system

Abstract

We provide the system. [Solution] A means of obtaining physical information from the user, A means of transmitting acquired physical information to a server, A means by which the server searches a database for similar past success stories based on the user's physical information, A means to analyze and generate suggestions for the optimal weight loss method based on the searched data, A means of providing the generated suggestions to the user, A means of selecting and presenting product information related to the proposed weight loss method to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Many people have difficulty finding a weight loss method suitable for themselves. This difficulty is because individual body shapes and constitutions are different, and a uniform method is often ineffective. Also, the process of selecting and purchasing appropriate products is complicated, imposing a heavy burden on users. The purpose of this invention is to solve these problems and provide support for users to effectively achieve weight loss.

Means for Solving the Problems

[0005] This invention provides a means for collecting a user's physical information and transmitting it to a server. Furthermore, the server uses a database to search for similar past success stories, analyzes the optimal weight loss method based on this, and generates suggestions. The generated suggestions are provided to the user, and relevant product information is also selected and presented. In addition, the accuracy of the suggestions is improved by collecting user feedback and storing it in the database. Furthermore, the invention includes a means for the user to purchase selected products via the server. This makes it possible to provide each user with a unified service that offers optimized weight loss methods and product purchase support.

[0006] A "user" refers to an individual who uses the system to provide their physical information and receive suggestions for the most suitable weight loss method.

[0007] "Physical information" refers to general data about a user's body shape and constitution, including height, weight, age, gender, activity level, and allergy information.

[0008] A "server" refers to a computing device that receives physical information sent by users and uses a database to analyze and suggest the most suitable weight loss methods.

[0009] A "database" refers to a system that systematically manages a large amount of information, such as past success stories of dieting and related data, for use in analysis.

[0010] "Similar past success stories" refer to data from other individuals who have similar physical characteristics to the target user and have a history of successful weight loss.

[0011] "Weight loss methods" refers to a series of strategies aimed at weight loss, including meal plans, exercise plans, and lifestyle improvement suggestions that are considered optimal for the user.

[0012] "Product information" refers to detailed information about available products, such as protein shakes and fitness equipment, related to the weight loss methods suggested to the user.

[0013] "Feedback" refers to the information that users provide to the system, such as their thoughts and evaluations about the weight loss methods suggested or the products they purchased.

[0014] "Purchase procedure" refers to the series of confirmation and payment processes necessary for a user to purchase a selected product. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when a sentiment engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when a sentiment engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be described.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system that uses AI to analyze and suggest weight loss methods tailored to individual users. Users access the system using a terminal such as a smartphone or computer and input their physical information. This information includes height, weight, age, gender, activity level, and allergy information. The terminal transmits the collected information to the server.

[0037] The server accesses a database to match the user's physical information with similar past success stories. The database contains detailed data on past weight loss successes, which is then analyzed by a machine learning algorithm. The server uses this analysis to generate the most suitable weight loss plan for the user. This plan includes a meal plan, an exercise plan, and lifestyle improvement suggestions.

[0038] Furthermore, the server selects products related to the proposed weight loss method. These selected products may include protein shakes and fitness equipment, designed to support the user in achieving their goals. This product information is then presented to the user.

[0039] When a user purchases a product based on the information provided, the terminal communicates the purchase intention to the server. The server then proceeds with the purchase process and performs the necessary steps for payment.

[0040] As a concrete example, let's consider a 30-year-old male user with a lifestyle that involves exercising twice a week. This user enters his height (170cm) and weight (70kg) into the terminal. The server searches its database for similar profiles and, based on successful weight loss cases, generates suggestions including calorie restriction and jogging three times a week. It also suggests products to encourage the purchase of protein shakes, and if the user shows interest, the server facilitates the purchase process. Through this process, the user receives a personalized weight loss strategy and support for purchasing appropriate products.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] The user enters physical information such as height, weight, age, gender, activity level, and allergy information through the device. The device checks this data and verifies that there are no missing entries or formatting errors.

[0044] Step 2:

[0045] The device converts the user's physical information into an appropriate format and sends it to the server. During this process, the data is encrypted to protect privacy.

[0046] Step 3:

[0047] Based on the physical information received by the server, it accesses an internal database. The server searches the database records to find past success stories similar to the user's information.

[0048] Step 4:

[0049] Based on the acquired similar data, the server uses statistics and machine learning algorithms to analyze the optimal weight loss method for the user. This analysis includes generating appropriate meal plans and exercise plans.

[0050] Step 5:

[0051] Based on the analysis results, the server creates a weight-loss plan proposal for the user. This proposal includes detailed action plans and suggestions for lifestyle improvements.

[0052] Step 6:

[0053] The server selects product information related to weight loss methods from its database and presents it to the user as suggested products. These products include protein shakes and fitness equipment.

[0054] Step 7:

[0055] The terminal displays proposals and product information received from the server to the user. The user reviews the proposals and selects products that interest them.

[0056] Step 8:

[0057] When a user indicates their intention to purchase a product, they send a purchase request to the server via their device. The server then begins processing the purchase.

[0058] Step 9:

[0059] The server receives the purchase request and processes the payment. It sends the necessary information to the payment system and confirms the payment. Once everything is complete, it sends a confirmation notification to the user.

[0060] Step 10:

[0061] The terminal collects suggestions and product feedback from users and sends it back to the server. The server analyzes the feedback and stores it in a database. This information is used to improve the accuracy of future suggestions.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] There is a need for an information processing system that can efficiently provide weight management methods optimized for individual users, while also enabling seamless selection and purchase of related products. Conventional systems have faced challenges in providing specific and personalized suggestions based on users' physical information, and in the complex process of purchasing related products based on those suggestions.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes means for analyzing past success stories using an AI model generated based on the user's biometric data, means for analyzing and proposing the optimal weight management method, means for selecting and presenting product information related to the proposal to the user, and means for carrying out the purchase procedure for the product selected by the user. This makes it possible to smoothly execute a series of processes for the user, from proposing a personalized weight management method to selecting and purchasing related products.

[0067] An "information processing device" is a device that has the function of acquiring biometric data from a user and transmitting that data to an information processing server via a network.

[0068] "Biometric data" refers to information related to a user's body, including data such as height, weight, age, gender, activity level, and allergy information.

[0069] An "information processing server" is a server that performs analysis based on biometric data received from an information processing device via a network, generates the optimal weight management method, and further selects related product information.

[0070] An "information storage device" is a storage device that stores data such as past success stories and user evaluations, and allows for retrieval and utilization as needed.

[0071] A "generative AI model" is an artificial intelligence model used to analyze past success stories and propose optimized weight management methods for users.

[0072] A "weight management method" is a method that includes meal plans, exercise plans, and lifestyle improvement suggestions designed based on the user's biometric data.

[0073] "Product information" refers to information about products related to the user's weight management methods, specifically including protein shakes and fitness equipment.

[0074] "Network" refers to communication infrastructure such as the internet that enables communication between information processing devices and information processing servers.

[0075] "Payment processing" refers to the series of procedures involved in the payment process when a user purchases a product they have selected.

[0076] This invention relates to an information processing system that allows users to receive a weight management method optimized for their individual needs. The system consists of a terminal used by the user, an information processing server operating on the cloud, and related software programs.

[0077] First, the user uses a device to input various physical data. This includes biometric data such as height, weight, age, gender, activity level, and allergy information. The device then transmits this information as encrypted data to an information processing server via the network.

[0078] Upon receiving this data, the server extracts past success stories from its information storage device and analyzes them using a generative AI model to generate an optimal management method based on the user's biometric data. This includes meal plans, exercise plans, and lifestyle improvement suggestions.

[0079] Furthermore, the server selects and presents products related to recommended management methods to the user. This includes product information that can help the user achieve their weight management goals, such as protein shakes and fitness equipment. If the user wishes to purchase these products, the terminal communicates this intention to the server, which then processes the payment securely and efficiently.

[0080] As a concrete example, let's assume a 30-year-old male user inputs information into the device such as his height (170cm), weight (70kg), and exercise habits (twice a week). Based on this information, the server uses a generative AI model to create a plan that includes calorie restriction and jogging three times a week as the optimal approach. Furthermore, it suggests products such as protein shakes.

[0081] As an example of a prompt for a generative AI model, we will use a text-based prompt that reads, "Please suggest the best weight management method for a 30-year-old male user who exercises twice a week, is 170cm tall, and weighs 70kg." This prompt provides the generative AI model with an appropriate analysis scheme and supports the construction of personalized suggestions.

[0082] In this way, this invention makes it possible to provide users with a practical and personalized weight management solution.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user enters their physical information using their device. In this step, the user fills in biometric data such as height, weight, age, gender, activity level, and allergy information in an input form. After tapping the submit button, the entered data is collected. This information becomes the input data for the next step.

[0086] Step 2:

[0087] The device transmits the entered biometric data to the server via the network. In this step, the device encrypts the collected data and sends it to the server using a secure communication protocol (e.g., HTTPS). This data serves as input for the next step.

[0088] Step 3:

[0089] The server analyzes the received biometric data. In this step, the server searches its internal data storage for past success stories and performs data analysis using a generative AI model. The input here is the user's biometric data, and the output is an optimal weight management plan based on the analysis.

[0090] Step 4:

[0091] The server generates and proposes an optimal weight management method to the user. In this step, the server constructs a method that includes a meal plan, exercise plan, and lifestyle improvement suggestions based on the results analyzed by the generative AI model. The output is personalized suggestion information presented to the user.

[0092] Step 5:

[0093] The server selects and presents products relevant to the proposal to the user. In this step, the server selects information on products (e.g., protein shakes or fitness equipment) that fit the generated weight management plan and provides it to the user as a list. The output is product information for the user to refer to.

[0094] Step 6:

[0095] If the user selects a product from the displayed options and wishes to purchase it, they communicate their intention to the server via their device. In this step, the user selects the item they wish to purchase from the product list and issues a purchase instruction through their device.

[0096] Step 7:

[0097] The server handles the purchase process. In this step, the server processes the payment for the selected items and performs the necessary authentication. This includes communication with the payment gateway and processing of payment information. The final output is confirmation that the purchase process is complete.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] Providing individual users with the most suitable weight management methods requires personalized suggestions based on diverse physical information and lifestyle habits. However, conventional systems have been unable to fully utilize this information, making it difficult to provide weight loss methods and lifestyle improvement guidelines optimized for each user. Furthermore, there is a problem of insufficient continuous support to help users implement the suggested methods in their daily lives.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for acquiring information about the user's body, means for an information processing device to search a recording medium for similar past success stories based on the user's information about their body, and means for interactively providing the user with exercise and dietary guidance through a home device. This makes it possible to provide the user with a personalized weight management method and support its practice in daily life.

[0103] A "user" is an individual who uses the system to provide physical information and receive personalized weight management methods.

[0104] "Physical information" refers to health-related data such as the user's height, weight, age, gender, activity level, and allergy information.

[0105] An "information processing device" is a server or computer system that receives a user's physical information, searches a database, and performs analysis.

[0106] "Recording medium" refers to databases and storage devices that store data on past success stories and other similar information.

[0107] A "weight management method" is an approach that includes meal plans, exercise plans, and lifestyle improvement suggestions generated based on the user's physical information.

[0108] "Home-use devices" refer to robots and smart devices used in the user's living space, and are used to provide guidance for weight management.

[0109] "Product information" refers to data on related products such as protein shakes and fitness equipment that are suggested to assist with weight management.

[0110] The system for implementing this invention aims to acquire information about the user's body and, based on that information, provide an optimal weight management method. The user inputs their body information through a home-use device such as a robot or smart device. This information is sent to a server, which accesses a recording medium to search for similar past success stories. Based on the information from the recording medium, the server performs data analysis using a generative AI model to generate an optimal weight management method for the user. This includes a meal plan, an exercise plan, and lifestyle improvement suggestions.

[0111] The generated suggestions are delivered interactively to the user via home devices. For example, a robot might encourage regular exercise and present product information as needed. If the user expresses interest, the purchase process is initiated through a server. This system allows users to receive a personalized health management approach on the spot, providing continuous support for a healthy lifestyle.

[0112] In this embodiment, the information processing device (server) uses software such as Python and TENSORFLOW® to perform database searches and analysis using AI models. As a home appliance, the robot is equipped with voice recognition and a touch interface, enabling two-way communication with the user.

[0113] For example, if a 40-year-old female user enters information such as height 160cm and weight 65kg, the server searches the database, and a generating AI model performs the analysis. This results in a suitable vegetarian meal plan and suggestions for two yoga exercises per week. Additionally, product information on organic supplements is presented, and if the user is interested, they can proceed with the purchase.

[0114] Example input prompts for a generative AI model:

[0115] "Use AI to propose the optimal weight loss plan based on the user's height, weight, age, gender, and activity level."

[0116] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0117] Step 1:

[0118] The device collects physical information from the user. The user inputs height, weight, age, gender, activity level, allergy information, etc., through voice recognition or a touch interface. This information is collected as raw data.

