device says, and the display shows images of oranges and kiwis and their nutritional information.

[0646] Step 10:

[0647] The user takes food in response to the advice and provides feedback to the device. When the device asks, "What did you think of today's advice?", the user responds, "I'd like to know more meat recipes."

[0648] Step 11:

[0649] The device collects the feedback and sends it to the server, which analyzes it and adjusts the AI ​​model to reflect the feedback in the next recommendation. For example, it may change the settings so that meat recipes are prioritized in future recommendations.

[0650] Example 2

[0651] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0652] In recent years, health problems such as lifestyle-related diseases and nutritional imbalances have been increasing. Many people want to take measures to address these problems, but self-management is difficult, and maintaining motivation is also challenging. Existing health management systems are unable to provide advice that takes into account the user's emotional state, tend to provide uniform information, and lack truly personalized support. This makes it difficult for users to realize the effects of self-management, making it difficult to continue.

[0653] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0654] In this invention, the server includes: [means equipped with voice interactive artificial intelligence and interacting with the user to collect information about the user's lifestyle and dietary habits; [means sending the collected information to the server for analysis and generating advice about the user's health condition and nutritional balance; [means providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation;] [means equipped with an emotion recognition engine that recognizes the user's emotional state from their speech and actions and adjusts the advice based on that information; and] [means collecting feedback from the user and adjusting the artificial intelligence model based on that. This makes it possible [to provide personalized health advice based on each user's emotional state and lifestyle habits and maintain motivation].

[0655] "Voice-activated interactive artificial intelligence" refers to artificial intelligence that can have natural conversations with users, and includes voice recognition, natural language processing, and voice synthesis technologies.

[0656] "User" means an individual who uses the system and receives health advice.

[0657] "Lifestyle habits" refers to the patterns of activities and behavior that a user engages in on a daily basis, including diet, exercise, sleep, and the like.

[0658] "Diet" refers to the food and eating habits that a user regularly consumes.

[0659] A "server" is a computer that performs the central calculations and data processing of a system.

[0660] "Advice" refers to suggestions and instructions generated by the system based on the user's health status and nutritional balance.

[0661] An "emotion recognition engine" is software that recognizes a user's emotional state from their speech and behavior, including analyzing voice tone and vocabulary.

[0662] "Feedback" refers to the thoughts and opinions that a user provides about advice, and is information that will be reflected in the next piece of advice.

[0663] An "artificial intelligence model" is a trained model for analyzing user data and generating health advice.

[0664] "Personalized" means optimized for the individual user.

[0665] "Motivation" refers to the will and enthusiasm a user has to achieve a goal.

[0666] A "goal" is a specific health-related standard or outcome that a user aims to achieve.

[0667] "Progress" refers to the progress or steps achieved towards a goal.

[0668] This invention relates to a "personal, nosy health advisor" that combines voice-activated interactive AI and an emotion recognition engine. This system collects and analyzes information about the user's lifestyle and dietary habits, and provides optimal health advice.

[0669] Hardware and software used

[0670] Hardware

[0671] Home devices: These are devices equipped with voice interactive artificial intelligence and enable real-time interaction with users.

[0672] software

[0673] Speech dialogue engine: Google Dialogflow

[0674] Emotion recognition engine: Affectiva SDK

[0675] Data analysis engine: Apache Spark

[0676] Database system: MySQL

[0677] Program processing description

[0678] 1. Initial device setup

[0679] During initial setup, a terminal (home device) asks the user for voice and conversation tone settings. First, the voice dialogue engine sets the voice gender and tone based on the user's preferences. For example, the terminal might say, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose from male, female, or other," and the user might respond, "I prefer a female voice."

[0680] 2. Daily interactions with users

[0681] The device routinely asks the user questions about their lifestyle and diet. For example, if the device asks, "What did you eat today?" and the user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner," the device collects and records that information. It also uses an emotion recognition engine to analyze the user's emotional state from their tone of voice and choice of words, providing feedback such as, "You seem happy today" or "You seem a little tired today."

[0682] 3. Data storage and transmission

[0683] The device stores the collected lifestyle and emotional state data in a MySQL database and sends it to the server. The device displays "Information recorded."

[0684] 4. Data analysis by the server

[0685] The server uses Apache Spark to analyze the data it receives, including evaluating lifestyle and emotional state data. For example, the server might determine that the user is deficient in vitamin C.

[0686] 5. Advice Generation

[0687] The server uses a generative AI model based on the analysis results to generate optimal health advice for the user, such as "You are lacking in vitamin C, so we recommend eating oranges and kiwis," and sends the advice to the device.

[0688] 6. Providing advice

[0689] The device provides the user with advice from the server both audibly and visually. The device will say, "You are lacking in vitamin C, so we recommend eating oranges and kiwis," and will show images of oranges and kiwis and their nutritional information on the display.

[0690] 7. Gathering User Feedback

[0691] After providing the advice, the device asks the user, "What did you think of today's advice?" If the user provides feedback such as "I'd like to know more meat recipes," that information is recorded by the device.

[0692] 8. Feedback analysis and model adjustment

[0693] The server analyzes user feedback and adjusts the generative AI model, so that future advice suggestions will be tailored to the user's preferences. For example, meat recipes will be prioritized from the next time.

[0694] Prompt Sentence Examples

[0695] "What did you eat today?"

[0696] "How are you feeling today?"

[0697] "What did you think of today's advice?"

[0698] As described above, the "your personal, nosy health advisor" of the present invention is a system that supports users in improving their lifestyle habits and maintaining their health by effectively combining voice-interactive artificial intelligence and an emotion recognition engine.

[0699] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0700] Processing Step Description

[0701] Step 1: Initial device setup

[0702] Specific behavior:

[0703] 1. The terminal presents the user with an initial setup start prompt.

[0704] 2. Device: "Hello! I'm your personal health advisor. First, please select the gender of my voice. You can choose male, female, or other."

[0705] 3. User: "Female voices are nice."

[0706] 4. Device: "Got it. I'll be speaking with a female voice from now on."

[0707] 5. Next, the device: "Next, please choose your conversational tone. You can choose casual, formal, or friendly."

[0708] 6. User: "Friendly."

[0709] 7. Terminal: "I'll speak to you in a friendly tone!"

[0710] Input: User voice preference, conversational tone preference

[0711] Data manipulation: Apply voice and speech tone settings based on user preferences

[0712] Output: Configured voice interaction environment

[0713] Step 2: Daily interactions with users

[0714] Specific behavior:

[0715] 1. At a set time each day, the device begins asking questions to the user.

[0716] 2. Terminal: "What did you eat today?"

[0717] 3. User: "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[0718] 4. Terminal: "Information recorded."

[0719] 5. Next, the device performs emotion recognition and provides feedback such as, "You seem happy today" or "You seem a little tired today."

[0720] Input: User's diet, voice tone, and language

[0721] Data processing: Record meal contents as text data and analyze emotions from voice tone and vocabulary

[0722] Output: Recorded food data, emotional state recognition

[0723] Step 3: Saving and sending data

[0724] Specific behavior:

[0725] 1. The device stores the collected data (lifestyle habits and emotional state) in an internal database.

[0726] 2. Terminal: Displays "Information sent to server."

[0727] 3. The data is sent over the internet to a server.

[0728] Input: User's lifestyle data, emotional state data

[0729] Data processing: storing data in a database and sending data to a server

[0730] Output: Records stored in the database, data sent to the server

[0731] Step 4: Data analysis by the server

[0732] Specific behavior:

[0733] 1. The server receives the transmitted data and stores it in a database.

[0734] 2. Analyze lifestyle data and emotional state data using Apache Spark.

[0735] 3. Evaluate the nutritional balance based on the meal content and also determine the user's psychological state based on their emotional state.

[0736] Input: Lifestyle data and emotional state data sent from the device

[0737] Data processing: Analyze data in the database and evaluate nutritional balance and emotional state

[0738] Output: Health status assessment report

[0739] Step 5: Advice generation

[0740] Specific behavior:

[0741] 1. The server uses a generative AI model to create health advice based on the analysis results.

[0742] 2. For example, if the nutritional balance is insufficient, we might suggest, "You are lacking vitamin C, so we recommend eating oranges or kiwis."

[0743] Input: Health Assessment Report

[0744] Data processing: Generate advice using AI models generated from analysis results

[0745] Output: Health advice to provide to the user

[0746] Step 6: Providing advice

[0747] Specific behavior:

[0748] 1. The server sends the generated advice to the device.

[0749] 2. The device will say, "You are lacking in vitamin C. We recommend eating oranges or kiwis."

[0750] 3. Display images and nutritional information of oranges and kiwis on the display.

[0751] Input: Generated health advice

[0752] Data processing: Sending advice data from the server to the device

[0753] Output: Advice and visual information provided to the user

[0754] Step 7: Gather user feedback

[0755] Specific behavior:

[0756] 1. Device: Ask the user, "What did you think about today's advice?"

[0757] 2. User: Provides feedback saying, "I'd like to see more meat recipes."

[0758] 3. Device: Display "Feedback recorded."

[0759] Input: User feedback

[0760] Data processing: recording and storing feedback data

[0761] Output: Recorded feedback data

[0762] Step 8: Feedback analysis and model adjustment

[0763] Specific behavior:

[0764] 1. The server analyzes the collected feedback.

[0765] 2. The generative AI model is adjusted based on the analysis results and reflected in the next advice generation.

[0766] 3. Update the model so that, for example, meat recipes are prioritized from the next time onwards.

[0767] Input: User feedback data

[0768] Data processing: feedback analysis, updating generative AI models

[0769] Output: The new model settings reflected in the next advice generation.

[0770] (Application example 2)

[0771] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0772] Conventional health advice systems collect information about a user's lifestyle and dietary habits and provide basic advice, but they struggle to provide personalized advice that takes into account the user's emotional state. It is also difficult to maintain consistency between health advice and dietary suggestions, which can lead to a continuous decline in user motivation. Furthermore, few systems effectively incorporate user feedback on the advice provided, making it difficult for users to continue receiving effective advice.

[0773] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0774] In this invention, the server includes: [means equipped with voice interactive artificial intelligence and interacting with the user to collect information about the user's lifestyle and dietary habits;] means for sending the collected information to the server and analyzing it to generate advice about the user's health condition and nutritional balance; [means for providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation;] means for collecting feedback from the user and adjusting the artificial intelligence model based thereon; [means for recognizing the user's emotional state using an emotion engine and adjusting the tone and content of health advice and dietary suggestions; and [means for providing a food delivery function that makes customized dietary suggestions based on the user's emotional state and dietary habits.] As a result, health advice and dietary suggestions are consistently provided taking the user's emotional state into consideration, thereby continuously motivating the user and enabling effective health maintenance and revitalization.

[0775] "Voice-based interactive AI" is an AI technology that interacts with users through voice input, understands their intentions and requests, and generates appropriate responses.

[0776] "Lifestyle" refers to behaviors and habits in daily life, such as exercise, sleep, and eating patterns.

[0777] "Diet" refers to habits related to eating behavior, such as the type of food consumed on a daily basis, the time of eating, and frequency of eating.

[0778] A "server" refers to a computer system that provides data and services to other computers and devices over a network.

[0779] "Health status" refers to information that indicates an individual's physical condition and level of health, including physical and mental status.

[0780] "Nutritional balance" refers to the appropriate intake of each nutrient (e.g., vitamins, minerals, proteins, carbohydrates, lipids, etc.) required by the body.

[0781] "Advice" refers to suggestions or advice that can help improve a user's health or diet.

[0782] An "emotion engine" refers to technology that analyzes a user's emotional state from their speech and behavior and recognizes their emotions.

[0783] "Feedback" refers to a user's evaluation or opinion of the advice or service provided.

[0784] "Personalized advice" refers to advice and suggestions that are customized to suit the individual characteristics and circumstances of a user.

[0785] "Meal suggestions" refers to proposing specific meal plans and menus to the user.

[0786] "Food delivery function" refers to a function that provides suggested foods and dishes to users as a delivery service.

[0787] "Customization" refers to adjusting settings and content to suit the user's preferences and needs.

[0788] This invention uses a system that combines voice-interactive AI and an emotion recognition engine to provide health advice based on a user's lifestyle and dietary habits. The system of this invention is mainly composed of three elements: a terminal (home device), a server, and a user.

[0789] 1. Terminal (home device)

[0790] The device is equipped with voice-activated interactive AI, enabling real-time dialogue with the user. When the user speaks to the device about their lifestyle and dietary habits, the device records the information and uses an emotion engine to identify the user's emotional state. This information and emotion data is sent to the server. The device's voice is customizable, allowing users to set gender, tone, and conversational style.

[0791] 2. Server

[0792] The server receives and analyzes the data sent from the device. Generative AI models such as the Transformer model (Hugging Face Transformers) are used for data analysis. The server evaluates the user's health and nutritional balance and generates optimal health advice based on that. It also adjusts the tone and content of the advice based on the user's emotional state, based on data from the emotion engine.

