system

Abstract

We provide the system. [Solution] Means of collecting employee health data, A means by which AI proposes the optimal company cafeteria menu based on the aforementioned health data, A means of providing the aforementioned proposed menu to employees and applying a discount when selected, A means for recording the aforementioned selection data and reflecting it in the next proposal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern corporate environments, the health management of employees and the improvement of their diet are highly regarded. However, regarding how employees should choose their daily meals, it is often left to individuals, and as a result, there is a problem that it is difficult to maintain a healthy diet. In addition, it is necessary to improve the utilization rate of the company cafeteria, but there is a lack of menu proposals tailored to the preferences and health conditions of employees, so there is a limit to promoting utilization. Under such circumstances, there is a need for a method to provide an optimal cafeteria menu based on the health status of employees and promote its utilization.

Means for Solving the Problems

[0005] This invention provides a system in which AI suggests the optimal company cafeteria menu based on employee health data. Specifically, it first includes means for collecting health checkup and daily health data to analyze employees' health status. Based on this data, it has means for the AI ​​to suggest a customized menu for each employee. Furthermore, it has a means for presenting the suggested menu to employees and applying a discount when they choose it to encourage selection. Finally, it includes a function to record employee choices and use them to improve future suggestions, thereby enabling continuous improvement. This simultaneously achieves improved employee health and increased cafeteria utilization.

[0006] "Employee health data" refers to information about the health status of employees managed within the company, including data such as weight, blood pressure, and allergy information.

[0007] "AI" refers to artificial intelligence, and is a technology or program that analyzes employees' health data to suggest the most suitable cafeteria menu.

[0008] "Company cafeteria menu" refers to the list of meals offered in the company cafeteria, encompassing the types and contents of meals that employees can choose from on a daily basis.

[0009] "Discount" refers to price adjustments made to offer the proposed cafeteria menu items at a lower price than the regular price.

[0010] "Selection data" refers to a collection of information recorded when an employee selects a specific meal from a suggested menu, and is used for subsequent AI analysis and menu suggestions. [Brief explanation of the drawing]

[0011] [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] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention provides a system for suggesting individually customized in-house cafeteria menus based on the health status of employees. The following describes an embodiment of the system.

[0033] Data collection and integration

[0034] The server periodically collects health data for each employee. This includes company health checkup results and data from wearable devices provided voluntarily by employees. The server centrally manages this data by linking it to individual employee IDs.

[0035] AI-based data analysis

[0036] The server uses an AI engine to analyze the collected health data. The AI ​​considers the employee's current health status and past cafeteria menu selection history, and generates an optimal meal plan based on nutritional balance, individual health goals, allergy information, and more.

[0037] Menu customization and suggestions

[0038] The server customizes and delivers the generated meal plan to each employee. This information is notified to the employee's device (smartphone or computer). The device displays an interface for selecting from the menu, and certain menu items are eligible for health-promoting discounts.

[0039] Recording of selections and history

[0040] Users select their desired items from a menu suggested on their terminal. This selection information is managed on the server side and used to improve future menu suggestions. This allows for continuous improvement based on the choices of each individual employee.

[0041] Specific example

[0042] For example, if employee B's recent health checkup reveals high cholesterol, the server will use AI analysis to suggest menus such as "low-fat, high-fiber salads and steamed dishes." These menus will be displayed on B's terminal with a discount, and once B selects a menu item, the history will be recorded and used to improve future AI suggestions.

[0043] This system makes it possible to improve the utilization rate of the cafeteria while maintaining the health of employees.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server collects employee health checkup data and daily health data. This includes various biometric information linked to employee IDs.

[0047] Step 2:

[0048] The server integrates the collected data and creates a dataset for analysis by the AI ​​engine. This dataset includes individual health status and dietary history.

[0049] Step 3:

[0050] The server uses an AI engine to analyze each employee's health status and eating history, and generates an optimal meal plan that takes into account nutritional balance and health goals.

[0051] Step 4:

[0052] The server sends the generated meal plan to the employee's terminal, which displays a customized menu. This menu includes discount information for each option.

[0053] Step 5:

[0054] The user makes a selection from a menu presented on the terminal. This selection has been verified by the server to be appropriate for the user's current health condition.

[0055] Step 6:

[0056] The server records the user's selection data and uses it for future analysis. This accumulated data contributes to improving the AI ​​model for future suggestions.

[0057] (Example 1)

[0058] 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."

[0059] The aim is to effectively improve employees' dietary habits and manage their health according to their individual health conditions, as well as to make the company cafeteria more accessible to employees and promote health maintenance. However, the conventional system made it difficult to suggest individually customized menus based on each employee's health condition and past preferences.

[0060] 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.

[0061] In this invention, the server includes means for acquiring employee biometric information, means for a machine learning engine to generate an optimal meal plan based on the biometric information, means for transferring the generated meal plan to the employee's computer and providing preferential treatment if selected, and means for recording the selection results and using them for future suggestions. This makes it possible to propose menus optimized for each employee's health condition, enabling the provision of a system that simultaneously achieves health maintenance and increases cafeteria utilization.

[0062] The term "employee" refers to an individual who belongs to a company or organization and performs duties within that organization.

[0063] "Biometric information" refers to data that indicates an individual's health status, including, for example, heart rate, steps taken, and sleep patterns.

[0064] A "machine learning engine" refers to a program that analyzes collected data, finds patterns, and executes algorithms to perform predictions and optimizations.

[0065] A "meal plan" refers to a set of menus proposed based on each employee's health condition and preferences.

[0066] "Computer equipment" refers to electronic devices used to receive, display, and process information, and includes personal computers and smartphones.

[0067] "Privilege" refers to discounts or benefits offered when certain conditions are met.

[0068] "Selection results" refers to the history of menus that employees actually chose from the proposed meal plans.

[0069] An "information processing system" refers to a system that includes a series of devices and software that solve problems through data input, processing, and output.

[0070] This invention is an information processing system for suggesting individually customized meal menus to employees in the company cafeteria based on their health status. The following describes a specific implementation of this system.

[0071] The server periodically collects employees' biometric information. This collection utilizes data from the company's health checkup database and data from wearable devices that employees voluntarily connect to. Data from wearable devices is retrieved via an API and securely stored in the database. This data collection can be automated using a task scheduler.

[0072] Subsequently, the server uses the collected data to run a machine learning engine, such as "TENSORFLOW®". The program on the server generates an optimized meal plan for each employee based on their health indicators and past eating history. This generating AI model considers biometric information and past history to analyze the content and nutritional balance of meals to promote the health of each individual employee.

[0073] The generated meal plan is sent via push notification from the server to the employee's device, such as a smartphone or PC. On the device, a user interface is generated where the suggested meal menu can be viewed, and in some cases, a discount may be applied to the selected menu.

[0074] Users, i.e., employees, select their desired items from the menus suggested through the provided interface. The user's selection is recorded on the server and used to generate future meal plans.

[0075] As a concrete example, if an employee's health checkup reveals that they need to limit their salt intake, the server uses a machine learning engine to generate a "low-salt snack and vegetable-centered menu" and notifies the employee of this plan on their device. If the employee selects the plan, a special offer is provided to encourage healthy choices.

[0076] Examples of prompts for the generating AI model include sentences that include specific instructions, such as, "Suggest an appropriate lunch menu based on the employee's health indicators. The reference biometric information should be the most up-to-date data."

[0077] In this way, this invention aims to make it easier for employees to choose a healthy diet, thereby improving health awareness throughout the company and promoting the use of the company cafeteria.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server collects employee biometric information. It receives data from the company's health checkup database and API data obtained from wearable devices as input. During data collection, the data (e.g., heart rate, steps, blood pressure) is stored in the database in a format that identifies each employee. This collection process is automated by a task scheduler and runs periodically.

[0081] Step 2:

[0082] The server feeds the collected biometric information into a machine learning engine. The set of data collected in step 1 is used as input. This data is processed by the AI ​​model to generate meal plans based on each employee's health status and past eating history. Data analysis is performed using Python scripts and the TensorFlow library, and optimized menu suggestions are output.