[0119] Step 2:

[0120] The device sends the collected physical information to the server. The device sends the data to the server using a secure communication protocol, and the server receives this data and converts its format into an analyzable data format.

[0121] Step 3:

[0122] The server searches the database based on the received physical information. Using an index search algorithm, the server matches similar past successes within the storage medium and extracts relevant entries. This process allows the server to obtain the historical data necessary for analysis.

[0123] Step 4:

[0124] The server performs data analysis using a generated AI model. Similar past cases and the user's physical information are provided as input. The AI ​​model uses machine learning algorithms to generate the optimal weight management method and outputs suggestions such as meal plans and exercise plans.

[0125] Step 5:

[0126] The server generates suggestions which are then delivered to the user using home devices. Specifically, a robot explains the suggestions using synthesized speech and a display, and interactively guides the user on how to implement them in their daily life. The user can provide feedback using voice or touch controls.

[0127] Step 6:

[0128] The server selects relevant product information and presents it to the user. Based on the generated weight management method, an algorithm selects information on protein shakes and fitness equipment. This selected information, along with purchase options, is then presented to the user via home devices.

[0129] Step 7:

[0130] The server handles the purchase process for products the user has expressed interest in. Once the user confirms their intention to purchase, the server calls the payment processing system and completes the purchase through a secure transaction. As a result, the user can obtain the selected product.

[0131] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0132] This invention combines an emotional engine with a system that suggests the optimal weight loss method for the user. The user accesses the system via a terminal, inputs physical information, and then uses the emotional engine to provide their current emotional state. This emotional information is obtained through a self-assessment provided by the user on the terminal. The emotional engine analyzes this data to infer the user's emotional state.

[0133] The device sends information, including this data, to the server. The server analyzes the received physical information and emotional state, and utilizes a database of past success stories to suggest the most suitable weight loss method for each individual user. Emotional state is particularly important and serves as an indicator to adjust the content and presentation of the suggestions. For example, if the user is feeling stressed, the server will suggest a weight loss method that incorporates more elements promoting relaxation.

[0134] The proposal includes meal plans, exercise plans, and lifestyle advice, all customized to the user's current emotional state. Furthermore, the server generates encouraging messages and advice to boost motivation based on the user's emotional state.

[0135] Product suggestions are also tailored to the user's emotions. For example, if a user lacks motivation to exercise, easy-to-use fitness equipment will be suggested. The terminal displays optimized suggestions and product information received from the server to the user.

[0136] As a concrete example, consider a case where a user is experiencing high levels of stress during weight loss. The user inputs this stress level through self-assessment, and the emotional engine recognizes that the user is in a stressed state. The server then suggests a plan that includes yoga and meditation to reduce stress, and also offers calming herbal tea as a suggested product. In this way, a detailed approach that takes into account the user's emotional state can be achieved to provide more effective weight loss support.

[0137] The following describes the processing flow.

[0138] Step 1:

[0139] Users input physical information such as height, weight, age, gender, activity level, and allergy information through their device. Furthermore, they input their current emotional state through a self-assessment for the emotion engine. This assessment is performed using scales such as stress, happiness, and fatigue.

[0140] Step 2:

[0141] The device collects the user's physical and emotional information, formats it, and sends it to the server. The transmitted data is encrypted to protect the user's privacy.

[0142] Step 3:

[0143] Based on the information received by the server, it accesses an internal database. The server searches for user-like profiles based on past success stories. This search is performed using statistical analysis and machine learning algorithms.

[0144] Step 4:

[0145] The server analyzes similar data and the user's current emotional state to generate the optimal weight loss method. This method generation includes special customization that reflects the user's emotions. For example, if the user is stressed, relaxation elements will be emphasized.

[0146] Step 5:

[0147] Based on the analysis results, the server creates a document containing specific weight loss suggestions. These suggestions include a meal plan, exercise plan, lifestyle improvement tips, and advice based on your emotional state.

[0148] Step 6:

[0149] The server selects relevant products based on the user's emotional state. The suggested products include items that suit the user's current situation, such as fitness aids to improve motivation or relaxation goods.

[0150] Step 7:

[0151] The terminal displays proposals and product information sent from the server to the user. The user can then use this information to proceed with their weight loss efforts and view options for products that interest them.

[0152] Step 8:

[0153] When a user decides to purchase an item, they send a purchase request to the server via their device. The server then initiates the purchase process and processes the necessary payment information.

[0154] Step 9:

[0155] The server completes the payment and notifies the user of the purchase confirmation. This completes the preparation for shipping the purchased items.

[0156] Step 10:

[0157] The terminal collects feedback from users about their weight loss experiences and products, and sends it to the server. The server analyzes the feedback and stores it in a database to use for future recommendations. This process enables the system to make more accurate recommendations.

[0158] (Example 2)

[0159] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0160] Until now, there has been no system that proposes effective weight loss methods that take into account the emotional state and physical information of individual users. Conventional weight loss support systems have the problem of low success rates due to stress and lack of motivation because they do not reflect the user's emotional state. Furthermore, they have not been able to provide motivation or product recommendations that are tailored to the user's emotional state.

[0161] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0162] In this invention, the server includes means for analyzing the user's emotional state using an emotion engine, means for selecting and presenting product information according to the emotional state to the user, and means for providing the user with encouraging messages generated based on the emotional state. This enables improvement measures that take into account the user's individual emotional state and engagement to motivate them, making more effective weight loss support possible.

[0163] A "user" is an individual or group that uses an information processing system and is the target of suggestions for weight loss methods.

[0164] "Physical information" refers to physiological data such as the user's height, weight, and age, and is basic information necessary for suggesting weight loss methods.

[0165] "Emotional state" refers to the user's current psychological state and emotions, and is information acquired as input based on their own evaluation.

[0166] An "emotion engine" is a software configuration for analyzing a user's emotional state, and it has the function of extracting emotional patterns using machine learning algorithms.

[0167] A "generative AI model" is an artificial intelligence program used for data analysis and proposal generation, and its role is to generate the optimal weight loss method for each individual user.

[0168] "Suggestions" refer to weight loss methods and lifestyle improvement measures provided to users based on the analyzed data.

[0169] A "server" refers to a part of an information processing system that analyzes data received from users via a network and generates and provides suggestions.

[0170] A "terminal" is a device operated by a user, used for inputting information and viewing suggestions.

[0171] A "database" is an information aggregation system that stores data on users' past successes and emotional states.

[0172] "Product information" refers to information about products and services that are suggested to meet the user's needs.

[0173] "Feedback" refers to the evaluations and opinions that users provide regarding the suggested weight loss methods, and these are stored in a database.

[0174] In order to implement this invention, it is necessary to construct an information processing system in which users, terminals, and servers collaborate.

[0175] The user first inputs their physical information and emotional state using a device. This device can be a personal computer, smartphone, or tablet, and must have an easily accessible interface. Emotional state input is provided using sliders or choices to indicate a self-assessed psychological state.

[0176] The terminal sends data acquired from the user to the server. The server is an information processing device with advanced computing capabilities that processes the received data. This server is equipped with an emotion engine and has the function of analyzing the received emotional information using machine learning algorithms. The analysis results must accurately capture the user's emotional state.

[0177] The server uses analyzed emotional state and physical information to apply a generative AI model, proposing weight loss methods optimized for each individual user. This generative model has the ability to generate effective suggestions in real time by referencing a database of past success stories. The suggestions also include generating encouraging messages tailored to the user's emotional state. This helps to maintain and increase the user's motivation for weight loss.

[0178] For example, if a user inputs that they are feeling stressed while on a diet, the server will suggest a relaxation plan that includes yoga and meditation, and recommend products with relaxation effects. An example of a prompt might be, "If the user is currently stressed, please suggest a weight loss plan and products that promote relaxation."

[0179] In this way, the seamless functioning of the entire system makes it possible to provide users with detailed weight loss support tailored to their individual needs.

[0180] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0181] Step 1:

[0182] The user uses the device to input physical information (e.g., height, weight, age, etc.) and emotional state. Emotional state is entered through a self-assessment interface, using sliders and multiple-choice options to select the current psychological state. The entered information is temporarily stored on the device.

[0183] Step 2:

[0184] The terminal transmits physical information and emotional states entered by the user to the server. This transmission is secure using an integrated encryption protocol. As a result, the server receives the user's physical information and emotional data as input data.

[0185] Step 3:

[0186] The server analyzes the received emotional data using an emotion engine. The emotion engine uses machine learning algorithms to analyze emotional patterns and quantify the user's emotional state. The output of this process is the analyzed emotional state information.

[0187] Step 4:

[0188] The server combines analyzed emotional states and physical information, applies a generative AI model, and proposes the optimal weight loss method. It compares this with a database of past success stories to generate personalized meal plans, exercise plans, and lifestyle suggestions for each user. These suggestions are then generated as the server's output.

[0189] Step 5:

[0190] The server generates encouraging messages based on the user's emotional state and selects product information suitable for the user. It then creates feedback, including this information, and sends it to the device.

[0191] Step 6:

[0192] The terminal displays suggestions, encouraging messages, and product information received from the server to the user. Based on the displayed information, the user can consider and implement more specific weight loss actions.

[0193] (Application Example 2)

[0194] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0195] In health management, there is a need to provide customized suggestions that take into account not only physical information but also individual mental states. However, currently, there is a lack of health management suggestions that consider the user's emotional state, which can lead to decreased motivation and difficulty in achieving efficient health management. Furthermore, unique support such as product suggestions or encouraging messages that respond to emotional states is not being provided, making it a challenge to provide more personalized support to users.

[0196] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0197] In this invention, the server includes means for acquiring physical and emotional information from the user, means for transmitting the acquired information to an information processing device, and means for generating and providing a message of encouragement based on the user's emotional information. This enables personalized motivation maintenance and efficient health management for the user by proposing an optimal health management method that takes into account the user's physical information and emotional state, and by providing support that is in line with their emotions.

[0198] A "user" refers to an individual who uses a health management system via a device to input physical and emotional information.

[0199] "Physical information" refers to basic data about the user's health, including information such as weight, height, and activity level.

[0200] "Emotional information" refers to data that users provide through self-assessment, indicating their current mental state.

[0201] An "information processing device" is a key system component that receives, analyzes, and stores data sent by users and generates suggestions.

[0202] A "success story" refers to an example of a health management method that has been effective under similar conditions in the past, and it forms the basis of the data used to generate proposals.

[0203] A "memory device" is a storage device within a system used to store information such as acquired data, generated suggestions, and messages of support.

[0204] "Health management methods" refer to management plans such as diet, exercise, and lifestyle that are proposed based on an analysis of the user's physical and emotional information.

[0205] "Product information" refers to information about products and services related to health management methods suggested to users.

[0206] A "message of support" is a message generated in response to the user's emotional state, intended to provide psychological support and encouragement.

[0207] The system for carrying out this invention comprises a terminal used by the user on a daily basis, a server, and an information processing device linked thereto. The user's terminal is a device such as a smartphone or smart glasses, and provides an interface for inputting physical information and emotional information.

[0208] Physical and emotional information entered by the user through the device is first temporarily stored on the device and then sent to a server in the cloud via the internet. The server has the functionality to receive this data using cloud infrastructure such as Amazon Web Services (AWS®). Furthermore, emotion analysis tools such as Google® Cloud AI are used to analyze the acquired emotional information and identify the user's emotional state.

[0209] The information processing device uses Hitachi's AI framework to search for past success stories from its storage device based on analyzed emotional and physical information. This generates the optimal health management method suited to the user's emotional state.

[0210] The generated health management methods and related product information are sent to and displayed on the user's device. In addition, encouraging messages tailored to the user's emotional state are automatically generated and provided to maintain motivation. For example, a user experiencing stress might be presented with suggestions for relaxation-oriented yoga exercises, along with product information on calming herbal teas.

[0211] As a concrete example, by feeding the AI ​​model a prompt such as, "Generate an appropriate exercise plan and relaxation methods to suggest when the user is feeling stressed. Also, customize the content by taking into account local event information," it is possible to generate a sophisticated health management plan in real time.

[0212] This system allows users to receive personalized health management suggestions, with a particular focus on emotional support.

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] Users input physical information (e.g., weight, height, activity level) and emotional information (e.g., stress levels, self-assessment of joy) using devices such as smartphones or smart glasses. The entered data is temporarily stored on the device.

[0216] Step 2:

[0217] The device transmits stored physical and emotional information to a server. This transmission takes place over the internet, and the data is securely uploaded using Amazon Web Services' cloud service.

[0218] Step 3:

[0219] The server uses Google Cloud AI to process emotional information in preparation for analyzing the received data. Based on the emotional information input, it identifies the user's emotional state (e.g., stress level) and passes this information to the next processing step.

[0220] Step 4:

[0221] Based on the analyzed emotional state and physical information, the server uses Hitachi's AI framework to search its memory for past success stories. It then calculates the optimal health management method based on the user information received as input and outputs this as a suggestion.

[0222] Step 5:

[0223] The server returns the generated health management methods and related product information to the client's terminal. It also generates and simultaneously sends encouraging messages based on the user's emotional state. This output is intended to maintain user motivation.