[0793] The generated advice is sent to the device as audio and visual information and provided to the user. Feedback data from the user is also sent to the server, and this is used to continuously optimize the generative AI model. For example, if a user provides feedback such as "I'd like to know more meat dish recipes," information about meat dishes will be prioritized in future advice suggestions.

[0794] 3. Users

[0795] Users can provide information about their lifestyle and dietary habits through interactions with the device. They can also receive health advice and meal suggestions from the server and improve their lifestyle based on those advice. The device recognizes the user's emotional state and provides appropriate advice based on their emotions, allowing users to enjoy a more personalized experience.

[0796] For example, if a user says, "I was full this morning, so I had just soup for lunch. It was delicious!", the emotion engine will recognize that the user is satisfied, and the server will generate the following advice: "I can see that you enjoyed a light lunch. However, you may be lacking in vitamins and minerals. Why not try a salad with lots of vegetables and some fruit for dinner?"

[0797] In addition, users can order the suggested meal menu as a delivery service, making it easy for them to get healthy meals. At this time, feedback on the advice provided is also collected and reflected in future advice.

[0798] The present invention consistently provides personalized health advice and dietary suggestions that are useful in daily life while taking into account the user's emotional state, thereby helping the user maintain their health and improve their motivation.

[0799] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0800] Step 1: Collecting user data on the device

[0801] The device collects information about the user's lifestyle and eating habits through dialogue with the user. If the user says, "I've been full since this morning, so I had just soup for lunch," the device uses voice recognition to record that information as text data. The input is the user's voice data, and the output is text data. This text data is temporarily stored in the device and later sent to the server.

[0802] Step 2: Recognizing your emotional state

[0803] The device uses an emotion engine to recognize the user's emotional state from the collected user speech data. From the tone and expression of the voice, the device determines the user's emotions, such as "happy" or "tired." In this case, the user is recognized as satisfied. The input is the user's voice data, and the output is emotional data.

[0804] Step 3: Sending data

[0805] The device sends the collected text data and emotion data to a server. The input is text data and emotion data, and the output is data transfer to the server. Specifically, the data is sent to the server via the Internet.

[0806] Step 4: Data analysis by the server

[0807] The server receives the data sent from the device and analyzes it using a generative AI model. The analysis uses a Transformer model (Hugging Face Transformers) to evaluate the user's diet and health status. The input is the user's text data and emotion data, and the output is the analysis results and health advice.

[0808] Step 5: Generate health advice

[0809] The server generates optimal health advice for the user based on the analysis results. For example, it generates advice such as, "We can see that you enjoyed a light lunch. However, you may be lacking in vitamins and minerals, so why not try a salad with plenty of vegetables and some fruit for dinner?" The input is the analysis results, and the output is specific advice statements.

[0810] Step 6: Providing advice

[0811] The server sends the generated advice to the terminal, which then provides it to the user as audio and visual information. The terminal will say aloud, "Today's lunch was good. For dinner, I recommend a salad and fruit, which will provide you with vitamins," and show specific meal examples on the display. The input is the advice text, and the output is the audio and visual information received by the user.

[0812] Step 7: Gather feedback

[0813] The user provides feedback on the advice provided. For example, the user might respond, "This advice is good, but I'd like to know more specific recipes." The device records this feedback and sends it to the server. The input is the user's feedback data, and the output is the transfer of the feedback data to the server.

[0814] Step 8: Tuning the generative AI model

[0815] The server analyzes the user's feedback and optimizes the generative AI model. It makes necessary adjustments to reflect the results in future advice. For example, it adjusts the model so that specific recipes are prioritized in future advice. The input is the user's feedback data, and the output is the adjusted generative AI model.

[0816] Example prompt sentence:

[0817] User input: "I was busy this morning and forgot to eat lunch. But I had some fruit as a snack in the evening."

[0818] Response prompt: "It's been a busy day, but I'd recommend including a protein-rich meal for dinner to ensure a balanced diet."

[0819] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0820] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0821] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0822] [Third embodiment]

[0823] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0824] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0825] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0826] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0827] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0828] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0829] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0830] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0831] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0832] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0833] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0834] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0835] The "Your Personal Nosy Health Advisor" of this invention is a system that supports the user's daily life using a home device equipped with voice-interactive AI. This system consists of the following steps:

[0836] System Configuration

[0837] This system is broadly composed of three elements: terminals, servers, and users.

[0838] 1. Terminal (home device)

[0839] The device is equipped with voice-activated interactive AI, enabling real-time dialogue with the user. The device asks questions about the user's lifestyle and dietary habits and records the responses. It also provides settings that allow users to customize the gender, tone, and conversational style of the voice. Furthermore, it sends the collected data to a server, displays the advice returned from the server, and relays it to the user via voice.

[0840] 2. Server

[0841] The server analyzes data about the user's lifestyle and dietary habits sent from the device and generates optimal health advice for the user. The server also performs a comprehensive analysis, including past data, to evaluate the user's health status and nutritional balance. The generated advice is sent to the device, and the artificial intelligence model is adjusted based on the feedback to improve the performance of the entire system.

[0842] 3. Users

[0843] Users interact with the device to provide information about their lifestyle and dietary habits and receive advice from the server. Not only can they improve their lifestyle based on the advice they receive, but they can also provide feedback that will be reflected in the next advice.

[0844] Example

[0845] Example 1: Customization settings according to usage

[0846] 1. Voice settings: During initial setup, the device asks the user for the gender and tone of their voice. For example, the device might say, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other." The user responds, "I prefer a female voice." The device records this and uses it for future interactions.

[0847] 2. Setting the conversation tone: The device then asks, "Next, please choose the conversation tone. You can choose from casual, formal, or friendly." The user requests "I prefer friendly." The device also records this and reflects it in future conversations.

[0848] Example 2: Data collection and advice provision

[0849] 1. Daily Questions: To understand daily habits and eating habits, the device asks, "What did you eat today?" The user answers, "I had toast and coffee for breakfast, pasta for lunch, and salad for dinner." The device records this information and sends it to the server.

[0850] 2. Data analysis: The server analyzes the collected data and evaluates the user's nutritional balance. For example, the server determines that the user is deficient in vitamin D based on their dietary data.

[0851] 3. Advice generation and provision: The server generates advice such as "You're lacking in vitamin D, so why not include fish in your dinner tonight?" and sends it to the device. The device then conveys this advice to the user via voice and also displays visual information (e.g., recipes using fish).

[0852] Example 3: User feedback and model adjustment

[0853] 1. Obtaining feedback: After providing advice, the device asks the user, "What did you think of today's advice? Is there anything else we can improve?" The user gives feedback such as, "I like fish dishes, so please tell me more recipes."

[0854] 2. Feedback analysis and model adjustment: The server analyzes this feedback and adjusts the AI ​​model to reflect it in the next recommendation. For example, a variety of fish-based recipes will be prioritized in the next recommendation.

[0855] As described above, the present invention provides an effective system that allows users to passively obtain health advice by combining voice dialogue-based artificial intelligence and data analysis technology.

[0856] The processing flow will be explained below.

[0857] Step 1:

[0858] The device will enter initial setup mode and ask the user to customize the gender, tone, and conversational style of the voice. For example, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other."

[0859] Step 2:

[0860] The user selects the gender of the voice and responds, "I prefer a female voice." The device then asks, "Next, please select the conversation tone. You can choose from casual, formal, or friendly." The user requests, "I prefer friendly."

[0861] Step 3:

[0862] The device records the settings and prepares to reflect them in subsequent interactions. It notifies the user that "the settings have been recorded," completing the initial setup.

[0863] Step 4:

[0864] The device periodically asks the user questions about their lifestyle and diet. For example, it asks, "What did you eat today?" The user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[0865] Step 5:

[0866] The device saves the collected data in its internal storage and then sends it to the server. It displays "Information recorded" and sends the data.

[0867] Step 6:

[0868] The server receives the data sent from the device and performs a comprehensive analysis, including the user's past data. It evaluates nutritional balance and health status and determines that the user has recently been lacking in vitamin C.

[0869] Step 7:

[0870] The server generates optimal advice for the user, for example, "We recommend eating oranges and kiwi fruits, which are rich in vitamin C," and sends it to the device.

[0871] Step 8:

[0872] The device receives advice from the server and provides it to the user both audibly and visually. It says, "You're lacking in vitamin C, so we recommend eating oranges and kiwis," and shows images of oranges and kiwis and nutritional information on the screen.

[0873] Step 9:

[0874] The device asks the user for feedback: "What did you think of today's advice?", and the user responds, "I'd like to know more meat recipes."

[0875] Step 10:

[0876] The device sends the collected feedback to a server, which analyzes it and adjusts the AI ​​model. From the next time onward, the settings are changed to prioritize meat recipes based on the user's preferences.

[0877] Example 1

[0878] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0879] Conventional health management systems often collect data about users' lifestyles and dietary habits and provide advice in a fragmented and inconsistent manner. They also lacked mechanisms for fully incorporating user preferences and feedback, making it difficult to provide personalized health advice. Furthermore, they were limited in their ability to customize the gender, tone, and style of the voice, limiting the user experience. There is a need to resolve these issues and provide a health management system that users will want to use on an ongoing basis.

[0880] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0881] In this invention, the server includes: a means for collecting information about a user's lifestyle and diet through real-time dialogue with the user, a means for converting the collected information into a data format and sending it to the server, which then analyzes the information to evaluate the user's health condition and nutritional balance and generate optimal health advice; a means for providing the generated advice to the user audibly and visually and notifying the advice in a set gender and tone; and a means for collecting feedback from the user after providing the advice, adjusting the AI ​​model based on the feedback, and reflecting the feedback in subsequent advice. This makes it possible to provide personalized health care optimized for the user's lifestyle and preferences, thereby continuously increasing the user's motivation.

[0882] "Voice-based interactive AI" refers to an AI technology that interacts with users via voice and collects and provides information in response to their instructions and questions.

[0883] "Real-time interaction" refers to a communication method in which interaction with a user occurs immediately without delay.

[0884] "Lifestyle habits" refers to the user's daily actions and patterns of behavior, including, for example, activities such as eating, exercise, and sleep.

[0885] "Diet" refers to the content and pattern of the food a user eats on a daily basis.

[0886] "Converting to data format" refers to the process of converting collected information into a format that can be analyzed by the server.

[0887] A "server" refers to a computer system that receives information, analyzes it, and returns the results.

[0888] "Health status" refers to the user's physical and mental condition, and is assessed based on medical indicators and lifestyle habits.

[0889] "Nutritional balance" refers to the balance of nutrients that the user consumes, and aims to ensure that the appropriate amount of nutrition is consumed without excess or deficiency.

[0890] "Health Advice" means specific suggestions or instructions provided to a User to improve or maintain their health.

[0891] "Audio and visual presentation" refers to the means by which generated information is conveyed to the user through audio and visual displays.

[0892] "Feedback" refers to reactions such as ratings and opinions provided by users.

[0893] "Artificial intelligence model" refers to machine learning algorithms and data analysis methods that automatically generate advice based on data.

[0894] "Customization" refers to the ability to change system settings based on user preferences and requirements.

[0895] "Conversational style" refers to the tone or style of a conversation, such as casual, formal, or friendly.

[0896] This invention is a system called "Your Personal Nosy Health Advisor," which uses a home device equipped with voice-interactive AI to support the user's daily life. This system is mainly composed of three elements: a terminal, a server, and a user.

[0897] Terminal (home device)

[0898] The device is equipped with voice interactive AI, which enables real-time voice interaction with the user. The device collects information from the user by asking questions such as:

[0899] "What did you eat today?"

[0900] "How is it going?"

[0901] The user's responses are recorded, converted into data, and sent to a server. The device also offers settings that allow users to customize the gender, tone, and conversational style of the voice.

[0902] server

[0903] The server analyzes the information sent from the device and generates appropriate health advice. Specifically, the server analyzes the user's dietary and lifestyle data to evaluate their nutritional balance and health status. If the server determines that the user is deficient in vitamin D, for example, it generates the following advice:

[0904] "You're lacking vitamin D, so why not try incorporating some fish into your dinner tonight?"

[0905] The analysis is performed using data analysis software such as Python or R, and optimal advice is provided using generative AI models, which are then sent to the device and notified to the user via audio and visual means.

[0906] User

[0907] Through interaction with the device, the user provides information about their lifestyle and dietary habits and receives advice from the server. Based on this advice, the user not only improves their daily life but also provides feedback. For example, if the device asks, "What did you think of today's advice? Is there anything else I can improve?" the user might respond, "I like fish dishes, so please tell me more recipes."

[0908] Prompt Sentence Examples

[0909] To actually use this system, users would enter prompts like the following to receive specific advice or information:

[0910] Please record what you ate today.