[0083] Step 3:

[0084] The server sends the generated meal plan to the employee's device. The input uses the meal plan obtained from the AI ​​model in step 2. Menu information is sent to the device via a push notification service and displayed on the device. This notification includes suggested menu items and special offers, allowing the employee to make a selection.

[0085] Step 4:

[0086] The user selects their desired meal from a menu displayed on the terminal. The terminal uses a visual interface to make the selection based on the menu information provided as input. The selection action is recognized by touch or click, and the result is sent to the server.

[0087] Step 5:

[0088] The server records the user's selections and updates the database for future suggestions. The selection information obtained in step 4 is used as input. The selection history is added to the database and used when generating the next meal plan to provide even more personalized suggestions. This process allows for the optimization of menus to better suit individual health conditions and preferences.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] Providing appropriate nutrition tailored to each employee's health condition is challenging, and individualized support is needed to maintain employee health and improve work efficiency. In this situation, there is a need for a method that efficiently presents meal menus tailored to each employee's health condition and preferences, and continuously optimizes them by utilizing their choices.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes means for acquiring information on the health of employees, means for an AI to present an optimal nutritional menu based on the health information, and means for providing the presented menu to the employee through an information display device and applying a value discount when selected. This enables the efficient presentation of personalized nutritional menus to employees and continuous optimization based on their selection history.

[0094] An "employee" is a person who works and performs duties in a factory, workplace, or similar location.

[0095] "Health-related information" refers to data related to an employee's health status, including their physical condition and health checkup results.

[0096] "AI" refers to artificial intelligence, a technology that analyzes large amounts of data to derive optimal results.

[0097] A "nutritional menu" refers to a meal plan proposed based on the health status of employees.

[0098] An "information display device" is a device used to visually present information to employees.

[0099] "Value discount" refers to a discount or reduction from the quoted price.

[0100] "Selection information" refers to data that records the choices made by employees from a presented menu.

[0101] This invention is a system that proposes individually customized nutritional menus based on employees' health information. The server first acquires information about the employees' health. This is done using data collected from wearable devices and the results of health checkups.

[0102] Next, the server uses a generative AI model to analyze the collected health information and create nutritional menus optimized for each employee's health condition. Python is used for processing this AI engine, and machine learning libraries are utilized for data analysis.

[0103] The terminal displays menus sent from the server on an information display device, such as a monitor, allowing employees to select their desired menu items. The selections are sent to and recorded by the server. The server uses this selection information to optimize menu suggestions for future visits.

[0104] This system allows for the suggestion of nutritious meals based on employees' health conditions, and enables the effective use of their selection information. For example, if employee C is identified as having a vitamin D deficiency, the server can suggest a meal rich in vitamin D. By selecting this suggestion, the employee can improve their health.

[0105] Examples of prompts for the generative AI model include: "Please suggest a meal plan for employee C based on recent health data," and "Please create a menu suitable for an employee with a vitamin D deficiency."

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

[0107] Step 1:

[0108] The server retrieves health information from employees' wearable devices and health checkup results. This input data includes heart rate, body temperature, blood pressure, and health checkup results. The server stores this data in a database, linked to the employee ID. This information retrieval is performed in real time or at regular intervals and is also managed as a history.

[0109] Step 2:

[0110] The server takes health information obtained from the database as input and performs data analysis using a generative AI model. Specifically, it analyzes employees' health status using Python and machine learning libraries and outputs an optimal nutritional menu that takes into account nutritional balance, allergy information, and health goals. In this process, it calculates the recommended amount of nutrients according to health status and evaluates the nutritional value of each menu.

[0111] Step 3:

[0112] The server sends a nutrition menu generated by AI to the terminal. The terminal displays the menu to the employee via an information display device. At this time, the user interface displays menu details and value information for promoting health. The user selects what they want from the displayed menu and sends that information as input.

[0113] Step 4:

[0114] Menu information selected on the terminal is sent to the server, which records this selection information in a database. This record includes information such as selection history and frequency, as it is used for data analysis to improve subsequent menu suggestions. The server analyzes the user's selection patterns and performs calculations to further improve future suggestions.

[0115] Step 5:

[0116] The server repeatedly learns health information and menu selection history based on the history and selection information recorded up to this point, in preparation for the next suggestion. By updating the generating AI model, the accuracy of the next menu suggestion is improved. Through this feedback loop, menu suggestions become dynamic and personalized, contributing to improved employee health.

[0117] 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.

[0118] This invention provides a system that enables the suggestion of cafeteria menus that simultaneously consider the health status and emotions of employees. The system incorporates an emotion engine that analyzes employee emotional data to support more appropriate menu selections.

[0119] Data collection and integration

[0120] The server collects health and emotional data for each employee. Health data includes physical information such as weight and blood pressure, while emotional data includes results from facial expression analysis during menu selections and automated questionnaires. The server manages this data based on the employee ID.

[0121] Data analysis using AI and emotion engines

[0122] The server analyzes collected health and emotional data using an AI engine and an emotion engine. The AI ​​engine generates menus that consider appropriate nutritional balance based on the user's health status, while the emotion engine optimizes menu suggestions from an emotional perspective, using the user's emotions at the time of selection and their past emotional history.

[0123] Menu suggestions and emotion-based customization

[0124] The server sends the generated menu to the employee's terminal. The terminal displays a customized menu based on the results of both the AI ​​and the emotion engine, presenting the user with the best options. These suggestions may include health promotion discounts and options designed to improve emotional satisfaction.

[0125] Recording of selections and history

[0126] Users make selections from a menu presented on their terminal. The server records these selections to ensure they are appropriate for the employee's current health and emotional state. Similarly, the results of the emotion engine's analysis are also recorded and used to improve subsequent menu suggestions.

[0127] Specific example

[0128] For example, if employee C is found to be deficient in vitamin D during a health checkup while experiencing a period of high stress, the server uses its AI engine to suggest vitamin-rich menus effective in reducing stress, and its emotion engine to present options that take employee C's stress level into consideration. This allows C to choose a menu that is emotionally satisfying, thus improving both their physical and emotional well-being.

[0129] This system enables support for employees' health and emotional well-being, leading to more effective promotion of cafeteria use.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The server collects employee health and emotional data. Health data comes from regular health checkup results and information from wearable devices, while emotional data is obtained from facial recognition software and automated questionnaires.

[0133] Step 2:

[0134] The server integrates the collected data and prepares it for analysis by the AI ​​engine and emotion engine. Health data and emotion data are linked to individual employee profiles.

[0135] Step 3:

[0136] The server uses an AI engine to generate a meal plan that considers nutritional balance based on health data. At the same time, an emotion engine analyzes the user's emotional data and customizes the menu according to their current emotional state.

[0137] Step 4:

[0138] The server sends the generated menu suggestions to the employee's terminal. The terminal receives this information and presents the suggested menu through a user-facing interface. Here, discount information and elements that enhance emotional satisfaction are specially displayed.

[0139] Step 5:

[0140] The user selects from a menu presented on the terminal. The selection matches the user's health and emotional state, and the server records the received selection in the system's database.

[0141] Step 6:

[0142] The server uses recorded selection and sentiment data to adjust its model for future menu suggestions. This ensures that subsequent suggestions are more personalized and emotionally and healthily appropriate.

[0143] (Example 2)

[0144] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0145] In employee health management, there is a need for a system that can propose meal plans that not only aim to improve physical health but also consider emotional satisfaction. Therefore, it is necessary to analyze health and emotional information in real time and provide the optimal cafeteria menu for each individual employee to increase corporate productivity.

[0146] 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.

[0147] In this invention, the server includes means for collecting employee health information and emotional information, means for a generative AI model to propose an optimal cafeteria menu based on the health information and emotional information, and means for displaying the proposed menu on a terminal and customizing it using the results of the emotional engine. This enables employees to make healthy and emotionally satisfying meal choices, thereby improving the overall vitality and productivity of the company.