[0224] Step 6:

[0225] The user's device displays the received health management instructions, product information, and encouraging messages. This allows the user to understand a concrete action plan and move forward to the next step.

[0226] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0227] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0228] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0229] [Second Embodiment]

[0230] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0231] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0232] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0233] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0234] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0235] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0236] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0237] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0238] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0239] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0240] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0241] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0242] This invention is a system that uses AI to analyze and suggest weight loss methods tailored to individual users. Users access the system using a terminal such as a smartphone or computer and input their physical information. This information includes height, weight, age, gender, activity level, and allergy information. The terminal transmits the collected information to the server.

[0243] The server accesses a database to match the user's physical information with similar past success stories. The database contains detailed data on past weight loss successes, which is then analyzed by a machine learning algorithm. The server uses this analysis to generate the most suitable weight loss plan for the user. This plan includes a meal plan, an exercise plan, and lifestyle improvement suggestions.

[0244] Furthermore, the server selects products related to the proposed weight loss method. These selected products may include protein shakes and fitness equipment, designed to support the user in achieving their goals. This product information is then presented to the user.

[0245] When a user purchases a product based on the information provided, the terminal communicates the purchase intention to the server. The server then proceeds with the purchase process and performs the necessary steps for payment.

[0246] As a concrete example, let's consider a 30-year-old male user with a lifestyle that involves exercising twice a week. This user enters his height (170cm) and weight (70kg) into the terminal. The server searches its database for similar profiles and, based on successful weight loss cases, generates suggestions including calorie restriction and jogging three times a week. It also suggests products to encourage the purchase of protein shakes, and if the user shows interest, the server facilitates the purchase process. Through this process, the user receives a personalized weight loss strategy and support for purchasing appropriate products.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] The user enters physical information such as height, weight, age, gender, activity level, and allergy information through the device. The device checks this data and verifies that there are no missing entries or formatting errors.

[0250] Step 2:

[0251] The device converts the user's physical information into an appropriate format and sends it to the server. During this process, the data is encrypted to protect privacy.

[0252] Step 3:

[0253] Based on the physical information received by the server, it accesses an internal database. The server searches the database records to find past success stories similar to the user's information.

[0254] Step 4:

[0255] Based on the acquired similar data, the server uses statistics and machine learning algorithms to analyze the optimal weight loss method for the user. This analysis includes generating appropriate meal plans and exercise plans.

[0256] Step 5:

[0257] Based on the analysis results, the server creates a weight-loss plan proposal for the user. This proposal includes detailed action plans and suggestions for lifestyle improvements.

[0258] Step 6:

[0259] The server selects product information related to weight loss methods from its database and presents it to the user as suggested products. These products include protein shakes and fitness equipment.

[0260] Step 7:

[0261] The terminal displays proposals and product information received from the server to the user. The user reviews the proposals and selects products that interest them.

[0262] Step 8:

[0263] When a user indicates their intention to purchase a product, they send a purchase request to the server via their device. The server then begins processing the purchase.

[0264] Step 9:

[0265] The server receives the purchase request and processes the payment. It sends the necessary information to the payment system and confirms the payment. Once everything is complete, it sends a confirmation notification to the user.

[0266] Step 10:

[0267] The terminal collects suggestions and product feedback from users and sends it back to the server. The server analyzes the feedback and stores it in a database. This information is used to improve the accuracy of future suggestions.

[0268] (Example 1)

[0269] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] There is a need for an information processing system that can efficiently provide weight management methods optimized for individual users, while also enabling seamless selection and purchase of related products. Conventional systems have faced challenges in providing specific and personalized suggestions based on users' physical information, and in the complex process of purchasing related products based on those suggestions.

[0271] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0272] In this invention, the server includes means for analyzing past success stories using an AI model generated based on the user's biometric data, means for analyzing and proposing the optimal weight management method, means for selecting and presenting product information related to the proposal to the user, and means for carrying out the purchase procedure for the product selected by the user. This makes it possible to smoothly execute a series of processes for the user, from proposing a personalized weight management method to selecting and purchasing related products.

[0273] An "information processing device" is a device that has the function of acquiring biometric data from a user and transmitting that data to an information processing server via a network.

[0274] "Biometric data" refers to information related to a user's body, including data such as height, weight, age, gender, activity level, and allergy information.

[0275] An "information processing server" is a server that performs analysis based on biometric data received from an information processing device via a network, generates the optimal weight management method, and further selects related product information.

[0276] An "information storage device" is a storage device that stores data such as past success stories and user evaluations, and allows for retrieval and utilization as needed.

[0277] A "generative AI model" is an artificial intelligence model used to analyze past success stories and propose optimized weight management methods for users.

[0278] A "weight management method" is a method that includes meal plans, exercise plans, and lifestyle improvement suggestions designed based on the user's biometric data.

[0279] "Product information" refers to information about products related to the user's weight management methods, specifically including protein shakes and fitness equipment.

[0280] "Network" refers to a communication infrastructure such as the Internet that enables communication between an information processing device and an information processing server.

[0281] "Payment processing" refers to a series of processes of payment procedures carried out when a user purchases selected goods.

[0282] This invention relates to an information processing system that enables a user to receive an individually optimized weight management method. This system is composed of a terminal used by the user, an information processing server operating on the cloud, and related software programs.

[0283] First, the user uses the terminal to input various body data. This includes, as biometric data, height, weight, age, gender, activity level, and allergy information. The terminal transmits this information as encrypted data to the information processing server via the network.

[0284] Upon receiving this data, the server extracts past successful cases from the information storage device and performs analysis using a generated AI model to generate an optimal management method based on the input user's biometric data. This includes a diet plan, an exercise plan, and improvement proposals regarding lifestyle.

[0285] Furthermore, the server selects products related to the recommended management method and presents them to the user. This includes product information such as protein shakes and fitness equipment that are useful for the user to achieve their weight management goals. When the user purchases these products, the terminal conveys their intention to the server, and the server performs secure and efficient payment processing.

[0286] As a specific example, assume that a 30-year-old male user inputs information such as a height of 170 cm, a weight of 70 kg, and exercise twice a week into the terminal. Based on this information, the server utilizes the generated AI model to prepare a plan that includes calorie restriction and jogging three times a week as the optimal method. Furthermore, a product recommendation for a protein shake is made.

[0287] As an example of a prompt sentence for the generated AI model, a text-form prompt sentence such as "Please propose an optimal weight management method for a user aged 30, male, exercising twice a week, with a height of 170 cm and a weight of 70 kg" is used. This prompt provides an appropriate analysis scheme for the generated AI model and supports the construction of individualized proposals.

[0288] In this way, this invention can provide a practical and individualized weight management solution for users.

[0289] The flow of the specific process in Example 1 will be described using FIG. 11.

[0290] Step 1:

[0291] The user inputs physical information using the terminal. In this step, the user fills in biometric data such as height, weight, age, gender, activity level, and allergy information in the input form. Then, by tapping the send button, the input data is collected. This information becomes the input data for the next step.

[0292] Step 2:

[0293] The terminal sends the input biometric data to the server via the network. In this step, the terminal encrypts the collected data and sends it to the server using a secure communication protocol (e.g., HTTPS). This data becomes the input for the next step.

[0294] Step 3:

[0295] The server analyzes the received biometric data. In this step, the server searches its internal data storage for past success stories and performs data analysis using a generative AI model. The input here is the user's biometric data, and the output is an optimal weight management plan based on the analysis.

[0296] Step 4:

[0297] The server generates and proposes an optimal weight management method to the user. In this step, the server constructs a method that includes a meal plan, exercise plan, and lifestyle improvement suggestions based on the results analyzed by the generative AI model. The output is personalized suggestion information presented to the user.

[0298] Step 5:

[0299] The server selects and presents products relevant to the proposal to the user. In this step, the server selects information on products (e.g., protein shakes or fitness equipment) that fit the generated weight management plan and provides it to the user as a list. The output is product information for the user to refer to.

[0300] Step 6:

[0301] If the user selects a product from the displayed options and wishes to purchase it, they communicate their intention to the server via their device. In this step, the user selects the item they wish to purchase from the product list and issues a purchase instruction through their device.

[0302] Step 7:

[0303] The server handles the purchase process. In this step, the server processes the payment for the selected items and performs the necessary authentication. This includes communication with the payment gateway and processing of payment information. The final output is confirmation that the purchase process is complete.

[0304] (Application Example 1)

[0305] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0306] In order to provide an optimal weight management method for each individual user, personalized proposals based on various physical information and lifestyle habits are required. However, in conventional systems, these pieces of information cannot be fully utilized, and it has been difficult to provide guidelines for weight loss methods and lifestyle improvement optimized for each individual user. Furthermore, there is a problem that continuous support for practicing the proposed methods in daily life is lacking.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0308] In this invention, the server includes means for acquiring information related to the user's body from the user, means for the information processing device to search for similar past success cases from a recording medium based on the information related to the user's body, and means for interactively providing guidance regarding exercise and diet to the user through household devices. Thereby, it becomes possible to provide a personalized weight management method to the user and support its practice in daily life.

[0309] A "user" is an individual who provides physical information using the system and receives a personalized weight management method.

[0310] "Information related to the body" refers to health-related data such as the user's height, weight, age, gender, activity level, allergy information, etc.

[0311] An "information processing device" is a server or computer system that receives the user's physical information, searches a database, and performs analysis.

[0312] A "recording medium" refers to a database or storage device that stores past success case data and the like.

[0313] A "weight management method" is an approach that includes meal plans, exercise plans, and lifestyle improvement suggestions generated based on the user's physical information.

[0314] "Home-use devices" refer to robots and smart devices used in the user's living space, and are used to provide guidance for weight management.

[0315] "Product information" refers to data on related products such as protein shakes and fitness equipment that are suggested to assist with weight management.

[0316] The system for implementing this invention aims to acquire information about the user's body and, based on that information, provide an optimal weight management method. The user inputs their body information through a home-use device such as a robot or smart device. This information is sent to a server, which accesses a recording medium to search for similar past success stories. Based on the information from the recording medium, the server performs data analysis using a generative AI model to generate an optimal weight management method for the user. This includes a meal plan, an exercise plan, and lifestyle improvement suggestions.

[0317] The generated suggestions are delivered interactively to the user via home devices. For example, a robot might encourage regular exercise and present product information as needed. If the user expresses interest, the purchase process is initiated through a server. This system allows users to receive a personalized health management approach on the spot, providing continuous support for a healthy lifestyle.

[0318] In this embodiment, the information processing device (server) uses software such as Python or TensorFlow to perform database searches and analysis using AI models. As a home device, the robot is equipped with voice recognition and a touch interface, enabling two-way communication with the user.

[0319] For example, if a 40-year-old female user enters information such as height 160cm and weight 65kg, the server searches the database, and a generating AI model performs the analysis. This results in a suitable vegetarian meal plan and suggestions for two yoga exercises per week. Additionally, product information on organic supplements is presented, and if the user is interested, they can proceed with the purchase.

[0320] Example input prompts for a generative AI model:

[0321] "Use AI to propose the optimal weight loss plan based on the user's height, weight, age, gender, and activity level."

[0322] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0323] Step 1:

[0324] The device collects physical information from the user. The user inputs height, weight, age, gender, activity level, allergy information, etc., through voice recognition or a touch interface. This information is collected as raw data.

[0325] Step 2:

[0326] The device sends the collected physical information to the server. The device sends the data to the server using a secure communication protocol, and the server receives this data and converts its format into an analyzable data format.

[0327] Step 3:

[0328] The server searches the database based on the received physical information. Using an index search algorithm, the server matches similar past successes within the storage medium and extracts relevant entries. This process allows the server to obtain the historical data necessary for analysis.

[0329] Step 4:

[0330] The server performs data analysis using a generated AI model. Similar past cases and the user's physical information are provided as input. The AI ​​model uses machine learning algorithms to generate the optimal weight management method and outputs suggestions such as meal plans and exercise plans.

[0331] Step 5:

[0332] The server generates suggestions which are then delivered to the user using home devices. Specifically, a robot explains the suggestions using synthesized speech and a display, and interactively guides the user on how to implement them in their daily life. The user can provide feedback using voice or touch controls.

[0333] Step 6:

[0334] The server selects relevant product information and presents it to the user. Based on the generated weight management method, an algorithm selects information on protein shakes and fitness equipment. This selected information, along with purchase options, is then presented to the user via home devices.

[0335] Step 7:

[0336] The server handles the purchase process for products the user has expressed interest in. Once the user confirms their intention to purchase, the server calls the payment processing system and completes the purchase through a secure transaction. As a result, the user can obtain the selected product.

[0337] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0338] This invention combines an emotional engine with a system that suggests the optimal weight loss method for the user. The user accesses the system via a terminal, inputs physical information, and then uses the emotional engine to provide their current emotional state. This emotional information is obtained through a self-assessment provided by the user on the terminal. The emotional engine analyzes this data to infer the user's emotional state.