[0911] "What ingredients should I include in my next meal?"

[0912] "Please give me some advice on how to improve my lifestyle."

[0913] "I'll provide feedback on yesterday's advice."

[0914] These prompts allow users to receive specific health advice and feedback, ultimately helping them optimize their lifestyle and nutritional balance.

[0915] Specific examples of hardware and software used

[0916] The following hardware and software are important for the implementation of the system:

[0917] Terminals (home devices): Smart speakers and smart displays equipped with voice-activated AI

[0918] Server: A server for high-performance data analysis (e.g., an environment for running scripts in Python or R)

[0919] Software: Machine learning frameworks (e.g., TensorFlow, PyTorch) for building generative AI models

[0920] Combining these elements makes it possible to provide users with ongoing personalized health advice.

[0921] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0922] Step 1: Reset audio settings

[0923] The device initializes the voice settings when the system starts up. The device asks the user, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other." The user responds, "I prefer a female voice." The input to this dialogue is the user's preference information, which the device converts into a data format and records. As an output, the device completes the voice settings to be used for subsequent interactions.

[0924] Step 2: Set the tone of the conversation

[0925] After setting the voice, the device sets the tone of the conversation. The device asks, "Next, please select the tone of the conversation. You can choose from casual, formal, or friendly," and the user requests, "I prefer friendly." This input information is also converted into data format and recorded. As an output, the device reflects the set tone in subsequent conversations.

[0926] Step 3: Daily Questioning and Data Collection

[0927] The device starts the user's daily assistance by asking, "What did you eat today?" The user answers, "I had toast and coffee for breakfast, pasta for lunch, and salad for dinner." This input information is recorded by the device and converted into a data format. As an output, the collected data is sent to the server.

[0928] Step 4: Data transmission and analysis

[0929] The server receives the data sent from the device. The input information is the user's dietary details, and the server analyzes this information using a data analysis script (e.g., Python). The data is processed by analyzing the dietary details and evaluating whether there are any nutrient deficiencies or excesses. The output is an evaluation of the user's nutritional balance.

[0930] Step 5: Advice Generation

[0931] The server generates specific health advice for the user based on the analysis results. For example, if it determines that the user is deficient in vitamin D, it generates the advice, "You're deficient in vitamin D, so why not try including some fish in your dinner tonight?" This advice is generated using a generative AI model. The generated advice is sent to the device as output.

[0932] Step 6: Providing advice

[0933] The device communicates the advice received from the server to the user via voice. The device notifies the user, saying, "You're lacking in vitamin D, so why not include fish in your dinner tonight?" and also displays a fish recipe as visual information. The input to this dialogue is the advice from the server, and the output is a voice notification and a visual presentation.

[0934] Step 7: Get user feedback

[0935] After providing the advice, the device asks the user for feedback, asking, "What did you think of today's advice? Is there anything else I can improve?" The user might reply, "I like fish dishes, so please tell me more recipes." This feedback information is recorded on the device, converted into a data format, and sent to the server. The feedback data is collected as output.

[0936] Step 8: Feedback analysis and model adjustment

[0937] The server analyzes the feedback sent. The input information is feedback from the user, and the server uses natural language processing technology to understand the user's wishes. As a data calculation, the AI ​​model is adjusted and reflected in the next advice. As an output, the adjusted AI model optimizes the advice for the next time and beyond.

[0938] (Application example 1)

[0939] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0940] In modern industrial facilities, managing the health status of workers is important, but traditionally workers have been expected to manage their health independently, resulting in a lack of effective support. Furthermore, the lack of personalized health advice tailored to the work environment has led to a decline in worker efficiency and motivation. The present invention aims to solve these problems and provide a system that supports worker health management.

[0941] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0942] In this invention, the server includes: [a means equipped with voice interactive artificial intelligence and interacting with a user to collect information about the user's lifestyle and dietary habits;] [a means for sending the collected information to the server and analyzing it to generate advice about the user's health condition and nutritional balance;] [a means for providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation;] [a means for collecting feedback from the user and adjusting the artificial intelligence model based thereon;] [a means for interacting with workers at industrial facilities and collecting information about the workers' health conditions; and] [a means for analyzing the collected information and providing the workers with advice about their health conditions audibly and visually. This makes it possible to grasp the health conditions of workers in real time and provide appropriate advice.

[0943] "Voice-based interactive AI" is AI that can naturally converse with users through voice, gather information, understand instructions, and respond accordingly.

[0944] "Worker" refers to an employee who performs various tasks in an industrial facility.

[0945] "health condition" means the state of a worker's physical and mental health;

[0946] "Nutritional balance" refers to the balance of nutrients that workers are properly consuming, and evaluates the balance of vitamins and minerals necessary to maintain good health.

[0947] "Advice" refers to advice and suggestions regarding health management provided to workers based on collected information and analysis results.

[0948] "Goals" refer to specific targets that are set for workers to achieve when managing their health.

[0949] "Progress" refers to data that indicates the current progress toward a set goal.

[0950] "Feedback" refers to the opinions, thoughts, and further requests that workers provide in response to advice.

[0951] "Artificial intelligence model" refers to an algorithm or system that generates advice based on collected data.

[0952] "Work environment" refers to the total physical and psychological environment in which workers perform their duties within an industrial establishment.

[0953] "Visual information" means advice or instructions provided through a display or other visual means.

[0954] This invention is a system for supporting the health management of workers in industrial facilities. This system consists of three main components: a robot equipped with voice-interactive AI, a server for analyzing data, and workers.

[0955] System Configuration

[0956] 1. Robot (voice-based AI-equipped terminal)

[0957] The robots can be placed anywhere in an industrial facility and interact with workers in real time. They have the following capabilities:

[0958] The voice dialogue function asks questions about the worker's health status and records their responses.

[0959] You can customize the gender, tone, and conversational style of the voice.

[0960] The collected data is sent to a server, and advice returned from the server is provided to the worker in the form of audio and visual information.

[0961] 2. Server

[0962] The server has the following features:

[0963] The robot analyzes data on the worker's health status and generates appropriate health advice. The server performs a comprehensive evaluation, including past data.

[0964] Collect worker feedback and adjust artificial intelligence models.

[0965] The specific technologies used by the server include data analysis algorithms (e.g., Python's Pandas, NumPy), voice dialogue AI engines (e.g., Google Dialogflow), and cloud databases (e.g., AWS DynamoDB).

[0966] 3. Workers

[0967] Workers use the system in the following steps:

[0968] Through dialogue with the robot, users can provide feedback on their health status.

[0969] Receive and act on advice provided by the robot and server.

[0970] They will provide feedback on how the advice provided was implemented and this will be reflected in the next advice.

[0971] Specific examples

[0972] Example 1: Everyday questions and advice

[0973] The robot asks, "How are you feeling today?" and the worker replies, "I'm a little tired." The robot sends this information to a server, which uses past data to generate advice such as, "You may not be hydrated enough today. Please drink some water," and provides this to the worker via the robot.

[0974] Example 2: Obtaining feedback and adjusting advice

[0975] After providing the advice, the robot asks, "What did you think of today's advice?", to which the worker responds, "It was helpful, but I'd like to know more about how to take breaks." Based on this feedback, the server adjusts the content of the next advice to include more detailed information about how to take breaks.

[0976] Prompt Sentence Examples

[0977] If a user reports "I have a headache today":

[0978] Robot: "You've reported a headache. Based on past data, it may be due to dehydration. Please try drinking some water now."

[0979] As shown in this example of a prompt, the robot will hold a brief dialogue, and the server will provide specific advice based on the analysis, allowing workers to understand their own health condition in real time and take appropriate measures.

[0980] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0981] Step 1:

[0982] The robot asks the worker, "How are you feeling today?" The input is the worker's verbal response, and the output is the voice data of that response. The robot converts this voice data into text data using speech recognition software (e.g., Google Dialogflow).

[0983] Step 2:

[0984] The robot collects the converted text data and sends it to a server via the Internet. The input is the text data, and the output is the data received by the server.

[0985] Step 3:

[0986] The server analyzes the received text data and performs a comprehensive evaluation, including past data. The input is the current text data and past data, and the output is the analysis result. The server uses a data analysis algorithm (e.g., Python's Pandas or NumPy) to derive a conclusion, such as "insufficient water."

[0987] Step 4:

[0988] The server generates health advice based on the analysis results. The input is the analysis results, and the output is text data of the advice. The server uses a generative AI model (e.g., GPT-3) to generate appropriate advice text. Take the prompt text "You are dehydrated, so please drink water" as an example.

[0989] Step 5:

[0990] The server sends the generated advice to the robot via the Internet. The input is the text data of the advice, and the output is the data received by the robot.

[0991] Step 6:

[0992] The robot receives advice and provides it to the worker as audio and visual information. The input is the text data of the advice, and the output is an audio message and visual information displayed on a display. The robot uses speech synthesis software to convert the text to audio, saying, "Drink some water."

[0993] Step 7:

[0994] The robot collects feedback from the worker. The input is the worker's verbal feedback, and the output is the voice data of that feedback. The robot converts this voice data into text data using speech recognition software (e.g., Google Dialogflow).

[0995] Step 8:

[0996] The robot sends text data as feedback to the server. The input is the text data, and the output is the feedback data received by the server.

[0997] Step 9:

[0998] The server analyzes the received feedback and adjusts the AI ​​model to reflect it in the next advice. The input is the text data of the feedback, and the output is the adjusted AI model. The server analyzes the feedback data and draws a conclusion, for example, that "detailed resting guidance is needed," and updates the model.

[0999] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1000] The "your personal 'meddling' health advisor" of this invention is a system that provides more precise support for the user's daily life by combining a home device equipped with voice-interactive AI and an emotion engine. This system consists of the following steps:

[1001] System Configuration

[1002] This system is broadly composed of three elements: terminals, servers, and users.

[1003] 1. Terminal (home device)

[1004] The device is equipped with voice-interactive AI, enabling real-time dialogue with the user. The device asks questions about the user's lifestyle and dietary habits and records the responses. In addition, by combining it with an emotion engine, it recognizes the user's emotional state from their speech and behavior and adjusts advice based on that information. It also provides settings that allow users to customize the gender, tone, and conversational style of the voice. The collected data is sent to a server, which generates advice and provides it to the user.

[1005] 2. Server

[1006] The server analyzes data about the user's lifestyle and dietary habits sent from the device and generates optimal health advice for the user. The server performs a comprehensive analysis, including past data, to evaluate the user's health condition and nutritional balance. The server also takes into account the user's emotional state as recognized by the emotion engine and adjusts the tone and content of the advice. The generated advice is sent to the device, and the artificial intelligence model is adjusted based on the feedback to improve the performance of the entire system.

[1007] 3. Users

[1008] Users interact with the device to provide information about their lifestyle and dietary habits and receive advice from the server. Based on the advice, users can improve their lifestyle habits and provide feedback, which will be reflected in future advice. The system also uses an emotion engine to understand the user's emotional state, allowing for more personalized service.

[1009] Example

[1010] Example 1: Customization settings according to usage

[1011] 1. Voice settings: During initial setup, the device asks the user for the gender and tone of the voice. For example, it says, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other." The user responds, "I prefer a female voice."

[1012] 2. Setting the conversation tone: The device then asks, "Next, please choose your conversation tone. You can choose from casual, formal, or friendly." The user requests "Friendly."

[1013] Example 2: Data collection and advice provision

[1014] 1. Daily Questions: To understand daily habits and eating habits, the device asks, "What did you eat today?" The user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[1015] 2. Emotion recognition: The device recognizes the user's emotional state from their tone of voice and choice of words, and provides feedback such as, "You seem happy today" or "You seem a little tired today."

[1016] 3. Data storage: The device stores the collected data and emotional state and sends it to the server. It displays "Information recorded."

[1017] 4. Data analysis and advice generation: The server analyzes the data and evaluates the user's health condition and nutritional balance. If the user is tired, the server recommends relaxing meals and exercise.

[1018] 5. Providing advice: The server generates a message saying, "You are lacking in vitamin C, so we recommend eating oranges and kiwis," and sends it to the device. The device then verbally conveys this advice to the user and displays images of oranges and kiwis and their nutritional information on the display.

[1019] Example 3: User feedback and model adjustment

[1020] 1. Feedback acquisition: After providing advice, the device asks the user, "What did you think of today's advice?" The user gives feedback such as, "I'd like to know more meat recipes."

[1021] 2. Feedback analysis and model adjustment: The server analyzes this feedback and adjusts the AI ​​model to reflect it in the next recommendation. For example, meat recipes will be prioritized in the next recommendation.

[1022] As described above, the present invention combines voice-based interactive AI with an emotion recognition engine to provide an effective system that allows users to passively receive health advice, thereby supporting users in maintaining and improving their health through a more personalized experience.