[0148] "Employee health information" refers to physical data such as weight and blood pressure, and is information used to assess an employee's health status.

[0149] "Emotional information" refers to data that represents the emotional state of employees, and is collected from sources such as facial expression analysis and survey results.

[0150] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to generate output based on a specific task, and in this context, it is used to suggest the optimal cafeteria menu.

[0151] "Methods for suggesting cafeteria menus" refers to a function that utilizes collected health and emotional information to present the most suitable cafeteria menu.

[0152] An "emotional engine" refers to software or algorithms that analyze the emotional state of employees and utilize the results for various suggestions and customizations.

[0153] "Terminal" refers to a device used by employees to access information, and includes personal computers and tablets.

[0154] In order to implement the invention, it is important to properly implement the various means of this system. This invention primarily operates around three main entities: a server, a terminal, and a user.

[0155] The server is responsible for collecting and managing employee health and emotional information. Health information is obtained through wearable devices and a database of regular health checkups. Emotional information is obtained through facial expression analysis of facial images captured using the terminal's camera and from questionnaires answered on the terminal. This allows the server to understand each employee's individual state in real time.

[0156] Next, the server uses the collected information to activate a generative AI model, which then suggests the optimal cafeteria menu for each employee. The generative AI model analyzes the data and generates a variety of menus that take into account nutritional balance and emotional satisfaction. The emotion engine further optimizes the impact of the suggested menus on employees' emotional well-being and incorporates this into the suggestions.

[0157] The terminal is responsible for displaying customized menus sent from the server to the user. The terminal provides an interface that visually presents the received menus in an easy-to-understand manner and allows sorting according to health-promoting effects and emotional satisfaction.

[0158] The user selects the option best suited to their situation from the menu displayed on the device. The selected menu is sent to the server, and the selection history and sentiment information are recorded in a data store for subsequent suggestions.

[0159] For example, if an employee is experiencing a period of high stress and is diagnosed with a vitamin D deficiency, the server uses a generative AI model to suggest a menu that includes fish dishes rich in vitamin D and herbal teas with stress-reducing effects. The emotion engine takes the employee's stress level into consideration to provide options that offer greater emotional satisfaction.

[0160] An example of a prompt message might be: "Could you please suggest cafeteria menus that take into account the health and emotional state of our employees? Specifically, we would like menu suggestions for employees who are experiencing stress and are deficient in vitamin D."

[0161] This system not only enables employees to make healthy and emotionally satisfying meal choices, but also contributes to maintaining employee health and improving the work environment.

[0162] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0163] Step 1:

[0164] The server collects employee health and emotional information. Inputs include physical data such as weight and blood pressure, obtained from each employee's wearable devices and health management systems. Emotional information inputs include facial expression data captured by the device's camera and emotional responses from a survey system. This data is linked to each employee's ID and stored in a database. The output is a unified health and emotional profile for each employee.

[0165] Step 2:

[0166] The server inputs the collected health and emotional information into a generating AI model and an emotion engine for analysis. First, the AI ​​model generates a nutritionally balanced cafeteria menu based on the health information. For example, for an employee who is deficient in vitamin D, a menu including fish and mushrooms will be generated. Meanwhile, the emotion engine analyzes the emotional information and evaluates the employee's current emotional state. The output of this process is a customized menu proposal that takes both health and emotional states into consideration.

[0167] Step 3:

[0168] The server sends the generated customized menu proposals to the terminal. The terminal builds a user interface based on the received data and displays the information. The input is the menu proposals sent from the server, and the output is a visually organized list of menus presented to the user. Filtering by health benefits and emotional satisfaction is also possible on the terminal.

[0169] Step 4:

[0170] The user makes their selection from a menu displayed on the device. The input is the menu list displayed on the device, and the output is information about the menu selected by the user. Through this process, the user can choose a meal that is best suited to their health and emotional state.

[0171] Step 5:

[0172] The server records data on the menu selected by the user and the emotional information associated with that selection. Inputs include the user's selection data and the results of the emotional analysis. This data is stored in a database to improve the accuracy of future menu suggestions. Output is the addition of new selection and emotional records to the updated database.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0175] In recent years, with the advancement of automation in manufacturing, efficient operational management of robots within factories has become a critical issue. However, current robot operation management systems do not adequately provide dynamic schedule optimization based on operational data or maintenance suggestions tailored to individual operating conditions. Therefore, there is a need for an integrated management system that proactively prevents robot failures and efficiency declines.

[0176] 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.

[0177] In this invention, the server includes means for collecting employee work data, means for artificial intelligence to propose an optimal machine work schedule based on the work data, and means for providing the proposed schedule to the system and evaluating the need for maintenance when selected. This enables dynamic and effective operational management to prevent robot failures.

[0178] "Operational data" refers to information that shows the working status of equipment and robots operating within a factory, and includes data such as vibration, temperature, and efficient work time measured by sensors.

[0179] "Artificial intelligence" is a technological system that learns and analyzes collected data to automatically perform judgments and predictions similar to those made by humans.

[0180] A "machine operation schedule" is a plan that defines the timetable and sequence for efficiently performing a series of tasks or operations that equipment or robots are responsible for.

[0181] "Maintenance" refers to maintenance work such as inspections, repairs, or adjustments performed to maintain the normal operation of equipment and robots and to prevent malfunctions.

[0182] "Selection data" refers to information about when a proposed work schedule or maintenance plan is selected and put into action, and is a record used to generate future proposals.

[0183] This system combines operational data collection, analysis, scheduling suggestions, and maintenance evaluation to achieve efficient equipment operation management in a factory. The server collects operational data through sensors attached to each piece of equipment within the factory. This data includes vibration, temperature, and work efficiency during operation.

[0184] Next, the server inputs the collected operational data into the AI ​​engine, which performs pattern recognition and anomaly detection. The AI ​​engine utilizes machine learning libraries such as TensorFlow and PyTorch to generate a schedule for optimizing the equipment's operating status. This analysis helps to determine the most efficient work sequence and time allocation.

[0185] Furthermore, the server uses an emotion engine to determine maintenance needs from operational data. This allows it to proactively detect potential failures and propose appropriate maintenance work. The generated proposals are sent in real time to the supervisor's or operator's terminal to support rapid decision-making on-site.

[0186] As a concrete example, if equipment A on a manufacturing line exhibits higher-than-normal vibrations, the AI ​​engine will immediately detect the anomaly, and the emotion engine will suggest maintenance. This information will then be displayed on a terminal, allowing the supervisor to decide on the maintenance.

[0187] An example of a prompt message for a generated AI model is: "The vibration of equipment A in the factory has become abnormally high. Please propose maintenance based on this data."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The server collects operational data from sensors installed on equipment within the factory. This data includes measurements of vibration, temperature, and work efficiency. The server periodically checks this data, cleans up the collected data, and converts it into a format suitable for analysis. The input is raw data from the sensors, and the output is formatted data stored in a database.

[0191] Step 2:

[0192] The server inputs the collected operational data into the AI ​​engine. Here, the AI ​​performs pattern recognition and applies anomaly detection algorithms to evaluate the operational status of the equipment. In this process, TensorFlow is used to run the model and calculate an anomaly score for each piece of equipment. The input is formatted operational data, and the output is a list of anomaly scores.

[0193] Step 3:

[0194] The server generates an optimal machine work schedule based on the output of the AI ​​engine. Here, it takes into account the anomaly score generated by the AI ​​model to formulate the most efficient schedule. This minimizes the risk of equipment failure while proposing an efficient work sequence. The input is the anomaly score, and the output is the work schedule.

[0195] Step 4:

[0196] The server uses an emotion engine to further analyze operational data and AI analysis results to determine the need for maintenance. The emotion engine examines the equipment's operational history and current anomaly score to make maintenance recommendations. This includes early prediction of anomalies and suggestions for specific maintenance methods. The inputs are operational history and anomaly scores, and the output is the maintenance recommendation.