[0339] The device sends information, including this data, to the server. The server analyzes the received physical information and emotional state, and utilizes a database of past success stories to suggest the most suitable weight loss method for each individual user. Emotional state is particularly important and serves as an indicator to adjust the content and presentation of the suggestions. For example, if the user is feeling stressed, the server will suggest a weight loss method that incorporates more elements promoting relaxation.

[0340] The proposal includes meal plans, exercise plans, and lifestyle advice, all customized to the user's current emotional state. Furthermore, the server generates encouraging messages and advice to boost motivation based on the user's emotional state.

[0341] Product suggestions are also tailored to the user's emotions. For example, if a user lacks motivation to exercise, easy-to-use fitness equipment will be suggested. The terminal displays optimized suggestions and product information received from the server to the user.

[0342] As a concrete example, consider a case where a user is experiencing high levels of stress during weight loss. The user inputs this stress level through self-assessment, and the emotional engine recognizes that the user is in a stressed state. The server then suggests a plan that includes yoga and meditation to reduce stress, and also offers calming herbal tea as a suggested product. In this way, a detailed approach that takes into account the user's emotional state can be achieved to provide more effective weight loss support.

[0343] The following describes the processing flow.

[0344] Step 1:

[0345] Users input physical information such as height, weight, age, gender, activity level, and allergy information through their device. Furthermore, they input their current emotional state through a self-assessment for the emotion engine. This assessment is performed using scales such as stress, happiness, and fatigue.

[0346] Step 2:

[0347] The device collects the user's physical and emotional information, formats it, and sends it to the server. The transmitted data is encrypted to protect the user's privacy.

[0348] Step 3:

[0349] Based on the information received by the server, it accesses an internal database. The server searches for user-like profiles based on past success stories. This search is performed using statistical analysis and machine learning algorithms.

[0350] Step 4:

[0351] The server analyzes similar data and the user's current emotional state to generate the optimal weight loss method. This method generation includes special customization that reflects the user's emotions. For example, if the user is stressed, relaxation elements will be emphasized.

[0352] Step 5:

[0353] Based on the analysis results, the server creates a document containing specific weight loss suggestions. These suggestions include a meal plan, exercise plan, lifestyle improvement tips, and advice based on your emotional state.

[0354] Step 6:

[0355] The server selects relevant products based on the user's emotional state. The suggested products include items that suit the user's current situation, such as fitness aids to improve motivation or relaxation goods.

[0356] Step 7:

[0357] The terminal displays proposals and product information sent from the server to the user. The user can then use this information to proceed with their weight loss efforts and view options for products that interest them.

[0358] Step 8:

[0359] When a user decides to purchase an item, they send a purchase request to the server via their device. The server then initiates the purchase process and processes the necessary payment information.

[0360] Step 9:

[0361] The server completes the payment and notifies the user of the purchase confirmation. This completes the preparation for shipping the purchased items.

[0362] Step 10:

[0363] The terminal collects feedback from users about their weight loss experiences and products, and sends it to the server. The server analyzes the feedback and stores it in a database to use for future recommendations. This process enables the system to make more accurate recommendations.

[0364] (Example 2)

[0365] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0366] Until now, there has been no system that proposes effective weight loss methods that take into account the emotional state and physical information of individual users. Conventional weight loss support systems have the problem of low success rates due to stress and lack of motivation because they do not reflect the user's emotional state. Furthermore, they have not been able to provide motivation or product recommendations that are tailored to the user's emotional state.

[0367] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0368] In this invention, the server includes means for analyzing the user's emotional state using an emotion engine, means for selecting and presenting product information according to the emotional state to the user, and means for providing the user with encouraging messages generated based on the emotional state. This enables improvement measures that take into account the user's individual emotional state and engagement to motivate them, making more effective weight loss support possible.

[0369] A "user" is an individual or group that uses an information processing system and is the target of suggestions for weight loss methods.

[0370] "Physical information" refers to physiological data such as the user's height, weight, and age, and is basic information necessary for suggesting weight loss methods.

[0371] "Emotional state" refers to the user's current psychological state and emotions, and is information acquired as input based on their own evaluation.

[0372] An "emotion engine" is a software configuration for analyzing a user's emotional state, and it has the function of extracting emotional patterns using machine learning algorithms.

[0373] A "generative AI model" is an artificial intelligence program used for data analysis and proposal generation, and its role is to generate the optimal weight loss method for each individual user.

[0374] "Suggestions" refer to weight loss methods and lifestyle improvement measures provided to users based on the analyzed data.

[0375] A "server" refers to a part of an information processing system that analyzes data received from users via a network and generates and provides suggestions.

[0376] A "terminal" is a device operated by a user, used for inputting information and viewing suggestions.

[0377] A "database" is an information aggregation system that stores data on users' past successes and emotional states.

[0378] "Product information" refers to information about products and services that are suggested to meet the user's needs.

[0379] "Feedback" refers to the evaluations and opinions that users provide regarding the suggested weight loss methods, and these are stored in a database.

[0380] In order to implement this invention, it is necessary to construct an information processing system in which users, terminals, and servers collaborate.

[0381] The user first inputs their physical information and emotional state using a device. This device can be a personal computer, smartphone, or tablet, and must have an easily accessible interface. Emotional state input is provided using sliders or choices to indicate a self-assessed psychological state.

[0382] The terminal sends data acquired from the user to the server. The server is an information processing device with advanced computing capabilities that processes the received data. This server is equipped with an emotion engine and has the function of analyzing the received emotional information using machine learning algorithms. The analysis results must accurately capture the user's emotional state.

[0383] The server uses analyzed emotional state and physical information to apply a generative AI model, proposing weight loss methods optimized for each individual user. This generative model has the ability to generate effective suggestions in real time by referencing a database of past success stories. The suggestions also include generating encouraging messages tailored to the user's emotional state. This helps to maintain and increase the user's motivation for weight loss.

[0384] For example, if a user inputs that they are feeling stressed while on a diet, the server will suggest a relaxation plan that includes yoga and meditation, and recommend products with relaxation effects. An example of a prompt might be, "If the user is currently stressed, please suggest a weight loss plan and products that promote relaxation."

[0385] In this way, the seamless functioning of the entire system makes it possible to provide users with detailed weight loss support tailored to their individual needs.

[0386] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0387] Step 1:

[0388] The user uses the device to input physical information (e.g., height, weight, age, etc.) and emotional state. Emotional state is entered through a self-assessment interface, using sliders and multiple-choice options to select the current psychological state. The entered information is temporarily stored on the device.

[0389] Step 2:

[0390] The terminal transmits physical information and emotional states entered by the user to the server. This transmission is secure using an integrated encryption protocol. As a result, the server receives the user's physical information and emotional data as input data.

[0391] Step 3:

[0392] The server analyzes the received emotional data using an emotion engine. The emotion engine uses machine learning algorithms to analyze emotional patterns and quantify the user's emotional state. The output of this process is the analyzed emotional state information.

[0393] Step 4:

[0394] The server combines analyzed emotional states and physical information, applies a generative AI model, and proposes the optimal weight loss method. It compares this with a database of past success stories to generate personalized meal plans, exercise plans, and lifestyle suggestions for each user. These suggestions are then generated as the server's output.

[0395] Step 5:

[0396] The server generates encouraging messages based on the user's emotional state and selects product information suitable for the user. It then creates feedback, including this information, and sends it to the device.

[0397] Step 6:

[0398] The terminal displays suggestions, encouraging messages, and product information received from the server to the user. Based on the displayed information, the user can consider and implement more specific weight loss actions.

[0399] (Application Example 2)

[0400] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0401] In health management, there is a need to provide customized suggestions that take into account not only physical information but also individual mental states. However, currently, there is a lack of health management suggestions that consider the user's emotional state, which can lead to decreased motivation and difficulty in achieving efficient health management. Furthermore, unique support such as product suggestions or encouraging messages that respond to emotional states is not being provided, making it a challenge to provide more personalized support to users.

[0402] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0403] In this invention, the server includes means for acquiring physical and emotional information from the user, means for transmitting the acquired information to an information processing device, and means for generating and providing a message of encouragement based on the user's emotional information. This enables personalized motivation maintenance and efficient health management for the user by proposing an optimal health management method that takes into account the user's physical information and emotional state, and by providing support that is in line with their emotions.

[0404] A "user" refers to an individual who uses a health management system via a device to input physical and emotional information.

[0405] "Physical information" refers to basic data about the user's health, including information such as weight, height, and activity level.

[0406] "Emotional information" refers to data that users provide through self-assessment, indicating their current mental state.

[0407] An "information processing device" is a key system component that receives, analyzes, and stores data sent by users and generates suggestions.

[0408] A "success story" refers to an example of a health management method that has been effective under similar conditions in the past, and it forms the basis of the data used to generate proposals.

[0409] A "memory device" is a storage device within a system used to store information such as acquired data, generated suggestions, and messages of support.

[0410] "Health management methods" refer to management plans such as diet, exercise, and lifestyle that are proposed based on an analysis of the user's physical and emotional information.

[0411] "Product information" refers to information about products and services related to health management methods suggested to users.

[0412] A "message of support" is a message generated in response to the user's emotional state, intended to provide psychological support and encouragement.

[0413] The system for carrying out this invention comprises a terminal used by the user on a daily basis, a server, and an information processing device linked thereto. The user's terminal is a device such as a smartphone or smart glasses, and provides an interface for inputting physical information and emotional information.

[0414] Physical and emotional information entered by the user through the device is first temporarily stored on the device and then sent to a server in the cloud via the internet. The server has the functionality to receive this data using cloud infrastructure such as Amazon Web Services (AWS). It also uses emotion analysis tools such as Google Cloud AI to analyze the acquired emotional information and identify the user's emotional state.

[0415] The information processing device uses Hitachi's AI framework to search for past success stories from its storage device based on analyzed emotional and physical information. This generates the optimal health management method suited to the user's emotional state.

[0416] The generated health management methods and related product information are sent to and displayed on the user's device. In addition, encouraging messages tailored to the user's emotional state are automatically generated and provided to maintain motivation. For example, a user experiencing stress might be presented with suggestions for relaxation-oriented yoga exercises, along with product information on calming herbal teas.

[0417] As a concrete example, by feeding the AI ​​model a prompt such as, "Generate an appropriate exercise plan and relaxation methods to suggest when the user is feeling stressed. Also, customize the content by taking into account local event information," it is possible to generate a sophisticated health management plan in real time.

[0418] This system allows users to receive personalized health management suggestions, with a particular focus on emotional support.

[0419] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0420] Step 1:

[0421] Users input physical information (e.g., weight, height, activity level) and emotional information (e.g., stress levels, self-assessment of joy) using devices such as smartphones or smart glasses. The entered data is temporarily stored on the device.

[0422] Step 2:

[0423] The device transmits stored physical and emotional information to a server. This transmission takes place over the internet, and the data is securely uploaded using Amazon Web Services' cloud service.

[0424] Step 3:

[0425] The server uses Google Cloud AI to process emotional information in preparation for analyzing the received data. Based on the emotional information input, it identifies the user's emotional state (e.g., stress level) and passes this information to the next processing step.

[0426] Step 4:

[0427] Based on the analyzed emotional state and physical information, the server uses Hitachi's AI framework to search its memory for past success stories. It then calculates the optimal health management method based on the user information received as input and outputs this as a suggestion.

[0428] Step 5:

[0429] The server returns the generated health management methods and related product information to the client's terminal. It also generates and simultaneously sends encouraging messages based on the user's emotional state. This output is intended to maintain user motivation.

[0430] Step 6:

[0431] The user's device displays the received health management instructions, product information, and encouraging messages. This allows the user to understand a concrete action plan and move forward to the next step.

[0432] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0433] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0435] [Third Embodiment]

[0436] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0437] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0438] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0439] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0440] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0441] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0442] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0443] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0444] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0445] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0446] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0447] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0448] This invention is a system that uses AI to analyze and suggest weight loss methods tailored to individual users. Users access the system using a terminal such as a smartphone or computer and input their physical information. This information includes height, weight, age, gender, activity level, and allergy information. The terminal transmits the collected information to the server.

[0449] The server accesses a database to match the user's physical information with similar past success stories. The database contains detailed data on past weight loss successes, which is then analyzed by a machine learning algorithm. The server uses this analysis to generate the most suitable weight loss plan for the user. This plan includes a meal plan, an exercise plan, and lifestyle improvement suggestions.

[0450] Furthermore, the server selects products related to the proposed weight loss method. These selected products may include protein shakes and fitness equipment, designed to support the user in achieving their goals. This product information is then presented to the user.

[0451] When a user purchases a product based on the information provided, the terminal communicates the purchase intention to the server. The server then proceeds with the purchase process and performs the necessary steps for payment.

[0452] As a concrete example, let's consider a 30-year-old male user with a lifestyle that involves exercising twice a week. This user enters his height (170cm) and weight (70kg) into the terminal. The server searches its database for similar profiles and, based on successful weight loss cases, generates suggestions including calorie restriction and jogging three times a week. It also suggests products to encourage the purchase of protein shakes, and if the user shows interest, the server facilitates the purchase process. Through this process, the user receives a personalized weight loss strategy and support for purchasing appropriate products.