[1023] The processing flow will be explained below.

[1024] Step 1:

[1025] The device will enter initial setup mode and ask the user to customize the gender, tone, and conversational style of the voice. For example, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other."

[1026] Step 2:

[1027] The user selects the gender of the voice and responds, "I prefer a female voice." The device then asks, "Next, please select the conversation tone. You can choose from casual, formal, or friendly." The user requests, "I prefer friendly."

[1028] Step 3:

[1029] The device records the settings and prepares to reflect them in subsequent interactions. It notifies the user that "the settings have been recorded," completing the initial setup.

[1030] Step 4:

[1031] The device periodically asks the user questions about their lifestyle and diet. For example, it asks, "What did you eat today?" The user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[1032] Step 5:

[1033] At the same time as listening to what the user is saying, the device uses an emotion engine to analyze the user's emotional state and provides feedback such as "You look happy today" or "You seem a little tired."

[1034] Step 6:

[1035] The device stores the collected data and emotional state in its internal storage, then transmits the data to the server. The device notifies the user that the information has been recorded, and transmits the data.

[1036] Step 7:

[1037] The server receives the data and performs a comprehensive analysis, including past data, to evaluate the user's nutritional balance and determine that the user has recently been lacking in vitamin C.

[1038] Step 8:

[1039] The server takes into account the user's current emotional state and generates appropriate advice. For example, if the user feels tired, the server generates advice recommending a relaxing meal or light exercise.

[1040] Step 9:

[1041] The server sends the generated advice to the device, which then provides it to the user both audibly and visually: "You're lacking in vitamin C, so we recommend eating oranges and kiwis," the device says, and the display shows images of oranges and kiwis and their nutritional information.

[1042] Step 10:

[1043] The user takes food in response to the advice and provides feedback to the device. When the device asks, "What did you think of today's advice?", the user responds, "I'd like to know more meat recipes."

[1044] Step 11:

[1045] The device collects the feedback and sends it to the server, which analyzes it and adjusts the AI ​​model to reflect the feedback in the next recommendation. For example, it may change the settings so that meat recipes are prioritized in future recommendations.

[1046] Example 2

[1047] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1048] In recent years, health problems such as lifestyle-related diseases and nutritional imbalances have been increasing. Many people want to take measures to address these problems, but self-management is difficult, and maintaining motivation is also challenging. Existing health management systems are unable to provide advice that takes into account the user's emotional state, tend to provide uniform information, and lack truly personalized support. This makes it difficult for users to realize the effects of self-management, making it difficult to continue.

[1049] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1050] In this invention, the server includes: [means equipped with voice interactive artificial intelligence and interacting with the user to collect information about the user's lifestyle and dietary habits; [means sending the collected information to the server for analysis and generating advice about the user's health condition and nutritional balance; [means providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation;] [means equipped with an emotion recognition engine that recognizes the user's emotional state from their speech and actions and adjusts the advice based on that information; and] [means collecting feedback from the user and adjusting the artificial intelligence model based on that. This makes it possible [to provide personalized health advice based on each user's emotional state and lifestyle habits and maintain motivation].

[1051] "Voice-activated interactive artificial intelligence" refers to artificial intelligence that can have natural conversations with users, and includes voice recognition, natural language processing, and voice synthesis technologies.

[1052] "User" means an individual who uses the system and receives health advice.

[1053] "Lifestyle habits" refers to the patterns of activities and behavior that a user engages in on a daily basis, including diet, exercise, sleep, and the like.

[1054] "Diet" refers to the food and eating habits that a user regularly consumes.

[1055] A "server" is a computer that performs the central calculations and data processing of a system.

[1056] "Advice" refers to suggestions and instructions generated by the system based on the user's health status and nutritional balance.

[1057] An "emotion recognition engine" is software that recognizes a user's emotional state from their speech and behavior, including analyzing voice tone and vocabulary.

[1058] "Feedback" refers to the thoughts and opinions that a user provides about advice, and is information that will be reflected in the next piece of advice.

[1059] An "artificial intelligence model" is a trained model for analyzing user data and generating health advice.

[1060] "Personalized" means optimized for the individual user.

[1061] "Motivation" refers to the will and enthusiasm a user has to achieve a goal.

[1062] A "goal" is a specific health-related standard or outcome that a user aims to achieve.

[1063] "Progress" refers to the progress or steps achieved towards a goal.

[1064] This invention relates to a "personal, nosy health advisor" that combines voice-activated interactive AI and an emotion recognition engine. This system collects and analyzes information about the user's lifestyle and dietary habits, and provides optimal health advice.

[1065] Hardware and software used

[1066] Hardware

[1067] Home devices: These are devices equipped with voice interactive artificial intelligence and enable real-time interaction with users.

[1068] software

[1069] Speech dialogue engine: Google Dialogflow

[1070] Emotion recognition engine: Affectiva SDK

[1071] Data analysis engine: Apache Spark

[1072] Database system: MySQL

[1073] Program processing description

[1074] 1. Initial device setup

[1075] During initial setup, a terminal (home device) asks the user for voice and conversation tone settings. First, the voice dialogue engine sets the voice gender and tone based on the user's preferences. For example, the terminal might say, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose from male, female, or other," and the user might respond, "I prefer a female voice."

[1076] 2. Daily interactions with users

[1077] The device routinely asks the user questions about their lifestyle and diet. For example, if the device asks, "What did you eat today?" and the user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner," the device collects and records that information. It also uses an emotion recognition engine to analyze the user's emotional state from their tone of voice and choice of words, providing feedback such as, "You seem happy today" or "You seem a little tired today."

[1078] 3. Data storage and transmission

[1079] The device stores the collected lifestyle and emotional state data in a MySQL database and sends it to the server. The device displays "Information recorded."

[1080] 4. Data analysis by the server

[1081] The server uses Apache Spark to analyze the data it receives, including evaluating lifestyle and emotional state data. For example, the server might determine that the user is deficient in vitamin C.

[1082] 5. Advice Generation

[1083] The server uses a generative AI model based on the analysis results to generate optimal health advice for the user, such as "You are lacking in vitamin C, so we recommend eating oranges and kiwis," and sends the advice to the device.

[1084] 6. Providing advice

[1085] The device provides the user with advice from the server both audibly and visually. The device will say, "You are lacking in vitamin C, so we recommend eating oranges and kiwis," and will show images of oranges and kiwis and their nutritional information on the display.

[1086] 7. Gathering User Feedback

[1087] After providing the advice, the device asks the user, "What did you think of today's advice?" If the user provides feedback such as "I'd like to know more meat recipes," that information is recorded by the device.

[1088] 8. Feedback analysis and model adjustment

[1089] The server analyzes user feedback and adjusts the generative AI model, so that future advice suggestions will be tailored to the user's preferences. For example, meat recipes will be prioritized from the next time.

[1090] Prompt Sentence Examples

[1091] "What did you eat today?"

[1092] "How are you feeling today?"

[1093] "What did you think of today's advice?"

[1094] As described above, the "your personal, nosy health advisor" of the present invention is a system that supports users in improving their lifestyle habits and maintaining their health by effectively combining voice-interactive artificial intelligence and an emotion recognition engine.

[1095] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1096] Processing Step Description

[1097] Step 1: Initial device setup

[1098] Specific behavior:

[1099] 1. The terminal presents the user with an initial setup start prompt.

[1100] 2. Device: "Hello! I'm your personal health advisor. First, please select the gender of my voice. You can choose male, female, or other."

[1101] 3. User: "Female voices are nice."

[1102] 4. Device: "Got it. I'll be speaking with a female voice from now on."

[1103] 5. Next, the device: "Next, please choose your conversational tone. You can choose casual, formal, or friendly."

[1104] 6. User: "Friendly."

[1105] 7. Terminal: "I'll speak to you in a friendly tone!"

[1106] Input: User voice preference, conversational tone preference

[1107] Data manipulation: Apply voice and speech tone settings based on user preferences

[1108] Output: Configured voice interaction environment

[1109] Step 2: Daily interactions with users

[1110] Specific behavior:

[1111] 1. At a set time each day, the device begins asking questions to the user.

[1112] 2. Terminal: "What did you eat today?"

[1113] 3. User: "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[1114] 4. Terminal: "Information recorded."

[1115] 5. Next, the device performs emotion recognition and provides feedback such as, "You seem happy today" or "You seem a little tired today."

[1116] Input: User's diet, voice tone, and language

[1117] Data processing: Record meal contents as text data and analyze emotions from voice tone and vocabulary

[1118] Output: Recorded food data, emotional state recognition

[1119] Step 3: Saving and sending data

[1120] Specific behavior:

[1121] 1. The device stores the collected data (lifestyle habits and emotional state) in an internal database.

[1122] 2. Terminal: Displays "Information sent to server."

[1123] 3. The data is sent over the internet to a server.

[1124] Input: User's lifestyle data, emotional state data

[1125] Data processing: storing data in a database and sending data to a server

[1126] Output: Records stored in the database, data sent to the server

[1127] Step 4: Data analysis by the server

[1128] Specific behavior:

[1129] 1. The server receives the transmitted data and stores it in a database.

[1130] 2. Analyze lifestyle data and emotional state data using Apache Spark.

[1131] 3. Evaluate the nutritional balance based on the meal content and also determine the user's psychological state based on their emotional state.

[1132] Input: Lifestyle data and emotional state data sent from the device

[1133] Data processing: Analyze data in the database and evaluate nutritional balance and emotional state

[1134] Output: Health status assessment report

[1135] Step 5: Advice generation

[1136] Specific behavior:

[1137] 1. The server uses a generative AI model to create health advice based on the analysis results.

[1138] 2. For example, if the nutritional balance is insufficient, we might suggest, "You are lacking vitamin C, so we recommend eating oranges or kiwis."

[1139] Input: Health Assessment Report

[1140] Data processing: Generate advice using AI models generated from analysis results

[1141] Output: Health advice to provide to the user

[1142] Step 6: Providing advice

[1143] Specific behavior:

[1144] 1. The server sends the generated advice to the device.

[1145] 2. The device will say, "You are lacking in vitamin C. We recommend eating oranges or kiwis."

[1146] 3. Display images and nutritional information of oranges and kiwis on the display.

[1147] Input: Generated health advice

[1148] Data processing: Sending advice data from the server to the device

[1149] Output: Advice and visual information provided to the user

[1150] Step 7: Gather user feedback

[1151] Specific behavior:

[1152] 1. Device: Ask the user, "What did you think about today's advice?"

[1153] 2. User: Provides feedback saying, "I'd like to see more meat recipes."

[1154] 3. Device: Display "Feedback recorded."

[1155] Input: User feedback

[1156] Data processing: recording and storing feedback data

[1157] Output: Recorded feedback data

[1158] Step 8: Feedback analysis and model adjustment

[1159] Specific behavior:

[1160] 1. The server analyzes the collected feedback.

[1161] 2. The generative AI model is adjusted based on the analysis results and reflected in the next advice generation.

[1162] 3. Update the model so that, for example, meat recipes are prioritized from the next time onwards.

[1163] Input: User feedback data

[1164] Data processing: feedback analysis, updating generative AI models

[1165] Output: The new model settings reflected in the next advice generation.

[1166] (Application example 2)

[1167] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1168] Conventional health advice systems collect information about a user's lifestyle and dietary habits and provide basic advice, but they struggle to provide personalized advice that takes into account the user's emotional state. It is also difficult to maintain consistency between health advice and dietary suggestions, which can lead to a continuous decline in user motivation. Furthermore, few systems effectively incorporate user feedback on the advice provided, making it difficult for users to continue receiving effective advice.

[1169] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1170] In this invention, the server includes: [means equipped with voice interactive artificial intelligence and interacting with the user to collect information about the user's lifestyle and dietary habits;] means for sending the collected information to the server and analyzing it to generate advice about the user's health condition and nutritional balance; [means for providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation;] means for collecting feedback from the user and adjusting the artificial intelligence model based thereon; [means for recognizing the user's emotional state using an emotion engine and adjusting the tone and content of health advice and dietary suggestions; and [means for providing a food delivery function that makes customized dietary suggestions based on the user's emotional state and dietary habits.] As a result, health advice and dietary suggestions are consistently provided taking the user's emotional state into consideration, thereby continuously motivating the user and enabling effective health maintenance and revitalization.

[1171] "Voice-based interactive AI" is an AI technology that interacts with users through voice input, understands their intentions and requests, and generates appropriate responses.

[1172] "Lifestyle" refers to behaviors and habits in daily life, such as exercise, sleep, and eating patterns.

[1173] "Diet" refers to habits related to eating behavior, such as the type of food consumed on a daily basis, the time of eating, and frequency of eating.

[1174] A "server" refers to a computer system that provides data and services to other computers and devices over a network.

[1175] "Health status" refers to information that indicates an individual's physical condition and level of health, including physical and mental status.