[0197] Step 5:

[0198] The server sends the generated work schedule and maintenance proposals to the supervisor's terminal. The terminal receives the schedule and proposals in real time, helping the supervisor make quick decisions based on them. The input is the work schedule and maintenance proposals, and the output is the information displayed on the terminal screen.

[0199] Step 6:

[0200] The user performs appropriate maintenance and schedule adjustments based on the suggestions displayed on the terminal. Once maintenance is decided, operational data and anomaly scores are updated and reflected in the next suggestion. The input is the suggestion on the terminal screen, and the output is the updated system data.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] This invention provides a system for suggesting individually customized in-house cafeteria menus based on the health status of employees. The following describes an embodiment of the system.

[0218] Data collection and integration

[0219] The server periodically collects health data for each employee. This includes company health checkup results and data from wearable devices provided voluntarily by employees. The server centrally manages this data by linking it to individual employee IDs.

[0220] AI-based data analysis

[0221] The server uses an AI engine to analyze the collected health data. The AI ​​considers the employee's current health status and past cafeteria menu selection history, and generates an optimal meal plan based on nutritional balance, individual health goals, allergy information, and more.

[0222] Menu customization and suggestions

[0223] The server customizes and delivers the generated meal plan to each employee. This information is notified to the employee's device (smartphone or computer). The device displays an interface for selecting from the menu, and certain menu items are eligible for health-promoting discounts.

[0224] Recording of selections and history

[0225] Users select their desired items from a menu suggested on their terminal. This selection information is managed on the server side and used to improve future menu suggestions. This allows for continuous improvement based on the choices of each individual employee.

[0226] Specific example

[0227] For example, if employee B's recent health checkup reveals high cholesterol, the server will use AI analysis to suggest menus such as "low-fat, high-fiber salads and steamed dishes." These menus will be displayed on B's terminal with a discount, and once B selects a menu item, the history will be recorded and used to improve future AI suggestions.

[0228] This system makes it possible to improve the utilization rate of the cafeteria while maintaining the health of employees.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The server collects employee health checkup data and daily health data. This includes various biometric information linked to employee IDs.

[0232] Step 2:

[0233] The server integrates the collected data and creates a dataset for analysis by the AI ​​engine. This dataset includes individual health status and dietary history.

[0234] Step 3:

[0235] The server uses an AI engine to analyze each employee's health status and eating history, and generates an optimal meal plan that takes into account nutritional balance and health goals.

[0236] Step 4:

[0237] The server sends the generated meal plan to the employee's terminal, which displays a customized menu. This menu includes discount information for each option.

[0238] Step 5:

[0239] The user makes a selection from a menu presented on the terminal. This selection has been verified by the server to be appropriate for the user's current health condition.

[0240] Step 6:

[0241] The server records the user's selection data and uses it for future analysis. This accumulated data contributes to improving the AI ​​model for future suggestions.

[0242] (Example 1)

[0243] 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".

[0244] The aim is to effectively improve employees' dietary habits and manage their health according to their individual health conditions, as well as to make the company cafeteria more accessible to employees and promote health maintenance. However, the conventional system made it difficult to suggest individually customized menus based on each employee's health condition and past preferences.

[0245] 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.

[0246] In this invention, the server includes means for acquiring employee biometric information, means for a machine learning engine to generate an optimal meal plan based on the biometric information, means for transferring the generated meal plan to the employee's computer and providing preferential treatment if selected, and means for recording the selection results and using them for future suggestions. This makes it possible to propose menus optimized for each employee's health condition, enabling the provision of a system that simultaneously achieves health maintenance and increases cafeteria utilization.

[0247] The term "employee" refers to an individual who belongs to a company or organization and performs duties within that organization.

[0248] "Biometric information" refers to data that indicates an individual's health status, including, for example, heart rate, steps taken, and sleep patterns.

[0249] A "machine learning engine" refers to a program that analyzes collected data, finds patterns, and executes algorithms to perform predictions and optimizations.

[0250] A "meal plan" refers to a set of menus proposed based on each employee's health condition and preferences.

[0251] "Computer equipment" refers to electronic devices used to receive, display, and process information, and includes personal computers and smartphones.

[0252] "Privilege" refers to discounts or benefits offered when certain conditions are met.

[0253] "Selection results" refers to the history of menus that employees actually chose from the proposed meal plans.

[0254] An "information processing system" refers to a system that includes a series of devices and software that solve problems through data input, processing, and output.

[0255] This invention is an information processing system for suggesting individually customized meal menus to employees in the company cafeteria based on their health status. The following describes a specific implementation of this system.

[0256] The server periodically collects employees' biometric information. This collection utilizes data from the company's health checkup database and data from wearable devices that employees voluntarily connect to. Data from wearable devices is retrieved via an API and securely stored in the database. This data collection can be automated using a task scheduler.

[0257] The server then uses the collected data to run a machine learning engine, such as TensorFlow. The program on the server generates an optimized meal plan for each employee based on their health indicators and past eating history. This generating AI model considers biometric information and past history to analyze the content and nutritional balance of meals to promote the health of each individual employee.

[0258] The generated meal plan is sent via push notification from the server to the employee's device, such as a smartphone or PC. On the device, a user interface is generated where the suggested meal menu can be viewed, and in some cases, a discount may be applied to the selected menu.

[0259] Users, i.e., employees, select their desired items from the menus suggested through the provided interface. The user's selection is recorded on the server and used to generate future meal plans.

[0260] As a concrete example, if an employee's health checkup reveals that they need to limit their salt intake, the server uses a machine learning engine to generate a "low-salt snack and vegetable-centered menu" and notifies the employee of this plan on their device. If the employee selects the plan, a special offer is provided to encourage healthy choices.

[0261] Examples of prompts for the generating AI model include sentences that include specific instructions, such as, "Suggest an appropriate lunch menu based on the employee's health indicators. The reference biometric information should be the most up-to-date data."

[0262] In this way, this invention aims to make it easier for employees to choose a healthy diet, thereby improving health awareness throughout the company and promoting the use of the company cafeteria.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The server collects employee biometric information. It receives data from the company's health checkup database and API data obtained from wearable devices as input. During data collection, the data (e.g., heart rate, steps, blood pressure) is stored in the database in a format that identifies each employee. This collection process is automated by a task scheduler and runs periodically.

[0266] Step 2:

[0267] The server feeds the collected biometric information into a machine learning engine. The set of data collected in step 1 is used as input. This data is processed by the AI ​​model to generate meal plans based on each employee's health status and past eating history. Data analysis is performed using Python scripts and the TensorFlow library, and optimized menu suggestions are output.

[0268] Step 3:

[0269] The server sends the generated meal plan to the employee's device. The input uses the meal plan obtained from the AI ​​model in step 2. Menu information is sent to the device via a push notification service and displayed on the device. This notification includes suggested menu items and special offers, allowing the employee to make a selection.

[0270] Step 4:

[0271] The user selects their desired meal from a menu displayed on the terminal. The terminal uses a visual interface to make the selection based on the menu information provided as input. The selection action is recognized by touch or click, and the result is sent to the server.

[0272] Step 5:

[0273] The server records the user's selections and updates the database for future suggestions. The selection information obtained in step 4 is used as input. The selection history is added to the database and used when generating the next meal plan to provide even more personalized suggestions. This process allows for the optimization of menus to better suit individual health conditions and preferences.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] It is difficult to provide appropriate nutrition according to the health conditions of employees, and individual measures are required to maintain the health of employees and improve work efficiency. In this situation, a method is needed to efficiently present diet menus according to the health conditions and preferences of each employee and continuously optimize them by utilizing the selected contents.

[0277] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes means for acquiring information related to the health of employees, means for presenting an optimal nutrition provision menu by AI based on the information related to health, and means for providing the presented menu to employees through an information display device and applying a price discount when it is selected. Thereby, an individualized nutrition provision menu can be efficiently presented to employees, and continuous optimization based on the selection history becomes possible.

[0279] "Employee" refers to a person who performs work while working in a factory, workplace, etc.