[0453] The following describes the processing flow.

[0454] Step 1:

[0455] The user enters physical information such as height, weight, age, gender, activity level, and allergy information through the device. The device checks this data and verifies that there are no missing entries or formatting errors.

[0456] Step 2:

[0457] The device converts the user's physical information into an appropriate format and sends it to the server. During this process, the data is encrypted to protect privacy.

[0458] Step 3:

[0459] Based on the physical information received by the server, it accesses an internal database. The server searches the database records to find past success stories similar to the user's information.

[0460] Step 4:

[0461] Based on the acquired similar data, the server uses statistics and machine learning algorithms to analyze the optimal weight loss method for the user. This analysis includes generating appropriate meal plans and exercise plans.

[0462] Step 5:

[0463] Based on the analysis results, the server creates a weight-loss plan proposal for the user. This proposal includes detailed action plans and suggestions for lifestyle improvements.

[0464] Step 6:

[0465] The server selects product information related to weight loss methods from its database and presents it to the user as suggested products. These products include protein shakes and fitness equipment.

[0466] Step 7:

[0467] The terminal displays proposals and product information received from the server to the user. The user reviews the proposals and selects products that interest them.

[0468] Step 8:

[0469] When a user indicates their intention to purchase a product, they send a purchase request to the server via their device. The server then begins processing the purchase.

[0470] Step 9:

[0471] The server receives the purchase request and processes the payment. It sends the necessary information to the payment system and confirms the payment. Once everything is complete, it sends a confirmation notification to the user.

[0472] Step 10:

[0473] The terminal collects suggestions and product feedback from users and sends it back to the server. The server analyzes the feedback and stores it in a database. This information is used to improve the accuracy of future suggestions.

[0474] (Example 1)

[0475] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0476] There is a need for an information processing system that can efficiently provide weight management methods optimized for individual users, while also enabling seamless selection and purchase of related products. Conventional systems have faced challenges in providing specific and personalized suggestions based on users' physical information, and in the complex process of purchasing related products based on those suggestions.

[0477] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0478] In this invention, the server includes means for analyzing past success stories using an AI model generated based on the user's biometric data, means for analyzing and proposing the optimal weight management method, means for selecting and presenting product information related to the proposal to the user, and means for carrying out the purchase procedure for the product selected by the user. This makes it possible to smoothly execute a series of processes for the user, from proposing a personalized weight management method to selecting and purchasing related products.

[0479] An "information processing device" is a device that has the function of acquiring biometric data from a user and transmitting that data to an information processing server via a network.

[0480] "Biometric data" refers to information related to a user's body, including data such as height, weight, age, gender, activity level, and allergy information.

[0481] An "information processing server" is a server that performs analysis based on biometric data received from an information processing device via a network, generates the optimal weight management method, and further selects related product information.

[0482] An "information storage device" is a storage device that stores data such as past success stories and user evaluations, and allows for retrieval and utilization as needed.

[0483] A "generative AI model" is an artificial intelligence model used to analyze past success stories and propose optimized weight management methods for users.

[0484] A "weight management method" is a method that includes meal plans, exercise plans, and lifestyle improvement suggestions designed based on the user's biometric data.

[0485] "Product information" refers to information about products related to the user's weight management methods, specifically including protein shakes and fitness equipment.

[0486] "Network" refers to communication infrastructure such as the internet that enables communication between information processing devices and information processing servers.

[0487] "Payment processing" refers to the series of procedures involved in the payment process when a user purchases a product they have selected.

[0488] This invention relates to an information processing system that allows users to receive a weight management method optimized for their individual needs. The system consists of a terminal used by the user, an information processing server operating on the cloud, and related software programs.

[0489] First, the user uses a device to input various physical data. This includes biometric data such as height, weight, age, gender, activity level, and allergy information. The device then transmits this information as encrypted data to an information processing server via the network.

[0490] Upon receiving this data, the server extracts past success stories from its information storage device and analyzes them using a generative AI model to generate an optimal management method based on the user's biometric data. This includes meal plans, exercise plans, and lifestyle improvement suggestions.

[0491] Furthermore, the server selects and presents products related to recommended management methods to the user. This includes product information that can help the user achieve their weight management goals, such as protein shakes and fitness equipment. If the user wishes to purchase these products, the terminal communicates this intention to the server, which then processes the payment securely and efficiently.

[0492] As a concrete example, let's assume a 30-year-old male user inputs information into the device such as his height (170cm), weight (70kg), and exercise habits (twice a week). Based on this information, the server uses a generative AI model to create a plan that includes calorie restriction and jogging three times a week as the optimal approach. Furthermore, it suggests products such as protein shakes.

[0493] As an example of a prompt for a generative AI model, we will use a text-based prompt that reads, "Please suggest the best weight management method for a 30-year-old male user who exercises twice a week, is 170cm tall, and weighs 70kg." This prompt provides the generative AI model with an appropriate analysis scheme and supports the construction of personalized suggestions.

[0494] In this way, this invention makes it possible to provide users with a practical and personalized weight management solution.

[0495] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0496] Step 1:

[0497] The user enters their physical information using their device. In this step, the user fills in biometric data such as height, weight, age, gender, activity level, and allergy information in an input form. After tapping the submit button, the entered data is collected. This information becomes the input data for the next step.

[0498] Step 2:

[0499] The device transmits the entered biometric data to the server via the network. In this step, the device encrypts the collected data and sends it to the server using a secure communication protocol (e.g., HTTPS). This data serves as input for the next step.

[0500] Step 3:

[0501] The server analyzes the received biometric data. In this step, the server searches its internal data storage for past success stories and performs data analysis using a generative AI model. The input here is the user's biometric data, and the output is an optimal weight management plan based on the analysis.

[0502] Step 4:

[0503] The server generates and proposes an optimal weight management method to the user. In this step, the server constructs a method that includes a meal plan, exercise plan, and lifestyle improvement suggestions based on the results analyzed by the generative AI model. The output is personalized suggestion information presented to the user.

[0504] Step 5:

[0505] The server selects and presents products relevant to the proposal to the user. In this step, the server selects information on products (e.g., protein shakes or fitness equipment) that fit the generated weight management plan and provides it to the user as a list. The output is product information for the user to refer to.

[0506] Step 6:

[0507] If the user selects a product from the displayed options and wishes to purchase it, they communicate their intention to the server via their device. In this step, the user selects the item they wish to purchase from the product list and issues a purchase instruction through their device.

[0508] Step 7:

[0509] The server handles the purchase process. In this step, the server processes the payment for the selected items and performs the necessary authentication. This includes communication with the payment gateway and processing of payment information. The final output is confirmation that the purchase process is complete.

[0510] (Application Example 1)

[0511] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0512] Providing individual users with the most suitable weight management methods requires personalized suggestions based on diverse physical information and lifestyle habits. However, conventional systems have been unable to fully utilize this information, making it difficult to provide weight loss methods and lifestyle improvement guidelines optimized for each user. Furthermore, there is a problem of insufficient continuous support to help users implement the suggested methods in their daily lives.

[0513] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0514] In this invention, the server includes means for acquiring information about the user's body, means for an information processing device to search a recording medium for similar past success stories based on the user's information about their body, and means for interactively providing the user with exercise and dietary guidance through a home device. This makes it possible to provide the user with a personalized weight management method and support its practice in daily life.

[0515] A "user" is an individual who uses the system to provide physical information and receive personalized weight management methods.

[0516] "Physical information" refers to health-related data such as the user's height, weight, age, gender, activity level, and allergy information.

[0517] An "information processing device" is a server or computer system that receives a user's physical information, searches a database, and performs analysis.

[0518] "Recording medium" refers to databases and storage devices that store data on past success stories and other similar information.

[0519] A "weight management method" is an approach that includes meal plans, exercise plans, and lifestyle improvement suggestions generated based on the user's physical information.

[0520] "Home-use devices" refer to robots and smart devices used in the user's living space, and are used to provide guidance for weight management.

[0521] "Product information" refers to data on related products such as protein shakes and fitness equipment that are suggested to assist with weight management.

[0522] The system for implementing this invention aims to acquire information about the user's body and, based on that information, provide an optimal weight management method. The user inputs their body information through a home-use device such as a robot or smart device. This information is sent to a server, which accesses a recording medium to search for similar past success stories. Based on the information from the recording medium, the server performs data analysis using a generative AI model to generate an optimal weight management method for the user. This includes a meal plan, an exercise plan, and lifestyle improvement suggestions.

[0523] The generated suggestions are delivered interactively to the user via home devices. For example, a robot might encourage regular exercise and present product information as needed. If the user expresses interest, the purchase process is initiated through a server. This system allows users to receive a personalized health management approach on the spot, providing continuous support for a healthy lifestyle.

[0524] In this embodiment, the information processing device (server) uses software such as Python or TensorFlow to perform database searches and analysis using AI models. As a home device, the robot is equipped with voice recognition and a touch interface, enabling two-way communication with the user.

[0525] For example, if a 40-year-old female user enters information such as height 160cm and weight 65kg, the server searches the database, and a generating AI model performs the analysis. This results in a suitable vegetarian meal plan and suggestions for two yoga exercises per week. Additionally, product information on organic supplements is presented, and if the user is interested, they can proceed with the purchase.

[0526] Example input prompts for a generative AI model:

[0527] "Use AI to propose the optimal weight loss plan based on the user's height, weight, age, gender, and activity level."

[0528] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0529] Step 1:

[0530] The device collects physical information from the user. The user inputs height, weight, age, gender, activity level, allergy information, etc., through voice recognition or a touch interface. This information is collected as raw data.

[0531] Step 2:

[0532] The device sends the collected physical information to the server. The device sends the data to the server using a secure communication protocol, and the server receives this data and converts its format into an analyzable data format.

[0533] Step 3:

[0534] The server searches the database based on the received physical information. Using an index search algorithm, the server matches similar past successes within the storage medium and extracts relevant entries. This process allows the server to obtain the historical data necessary for analysis.

[0535] Step 4:

[0536] The server performs data analysis using a generated AI model. Similar past cases and the user's physical information are provided as input. The AI ​​model uses machine learning algorithms to generate the optimal weight management method and outputs suggestions such as meal plans and exercise plans.

[0537] Step 5:

[0538] The server generates suggestions which are then delivered to the user using home devices. Specifically, a robot explains the suggestions using synthesized speech and a display, and interactively guides the user on how to implement them in their daily life. The user can provide feedback using voice or touch controls.

[0539] Step 6:

[0540] The server selects relevant product information and presents it to the user. Based on the generated weight management method, an algorithm selects information on protein shakes and fitness equipment. This selected information, along with purchase options, is then presented to the user via home devices.

[0541] Step 7:

[0542] The server handles the purchase process for products the user has expressed interest in. Once the user confirms their intention to purchase, the server calls the payment processing system and completes the purchase through a secure transaction. As a result, the user can obtain the selected product.

[0543] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0544] This invention combines an emotional engine with a system that suggests the optimal weight loss method for the user. The user accesses the system via a terminal, inputs physical information, and then uses the emotional engine to provide their current emotional state. This emotional information is obtained through a self-assessment provided by the user on the terminal. The emotional engine analyzes this data to infer the user's emotional state.

[0545] The device sends information, including this data, to the server. The server analyzes the received physical information and emotional state, and utilizes a database of past success stories to suggest the most suitable weight loss method for each individual user. Emotional state is particularly important and serves as an indicator to adjust the content and presentation of the suggestions. For example, if the user is feeling stressed, the server will suggest a weight loss method that incorporates more elements promoting relaxation.

[0546] The proposal includes meal plans, exercise plans, and lifestyle advice, all customized to the user's current emotional state. Furthermore, the server generates encouraging messages and advice to boost motivation based on the user's emotional state.

[0547] Product suggestions are also tailored to the user's emotions. For example, if a user lacks motivation to exercise, easy-to-use fitness equipment will be suggested. The terminal displays optimized suggestions and product information received from the server to the user.

[0548] As a concrete example, consider a case where a user is experiencing high levels of stress during weight loss. The user inputs this stress level through self-assessment, and the emotional engine recognizes that the user is in a stressed state. The server then suggests a plan that includes yoga and meditation to reduce stress, and also offers calming herbal tea as a suggested product. In this way, a detailed approach that takes into account the user's emotional state can be achieved to provide more effective weight loss support.

[0549] The following describes the processing flow.

[0550] Step 1:

[0551] Users input physical information such as height, weight, age, gender, activity level, and allergy information through their device. Furthermore, they input their current emotional state through a self-assessment for the emotion engine. This assessment is performed using scales such as stress, happiness, and fatigue.

[0552] Step 2:

[0553] The device collects the user's physical and emotional information, formats it, and sends it to the server. The transmitted data is encrypted to protect the user's privacy.