[1176] "Nutritional balance" refers to the appropriate intake of each nutrient (e.g., vitamins, minerals, proteins, carbohydrates, lipids, etc.) required by the body.

[1177] "Advice" refers to suggestions or advice that can help improve a user's health or diet.

[1178] An "emotion engine" refers to technology that analyzes a user's emotional state from their speech and behavior and recognizes their emotions.

[1179] "Feedback" refers to a user's evaluation or opinion of the advice or service provided.

[1180] "Personalized advice" refers to advice and suggestions that are customized to suit the individual characteristics and circumstances of a user.

[1181] "Meal suggestions" refers to proposing specific meal plans and menus to the user.

[1182] "Food delivery function" refers to a function that provides suggested foods and dishes to users as a delivery service.

[1183] "Customization" refers to adjusting settings and content to suit the user's preferences and needs.

[1184] This invention uses a system that combines voice-interactive AI and an emotion recognition engine to provide health advice based on a user's lifestyle and dietary habits. The system of this invention is mainly composed of three elements: a terminal (home device), a server, and a user.

[1185] 1. Terminal (home device)

[1186] The device is equipped with voice-activated interactive AI, enabling real-time dialogue with the user. When the user speaks to the device about their lifestyle and dietary habits, the device records the information and uses an emotion engine to identify the user's emotional state. This information and emotion data is sent to the server. The device's voice is customizable, allowing users to set gender, tone, and conversational style.

[1187] 2. Server

[1188] The server receives and analyzes the data sent from the device. Generative AI models such as the Transformer model (Hugging Face Transformers) are used for data analysis. The server evaluates the user's health and nutritional balance and generates optimal health advice based on that. It also adjusts the tone and content of the advice based on the user's emotional state, based on data from the emotion engine.

[1189] The generated advice is sent to the device as audio and visual information and provided to the user. Feedback data from the user is also sent to the server, and this is used to continuously optimize the generative AI model. For example, if a user provides feedback such as "I'd like to know more meat dish recipes," information about meat dishes will be prioritized in future advice suggestions.

[1190] 3. Users

[1191] Users can provide information about their lifestyle and dietary habits through interactions with the device. They can also receive health advice and meal suggestions from the server and improve their lifestyle based on those advice. The device recognizes the user's emotional state and provides appropriate advice based on their emotions, allowing users to enjoy a more personalized experience.

[1192] For example, if a user says, "I was full this morning, so I had just soup for lunch. It was delicious!", the emotion engine will recognize that the user is satisfied, and the server will generate the following advice: "I can see that you enjoyed a light lunch. However, you may be lacking in vitamins and minerals. Why not try a salad with lots of vegetables and some fruit for dinner?"

[1193] In addition, users can order the suggested meal menu as a delivery service, making it easy for them to get healthy meals. At this time, feedback on the advice provided is also collected and reflected in future advice.

[1194] The present invention consistently provides personalized health advice and dietary suggestions that are useful in daily life while taking into account the user's emotional state, thereby helping the user maintain their health and improve their motivation.

[1195] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1196] Step 1: Collecting user data on the device

[1197] The device collects information about the user's lifestyle and eating habits through dialogue with the user. If the user says, "I've been full since this morning, so I had just soup for lunch," the device uses voice recognition to record that information as text data. The input is the user's voice data, and the output is text data. This text data is temporarily stored in the device and later sent to the server.

[1198] Step 2: Recognizing your emotional state

[1199] The device uses an emotion engine to recognize the user's emotional state from the collected user speech data. From the tone and expression of the voice, the device determines the user's emotions, such as "happy" or "tired." In this case, the user is recognized as satisfied. The input is the user's voice data, and the output is emotional data.

[1200] Step 3: Sending data

[1201] The device sends the collected text data and emotion data to a server. The input is text data and emotion data, and the output is data transfer to the server. Specifically, the data is sent to the server via the Internet.

[1202] Step 4: Data analysis by the server

[1203] The server receives the data sent from the device and analyzes it using a generative AI model. The analysis uses a Transformer model (Hugging Face Transformers) to evaluate the user's diet and health status. The input is the user's text data and emotion data, and the output is the analysis results and health advice.

[1204] Step 5: Generate health advice

[1205] The server generates optimal health advice for the user based on the analysis results. For example, it generates advice such as, "We can see that you enjoyed a light lunch. However, you may be lacking in vitamins and minerals, so why not try a salad with plenty of vegetables and some fruit for dinner?" The input is the analysis results, and the output is specific advice statements.

[1206] Step 6: Providing advice

[1207] The server sends the generated advice to the terminal, which then provides it to the user as audio and visual information. The terminal will say aloud, "Today's lunch was good. For dinner, I recommend a salad and fruit, which will provide you with vitamins," and show specific meal examples on the display. The input is the advice text, and the output is the audio and visual information received by the user.

[1208] Step 7: Gather feedback

[1209] The user provides feedback on the advice provided. For example, the user might respond, "This advice is good, but I'd like to know more specific recipes." The device records this feedback and sends it to the server. The input is the user's feedback data, and the output is the transfer of the feedback data to the server.

[1210] Step 8: Tuning the generative AI model

[1211] The server analyzes the user's feedback and optimizes the generative AI model. It makes necessary adjustments to reflect the results in future advice. For example, it adjusts the model so that specific recipes are prioritized in future advice. The input is the user's feedback data, and the output is the adjusted generative AI model.

[1212] Example prompt sentence:

[1213] User input: "I was busy this morning and forgot to eat lunch. But I had some fruit as a snack in the evening."

[1214] Response prompt: "It's been a busy day, but I'd recommend including a protein-rich meal for dinner to ensure a balanced diet."

[1215] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1216] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1217] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1218] [Fourth embodiment]

[1219] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1220] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1221] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1222] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1223] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1224] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1225] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1226] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1227] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1228] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1229] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1230] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1231] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1232] The "Your Personal Nosy Health Advisor" of this invention is a system that supports the user's daily life using a home device equipped with voice-interactive AI. This system consists of the following steps:

[1233] System Configuration

[1234] This system is broadly composed of three elements: terminals, servers, and users.

[1235] 1. Terminal (home device)

[1236] The device is equipped with voice-activated interactive AI, enabling real-time dialogue with the user. The device asks questions about the user's lifestyle and dietary habits and records the responses. It also provides settings that allow users to customize the gender, tone, and conversational style of the voice. Furthermore, it sends the collected data to a server, displays the advice returned from the server, and relays it to the user via voice.

[1237] 2. Server

[1238] The server analyzes data about the user's lifestyle and dietary habits sent from the device and generates optimal health advice for the user. The server also performs a comprehensive analysis, including past data, to evaluate the user's health status and nutritional balance. The generated advice is sent to the device, and the artificial intelligence model is adjusted based on the feedback to improve the performance of the entire system.

[1239] 3. Users

[1240] Users interact with the device to provide information about their lifestyle and dietary habits and receive advice from the server. Not only can they improve their lifestyle based on the advice they receive, but they can also provide feedback that will be reflected in the next advice.

[1241] Example

[1242] Example 1: Customization settings according to usage

[1243] 1. Voice settings: During initial setup, the device asks the user for the gender and tone of their voice. For example, the device might say, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other." The user responds, "I prefer a female voice." The device records this and uses it for future interactions.

[1244] 2. Setting the conversation tone: The device then asks, "Next, please choose the conversation tone. You can choose from casual, formal, or friendly." The user requests "I prefer friendly." The device also records this and reflects it in future conversations.

[1245] Example 2: Data collection and advice provision

[1246] 1. Daily Questions: To understand daily habits and eating habits, the device asks, "What did you eat today?" The user answers, "I had toast and coffee for breakfast, pasta for lunch, and salad for dinner." The device records this information and sends it to the server.

[1247] 2. Data analysis: The server analyzes the collected data and evaluates the user's nutritional balance. For example, the server determines that the user is deficient in vitamin D based on their dietary data.

[1248] 3. Advice generation and provision: The server generates advice such as "You're lacking in vitamin D, so why not include fish in your dinner tonight?" and sends it to the device. The device then conveys this advice to the user via voice and also displays visual information (e.g., recipes using fish).

[1249] Example 3: User feedback and model adjustment

[1250] 1. Obtaining feedback: After providing advice, the device asks the user, "What did you think of today's advice? Is there anything else we can improve?" The user gives feedback such as, "I like fish dishes, so please tell me more recipes."

[1251] 2. Feedback analysis and model adjustment: The server analyzes this feedback and adjusts the AI ​​model to reflect it in the next recommendation. For example, a variety of fish-based recipes will be prioritized in the next recommendation.

[1252] As described above, the present invention provides an effective system that allows users to passively obtain health advice by combining voice dialogue-based artificial intelligence and data analysis technology.

[1253] The processing flow will be explained below.

[1254] Step 1:

[1255] The device will enter initial setup mode and ask the user to customize the gender, tone, and conversational style of the voice. For example, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other."

[1256] Step 2:

[1257] The user selects the gender of the voice and responds, "I prefer a female voice." The device then asks, "Next, please select the conversation tone. You can choose from casual, formal, or friendly." The user requests, "I prefer friendly."

[1258] Step 3:

[1259] The device records the settings and prepares to reflect them in subsequent interactions. It notifies the user that "the settings have been recorded," completing the initial setup.

[1260] Step 4:

[1261] The device periodically asks the user questions about their lifestyle and diet. For example, it asks, "What did you eat today?" The user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[1262] Step 5:

[1263] The device saves the collected data in its internal storage and then sends it to the server. It displays "Information recorded" and sends the data.

[1264] Step 6:

[1265] The server receives the data sent from the device and performs a comprehensive analysis, including the user's past data. It evaluates nutritional balance and health status and determines that the user has recently been lacking in vitamin C.

[1266] Step 7:

[1267] The server generates optimal advice for the user, for example, "We recommend eating oranges and kiwi fruits, which are rich in vitamin C," and sends it to the device.

[1268] Step 8:

[1269] The device receives advice from the server and provides it to the user both audibly and visually. It says, "You're lacking in vitamin C, so we recommend eating oranges and kiwis," and shows images of oranges and kiwis and nutritional information on the screen.

[1270] Step 9:

[1271] The device asks the user for feedback: "What did you think of today's advice?", and the user responds, "I'd like to know more meat recipes."

[1272] Step 10:

[1273] The device sends the collected feedback to a server, which analyzes it and adjusts the AI ​​model. From the next time onward, the settings are changed to prioritize meat recipes based on the user's preferences.

[1274] Example 1

[1275] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1276] Conventional health management systems often collect data about users' lifestyles and dietary habits and provide advice in a fragmented and inconsistent manner. They also lacked mechanisms for fully incorporating user preferences and feedback, making it difficult to provide personalized health advice. Furthermore, they were limited in their ability to customize the gender, tone, and style of the voice, limiting the user experience. There is a need to resolve these issues and provide a health management system that users will want to use on an ongoing basis.

[1277] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1278] In this invention, the server includes: a means for collecting information about a user's lifestyle and diet through real-time dialogue with the user, a means for converting the collected information into a data format and sending it to the server, which then analyzes the information to evaluate the user's health condition and nutritional balance and generate optimal health advice; a means for providing the generated advice to the user audibly and visually and notifying the advice in a set gender and tone; and a means for collecting feedback from the user after providing the advice, adjusting the AI ​​model based on the feedback, and reflecting the feedback in subsequent advice. This makes it possible to provide personalized health care optimized for the user's lifestyle and preferences, thereby continuously increasing the user's motivation.

[1279] "Voice-based interactive AI" refers to an AI technology that interacts with users via voice and collects and provides information in response to their instructions and questions.

[1280] "Real-time interaction" refers to a communication method in which interaction with a user occurs immediately without delay.

[1281] "Lifestyle habits" refers to the user's daily actions and patterns of behavior, including, for example, activities such as eating, exercise, and sleep.

[1282] "Diet" refers to the content and pattern of the food a user eats on a daily basis.

[1283] "Converting to data format" refers to the process of converting collected information into a format that can be analyzed by the server.

[1284] A "server" refers to a computer system that receives information, analyzes it, and returns the results.

[1285] "Health status" refers to the user's physical and mental condition, and is assessed based on medical indicators and lifestyle habits.

[1286] "Nutritional balance" refers to the balance of nutrients that the user consumes, and aims to ensure that the appropriate amount of nutrition is consumed without excess or deficiency.

[1287] "Health Advice" means specific suggestions or instructions provided to a User to improve or maintain their health.

[1288] "Audio and visual presentation" refers to the means by which generated information is conveyed to the user through audio and visual displays.

[1289] "Feedback" refers to reactions such as ratings and opinions provided by users.

[1290] "Artificial intelligence model" refers to machine learning algorithms and data analysis methods that automatically generate advice based on data.

[1291] "Customization" refers to the ability to change system settings based on user preferences and requirements.