[0280] "Information related to health" refers to data related to the health state, including the physical state and health examination results of employees.

[0281] "AI" refers to artificial intelligence, which is a technology that derives optimal results by analyzing a large amount of data.

[0282] "Nutrition provision menu" refers to a diet plan proposed based on the health state of employees.

[0283] "Information display device" refers to a device for visually presenting information to employees.

[0284] "Price discount" refers to a discount or price reduction from the presented price.

[0285] "Selection information" refers to data that records the selection content chosen by an employee from the presented menu.

[0286] This invention is a system that proposes a nutrition provision menu customized individually based on the health information of employees. First, the server obtains information regarding the health of employees. This is done by collecting data from wearable devices and using the results of health check-ups.

[0287] Next, the server performs analysis using a generated AI model based on the collected health information to create a nutrition provision menu optimized for each employee's health condition. Python is used for the processing of this AI engine, and machine learning libraries are utilized for data analysis.

[0288] The terminal displays the menu sent from the server on an information display device, such as a monitor, enabling the employee to select the desired menu from it. The selection content is sent to and recorded by the server. By utilizing this selection information for the next menu proposal, the server aims to achieve more refined menu optimization.

[0289] With this system, based on the health condition of employees, a high-nutrition diet can be proposed, and the selection information can be effectively utilized. As a specific example, if it is pointed out that employee C has a vitamin D deficiency, the server can propose a diet rich in vitamin D. By the employee selecting this proposal, it becomes possible to improve the health condition.

[0290] Examples of prompt sentences for the generated AI model include "Please propose a meal plan for employee C based on the recent health data." and "Please create a menu suitable for employees with vitamin D deficiency."

[0291] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0292] Step 1:

[0293] The server retrieves health information from employees' wearable devices and health checkup results. This input data includes heart rate, body temperature, blood pressure, and health checkup results. The server stores this data in a database, linked to the employee ID. This information retrieval is performed in real time or at regular intervals and is also managed as a history.

[0294] Step 2:

[0295] The server takes health information obtained from the database as input and performs data analysis using a generative AI model. Specifically, it analyzes employees' health status using Python and machine learning libraries and outputs an optimal nutritional menu that takes into account nutritional balance, allergy information, and health goals. In this process, it calculates the recommended amount of nutrients according to health status and evaluates the nutritional value of each menu.

[0296] Step 3:

[0297] The server sends a nutrition menu generated by AI to the terminal. The terminal displays the menu to the employee via an information display device. At this time, the user interface displays menu details and value information for promoting health. The user selects what they want from the displayed menu and sends that information as input.

[0298] Step 4:

[0299] Menu information selected on the terminal is sent to the server, which records this selection information in a database. This record includes information such as selection history and frequency, as it is used for data analysis to improve subsequent menu suggestions. The server analyzes the user's selection patterns and performs calculations to further improve future suggestions.

[0300] Step 5:

[0301] In preparation for the following suggestions, the server repeatedly learns the health information and menu selection history based on the history and selection information recorded so far. By updating the generative AI model, the accuracy of the next menu suggestion is improved. Through this feedback loop, the menu suggestions are dynamic and individualized, contributing to the improvement of employees' health conditions.

[0302] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0303] This invention provides a system that enables the proposal of cafeteria menus considering the health conditions and emotions of employees simultaneously. The system incorporates an emotion engine, analyzes the emotion data of employees, and supports more appropriate menu selections.

[0304] Data collection and integration

[0305] The server collects health data and emotion data for each employee. The health data includes physical information such as weight and blood pressure, and the emotion data includes the results of facial expression analysis and the results of automatic questionnaires when employees select menus. The server manages these data based on the employee ID.

[0306] Data analysis by AI and emotion engine

[0307] The server analyzes the collected health data and emotion data using an AI engine and an emotion engine. The AI engine generates a menu considering a suitable nutritional balance based on the health condition, and the emotion engine optimizes the menu proposal from the emotional aspect using the emotions at the time of selection and the past emotion history.

[0308] Menu proposal and emotion-based customization

[0309] The server sends the generated menu to the employee's terminal. The terminal displays a customized menu based on the results of both the AI ​​and the emotion engine, presenting the user with the best options. These suggestions may include health promotion discounts and options designed to improve emotional satisfaction.

[0310] Recording of selections and history

[0311] Users make selections from a menu presented on their terminal. The server records these selections to ensure they are appropriate for the employee's current health and emotional state. Similarly, the results of the emotion engine's analysis are also recorded and used to improve subsequent menu suggestions.

[0312] Specific example

[0313] For example, if employee C is found to be deficient in vitamin D during a health checkup while experiencing a period of high stress, the server uses its AI engine to suggest vitamin-rich menus effective in reducing stress, and its emotion engine to present options that take employee C's stress level into consideration. This allows C to choose a menu that is emotionally satisfying, thus improving both their physical and emotional well-being.

[0314] This system enables support for employees' health and emotional well-being, leading to more effective promotion of cafeteria use.

[0315] The following describes the processing flow.

[0316] Step 1:

[0317] The server collects employee health and emotional data. Health data comes from regular health checkup results and information from wearable devices, while emotional data is obtained from facial recognition software and automated questionnaires.

[0318] Step 2:

[0319] The server integrates the collected data and prepares it for analysis by the AI ​​engine and emotion engine. Health data and emotion data are linked to individual employee profiles.

[0320] Step 3:

[0321] The server uses an AI engine to generate a meal plan that considers nutritional balance based on health data. At the same time, an emotion engine analyzes the user's emotional data and customizes the menu according to their current emotional state.

[0322] Step 4:

[0323] The server sends the generated menu suggestions to the employee's terminal. The terminal receives this information and presents the suggested menu through a user-facing interface. Here, discount information and elements that enhance emotional satisfaction are specially displayed.

[0324] Step 5:

[0325] The user selects from a menu presented on the terminal. The selection matches the user's health and emotional state, and the server records the received selection in the system's database.

[0326] Step 6:

[0327] The server uses recorded selection and sentiment data to adjust its model for future menu suggestions. This ensures that subsequent suggestions are more personalized and emotionally and healthily appropriate.

[0328] (Example 2)

[0329] 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".

[0330] In employee health management, there is a need for a system that can propose meal plans that not only aim to improve physical health but also consider emotional satisfaction. Therefore, it is necessary to analyze health and emotional information in real time and provide the optimal cafeteria menu for each individual employee to increase corporate productivity.

[0331] 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.

[0332] In this invention, the server includes means for collecting employee health information and emotional information, means for a generative AI model to propose an optimal cafeteria menu based on the health information and emotional information, and means for displaying the proposed menu on a terminal and customizing it using the results of the emotional engine. This enables employees to make healthy and emotionally satisfying meal choices, thereby improving the overall vitality and productivity of the company.

[0333] "Employee health information" refers to physical data such as weight and blood pressure, and is information used to assess an employee's health status.

[0334] "Emotional information" refers to data that represents the emotional state of employees, and is collected from sources such as facial expression analysis and survey results.

[0335] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to generate output based on a specific task, and in this context, it is used to suggest the optimal cafeteria menu.

[0336] "Methods for suggesting cafeteria menus" refers to a function that utilizes collected health and emotional information to present the most suitable cafeteria menu.

[0337] An "emotional engine" refers to software or algorithms that analyze the emotional state of employees and utilize the results for various suggestions and customizations.

[0338] "Terminal" refers to a device used by employees to access information, and includes personal computers and tablets.

[0339] In order to implement the invention, it is important to properly implement the various means of this system. This invention primarily operates around three main entities: a server, a terminal, and a user.

[0340] The server is responsible for collecting and managing employee health and emotional information. Health information is obtained through wearable devices and a database of regular health checkups. Emotional information is obtained through facial expression analysis of facial images captured using the terminal's camera and from questionnaires answered on the terminal. This allows the server to understand each employee's individual state in real time.