[0554] Step 3:

[0555] Based on the information received by the server, it accesses an internal database. The server searches for user-like profiles based on past success stories. This search is performed using statistical analysis and machine learning algorithms.

[0556] Step 4:

[0557] The server analyzes similar data and the user's current emotional state to generate the optimal weight loss method. This method generation includes special customization that reflects the user's emotions. For example, if the user is stressed, relaxation elements will be emphasized.

[0558] Step 5:

[0559] Based on the analysis results, the server creates a document containing specific weight loss suggestions. These suggestions include a meal plan, exercise plan, lifestyle improvement tips, and advice based on your emotional state.

[0560] Step 6:

[0561] The server selects relevant products based on the user's emotional state. The suggested products include items that suit the user's current situation, such as fitness aids to improve motivation or relaxation goods.

[0562] Step 7:

[0563] The terminal displays proposals and product information sent from the server to the user. The user can then use this information to proceed with their weight loss efforts and view options for products that interest them.

[0564] Step 8:

[0565] When a user decides to purchase an item, they send a purchase request to the server via their device. The server then initiates the purchase process and processes the necessary payment information.

[0566] Step 9:

[0567] The server completes the payment and notifies the user of the purchase confirmation. This completes the preparation for shipping the purchased items.

[0568] Step 10:

[0569] The terminal collects feedback from users about their weight loss experiences and products, and sends it to the server. The server analyzes the feedback and stores it in a database to use for future recommendations. This process enables the system to make more accurate recommendations.

[0570] (Example 2)

[0571] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0572] Until now, there has been no system that proposes effective weight loss methods that take into account the emotional state and physical information of individual users. Conventional weight loss support systems have the problem of low success rates due to stress and lack of motivation because they do not reflect the user's emotional state. Furthermore, they have not been able to provide motivation or product recommendations that are tailored to the user's emotional state.

[0573] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0574] In this invention, the server includes means for analyzing the user's emotional state using an emotion engine, means for selecting and presenting product information according to the emotional state to the user, and means for providing the user with encouraging messages generated based on the emotional state. This enables improvement measures that take into account the user's individual emotional state and engagement to motivate them, making more effective weight loss support possible.

[0575] A "user" is an individual or group that uses an information processing system and is the target of suggestions for weight loss methods.

[0576] "Physical information" refers to physiological data such as the user's height, weight, and age, and is basic information necessary for suggesting weight loss methods.

[0577] "Emotional state" refers to the user's current psychological state and emotions, and is information acquired as input based on their own evaluation.

[0578] An "emotion engine" is a software configuration for analyzing a user's emotional state, and it has the function of extracting emotional patterns using machine learning algorithms.

[0579] A "generative AI model" is an artificial intelligence program used for data analysis and proposal generation, and its role is to generate the optimal weight loss method for each individual user.

[0580] "Suggestions" refer to weight loss methods and lifestyle improvement measures provided to users based on the analyzed data.

[0581] A "server" refers to a part of an information processing system that analyzes data received from users via a network and generates and provides suggestions.

[0582] A "terminal" is a device operated by a user, used for inputting information and viewing suggestions.

[0583] A "database" is an information aggregation system that stores data on users' past successes and emotional states.

[0584] "Product information" refers to information about products and services that are suggested to meet the user's needs.

[0585] "Feedback" refers to the evaluations and opinions that users provide regarding the suggested weight loss methods, and these are stored in a database.

[0586] In order to implement this invention, it is necessary to construct an information processing system in which users, terminals, and servers collaborate.

[0587] The user first inputs their physical information and emotional state using a device. This device can be a personal computer, smartphone, or tablet, and must have an easily accessible interface. Emotional state input is provided using sliders or choices to indicate a self-assessed psychological state.

[0588] The terminal sends data acquired from the user to the server. The server is an information processing device with advanced computing capabilities that processes the received data. This server is equipped with an emotion engine and has the function of analyzing the received emotional information using machine learning algorithms. The analysis results must accurately capture the user's emotional state.

[0589] The server uses analyzed emotional state and physical information to apply a generative AI model, proposing weight loss methods optimized for each individual user. This generative model has the ability to generate effective suggestions in real time by referencing a database of past success stories. The suggestions also include generating encouraging messages tailored to the user's emotional state. This helps to maintain and increase the user's motivation for weight loss.

[0590] For example, if a user inputs that they are feeling stressed while on a diet, the server will suggest a relaxation plan that includes yoga and meditation, and recommend products with relaxation effects. An example of a prompt might be, "If the user is currently stressed, please suggest a weight loss plan and products that promote relaxation."

[0591] In this way, the seamless functioning of the entire system makes it possible to provide users with detailed weight loss support tailored to their individual needs.

[0592] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0593] Step 1:

[0594] The user uses the device to input physical information (e.g., height, weight, age, etc.) and emotional state. Emotional state is entered through a self-assessment interface, using sliders and multiple-choice options to select the current psychological state. The entered information is temporarily stored on the device.

[0595] Step 2:

[0596] The terminal transmits physical information and emotional states entered by the user to the server. This transmission is secure using an integrated encryption protocol. As a result, the server receives the user's physical information and emotional data as input data.

[0597] Step 3:

[0598] The server analyzes the received emotional data using an emotion engine. The emotion engine uses machine learning algorithms to analyze emotional patterns and quantify the user's emotional state. The output of this process is the analyzed emotional state information.

[0599] Step 4:

[0600] The server combines analyzed emotional states and physical information, applies a generative AI model, and proposes the optimal weight loss method. It compares this with a database of past success stories to generate personalized meal plans, exercise plans, and lifestyle suggestions for each user. These suggestions are then generated as the server's output.

[0601] Step 5:

[0602] The server generates encouraging messages based on the user's emotional state and selects product information suitable for the user. It then creates feedback, including this information, and sends it to the device.

[0603] Step 6:

[0604] The terminal displays suggestions, encouraging messages, and product information received from the server to the user. Based on the displayed information, the user can consider and implement more specific weight loss actions.

[0605] (Application Example 2)

[0606] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0607] In health management, there is a need to provide customized suggestions that take into account not only physical information but also individual mental states. However, currently, there is a lack of health management suggestions that consider the user's emotional state, which can lead to decreased motivation and difficulty in achieving efficient health management. Furthermore, unique support such as product suggestions or encouraging messages that respond to emotional states is not being provided, making it a challenge to provide more personalized support to users.

[0608] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0609] In this invention, the server includes means for acquiring physical and emotional information from the user, means for transmitting the acquired information to an information processing device, and means for generating and providing a message of encouragement based on the user's emotional information. This enables personalized motivation maintenance and efficient health management for the user by proposing an optimal health management method that takes into account the user's physical information and emotional state, and by providing support that is in line with their emotions.

[0610] A "user" refers to an individual who uses a health management system via a device to input physical and emotional information.

[0611] "Physical information" refers to basic data about the user's health, including information such as weight, height, and activity level.

[0612] "Emotional information" refers to data that users provide through self-assessment, indicating their current mental state.

[0613] An "information processing device" is a key system component that receives, analyzes, and stores data sent by users and generates suggestions.

[0614] A "success story" refers to an example of a health management method that has been effective under similar conditions in the past, and it forms the basis of the data used to generate proposals.

[0615] A "memory device" is a storage device within a system used to store information such as acquired data, generated suggestions, and messages of support.

[0616] "Health management methods" refer to management plans such as diet, exercise, and lifestyle that are proposed based on an analysis of the user's physical and emotional information.

[0617] "Product information" refers to information about products and services related to health management methods suggested to users.

[0618] A "message of support" is a message generated in response to the user's emotional state, intended to provide psychological support and encouragement.

[0619] The system for carrying out this invention comprises a terminal used by the user on a daily basis, a server, and an information processing device linked thereto. The user's terminal is a device such as a smartphone or smart glasses, and provides an interface for inputting physical information and emotional information.

[0620] Physical and emotional information entered by the user through the device is first temporarily stored on the device and then sent to a server in the cloud via the internet. The server has the functionality to receive this data using cloud infrastructure such as Amazon Web Services (AWS). It also uses emotion analysis tools such as Google Cloud AI to analyze the acquired emotional information and identify the user's emotional state.

[0621] The information processing device uses Hitachi's AI framework to search for past success stories from its storage device based on analyzed emotional and physical information. This generates the optimal health management method suited to the user's emotional state.

[0622] The generated health management methods and related product information are sent to and displayed on the user's device. In addition, encouraging messages tailored to the user's emotional state are automatically generated and provided to maintain motivation. For example, a user experiencing stress might be presented with suggestions for relaxation-oriented yoga exercises, along with product information on calming herbal teas.

[0623] As a concrete example, by feeding the AI ​​model a prompt such as, "Generate an appropriate exercise plan and relaxation methods to suggest when the user is feeling stressed. Also, customize the content by taking into account local event information," it is possible to generate a sophisticated health management plan in real time.

[0624] This system allows users to receive personalized health management suggestions, with a particular focus on emotional support.

[0625] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0626] Step 1:

[0627] Users input physical information (e.g., weight, height, activity level) and emotional information (e.g., stress levels, self-assessment of joy) using devices such as smartphones or smart glasses. The entered data is temporarily stored on the device.

[0628] Step 2:

[0629] The device transmits stored physical and emotional information to a server. This transmission takes place over the internet, and the data is securely uploaded using Amazon Web Services' cloud service.

[0630] Step 3:

[0631] The server uses Google Cloud AI to process emotional information in preparation for analyzing the received data. Based on the emotional information input, it identifies the user's emotional state (e.g., stress level) and passes this information to the next processing step.

[0632] Step 4:

[0633] Based on the analyzed emotional state and physical information, the server uses Hitachi's AI framework to search its memory for past success stories. It then calculates the optimal health management method based on the user information received as input and outputs this as a suggestion.

[0634] Step 5:

[0635] The server returns the generated health management methods and related product information to the client's terminal. It also generates and simultaneously sends encouraging messages based on the user's emotional state. This output is intended to maintain user motivation.

[0636] Step 6:

[0637] The user's device displays the received health management instructions, product information, and encouraging messages. This allows the user to understand a concrete action plan and move forward to the next step.

[0638] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0639] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0640] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0641] [Fourth Embodiment]

[0642] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0643] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0644] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0645] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0646] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0647] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0648] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0649] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0650] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0651] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0652] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0653] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0654] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0655] This invention is a system that uses AI to analyze and suggest weight loss methods tailored to individual users. Users access the system using a terminal such as a smartphone or computer and input their physical information. This information includes height, weight, age, gender, activity level, and allergy information. The terminal transmits the collected information to the server.

[0656] The server accesses a database to match the user's physical information with similar past success stories. The database contains detailed data on past weight loss successes, which is then analyzed by a machine learning algorithm. The server uses this analysis to generate the most suitable weight loss plan for the user. This plan includes a meal plan, an exercise plan, and lifestyle improvement suggestions.

[0657] Furthermore, the server selects products related to the proposed weight loss method. These selected products may include protein shakes and fitness equipment, designed to support the user in achieving their goals. This product information is then presented to the user.

[0658] When a user purchases a product based on the information provided, the terminal communicates the purchase intention to the server. The server then proceeds with the purchase process and performs the necessary steps for payment.

[0659] As a concrete example, let's consider a 30-year-old male user with a lifestyle that involves exercising twice a week. This user enters his height (170cm) and weight (70kg) into the terminal. The server searches its database for similar profiles and, based on successful weight loss cases, generates suggestions including calorie restriction and jogging three times a week. It also suggests products to encourage the purchase of protein shakes, and if the user shows interest, the server facilitates the purchase process. Through this process, the user receives a personalized weight loss strategy and support for purchasing appropriate products.

[0660] The following describes the processing flow.

[0661] Step 1:

[0662] The user enters physical information such as height, weight, age, gender, activity level, and allergy information through the device. The device checks this data and verifies that there are no missing entries or formatting errors.

[0663] Step 2:

[0664] The device converts the user's physical information into an appropriate format and sends it to the server. During this process, the data is encrypted to protect privacy.

[0665] Step 3:

[0666] Based on the physical information received by the server, it accesses an internal database. The server searches the database records to find past success stories similar to the user's information.

[0667] Step 4:

[0668] Based on the acquired similar data, the server uses statistics and machine learning algorithms to analyze the optimal weight loss method for the user. This analysis includes generating appropriate meal plans and exercise plans.

[0669] Step 5:

[0670] Based on the analysis results, the server creates a weight-loss plan proposal for the user. This proposal includes detailed action plans and suggestions for lifestyle improvements.

[0671] Step 6:

[0672] The server selects product information related to weight loss methods from its database and presents it to the user as suggested products. These products include protein shakes and fitness equipment.

[0673] Step 7:

[0674] The terminal displays proposals and product information received from the server to the user. The user reviews the proposals and selects products that interest them.

[0675] Step 8:

[0676] When a user indicates their intention to purchase a product, they send a purchase request to the server via their device. The server then begins processing the purchase.

[0677] Step 9:

[0678] The server receives the purchase request and processes the payment. It sends the necessary information to the payment system and confirms the payment. Once everything is complete, it sends a confirmation notification to the user.