[1292] "Conversational style" refers to the tone or style of a conversation, such as casual, formal, or friendly.

[1293] This invention is a system called "Your Personal Nosy Health Advisor," which uses a home device equipped with voice-interactive AI to support the user's daily life. This system is mainly composed of three elements: a terminal, a server, and a user.

[1294] Terminal (home device)

[1295] The device is equipped with voice interactive AI, which enables real-time voice interaction with the user. The device collects information from the user by asking questions such as:

[1296] "What did you eat today?"

[1297] "How is it going?"

[1298] The user's responses are recorded, converted into data, and sent to a server. The device also offers settings that allow users to customize the gender, tone, and conversational style of the voice.

[1299] server

[1300] The server analyzes the information sent from the device and generates appropriate health advice. Specifically, the server analyzes the user's dietary and lifestyle data to evaluate their nutritional balance and health status. If the server determines that the user is deficient in vitamin D, for example, it generates the following advice:

[1301] "You're lacking vitamin D, so why not try incorporating some fish into your dinner tonight?"

[1302] The analysis is performed using data analysis software such as Python or R, and optimal advice is provided using generative AI models, which are then sent to the device and notified to the user via audio and visual means.

[1303] User

[1304] Through interaction with the device, the user provides information about their lifestyle and dietary habits and receives advice from the server. Based on this advice, the user not only improves their daily life but also provides feedback. For example, if the device asks, "What did you think of today's advice? Is there anything else I can improve?" the user might respond, "I like fish dishes, so please tell me more recipes."

[1305] Prompt Sentence Examples

[1306] To actually use this system, users would enter prompts like the following to receive specific advice or information:

[1307] Please record what you ate today.

[1308] "What ingredients should I include in my next meal?"

[1309] "Please give me some advice on how to improve my lifestyle."

[1310] "I'll provide feedback on yesterday's advice."

[1311] These prompts allow users to receive specific health advice and feedback, ultimately helping them optimize their lifestyle and nutritional balance.

[1312] Specific examples of hardware and software used

[1313] The following hardware and software are important for the implementation of the system:

[1314] Terminals (home devices): Smart speakers and smart displays equipped with voice-activated AI

[1315] Server: A server for high-performance data analysis (e.g., an environment for running scripts in Python or R)

[1316] Software: Machine learning frameworks (e.g., TensorFlow, PyTorch) for building generative AI models

[1317] Combining these elements makes it possible to provide users with ongoing personalized health advice.

[1318] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1319] Step 1: Reset audio settings

[1320] The device initializes the voice settings when the system starts up. The device asks the user, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other." The user responds, "I prefer a female voice." The input to this dialogue is the user's preference information, which the device converts into a data format and records. As an output, the device completes the voice settings to be used for subsequent interactions.

[1321] Step 2: Set the tone of the conversation

[1322] After setting the voice, the device sets the tone of the conversation. The device asks, "Next, please select the tone of the conversation. You can choose from casual, formal, or friendly," and the user requests, "I prefer friendly." This input information is also converted into data format and recorded. As an output, the device reflects the set tone in subsequent conversations.

[1323] Step 3: Daily Questioning and Data Collection

[1324] The device starts the user's daily assistance by asking, "What did you eat today?" The user answers, "I had toast and coffee for breakfast, pasta for lunch, and salad for dinner." This input information is recorded by the device and converted into a data format. As an output, the collected data is sent to the server.

[1325] Step 4: Data transmission and analysis

[1326] The server receives the data sent from the device. The input information is the user's dietary details, and the server analyzes this information using a data analysis script (e.g., Python). The data is processed by analyzing the dietary details and evaluating whether there are any nutrient deficiencies or excesses. The output is an evaluation of the user's nutritional balance.

[1327] Step 5: Advice Generation

[1328] The server generates specific health advice for the user based on the analysis results. For example, if it determines that the user is deficient in vitamin D, it generates the advice, "You're deficient in vitamin D, so why not try including some fish in your dinner tonight?" This advice is generated using a generative AI model. The generated advice is sent to the device as output.

[1329] Step 6: Providing advice

[1330] The device communicates the advice received from the server to the user via voice. The device notifies the user, saying, "You're lacking in vitamin D, so why not include fish in your dinner tonight?" and also displays a fish recipe as visual information. The input to this dialogue is the advice from the server, and the output is a voice notification and a visual presentation.

[1331] Step 7: Get user feedback

[1332] After providing the advice, the device asks the user for feedback, asking, "What did you think of today's advice? Is there anything else I can improve?" The user might reply, "I like fish dishes, so please tell me more recipes." This feedback information is recorded on the device, converted into a data format, and sent to the server. The feedback data is collected as output.

[1333] Step 8: Feedback analysis and model adjustment

[1334] The server analyzes the feedback sent. The input information is feedback from the user, and the server uses natural language processing technology to understand the user's wishes. As a data calculation, the AI ​​model is adjusted and reflected in the next advice. As an output, the adjusted AI model optimizes the advice for the next time and beyond.

[1335] (Application example 1)

[1336] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1337] In modern industrial facilities, managing the health status of workers is important, but traditionally workers have been expected to manage their health independently, resulting in a lack of effective support. Furthermore, the lack of personalized health advice tailored to the work environment has led to a decline in worker efficiency and motivation. The present invention aims to solve these problems and provide a system that supports worker health management.

[1338] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1339] In this invention, the server includes: [a means equipped with voice interactive artificial intelligence and interacting with a user to collect information about the user's lifestyle and dietary habits;] [a means for sending the collected information to the server and analyzing it to generate advice about the user's health condition and nutritional balance;] [a means for providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation;] [a means for collecting feedback from the user and adjusting the artificial intelligence model based thereon;] [a means for interacting with workers at industrial facilities and collecting information about the workers' health conditions; and] [a means for analyzing the collected information and providing the workers with advice about their health conditions audibly and visually. This makes it possible to grasp the health conditions of workers in real time and provide appropriate advice.

[1340] "Voice-based interactive AI" is AI that can naturally converse with users through voice, gather information, understand instructions, and respond accordingly.

[1341] "Worker" refers to an employee who performs various tasks in an industrial facility.

[1342] "health condition" means the state of a worker's physical and mental health;

[1343] "Nutritional balance" refers to the balance of nutrients that workers are properly consuming, and evaluates the balance of vitamins and minerals necessary to maintain good health.

[1344] "Advice" refers to advice and suggestions regarding health management provided to workers based on collected information and analysis results.

[1345] "Goals" refer to specific targets that are set for workers to achieve when managing their health.

[1346] "Progress" refers to data that indicates the current progress toward a set goal.

[1347] "Feedback" refers to the opinions, thoughts, and further requests that workers provide in response to advice.

[1348] "Artificial intelligence model" refers to an algorithm or system that generates advice based on collected data.

[1349] "Work environment" refers to the total physical and psychological environment in which workers perform their duties within an industrial establishment.

[1350] "Visual information" means advice or instructions provided through a display or other visual means.

[1351] This invention is a system for supporting the health management of workers in industrial facilities. This system consists of three main components: a robot equipped with voice-interactive AI, a server for analyzing data, and workers.

[1352] System Configuration

[1353] 1. Robot (voice-based AI-equipped terminal)

[1354] The robots can be placed anywhere in an industrial facility and interact with workers in real time. They have the following capabilities:

[1355] The voice dialogue function asks questions about the worker's health status and records their responses.

[1356] You can customize the gender, tone, and conversational style of the voice.

[1357] The collected data is sent to a server, and advice returned from the server is provided to the worker in the form of audio and visual information.

[1358] 2. Server

[1359] The server has the following features:

[1360] The robot analyzes data on the worker's health status and generates appropriate health advice. The server performs a comprehensive evaluation, including past data.

[1361] Collect worker feedback and adjust artificial intelligence models.

[1362] The specific technologies used by the server include data analysis algorithms (e.g., Python's Pandas, NumPy), voice dialogue AI engines (e.g., Google Dialogflow), and cloud databases (e.g., AWS DynamoDB).

[1363] 3. Workers

[1364] Workers use the system in the following steps:

[1365] Through dialogue with the robot, users can provide feedback on their health status.

[1366] Receive and act on advice provided by the robot and server.

[1367] They will provide feedback on how the advice provided was implemented and this will be reflected in the next advice.

[1368] Specific examples

[1369] Example 1: Everyday questions and advice

[1370] The robot asks, "How are you feeling today?" and the worker replies, "I'm a little tired." The robot sends this information to a server, which uses past data to generate advice such as, "You may not be hydrated enough today. Please drink some water," and provides this to the worker via the robot.

[1371] Example 2: Obtaining feedback and adjusting advice

[1372] After providing the advice, the robot asks, "What did you think of today's advice?", to which the worker responds, "It was helpful, but I'd like to know more about how to take breaks." Based on this feedback, the server adjusts the content of the next advice to include more detailed information about how to take breaks.

[1373] Prompt Sentence Examples

[1374] If a user reports "I have a headache today":

[1375] Robot: "You've reported a headache. Based on past data, it may be due to dehydration. Please try drinking some water now."

[1376] As shown in this example of a prompt, the robot will hold a brief dialogue, and the server will provide specific advice based on the analysis, allowing workers to understand their own health condition in real time and take appropriate measures.

[1377] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1378] Step 1:

[1379] The robot asks the worker, "How are you feeling today?" The input is the worker's verbal response, and the output is the voice data of that response. The robot converts this voice data into text data using speech recognition software (e.g., Google Dialogflow).

[1380] Step 2:

[1381] The robot collects the converted text data and sends it to a server via the Internet. The input is the text data, and the output is the data received by the server.

[1382] Step 3:

[1383] The server analyzes the received text data and performs a comprehensive evaluation, including past data. The input is the current text data and past data, and the output is the analysis result. The server uses a data analysis algorithm (e.g., Python's Pandas or NumPy) to derive a conclusion, such as "insufficient water."

[1384] Step 4:

[1385] The server generates health advice based on the analysis results. The input is the analysis results, and the output is text data of the advice. The server uses a generative AI model (e.g., GPT-3) to generate appropriate advice text. Take the prompt text "You are dehydrated, so please drink water" as an example.

[1386] Step 5:

[1387] The server sends the generated advice to the robot via the Internet. The input is the text data of the advice, and the output is the data received by the robot.

[1388] Step 6:

[1389] The robot receives advice and provides it to the worker as audio and visual information. The input is the text data of the advice, and the output is an audio message and visual information displayed on a display. The robot uses speech synthesis software to convert the text to audio, saying, "Drink some water."

[1390] Step 7:

[1391] The robot collects feedback from the worker. The input is the worker's verbal feedback, and the output is the voice data of that feedback. The robot converts this voice data into text data using speech recognition software (e.g., Google Dialogflow).

[1392] Step 8:

[1393] The robot sends text data as feedback to the server. The input is the text data, and the output is the feedback data received by the server.

[1394] Step 9:

[1395] The server analyzes the received feedback and adjusts the AI ​​model to reflect it in the next advice. The input is the text data of the feedback, and the output is the adjusted AI model. The server analyzes the feedback data and draws a conclusion, for example, that "detailed resting guidance is needed," and updates the model.

[1396] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1397] The "your personal 'meddling' health advisor" of this invention is a system that provides more precise support for the user's daily life by combining a home device equipped with voice-interactive AI and an emotion engine. This system consists of the following steps:

[1398] System Configuration

[1399] This system is broadly composed of three elements: terminals, servers, and users.

[1400] 1. Terminal (home device)

[1401] The device is equipped with voice-interactive AI, enabling real-time dialogue with the user. The device asks questions about the user's lifestyle and dietary habits and records the responses. In addition, by combining it with an emotion engine, it recognizes the user's emotional state from their speech and behavior and adjusts advice based on that information. It also provides settings that allow users to customize the gender, tone, and conversational style of the voice. The collected data is sent to a server, which generates advice and provides it to the user.

[1402] 2. Server

[1403] The server analyzes data about the user's lifestyle and dietary habits sent from the device and generates optimal health advice for the user. The server performs a comprehensive analysis, including past data, to evaluate the user's health condition and nutritional balance. The server also takes into account the user's emotional state as recognized by the emotion engine and adjusts the tone and content of the advice. The generated advice is sent to the device, and the artificial intelligence model is adjusted based on the feedback to improve the performance of the entire system.

[1404] 3. Users

[1405] Users interact with the device to provide information about their lifestyle and dietary habits and receive advice from the server. Based on the advice, users can improve their lifestyle habits and provide feedback, which will be reflected in future advice. The system also uses an emotion engine to understand the user's emotional state, allowing for more personalized service.

[1406] Example

[1407] Example 1: Customization settings according to usage

[1408] 1. Voice settings: During initial setup, the device asks the user for the gender and tone of the voice. For example, it says, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other." The user responds, "I prefer a female voice."