[0341] Next, the server uses the collected information to activate a generative AI model, which then suggests the optimal cafeteria menu for each employee. The generative AI model analyzes the data and generates a variety of menus that take into account nutritional balance and emotional satisfaction. The emotion engine further optimizes the impact of the suggested menus on employees' emotional well-being and incorporates this into the suggestions.

[0342] The terminal is responsible for displaying customized menus sent from the server to the user. The terminal provides an interface that visually presents the received menus in an easy-to-understand manner and allows sorting according to health-promoting effects and emotional satisfaction.

[0343] The user selects the option best suited to their situation from the menu displayed on the device. The selected menu is sent to the server, and the selection history and sentiment information are recorded in a data store for subsequent suggestions.

[0344] For example, if an employee is experiencing a period of high stress and is diagnosed with a vitamin D deficiency, the server uses a generative AI model to suggest a menu that includes fish dishes rich in vitamin D and herbal teas with stress-reducing effects. The emotion engine takes the employee's stress level into consideration to provide options that offer greater emotional satisfaction.

[0345] An example of a prompt message might be: "Could you please suggest cafeteria menus that take into account the health and emotional state of our employees? Specifically, we would like menu suggestions for employees who are experiencing stress and are deficient in vitamin D."

[0346] This system not only enables employees to make healthy and emotionally satisfying meal choices, but also contributes to maintaining employee health and improving the work environment.

[0347] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0348] Step 1:

[0349] The server collects employee health and emotional information. Inputs include physical data such as weight and blood pressure, obtained from each employee's wearable devices and health management systems. Emotional information inputs include facial expression data captured by the device's camera and emotional responses from a survey system. This data is linked to each employee's ID and stored in a database. The output is a unified health and emotional profile for each employee.

[0350] Step 2:

[0351] The server inputs the collected health and emotional information into a generating AI model and an emotion engine for analysis. First, the AI ​​model generates a nutritionally balanced cafeteria menu based on the health information. For example, for an employee who is deficient in vitamin D, a menu including fish and mushrooms will be generated. Meanwhile, the emotion engine analyzes the emotional information and evaluates the employee's current emotional state. The output of this process is a customized menu proposal that takes both health and emotional states into consideration.

[0352] Step 3:

[0353] The server sends the generated customized menu proposals to the terminal. The terminal builds a user interface based on the received data and displays the information. The input is the menu proposals sent from the server, and the output is a visually organized list of menus presented to the user. Filtering by health benefits and emotional satisfaction is also possible on the terminal.

[0354] Step 4:

[0355] The user makes their selection from a menu displayed on the device. The input is the menu list displayed on the device, and the output is information about the menu selected by the user. Through this process, the user can choose a meal that is best suited to their health and emotional state.

[0356] Step 5:

[0357] The server records data on the menu selected by the user and the emotional information associated with that selection. Inputs include the user's selection data and the results of the emotional analysis. This data is stored in a database to improve the accuracy of future menu suggestions. Output is the addition of new selection and emotional records to the updated database.

[0358] (Application Example 2)

[0359] 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."

[0360] In recent years, with the advancement of automation in manufacturing, efficient operational management of robots within factories has become a critical issue. However, current robot operation management systems do not adequately provide dynamic schedule optimization based on operational data or maintenance suggestions tailored to individual operating conditions. Therefore, there is a need for an integrated management system that proactively prevents robot failures and efficiency declines.

[0361] 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.

[0362] In this invention, the server includes means for collecting employee work data, means for artificial intelligence to propose an optimal machine work schedule based on the work data, and means for providing the proposed schedule to the system and evaluating the need for maintenance when selected. This enables dynamic and effective operational management to prevent robot failures.

[0363] "Operational data" refers to information that shows the working status of equipment and robots operating within a factory, and includes data such as vibration, temperature, and efficient work time measured by sensors.

[0364] "Artificial intelligence" is a technological system that learns and analyzes collected data to automatically perform judgments and predictions similar to those made by humans.

[0365] A "machine operation schedule" is a plan that defines the timetable and sequence for efficiently performing a series of tasks or operations that equipment or robots are responsible for.

[0366] "Maintenance" refers to maintenance work such as inspections, repairs, or adjustments performed to maintain the normal operation of equipment and robots and to prevent malfunctions.

[0367] "Selection data" refers to information about when a proposed work schedule or maintenance plan is selected and put into action, and is a record used to generate future proposals.

[0368] This system combines operational data collection, analysis, scheduling suggestions, and maintenance evaluation to achieve efficient equipment operation management in a factory. The server collects operational data through sensors attached to each piece of equipment within the factory. This data includes vibration, temperature, and work efficiency during operation.

[0369] Next, the server inputs the collected operational data into the AI ​​engine, which performs pattern recognition and anomaly detection. The AI ​​engine utilizes machine learning libraries such as TensorFlow and PyTorch to generate a schedule for optimizing the equipment's operating status. This analysis helps to determine the most efficient work sequence and time allocation.

[0370] Furthermore, the server uses an emotion engine to determine maintenance needs from operational data. This allows it to proactively detect potential failures and propose appropriate maintenance work. The generated proposals are sent in real time to the supervisor's or operator's terminal to support rapid decision-making on-site.

[0371] As a concrete example, if equipment A on a manufacturing line exhibits higher-than-normal vibrations, the AI ​​engine will immediately detect the anomaly, and the emotion engine will suggest maintenance. This information will then be displayed on a terminal, allowing the supervisor to decide on the maintenance.

[0372] An example of a prompt message for a generated AI model is: "The vibration of equipment A in the factory has become abnormally high. Please propose maintenance based on this data."

[0373] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0374] Step 1:

[0375] The server collects operational data from sensors installed on equipment within the factory. This data includes measurements of vibration, temperature, and work efficiency. The server periodically checks this data, cleans up the collected data, and converts it into a format suitable for analysis. The input is raw data from the sensors, and the output is formatted data stored in a database.

[0376] Step 2:

[0377] The server inputs the collected operational data into the AI ​​engine. Here, the AI ​​performs pattern recognition and applies anomaly detection algorithms to evaluate the operational status of the equipment. In this process, TensorFlow is used to run the model and calculate an anomaly score for each piece of equipment. The input is formatted operational data, and the output is a list of anomaly scores.

[0378] Step 3:

[0379] The server generates an optimal machine work schedule based on the output of the AI ​​engine. Here, it takes into account the anomaly score generated by the AI ​​model to formulate the most efficient schedule. This minimizes the risk of equipment failure while proposing an efficient work sequence. The input is the anomaly score, and the output is the work schedule.

[0380] Step 4:

[0381] The server uses an emotion engine to further analyze operational data and AI analysis results to determine the need for maintenance. The emotion engine examines the equipment's operational history and current anomaly score to make maintenance recommendations. This includes early prediction of anomalies and suggestions for specific maintenance methods. The inputs are operational history and anomaly scores, and the output is the maintenance recommendation.

[0382] Step 5:

[0383] The server sends the generated work schedule and maintenance proposals to the supervisor's terminal. The terminal receives the schedule and proposals in real time, helping the supervisor make quick decisions based on them. The input is the work schedule and maintenance proposals, and the output is the information displayed on the terminal screen.

[0384] Step 6:

[0385] The user performs appropriate maintenance and schedule adjustments based on the suggestions displayed on the terminal. Once maintenance is decided, operational data and anomaly scores are updated and reflected in the next suggestion. The input is the suggestion on the terminal screen, and the output is the updated system data.

[0386] 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.

[0387] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0388] 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.

[0389] [Third Embodiment]

[0390] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0391] 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.

[0392] 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).

[0393] 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.

[0394] 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.

[0395] 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).

[0396] 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.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] 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".

[0402] This invention provides a system for suggesting individually customized in-house cafeteria menus based on the health status of employees. The following describes an embodiment of the system.

[0403] Data collection and integration

[0404] The server periodically collects health data for each employee. This includes company health checkup results and data from wearable devices provided voluntarily by employees. The server centrally manages this data by linking it to individual employee IDs.