[0679] Step 10:

[0680] The terminal collects suggestions and product feedback from users and sends it back to the server. The server analyzes the feedback and stores it in a database. This information is used to improve the accuracy of future suggestions.

[0681] (Example 1)

[0682] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0683] There is a need for an information processing system that can efficiently provide weight management methods optimized for individual users, while also enabling seamless selection and purchase of related products. Conventional systems have faced challenges in providing specific and personalized suggestions based on users' physical information, and in the complex process of purchasing related products based on those suggestions.

[0684] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0685] In this invention, the server includes means for analyzing past success stories using an AI model generated based on the user's biometric data, means for analyzing and proposing the optimal weight management method, means for selecting and presenting product information related to the proposal to the user, and means for carrying out the purchase procedure for the product selected by the user. This makes it possible to smoothly execute a series of processes for the user, from proposing a personalized weight management method to selecting and purchasing related products.

[0686] An "information processing device" is a device that has the function of acquiring biometric data from a user and transmitting that data to an information processing server via a network.

[0687] "Biometric data" refers to information related to a user's body, including data such as height, weight, age, gender, activity level, and allergy information.

[0688] An "information processing server" is a server that performs analysis based on biometric data received from an information processing device via a network, generates the optimal weight management method, and further selects related product information.

[0689] An "information storage device" is a storage device that stores data such as past success stories and user evaluations, and allows for retrieval and utilization as needed.

[0690] A "generative AI model" is an artificial intelligence model used to analyze past success stories and propose optimized weight management methods for users.

[0691] A "weight management method" is a method that includes meal plans, exercise plans, and lifestyle improvement suggestions designed based on the user's biometric data.

[0692] "Product information" refers to information about products related to the user's weight management methods, specifically including protein shakes and fitness equipment.

[0693] "Network" refers to communication infrastructure such as the internet that enables communication between information processing devices and information processing servers.

[0694] "Payment processing" refers to the series of procedures involved in the payment process when a user purchases a product they have selected.

[0695] This invention relates to an information processing system that allows users to receive a weight management method optimized for their individual needs. The system consists of a terminal used by the user, an information processing server operating on the cloud, and related software programs.

[0696] First, the user uses a device to input various physical data. This includes biometric data such as height, weight, age, gender, activity level, and allergy information. The device then transmits this information as encrypted data to an information processing server via the network.

[0697] Upon receiving this data, the server extracts past success stories from its information storage device and analyzes them using a generative AI model to generate an optimal management method based on the user's biometric data. This includes meal plans, exercise plans, and lifestyle improvement suggestions.

[0698] Furthermore, the server selects and presents products related to recommended management methods to the user. This includes product information that can help the user achieve their weight management goals, such as protein shakes and fitness equipment. If the user wishes to purchase these products, the terminal communicates this intention to the server, which then processes the payment securely and efficiently.

[0699] As a concrete example, let's assume a 30-year-old male user inputs information into the device such as his height (170cm), weight (70kg), and exercise habits (twice a week). Based on this information, the server uses a generative AI model to create a plan that includes calorie restriction and jogging three times a week as the optimal approach. Furthermore, it suggests products such as protein shakes.

[0700] As an example of a prompt for a generative AI model, we will use a text-based prompt that reads, "Please suggest the best weight management method for a 30-year-old male user who exercises twice a week, is 170cm tall, and weighs 70kg." This prompt provides the generative AI model with an appropriate analysis scheme and supports the construction of personalized suggestions.

[0701] In this way, this invention makes it possible to provide users with a practical and personalized weight management solution.

[0702] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0703] Step 1:

[0704] The user enters their physical information using their device. In this step, the user fills in biometric data such as height, weight, age, gender, activity level, and allergy information in an input form. After tapping the submit button, the entered data is collected. This information becomes the input data for the next step.

[0705] Step 2:

[0706] The device transmits the entered biometric data to the server via the network. In this step, the device encrypts the collected data and sends it to the server using a secure communication protocol (e.g., HTTPS). This data serves as input for the next step.

[0707] Step 3:

[0708] The server analyzes the received biometric data. In this step, the server searches its internal data storage for past success stories and performs data analysis using a generative AI model. The input here is the user's biometric data, and the output is an optimal weight management plan based on the analysis.

[0709] Step 4:

[0710] The server generates and proposes an optimal weight management method to the user. In this step, the server constructs a method that includes a meal plan, exercise plan, and lifestyle improvement suggestions based on the results analyzed by the generative AI model. The output is personalized suggestion information presented to the user.

[0711] Step 5:

[0712] The server selects and presents products relevant to the proposal to the user. In this step, the server selects information on products (e.g., protein shakes or fitness equipment) that fit the generated weight management plan and provides it to the user as a list. The output is product information for the user to refer to.

[0713] Step 6:

[0714] If the user selects a product from the displayed options and wishes to purchase it, they communicate their intention to the server via their device. In this step, the user selects the item they wish to purchase from the product list and issues a purchase instruction through their device.

[0715] Step 7:

[0716] The server handles the purchase process. In this step, the server processes the payment for the selected items and performs the necessary authentication. This includes communication with the payment gateway and processing of payment information. The final output is confirmation that the purchase process is complete.

[0717] (Application Example 1)

[0718] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] Providing individual users with the most suitable weight management methods requires personalized suggestions based on diverse physical information and lifestyle habits. However, conventional systems have been unable to fully utilize this information, making it difficult to provide weight loss methods and lifestyle improvement guidelines optimized for each user. Furthermore, there is a problem of insufficient continuous support to help users implement the suggested methods in their daily lives.

[0720] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0721] In this invention, the server includes means for acquiring information about the user's body, means for an information processing device to search a recording medium for similar past success stories based on the user's information about their body, and means for interactively providing the user with exercise and dietary guidance through a home device. This makes it possible to provide the user with a personalized weight management method and support its practice in daily life.

[0722] A "user" is an individual who uses the system to provide physical information and receive personalized weight management methods.

[0723] "Physical information" refers to health-related data such as the user's height, weight, age, gender, activity level, and allergy information.

[0724] An "information processing device" is a server or computer system that receives a user's physical information, searches a database, and performs analysis.

[0725] "Recording medium" refers to databases and storage devices that store data on past success stories and other similar information.

[0726] A "weight management method" is an approach that includes meal plans, exercise plans, and lifestyle improvement suggestions generated based on the user's physical information.

[0727] "Home-use devices" refer to robots and smart devices used in the user's living space, and are used to provide guidance for weight management.

[0728] "Product information" refers to data on related products such as protein shakes and fitness equipment that are suggested to assist with weight management.

[0729] The system for implementing this invention aims to acquire information about the user's body and, based on that information, provide an optimal weight management method. The user inputs their body information through a home-use device such as a robot or smart device. This information is sent to a server, which accesses a recording medium to search for similar past success stories. Based on the information from the recording medium, the server performs data analysis using a generative AI model to generate an optimal weight management method for the user. This includes a meal plan, an exercise plan, and lifestyle improvement suggestions.

[0730] The generated suggestions are delivered interactively to the user via home devices. For example, a robot might encourage regular exercise and present product information as needed. If the user expresses interest, the purchase process is initiated through a server. This system allows users to receive a personalized health management approach on the spot, providing continuous support for a healthy lifestyle.

[0731] In this embodiment, the information processing device (server) uses software such as Python or TensorFlow to perform database searches and analysis using AI models. As a home device, the robot is equipped with voice recognition and a touch interface, enabling two-way communication with the user.

[0732] For example, if a 40-year-old female user enters information such as height 160cm and weight 65kg, the server searches the database, and a generating AI model performs the analysis. This results in a suitable vegetarian meal plan and suggestions for two yoga exercises per week. Additionally, product information on organic supplements is presented, and if the user is interested, they can proceed with the purchase.

[0733] Example input prompts for a generative AI model:

[0734] "Use AI to propose the optimal weight loss plan based on the user's height, weight, age, gender, and activity level."

[0735] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0736] Step 1:

[0737] The device collects physical information from the user. The user inputs height, weight, age, gender, activity level, allergy information, etc., through voice recognition or a touch interface. This information is collected as raw data.

[0738] Step 2:

[0739] The device sends the collected physical information to the server. The device sends the data to the server using a secure communication protocol, and the server receives this data and converts its format into an analyzable data format.

[0740] Step 3:

[0741] The server searches the database based on the received physical information. Using an index search algorithm, the server matches similar past successes within the storage medium and extracts relevant entries. This process allows the server to obtain the historical data necessary for analysis.

[0742] Step 4:

[0743] The server performs data analysis using a generated AI model. Similar past cases and the user's physical information are provided as input. The AI ​​model uses machine learning algorithms to generate the optimal weight management method and outputs suggestions such as meal plans and exercise plans.

[0744] Step 5:

[0745] The server generates suggestions which are then delivered to the user using home devices. Specifically, a robot explains the suggestions using synthesized speech and a display, and interactively guides the user on how to implement them in their daily life. The user can provide feedback using voice or touch controls.

[0746] Step 6:

[0747] The server selects relevant product information and presents it to the user. Based on the generated weight management method, an algorithm selects information on protein shakes and fitness equipment. This selected information, along with purchase options, is then presented to the user via home devices.

[0748] Step 7:

[0749] The server handles the purchase process for products the user has expressed interest in. Once the user confirms their intention to purchase, the server calls the payment processing system and completes the purchase through a secure transaction. As a result, the user can obtain the selected product.

[0750] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0751] This invention combines an emotional engine with a system that suggests the optimal weight loss method for the user. The user accesses the system via a terminal, inputs physical information, and then uses the emotional engine to provide their current emotional state. This emotional information is obtained through a self-assessment provided by the user on the terminal. The emotional engine analyzes this data to infer the user's emotional state.

[0752] The device sends information, including this data, to the server. The server analyzes the received physical information and emotional state, and utilizes a database of past success stories to suggest the most suitable weight loss method for each individual user. Emotional state is particularly important and serves as an indicator to adjust the content and presentation of the suggestions. For example, if the user is feeling stressed, the server will suggest a weight loss method that incorporates more elements promoting relaxation.

[0753] The proposal includes meal plans, exercise plans, and lifestyle advice, all customized to the user's current emotional state. Furthermore, the server generates encouraging messages and advice to boost motivation based on the user's emotional state.

[0754] Product suggestions are also tailored to the user's emotions. For example, if a user lacks motivation to exercise, easy-to-use fitness equipment will be suggested. The terminal displays optimized suggestions and product information received from the server to the user.

[0755] As a concrete example, consider a case where a user is experiencing high levels of stress during weight loss. The user inputs this stress level through self-assessment, and the emotional engine recognizes that the user is in a stressed state. The server then suggests a plan that includes yoga and meditation to reduce stress, and also offers calming herbal tea as a suggested product. In this way, a detailed approach that takes into account the user's emotional state can be achieved to provide more effective weight loss support.

[0756] The following describes the processing flow.

[0757] Step 1:

[0758] Users input physical information such as height, weight, age, gender, activity level, and allergy information through their device. Furthermore, they input their current emotional state through a self-assessment for the emotion engine. This assessment is performed using scales such as stress, happiness, and fatigue.

[0759] Step 2:

[0760] The device collects the user's physical and emotional information, formats it, and sends it to the server. The transmitted data is encrypted to protect the user's privacy.

[0761] Step 3:

[0762] Based on the information received by the server, it accesses an internal database. The server searches for user-like profiles based on past success stories. This search is performed using statistical analysis and machine learning algorithms.

[0763] Step 4:

[0764] The server analyzes similar data and the user's current emotional state to generate the optimal weight loss method. This method generation includes special customization that reflects the user's emotions. For example, if the user is stressed, relaxation elements will be emphasized.

[0765] Step 5:

[0766] Based on the analysis results, the server creates a document containing specific weight loss suggestions. These suggestions include a meal plan, exercise plan, lifestyle improvement tips, and advice based on your emotional state.

[0767] Step 6:

[0768] The server selects relevant products based on the user's emotional state. The suggested products include items that suit the user's current situation, such as fitness aids to improve motivation or relaxation goods.

[0769] Step 7:

[0770] The terminal displays proposals and product information sent from the server to the user. The user can then use this information to proceed with their weight loss efforts and view options for products that interest them.

[0771] Step 8:

[0772] When a user decides to purchase an item, they send a purchase request to the server via their device. The server then initiates the purchase process and processes the necessary payment information.

[0773] Step 9:

[0774] The server completes the payment and notifies the user of the purchase confirmation. This completes the preparation for shipping the purchased items.

[0775] Step 10:

[0776] The terminal collects feedback from users about their weight loss experiences and products, and sends it to the server. The server analyzes the feedback and stores it in a database to use for future recommendations. This process enables the system to make more accurate recommendations.

[0777] (Example 2)

[0778] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0779] Until now, there has been no system that proposes effective weight loss methods that take into account the emotional state and physical information of individual users. Conventional weight loss support systems have the problem of low success rates due to stress and lack of motivation because they do not reflect the user's emotional state. Furthermore, they have not been able to provide motivation or product recommendations that are tailored to the user's emotional state.