[1409] 2. Setting the conversation tone: The device then asks, "Next, please choose your conversation tone. You can choose from casual, formal, or friendly." The user requests "Friendly."

[1410] Example 2: Data collection and advice provision

[1411] 1. Daily Questions: To understand daily habits and eating habits, the device asks, "What did you eat today?" The user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[1412] 2. Emotion recognition: The device recognizes the user's emotional state from their tone of voice and choice of words, and provides feedback such as, "You seem happy today" or "You seem a little tired today."

[1413] 3. Data storage: The device stores the collected data and emotional state and sends it to the server. It displays "Information recorded."

[1414] 4. Data analysis and advice generation: The server analyzes the data and evaluates the user's health condition and nutritional balance. If the user is tired, the server recommends relaxing meals and exercise.

[1415] 5. Providing advice: The server generates a message saying, "You are lacking in vitamin C, so we recommend eating oranges and kiwis," and sends it to the device. The device then verbally conveys this advice to the user and displays images of oranges and kiwis and their nutritional information on the display.

[1416] Example 3: User feedback and model adjustment

[1417] 1. Feedback acquisition: After providing advice, the device asks the user, "What did you think of today's advice?" The user gives feedback such as, "I'd like to know more meat recipes."

[1418] 2. Feedback analysis and model adjustment: The server analyzes this feedback and adjusts the AI ​​model to reflect it in the next recommendation. For example, meat recipes will be prioritized in the next recommendation.

[1419] As described above, the present invention combines voice-based interactive AI with an emotion recognition engine to provide an effective system that allows users to passively receive health advice, thereby supporting users in maintaining and improving their health through a more personalized experience.

[1420] The processing flow will be explained below.

[1421] Step 1:

[1422] The device will enter initial setup mode and ask the user to customize the gender, tone, and conversational style of the voice. For example, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose male, female, or other."

[1423] Step 2:

[1424] The user selects the gender of the voice and responds, "I prefer a female voice." The device then asks, "Next, please select the conversation tone. You can choose from casual, formal, or friendly." The user requests, "I prefer friendly."

[1425] Step 3:

[1426] The device records the settings and prepares to reflect them in subsequent interactions. It notifies the user that "the settings have been recorded," completing the initial setup.

[1427] Step 4:

[1428] The device periodically asks the user questions about their lifestyle and diet. For example, it asks, "What did you eat today?" The user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[1429] Step 5:

[1430] At the same time as listening to what the user is saying, the device uses an emotion engine to analyze the user's emotional state and provides feedback such as "You look happy today" or "You seem a little tired."

[1431] Step 6:

[1432] The device stores the collected data and emotional state in its internal storage, then transmits the data to the server. The device notifies the user that the information has been recorded, and transmits the data.

[1433] Step 7:

[1434] The server receives the data and performs a comprehensive analysis, including past data, to evaluate the user's nutritional balance and determine that the user has recently been lacking in vitamin C.

[1435] Step 8:

[1436] The server takes into account the user's current emotional state and generates appropriate advice. For example, if the user feels tired, the server generates advice recommending a relaxing meal or light exercise.

[1437] Step 9:

[1438] The server sends the generated advice to the device, which then provides it to the user both audibly and visually: "You're lacking in vitamin C, so we recommend eating oranges and kiwis," the device says, and the display shows images of oranges and kiwis and their nutritional information.

[1439] Step 10:

[1440] The user takes food in response to the advice and provides feedback to the device. When the device asks, "What did you think of today's advice?", the user responds, "I'd like to know more meat recipes."

[1441] Step 11:

[1442] The device collects the feedback and sends it to the server, which analyzes it and adjusts the AI ​​model to reflect the feedback in the next recommendation. For example, it may change the settings so that meat recipes are prioritized in future recommendations.

[1443] Example 2

[1444] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1445] In recent years, health problems such as lifestyle-related diseases and nutritional imbalances have been increasing. Many people want to take measures to address these problems, but self-management is difficult, and maintaining motivation is also challenging. Existing health management systems are unable to provide advice that takes into account the user's emotional state, tend to provide uniform information, and lack truly personalized support. This makes it difficult for users to realize the effects of self-management, making it difficult to continue.

[1446] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1447] In this invention, the server includes: [means equipped with voice interactive artificial intelligence and interacting with the user to collect information about the user's lifestyle and dietary habits; [means sending the collected information to the server for analysis and generating advice about the user's health condition and nutritional balance; [means providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation;] [means equipped with an emotion recognition engine that recognizes the user's emotional state from their speech and actions and adjusts the advice based on that information; and] [means collecting feedback from the user and adjusting the artificial intelligence model based on that. This makes it possible [to provide personalized health advice based on each user's emotional state and lifestyle habits and maintain motivation].

[1448] "Voice-activated interactive artificial intelligence" refers to artificial intelligence that can have natural conversations with users, and includes voice recognition, natural language processing, and voice synthesis technologies.

[1449] "User" means an individual who uses the system and receives health advice.

[1450] "Lifestyle habits" refers to the patterns of activities and behavior that a user engages in on a daily basis, including diet, exercise, sleep, and the like.

[1451] "Diet" refers to the food and eating habits that a user regularly consumes.

[1452] A "server" is a computer that performs the central calculations and data processing of a system.

[1453] "Advice" refers to suggestions and instructions generated by the system based on the user's health status and nutritional balance.

[1454] An "emotion recognition engine" is software that recognizes a user's emotional state from their speech and behavior, including analyzing voice tone and vocabulary.

[1455] "Feedback" refers to the thoughts and opinions that a user provides about advice, and is information that will be reflected in the next piece of advice.

[1456] An "artificial intelligence model" is a trained model for analyzing user data and generating health advice.

[1457] "Personalized" means optimized for the individual user.

[1458] "Motivation" refers to the will and enthusiasm a user has to achieve a goal.

[1459] A "goal" is a specific health-related standard or outcome that a user aims to achieve.

[1460] "Progress" refers to the progress or steps achieved towards a goal.

[1461] This invention relates to a "personal, nosy health advisor" that combines voice-activated interactive AI and an emotion recognition engine. This system collects and analyzes information about the user's lifestyle and dietary habits, and provides optimal health advice.

[1462] Hardware and software used

[1463] Hardware

[1464] Home devices: These are devices equipped with voice interactive artificial intelligence and enable real-time interaction with users.

[1465] software

[1466] Speech dialogue engine: Google Dialogflow

[1467] Emotion recognition engine: Affectiva SDK

[1468] Data analysis engine: Apache Spark

[1469] Database system: MySQL

[1470] Program processing description

[1471] 1. Initial device setup

[1472] During initial setup, a terminal (home device) asks the user for voice and conversation tone settings. First, the voice dialogue engine sets the voice gender and tone based on the user's preferences. For example, the terminal might say, "Hello! I'm your personal health advisor. First, please choose the gender of my voice. You can choose from male, female, or other," and the user might respond, "I prefer a female voice."

[1473] 2. Daily interactions with users

[1474] The device routinely asks the user questions about their lifestyle and diet. For example, if the device asks, "What did you eat today?" and the user responds, "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner," the device collects and records that information. It also uses an emotion recognition engine to analyze the user's emotional state from their tone of voice and choice of words, providing feedback such as, "You seem happy today" or "You seem a little tired today."

[1475] 3. Data storage and transmission

[1476] The device stores the collected lifestyle and emotional state data in a MySQL database and sends it to the server. The device displays "Information recorded."

[1477] 4. Data analysis by the server

[1478] The server uses Apache Spark to analyze the data it receives, including evaluating lifestyle and emotional state data. For example, the server might determine that the user is deficient in vitamin C.

[1479] 5. Advice Generation

[1480] The server uses a generative AI model based on the analysis results to generate optimal health advice for the user, such as "You are lacking in vitamin C, so we recommend eating oranges and kiwis," and sends the advice to the device.

[1481] 6. Providing advice

[1482] The device provides the user with advice from the server both audibly and visually. The device will say, "You are lacking in vitamin C, so we recommend eating oranges and kiwis," and will show images of oranges and kiwis and their nutritional information on the display.

[1483] 7. Gathering User Feedback

[1484] After providing the advice, the device asks the user, "What did you think of today's advice?" If the user provides feedback such as "I'd like to know more meat recipes," that information is recorded by the device.

[1485] 8. Feedback analysis and model adjustment

[1486] The server analyzes user feedback and adjusts the generative AI model, so that future advice suggestions will be tailored to the user's preferences. For example, meat recipes will be prioritized from the next time.

[1487] Prompt Sentence Examples

[1488] "What did you eat today?"

[1489] "How are you feeling today?"

[1490] "What did you think of today's advice?"

[1491] As described above, the "your personal, nosy health advisor" of the present invention is a system that supports users in improving their lifestyle habits and maintaining their health by effectively combining voice-interactive artificial intelligence and an emotion recognition engine.

[1492] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1493] Processing Step Description

[1494] Step 1: Initial device setup

[1495] Specific behavior:

[1496] 1. The terminal presents the user with an initial setup start prompt.

[1497] 2. Device: "Hello! I'm your personal health advisor. First, please select the gender of my voice. You can choose male, female, or other."

[1498] 3. User: "Female voices are nice."

[1499] 4. Device: "Got it. I'll be speaking with a female voice from now on."

[1500] 5. Next, the device: "Next, please choose your conversational tone. You can choose casual, formal, or friendly."

[1501] 6. User: "Friendly."

[1502] 7. Terminal: "I'll speak to you in a friendly tone!"

[1503] Input: User voice preference, conversational tone preference

[1504] Data manipulation: Apply voice and speech tone settings based on user preferences

[1505] Output: Configured voice interaction environment

[1506] Step 2: Daily interactions with users

[1507] Specific behavior:

[1508] 1. At a set time each day, the device begins asking questions to the user.

[1509] 2. Terminal: "What did you eat today?"

[1510] 3. User: "I had yogurt for breakfast, a sandwich for lunch, and curry rice for dinner."

[1511] 4. Terminal: "Information recorded."

[1512] 5. Next, the device performs emotion recognition and provides feedback such as, "You seem happy today" or "You seem a little tired today."

[1513] Input: User's diet, voice tone, and language

[1514] Data processing: Record meal contents as text data and analyze emotions from voice tone and vocabulary

[1515] Output: Recorded food data, emotional state recognition

[1516] Step 3: Saving and sending data

[1517] Specific behavior:

[1518] 1. The device stores the collected data (lifestyle habits and emotional state) in an internal database.

[1519] 2. Terminal: Displays "Information sent to server."

[1520] 3. The data is sent over the internet to a server.

[1521] Input: User's lifestyle data, emotional state data

[1522] Data processing: storing data in a database and sending data to a server

[1523] Output: Records stored in the database, data sent to the server

[1524] Step 4: Data analysis by the server

[1525] Specific behavior:

[1526] 1. The server receives the transmitted data and stores it in a database.

[1527] 2. Analyze lifestyle data and emotional state data using Apache Spark.

[1528] 3. Evaluate the nutritional balance based on the meal content and also determine the user's psychological state based on their emotional state.

[1529] Input: Lifestyle data and emotional state data sent from the device

[1530] Data processing: Analyze data in the database and evaluate nutritional balance and emotional state

[1531] Output: Health status assessment report

[1532] Step 5: Advice generation

[1533] Specific behavior:

[1534] 1. The server uses a generative AI model to create health advice based on the analysis results.

[1535] 2. For example, if the nutritional balance is insufficient, we might suggest, "You are lacking vitamin C, so we recommend eating oranges or kiwis."

[1536] Input: Health Assessment Report

[1537] Data processing: Generate advice using AI models generated from analysis results

[1538] Output: Health advice to provide to the user

[1539] Step 6: Providing advice

[1540] Specific behavior:

[1541] 1. The server sends the generated advice to the device.

[1542] 2. The device will say, "You are lacking in vitamin C. We recommend eating oranges or kiwis."

[1543] 3. Display images and nutritional information of oranges and kiwis on the display.

[1544] Input: Generated health advice

[1545] Data processing: Sending advice data from the server to the device

[1546] Output: Advice and visual information provided to the user

[1547] Step 7: Gather user feedback

[1548] Specific behavior:

[1549] 1. Device: Ask the user, "What did you think about today's advice?"

[1550] 2. User: Provides feedback saying, "I'd like to see more meat recipes."

[1551] 3. Device: Display "Feedback recorded."

[1552] Input: User feedback

[1553] Data processing: recording and storing feedback data

[1554] Output: Recorded feedback data

[1555] Step 8: Feedback analysis and model adjustment

[1556] Specific behavior:

[1557] 1. The server analyzes the collected feedback.

[1558] 2. The generative AI model is adjusted based on the analysis results and reflected in the next advice generation.

[1559] 3. Update the model so that, for example, meat recipes are prioritized from the next time onwards.