[0405] AI-based data analysis

[0406] The server uses an AI engine to analyze the collected health data. The AI ​​considers the employee's current health status and past cafeteria menu selection history, and generates an optimal meal plan based on nutritional balance, individual health goals, allergy information, and more.

[0407] Menu customization and suggestions

[0408] The server customizes and delivers the generated meal plan to each employee. This information is notified to the employee's device (smartphone or computer). The device displays an interface for selecting from the menu, and certain menu items are eligible for health-promoting discounts.

[0409] Recording of selections and history

[0410] Users select their desired items from a menu suggested on their terminal. This selection information is managed on the server side and used to improve future menu suggestions. This allows for continuous improvement based on the choices of each individual employee.

[0411] Specific example

[0412] For example, if employee B's recent health checkup reveals high cholesterol, the server will use AI analysis to suggest menus such as "low-fat, high-fiber salads and steamed dishes." These menus will be displayed on B's terminal with a discount, and once B selects a menu item, the history will be recorded and used to improve future AI suggestions.

[0413] This system makes it possible to improve the utilization rate of the cafeteria while maintaining the health of employees.

[0414] The following describes the processing flow.

[0415] Step 1:

[0416] The server collects employee health checkup data and daily health data. This includes various biometric information linked to employee IDs.

[0417] Step 2:

[0418] The server integrates the collected data and creates a dataset for analysis by the AI ​​engine. This dataset includes individual health status and dietary history.

[0419] Step 3:

[0420] The server uses an AI engine to analyze each employee's health status and eating history, and generates an optimal meal plan that takes into account nutritional balance and health goals.

[0421] Step 4:

[0422] The server sends the generated meal plan to the employee's terminal, which displays a customized menu. This menu includes discount information for each option.

[0423] Step 5:

[0424] The user makes a selection from a menu presented on the terminal. This selection has been verified by the server to be appropriate for the user's current health condition.

[0425] Step 6:

[0426] The server records the user's selection data and uses it for future analysis. This accumulated data contributes to improving the AI ​​model for future suggestions.

[0427] (Example 1)

[0428] 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."

[0429] The aim is to effectively improve employees' dietary habits and manage their health according to their individual health conditions, as well as to make the company cafeteria more accessible to employees and promote health maintenance. However, the conventional system made it difficult to suggest individually customized menus based on each employee's health condition and past preferences.

[0430] 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.

[0431] In this invention, the server includes means for acquiring employee biometric information, means for a machine learning engine to generate an optimal meal plan based on the biometric information, means for transferring the generated meal plan to the employee's computer and providing preferential treatment if selected, and means for recording the selection results and using them for future suggestions. This makes it possible to propose menus optimized for each employee's health condition, enabling the provision of a system that simultaneously achieves health maintenance and increases cafeteria utilization.

[0432] The term "employee" refers to an individual who belongs to a company or organization and performs duties within that organization.

[0433] "Biometric information" refers to data that indicates an individual's health status, including, for example, heart rate, steps taken, and sleep patterns.

[0434] A "machine learning engine" refers to a program that analyzes collected data, finds patterns, and executes algorithms to perform predictions and optimizations.

[0435] A "meal plan" refers to a set of menus proposed based on each employee's health condition and preferences.

[0436] "Computer equipment" refers to electronic devices used to receive, display, and process information, and includes personal computers and smartphones.

[0437] "Privilege" refers to discounts or benefits offered when certain conditions are met.

[0438] "Selection results" refers to the history of menus that employees actually chose from the proposed meal plans.

[0439] An "information processing system" refers to a system that includes a series of devices and software that solve problems through data input, processing, and output.

[0440] This invention is an information processing system for suggesting individually customized meal menus to employees in the company cafeteria based on their health status. The following describes a specific implementation of this system.

[0441] The server periodically collects employees' biometric information. This collection utilizes data from the company's health checkup database and data from wearable devices that employees voluntarily connect to. Data from wearable devices is retrieved via an API and securely stored in the database. This data collection can be automated using a task scheduler.

[0442] The server then uses the collected data to run a machine learning engine, such as TensorFlow. The program on the server generates an optimized meal plan for each employee based on their health indicators and past eating history. This generating AI model considers biometric information and past history to analyze the content and nutritional balance of meals to promote the health of each individual employee.

[0443] The generated meal plan is sent via push notification from the server to the employee's device, such as a smartphone or PC. On the device, a user interface is generated where the suggested meal menu can be viewed, and in some cases, a discount may be applied to the selected menu.

[0444] Users, i.e., employees, select their desired items from the menus suggested through the provided interface. The user's selection is recorded on the server and used to generate future meal plans.

[0445] As a concrete example, if an employee's health checkup reveals that they need to limit their salt intake, the server uses a machine learning engine to generate a "low-salt snack and vegetable-centered menu" and notifies the employee of this plan on their device. If the employee selects the plan, a special offer is provided to encourage healthy choices.

[0446] Examples of prompts for the generating AI model include sentences that include specific instructions, such as, "Suggest an appropriate lunch menu based on the employee's health indicators. The reference biometric information should be the most up-to-date data."

[0447] In this way, this invention aims to make it easier for employees to choose a healthy diet, thereby improving health awareness throughout the company and promoting the use of the company cafeteria.

[0448] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0449] Step 1:

[0450] The server collects employee biometric information. It receives data from the company's health checkup database and API data obtained from wearable devices as input. During data collection, the data (e.g., heart rate, steps, blood pressure) is stored in the database in a format that identifies each employee. This collection process is automated by a task scheduler and runs periodically.

[0451] Step 2:

[0452] The server feeds the collected biometric information into a machine learning engine. The set of data collected in step 1 is used as input. This data is processed by the AI ​​model to generate meal plans based on each employee's health status and past eating history. Data analysis is performed using Python scripts and the TensorFlow library, and optimized menu suggestions are output.

[0453] Step 3:

[0454] The server sends the generated meal plan to the employee's device. The input uses the meal plan obtained from the AI ​​model in step 2. Menu information is sent to the device via a push notification service and displayed on the device. This notification includes suggested menu items and special offers, allowing the employee to make a selection.

[0455] Step 4:

[0456] The user selects their desired meal from a menu displayed on the terminal. The terminal uses a visual interface to make the selection based on the menu information provided as input. The selection action is recognized by touch or click, and the result is sent to the server.

[0457] Step 5:

[0458] The server records the user's selections and updates the database for future suggestions. The selection information obtained in step 4 is used as input. The selection history is added to the database and used when generating the next meal plan to provide even more personalized suggestions. This process allows for the optimization of menus to better suit individual health conditions and preferences.

[0459] (Application Example 1)

[0460] 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."

[0461] Providing appropriate nutrition tailored to each employee's health condition is challenging, and individualized support is needed to maintain employee health and improve work efficiency. In this situation, there is a need for a method that efficiently presents meal menus tailored to each employee's health condition and preferences, and continuously optimizes them by utilizing their choices.

[0462] 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.

[0463] In this invention, the server includes means for acquiring information on the health of employees, means for an AI to present an optimal nutritional menu based on the health information, and means for providing the presented menu to the employee through an information display device and applying a value discount when selected. This enables the efficient presentation of personalized nutritional menus to employees and continuous optimization based on their selection history.

[0464] An "employee" is a person who works and performs duties in a factory, workplace, or similar location.

[0465] "Health-related information" refers to data related to an employee's health status, including their physical condition and health checkup results.

[0466] "AI" refers to artificial intelligence, a technology that analyzes large amounts of data to derive optimal results.

[0467] A "nutritional menu" refers to a meal plan proposed based on the health status of employees.

[0468] An "information display device" is a device used to visually present information to employees.

[0469] "Value discount" refers to a discount or reduction from the quoted price.

[0470] "Selection information" refers to data that records the choices made by employees from a presented menu.

[0471] This invention is a system that proposes individually customized nutritional menus based on employees' health information. The server first acquires information about the employees' health. This is done using data collected from wearable devices and the results of health checkups.

[0472] Next, the server uses a generative AI model to analyze the collected health information and create nutritional menus optimized for each employee's health condition. Python is used for processing this AI engine, and machine learning libraries are utilized for data analysis.