[0780] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0781] In this invention, the server includes means for analyzing the user's emotional state using an emotion engine, means for selecting and presenting product information according to the emotional state to the user, and means for providing the user with encouraging messages generated based on the emotional state. This enables improvement measures that take into account the user's individual emotional state and engagement to motivate them, making more effective weight loss support possible.

[0782] A "user" is an individual or group that uses an information processing system and is the target of suggestions for weight loss methods.

[0783] "Physical information" refers to physiological data such as the user's height, weight, and age, and is basic information necessary for suggesting weight loss methods.

[0784] "Emotional state" refers to the user's current psychological state and emotions, and is information acquired as input based on their own evaluation.

[0785] An "emotion engine" is a software configuration for analyzing a user's emotional state, and it has the function of extracting emotional patterns using machine learning algorithms.

[0786] A "generative AI model" is an artificial intelligence program used for data analysis and proposal generation, and its role is to generate the optimal weight loss method for each individual user.

[0787] "Suggestions" refer to weight loss methods and lifestyle improvement measures provided to users based on the analyzed data.

[0788] A "server" refers to a part of an information processing system that analyzes data received from users via a network and generates and provides suggestions.

[0789] A "terminal" is a device operated by a user, used for inputting information and viewing suggestions.

[0790] A "database" is an information aggregation system that stores data on users' past successes and emotional states.

[0791] "Product information" refers to information about products and services that are suggested to meet the user's needs.

[0792] "Feedback" refers to the evaluations and opinions that users provide regarding the suggested weight loss methods, and these are stored in a database.

[0793] In order to implement this invention, it is necessary to construct an information processing system in which users, terminals, and servers collaborate.

[0794] The user first inputs their physical information and emotional state using a device. This device can be a personal computer, smartphone, or tablet, and must have an easily accessible interface. Emotional state input is provided using sliders or choices to indicate a self-assessed psychological state.

[0795] The terminal sends data acquired from the user to the server. The server is an information processing device with advanced computing capabilities that processes the received data. This server is equipped with an emotion engine and has the function of analyzing the received emotional information using machine learning algorithms. The analysis results must accurately capture the user's emotional state.

[0796] The server uses analyzed emotional state and physical information to apply a generative AI model, proposing weight loss methods optimized for each individual user. This generative model has the ability to generate effective suggestions in real time by referencing a database of past success stories. The suggestions also include generating encouraging messages tailored to the user's emotional state. This helps to maintain and increase the user's motivation for weight loss.

[0797] For example, if a user inputs that they are feeling stressed while on a diet, the server will suggest a relaxation plan that includes yoga and meditation, and recommend products with relaxation effects. An example of a prompt might be, "If the user is currently stressed, please suggest a weight loss plan and products that promote relaxation."

[0798] In this way, the seamless functioning of the entire system makes it possible to provide users with detailed weight loss support tailored to their individual needs.

[0799] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0800] Step 1:

[0801] The user uses the device to input physical information (e.g., height, weight, age, etc.) and emotional state. Emotional state is entered through a self-assessment interface, using sliders and multiple-choice options to select the current psychological state. The entered information is temporarily stored on the device.

[0802] Step 2:

[0803] The terminal transmits physical information and emotional states entered by the user to the server. This transmission is secure using an integrated encryption protocol. As a result, the server receives the user's physical information and emotional data as input data.

[0804] Step 3:

[0805] The server analyzes the received emotional data using an emotion engine. The emotion engine uses machine learning algorithms to analyze emotional patterns and quantify the user's emotional state. The output of this process is the analyzed emotional state information.

[0806] Step 4:

[0807] The server combines analyzed emotional states and physical information, applies a generative AI model, and proposes the optimal weight loss method. It compares this with a database of past success stories to generate personalized meal plans, exercise plans, and lifestyle suggestions for each user. These suggestions are then generated as the server's output.

[0808] Step 5:

[0809] The server generates encouraging messages based on the user's emotional state and selects product information suitable for the user. It then creates feedback, including this information, and sends it to the device.

[0810] Step 6:

[0811] The terminal displays suggestions, encouraging messages, and product information received from the server to the user. Based on the displayed information, the user can consider and implement more specific weight loss actions.

[0812] (Application Example 2)

[0813] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0814] In health management, there is a need to provide customized suggestions that take into account not only physical information but also individual mental states. However, currently, there is a lack of health management suggestions that consider the user's emotional state, which can lead to decreased motivation and difficulty in achieving efficient health management. Furthermore, unique support such as product suggestions or encouraging messages that respond to emotional states is not being provided, making it a challenge to provide more personalized support to users.

[0815] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0816] In this invention, the server includes means for acquiring physical and emotional information from the user, means for transmitting the acquired information to an information processing device, and means for generating and providing a message of encouragement based on the user's emotional information. This enables personalized motivation maintenance and efficient health management for the user by proposing an optimal health management method that takes into account the user's physical information and emotional state, and by providing support that is in line with their emotions.

[0817] A "user" refers to an individual who uses a health management system via a device to input physical and emotional information.

[0818] "Physical information" refers to basic data about the user's health, including information such as weight, height, and activity level.

[0819] "Emotional information" refers to data that users provide through self-assessment, indicating their current mental state.

[0820] An "information processing device" is a key system component that receives, analyzes, and stores data sent by users and generates suggestions.

[0821] A "success story" refers to an example of a health management method that has been effective under similar conditions in the past, and it forms the basis of the data used to generate proposals.

[0822] A "memory device" is a storage device within a system used to store information such as acquired data, generated suggestions, and messages of support.

[0823] "Health management methods" refer to management plans such as diet, exercise, and lifestyle that are proposed based on an analysis of the user's physical and emotional information.

[0824] "Product information" refers to information about products and services related to health management methods suggested to users.

[0825] A "message of support" is a message generated in response to the user's emotional state, intended to provide psychological support and encouragement.

[0826] The system for carrying out this invention comprises a terminal used by the user on a daily basis, a server, and an information processing device linked thereto. The user's terminal is a device such as a smartphone or smart glasses, and provides an interface for inputting physical information and emotional information.

[0827] Physical and emotional information entered by the user through the device is first temporarily stored on the device and then sent to a server in the cloud via the internet. The server has the functionality to receive this data using cloud infrastructure such as Amazon Web Services (AWS). It also uses emotion analysis tools such as Google Cloud AI to analyze the acquired emotional information and identify the user's emotional state.

[0828] The information processing device uses Hitachi's AI framework to search for past success stories from its storage device based on analyzed emotional and physical information. This generates the optimal health management method suited to the user's emotional state.

[0829] The generated health management methods and related product information are sent to and displayed on the user's device. In addition, encouraging messages tailored to the user's emotional state are automatically generated and provided to maintain motivation. For example, a user experiencing stress might be presented with suggestions for relaxation-oriented yoga exercises, along with product information on calming herbal teas.

[0830] As a concrete example, by feeding the AI ​​model a prompt such as, "Generate an appropriate exercise plan and relaxation methods to suggest when the user is feeling stressed. Also, customize the content by taking into account local event information," it is possible to generate a sophisticated health management plan in real time.

[0831] This system allows users to receive personalized health management suggestions, with a particular focus on emotional support.

[0832] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0833] Step 1:

[0834] Users input physical information (e.g., weight, height, activity level) and emotional information (e.g., stress levels, self-assessment of joy) using devices such as smartphones or smart glasses. The entered data is temporarily stored on the device.

[0835] Step 2:

[0836] The device transmits stored physical and emotional information to a server. This transmission takes place over the internet, and the data is securely uploaded using Amazon Web Services' cloud service.

[0837] Step 3:

[0838] The server uses Google Cloud AI to process emotional information in preparation for analyzing the received data. Based on the emotional information input, it identifies the user's emotional state (e.g., stress level) and passes this information to the next processing step.

[0839] Step 4:

[0840] Based on the analyzed emotional state and physical information, the server uses Hitachi's AI framework to search its memory for past success stories. It then calculates the optimal health management method based on the user information received as input and outputs this as a suggestion.

[0841] Step 5:

[0842] The server returns the generated health management methods and related product information to the client's terminal. It also generates and simultaneously sends encouraging messages based on the user's emotional state. This output is intended to maintain user motivation.

[0843] Step 6:

[0844] The user's device displays the received health management instructions, product information, and encouraging messages. This allows the user to understand a concrete action plan and move forward to the next step.

[0845] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0846] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0847] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0848] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0849] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0850] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0851] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0852] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0853] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0854] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0855] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0856] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0857] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0858] 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.

[0859] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0860] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0861] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0862] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0863] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0864] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0865] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0866] The following is further disclosed regarding the embodiments described above.

[0867] (Claim 1)

[0868] A means of obtaining physical information from the user,

[0869] A means of transmitting acquired physical information to a server,

[0870] A means by which the server searches a database for similar past success stories based on the user's physical information,

[0871] A means to analyze and generate suggestions for the optimal weight loss method based on the searched data,

[0872] A means of providing the generated suggestions to the user,

[0873] A means of selecting and presenting product information related to the proposed weight loss method to the user,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, comprising means for collecting feedback from users on weight loss methods and storing it in a database.

[0877] (Claim 3)

[0878] The system according to claim 1, comprising means for performing the purchase procedure for a product selected by the user via a server.

[0879] "Example 1"

[0880] (Claim 1)

[0881] A means by which an information processing device acquires biometric data from a user,

[0882] A means for transmitting acquired biometric data to an information processing server via a network device,

[0883] A means by which an information processing server searches for past success stories from its information storage device based on the user's biometric data,

[0884] A means to analyze and generate suggestions for the optimal weight management method based on the searched information,

[0885] A means of providing the generated suggestions to the user,

[0886] A means of selecting and presenting product information related to the proposed weight management method to the user,

[0887] A means by which the user selects a product presented and processes the payment via an information processing server,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, which collects evaluation information from users regarding weight management methods and stores it in an information storage device.

[0891] (Claim 3)

[0892] The system according to claim 1, which analyzes similar past success stories using a generative AI model.

[0893] "Application Example 1"

[0894] (Claim 1)

[0895] Means of obtaining information about the user's body,

[0896] A means for transmitting acquired information about the body to an information processing device,

[0897] An information processing device provides a means for searching for similar past success stories from a recording medium based on information about the user's body,

[0898] A means to analyze and generate suggestions for the optimal weight management method based on the searched records,

[0899] A means of providing the generated suggestions to users,

[0900] A means of selecting and presenting product information related to the proposed weight management method to the user,

[0901] A means of interactively providing users with guidance on exercise and diet through home-use devices,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, comprising means for collecting feedback from users regarding weight management methods and storing it on a recording medium.

[0905] (Claim 3)

[0906] The system according to claim 1, comprising means for performing the purchase procedure of a product selected by a user via an information processing device.

[0907] "Example 2 of combining an emotion engine"

[0908] (Claim 1)

[0909] A means of obtaining physical information and emotional state from the user,

[0910] A means of transmitting acquired physical information and emotional state to a server,

[0911] A means by which the server analyzes the user's emotional state using an emotion engine,

[0912] The server uses the user's physical information and analyzed emotional state to search a database for similar past success stories.

[0913] A method for analyzing the optimal weight loss method based on searched data and generating suggestions using an AI model,

[0914] A means of providing users with generated suggestions and encouraging messages generated based on their emotional state,

[0915] A means of selecting and presenting product information to users based on their emotional state,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, which collects user feedback on weight loss methods and stores it in a database.

[0919] (Claim 3)

[0920] The system according to claim 1, which performs the purchase procedure for a product selected by the user via a server.

[0921] "Application example 2 when combining with an emotional engine"

[0922] (Claim 1)

[0923] A means of obtaining physical and emotional information from the user,

[0924] Means for transmitting acquired information to an information processing device,

[0925] An information processing device provides a means for searching for similar past success stories from a storage device based on the user's physical and emotional information,

[0926] A means of analyzing and generating suggestions for optimal health management methods based on searched data,

[0927] A means of providing the generated suggestions to the user,

[0928] A means of selecting and presenting product information related to the proposed health management method to the user,

[0929] A means of generating and providing supportive messages based on user emotional information,

[0930] A system that includes this.

[0931] (Claim 2)

[0932] The system according to claim 1, which collects user feedback on health management methods and stores it in a storage device.

[0933] (Claim 3)

[0934] The system according to claim 1, which performs the purchase procedure for a product selected by the user via an information processing device. [Explanation of Symbols]

[0935] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining physical information from the user, A means of transmitting acquired physical information to a server, A means by which the server searches a database for similar past success stories based on the user's physical information, A means to analyze and generate suggestions for the optimal weight loss method based on the searched data, A means of providing the generated suggestions to the user, A means of selecting and presenting product information related to the proposed weight loss method to the user, A system that includes this.

2. The system according to claim 1, comprising means for collecting feedback from users on weight loss methods and storing it in a database.

3. The system according to claim 1, comprising means for performing the purchase procedure for a product selected by the user via a server.

Images