[1560] Input: User feedback data

[1561] Data processing: feedback analysis, updating generative AI models

[1562] Output: The new model settings reflected in the next advice generation.

[1563] (Application example 2)

[1564] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1565] Conventional health advice systems collect information about a user's lifestyle and dietary habits and provide basic advice, but they struggle to provide personalized advice that takes into account the user's emotional state. It is also difficult to maintain consistency between health advice and dietary suggestions, which can lead to a continuous decline in user motivation. Furthermore, few systems effectively incorporate user feedback on the advice provided, making it difficult for users to continue receiving effective advice.

[1566] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1567] In this invention, the server includes: [means equipped with voice interactive artificial intelligence and interacting with the user to collect information about the user's lifestyle and dietary habits;] means for sending the collected information to the server and analyzing it to generate advice about the user's health condition and nutritional balance; [means for providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation;] means for collecting feedback from the user and adjusting the artificial intelligence model based thereon; [means for recognizing the user's emotional state using an emotion engine and adjusting the tone and content of health advice and dietary suggestions; and [means for providing a food delivery function that makes customized dietary suggestions based on the user's emotional state and dietary habits.] As a result, health advice and dietary suggestions are consistently provided taking the user's emotional state into consideration, thereby continuously motivating the user and enabling effective health maintenance and revitalization.

[1568] "Voice-based interactive AI" is an AI technology that interacts with users through voice input, understands their intentions and requests, and generates appropriate responses.

[1569] "Lifestyle" refers to behaviors and habits in daily life, such as exercise, sleep, and eating patterns.

[1570] "Diet" refers to habits related to eating behavior, such as the type of food consumed on a daily basis, the time of eating, and frequency of eating.

[1571] A "server" refers to a computer system that provides data and services to other computers and devices over a network.

[1572] "Health status" refers to information that indicates an individual's physical condition and level of health, including physical and mental status.

[1573] "Nutritional balance" refers to the appropriate intake of each nutrient (e.g., vitamins, minerals, proteins, carbohydrates, lipids, etc.) required by the body.

[1574] "Advice" refers to suggestions or advice that can help improve a user's health or diet.

[1575] An "emotion engine" refers to technology that analyzes a user's emotional state from their speech and behavior and recognizes their emotions.

[1576] "Feedback" refers to a user's evaluation or opinion of the advice or service provided.

[1577] "Personalized advice" refers to advice and suggestions that are customized to suit the individual characteristics and circumstances of a user.

[1578] "Meal suggestions" refers to proposing specific meal plans and menus to the user.

[1579] "Food delivery function" refers to a function that provides suggested foods and dishes to users as a delivery service.

[1580] "Customization" refers to adjusting settings and content to suit the user's preferences and needs.

[1581] This invention uses a system that combines voice-interactive AI and an emotion recognition engine to provide health advice based on a user's lifestyle and dietary habits. The system of this invention is mainly composed of three elements: a terminal (home device), a server, and a user.

[1582] 1. Terminal (home device)

[1583] The device is equipped with voice-activated interactive AI, enabling real-time dialogue with the user. When the user speaks to the device about their lifestyle and dietary habits, the device records the information and uses an emotion engine to identify the user's emotional state. This information and emotion data is sent to the server. The device's voice is customizable, allowing users to set gender, tone, and conversational style.

[1584] 2. Server

[1585] The server receives and analyzes the data sent from the device. Generative AI models such as the Transformer model (Hugging Face Transformers) are used for data analysis. The server evaluates the user's health and nutritional balance and generates optimal health advice based on that. It also adjusts the tone and content of the advice based on the user's emotional state, based on data from the emotion engine.

[1586] The generated advice is sent to the device as audio and visual information and provided to the user. Feedback data from the user is also sent to the server, and this is used to continuously optimize the generative AI model. For example, if a user provides feedback such as "I'd like to know more meat dish recipes," information about meat dishes will be prioritized in future advice suggestions.

[1587] 3. Users

[1588] Users can provide information about their lifestyle and dietary habits through interactions with the device. They can also receive health advice and meal suggestions from the server and improve their lifestyle based on those advice. The device recognizes the user's emotional state and provides appropriate advice based on their emotions, allowing users to enjoy a more personalized experience.

[1589] For example, if a user says, "I was full this morning, so I had just soup for lunch. It was delicious!", the emotion engine will recognize that the user is satisfied, and the server will generate the following advice: "I can see that you enjoyed a light lunch. However, you may be lacking in vitamins and minerals. Why not try a salad with lots of vegetables and some fruit for dinner?"

[1590] In addition, users can order the suggested meal menu as a delivery service, making it easy for them to get healthy meals. At this time, feedback on the advice provided is also collected and reflected in future advice.

[1591] The present invention consistently provides personalized health advice and dietary suggestions that are useful in daily life while taking into account the user's emotional state, thereby helping the user maintain their health and improve their motivation.

[1592] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1593] Step 1: Collecting user data on the device

[1594] The device collects information about the user's lifestyle and eating habits through dialogue with the user. If the user says, "I've been full since this morning, so I had just soup for lunch," the device uses voice recognition to record that information as text data. The input is the user's voice data, and the output is text data. This text data is temporarily stored in the device and later sent to the server.

[1595] Step 2: Recognizing your emotional state

[1596] The device uses an emotion engine to recognize the user's emotional state from the collected user speech data. From the tone and expression of the voice, the device determines the user's emotions, such as "happy" or "tired." In this case, the user is recognized as satisfied. The input is the user's voice data, and the output is emotional data.

[1597] Step 3: Sending data

[1598] The device sends the collected text data and emotion data to a server. The input is text data and emotion data, and the output is data transfer to the server. Specifically, the data is sent to the server via the Internet.

[1599] Step 4: Data analysis by the server

[1600] The server receives the data sent from the device and analyzes it using a generative AI model. The analysis uses a Transformer model (Hugging Face Transformers) to evaluate the user's diet and health status. The input is the user's text data and emotion data, and the output is the analysis results and health advice.

[1601] Step 5: Generate health advice

[1602] The server generates optimal health advice for the user based on the analysis results. For example, it generates advice such as, "We can see that you enjoyed a light lunch. However, you may be lacking in vitamins and minerals, so why not try a salad with plenty of vegetables and some fruit for dinner?" The input is the analysis results, and the output is specific advice statements.

[1603] Step 6: Providing advice

[1604] The server sends the generated advice to the terminal, which then provides it to the user as audio and visual information. The terminal will say aloud, "Today's lunch was good. For dinner, I recommend a salad and fruit, which will provide you with vitamins," and show specific meal examples on the display. The input is the advice text, and the output is the audio and visual information received by the user.

[1605] Step 7: Gather feedback

[1606] The user provides feedback on the advice provided. For example, the user might respond, "This advice is good, but I'd like to know more specific recipes." The device records this feedback and sends it to the server. The input is the user's feedback data, and the output is the transfer of the feedback data to the server.

[1607] Step 8: Tuning the generative AI model

[1608] The server analyzes the user's feedback and optimizes the generative AI model. It makes necessary adjustments to reflect the results in future advice. For example, it adjusts the model so that specific recipes are prioritized in future advice. The input is the user's feedback data, and the output is the adjusted generative AI model.

[1609] Example prompt sentence:

[1610] User input: "I was busy this morning and forgot to eat lunch. But I had some fruit as a snack in the evening."

[1611] Response prompt: "It's been a busy day, but I'd recommend including a protein-rich meal for dinner to ensure a balanced diet."

[1612] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1613] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1614] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1615] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1616] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1617] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1618] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1619] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1620] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1621] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1622] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1623] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1624] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1625] 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.

[1626] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1627] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1628] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1629] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1630] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1631] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1632] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1633] The following is further disclosed regarding the above embodiment.

[1634] (Claim 1)

[1635] [A means for collecting information on the user's lifestyle and dietary habits by having a voice-interactive AI and interacting with the user;

[1636] [Means for transmitting the collected information to a server for analysis and generating advice on the user's health status and nutritional balance;

[1637] [means for providing generated advice to the user audibly and visually and displaying goals and progress to keep the user motivated;

[1638] [Means of collecting user feedback and adjusting the AI ​​model based on it; and

[1639] A system including:

[1640] (Claim 2)

[1641] [The system of claim 1 allows for customization of the gender, tone, and conversational style of the voice.

[1642] (Claim 3)

[1643] [The system of claim 1, which visually displays goals and progress and incorporates achievement rewards and gamification elements to enhance the user's ongoing motivation.

[1644] "Example 1"

[1645] (Claim 1)

[1646] [Equipped with voice interactive AI, it is a means of collecting information about the user's lifestyle and dietary habits through real-time dialogue with the user;

[1647] [Means for converting the collected information into a data format and sending it to a server, which then analyzes the information to evaluate the user's health condition and nutritional balance and generate optimal health advice;

[1648] [Means for providing the generated advice to the user audibly and visually, and announcing the advice in a set gender and tone;

[1649] [Means of collecting feedback from users after providing advice, adjusting the AI ​​model based on that feedback, and reflecting it in future advice, and

[1650] A system including:

[1651] (Claim 2)

[1652] [The system of claim 1 allows for customization of the gender, tone, and conversational style of the voice.

[1653] (Claim 3)

[1654] [The system of claim 1, which visually displays goals and progress and incorporates achievement rewards and gamification elements to enhance the user's ongoing motivation.

[1655] "Application Example 1"

[1656] (Claim 1)

[1657] [A means for collecting information on the user's lifestyle and dietary habits by having a voice-interactive AI and interacting with the user;

[1658] [Means for transmitting the collected information to a server for analysis and generating advice on the user's health status and nutritional balance;

[1659] [means for providing generated advice to the user audibly and visually and displaying goals and progress to keep the user motivated;

[1660] [Means of collecting user feedback and adjusting the AI ​​model based on it; and

[1661] [means for interacting with workers at industrial facilities and collecting information regarding the health status of the workers;

[1662] [Means of analyzing the collected information and providing audio and visual advice to workers regarding their health status;

[1663] A system including:

[1664] (Claim 2)

[1665] [The system according to claim 1, wherein the gender, tone and style of the voice can be customized to enable dialogue adapted to the working environment.

[1666] (Claim 3)

[1667] [The system of claim 1, which visually displays goals and progress and incorporates achievement rewards and gamification elements to enhance the ongoing motivation of users and workers.

[1668] "Example 2: Combining Emotion Engines"

[1669] (Claim 1)

[1670] [A means for collecting information on the user's lifestyle and dietary habits by having a voice-interactive AI and interacting with the user;

[1671] [Means for transmitting the collected information to a server for analysis and generating advice on the user's health status and nutritional balance;

[1672] [means for providing generated advice to the user audibly and visually and displaying goals and progress to keep the user motivated;

[1673] [Means for incorporating an emotion recognition engine that recognizes the user's emotional state from their speech and behavior and adjusts advice based on that information;

[1674] [Means of collecting user feedback and adjusting the AI ​​model based on it; and

[1675] A system including:

[1676] (Claim 2)

[1677] [The system of claim 1 allows for customization of the gender, tone, and conversational style of the voice.

[1678] (Claim 3)

[1679] [The system of claim 1, which visually displays goals and progress and incorporates achievement rewards and gamification elements to enhance the user's ongoing motivation.

[1680] "Application example 2 when combining emotion engines"

[1681] (Claim 1)

[1682] [A means for collecting information on the user's lifestyle and dietary habits by having a voice-interactive AI and interacting with the user;

[1683] [Means for transmitting the collected information to a server for analysis and generating advice on the user's health status and nutritional balance;

[1684] [means for providing generated advice to the user audibly and visually and displaying goals and progress to keep the user motivated;

[1685] [Means of collecting user feedback and adjusting the AI ​​model based on it; and

[1686] [Means of using an emotion engine to recognize the user's emotional state and adjust the tone and content of health advice and dietary suggestions; and

[1687] [Means for providing a food delivery function that provides customized meal suggestions based on the user's emotional state and eating habits;

[1688] A system including:

[1689] (Claim 2)

[1690] [The system of claim 1 allows for customization of the gender, tone, and conversational style of the voice.

[1691] (Claim 3)

[1692] [The system of claim 1, which visually displays goals and progress and incorporates achievement rewards and gamification elements to enhance the user's ongoing motivation. [Explanation of symbols]

[1693] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for collecting information on the user's lifestyle and dietary habits by having a voice-interactive artificial intelligence and interacting with the user; A means for transmitting the collected information to a server for analysis and generating advice regarding the user's health condition and nutritional balance; means for providing the generated advice to the user audibly and visually and displaying goals and progress to maintain the user's motivation; a means for collecting user feedback and adjusting the artificial intelligence model based thereon; A system including:

2. [The system of claim 1 allows customization of the gender, tone, and conversational style of the voice.]

3. The system according to claim 1, which visually displays goals and progress, and incorporates achievement rewards and gamification elements to enhance the user's ongoing motivation.

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