[0473] The terminal displays menus sent from the server on an information display device, such as a monitor, allowing employees to select their desired menu items. The selections are sent to and recorded by the server. The server uses this selection information to optimize menu suggestions for future visits.

[0474] This system allows for the suggestion of nutritious meals based on employees' health conditions, and enables the effective use of their selection information. For example, if employee C is identified as having a vitamin D deficiency, the server can suggest a meal rich in vitamin D. By selecting this suggestion, the employee can improve their health.

[0475] Examples of prompts for the generative AI model include: "Please suggest a meal plan for employee C based on recent health data," and "Please create a menu suitable for an employee with a vitamin D deficiency."

[0476] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0477] Step 1:

[0478] The server retrieves health information from employees' wearable devices and health checkup results. This input data includes heart rate, body temperature, blood pressure, and health checkup results. The server stores this data in a database, linked to the employee ID. This information retrieval is performed in real time or at regular intervals and is also managed as a history.

[0479] Step 2:

[0480] The server takes health information obtained from the database as input and performs data analysis using a generative AI model. Specifically, it analyzes employees' health status using Python and machine learning libraries and outputs an optimal nutritional menu that takes into account nutritional balance, allergy information, and health goals. In this process, it calculates the recommended amount of nutrients according to health status and evaluates the nutritional value of each menu.

[0481] Step 3:

[0482] The server sends a nutrition menu generated by AI to the terminal. The terminal displays the menu to the employee via an information display device. At this time, the user interface displays menu details and value information for promoting health. The user selects what they want from the displayed menu and sends that information as input.

[0483] Step 4:

[0484] Menu information selected on the terminal is sent to the server, which records this selection information in a database. This record includes information such as selection history and frequency, as it is used for data analysis to improve subsequent menu suggestions. The server analyzes the user's selection patterns and performs calculations to further improve future suggestions.

[0485] Step 5:

[0486] The server repeatedly learns health information and menu selection history based on the history and selection information recorded up to this point, in preparation for the next suggestion. By updating the generating AI model, the accuracy of the next menu suggestion is improved. Through this feedback loop, menu suggestions become dynamic and personalized, contributing to improved employee health.

[0487] 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.

[0488] This invention provides a system that enables the suggestion of cafeteria menus that simultaneously consider the health status and emotions of employees. The system incorporates an emotion engine that analyzes employee emotional data to support more appropriate menu selections.

[0489] Data collection and integration

[0490] The server collects health and emotional data for each employee. Health data includes physical information such as weight and blood pressure, while emotional data includes results from facial expression analysis during menu selections and automated questionnaires. The server manages this data based on the employee ID.

[0491] Data analysis using AI and emotion engines

[0492] The server analyzes collected health and emotional data using an AI engine and an emotion engine. The AI ​​engine generates menus that consider appropriate nutritional balance based on the user's health status, while the emotion engine optimizes menu suggestions from an emotional perspective, using the user's emotions at the time of selection and their past emotional history.

[0493] Menu suggestions and emotion-based customization

[0494] The server sends the generated menu to the employee's terminal. The terminal displays a customized menu based on the results of both the AI ​​and the emotion engine, presenting the user with the best options. These suggestions may include health promotion discounts and options designed to improve emotional satisfaction.

[0495] Recording of selections and history

[0496] Users make selections from a menu presented on their terminal. The server records these selections to ensure they are appropriate for the employee's current health and emotional state. Similarly, the results of the emotion engine's analysis are also recorded and used to improve subsequent menu suggestions.

[0497] Specific example

[0498] For example, if employee C is found to be deficient in vitamin D during a health checkup while experiencing a period of high stress, the server uses its AI engine to suggest vitamin-rich menus effective in reducing stress, and its emotion engine to present options that take employee C's stress level into consideration. This allows C to choose a menu that is emotionally satisfying, thus improving both their physical and emotional well-being.

[0499] This system enables support for employees' health and emotional well-being, leading to more effective promotion of cafeteria use.

[0500] The following describes the processing flow.

[0501] Step 1:

[0502] The server collects employee health and emotional data. Health data comes from regular health checkup results and information from wearable devices, while emotional data is obtained from facial recognition software and automated questionnaires.

[0503] Step 2:

[0504] The server integrates the collected data and prepares it for analysis by the AI ​​engine and emotion engine. Health data and emotion data are linked to individual employee profiles.

[0505] Step 3:

[0506] The server uses an AI engine to generate a meal plan that considers nutritional balance based on health data. At the same time, an emotion engine analyzes the user's emotional data and customizes the menu according to their current emotional state.

[0507] Step 4:

[0508] The server sends the generated menu suggestions to the employee's terminal. The terminal receives this information and presents the suggested menu through a user-facing interface. Here, discount information and elements that enhance emotional satisfaction are specially displayed.

[0509] Step 5:

[0510] The user selects from a menu presented on the terminal. The selection matches the user's health and emotional state, and the server records the received selection in the system's database.

[0511] Step 6:

[0512] The server uses recorded selection and sentiment data to adjust its model for future menu suggestions. This ensures that subsequent suggestions are more personalized and emotionally and healthily appropriate.

[0513] (Example 2)

[0514] 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."

[0515] In employee health management, there is a need for a system that can propose meal plans that not only aim to improve physical health but also consider emotional satisfaction. Therefore, it is necessary to analyze health and emotional information in real time and provide the optimal cafeteria menu for each individual employee to increase corporate productivity.

[0516] 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.

[0517] In this invention, the server includes means for collecting employee health information and emotional information, means for a generative AI model to propose an optimal cafeteria menu based on the health information and emotional information, and means for displaying the proposed menu on a terminal and customizing it using the results of the emotional engine. This enables employees to make healthy and emotionally satisfying meal choices, thereby improving the overall vitality and productivity of the company.

[0518] "Employee health information" refers to physical data such as weight and blood pressure, and is information used to assess an employee's health status.

[0519] "Emotional information" refers to data that represents the emotional state of employees, and is collected from sources such as facial expression analysis and survey results.

[0520] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to generate output based on a specific task, and in this context, it is used to suggest the optimal cafeteria menu.

[0521] "Methods for suggesting cafeteria menus" refers to a function that utilizes collected health and emotional information to present the most suitable cafeteria menu.

[0522] An "emotional engine" refers to software or algorithms that analyze the emotional state of employees and utilize the results for various suggestions and customizations.

[0523] "Terminal" refers to a device used by employees to access information, and includes personal computers and tablets.

[0524] In order to implement the invention, it is important to properly implement the various means of this system. This invention primarily operates around three main entities: a server, a terminal, and a user.

[0525] The server is responsible for collecting and managing employee health and emotional information. Health information is obtained through wearable devices and a database of regular health checkups. Emotional information is obtained through facial expression analysis of facial images captured using the terminal's camera and from questionnaires answered on the terminal. This allows the server to understand each employee's individual state in real time.

[0526] Next, the server uses the collected information to activate a generative AI model, which then suggests the optimal cafeteria menu for each employee. The generative AI model analyzes the data and generates a variety of menus that take into account nutritional balance and emotional satisfaction. The emotion engine further optimizes the impact of the suggested menus on employees' emotional well-being and incorporates this into the suggestions.

[0527] The terminal is responsible for displaying customized menus sent from the server to the user. The terminal provides an interface that visually presents the received menus in an easy-to-understand manner and allows sorting according to health-promoting effects and emotional satisfaction.

[0528] The user selects the option best suited to their situation from the menu displayed on the device. The selected menu is sent to the server, and the selection history and sentiment information are recorded in a data store for subsequent suggestions.

[0529] For example, if an employee is experiencing a period of high stress and is diagnosed with a vitamin D deficiency, the server uses a generative AI model to suggest a menu that includes fish dishes rich in vitamin D and herbal teas with stress-reducing effects. The emotion engine takes the employee's stress level into consideration to provide options that offer greater emotional satisfaction.

[0530] An example of a prompt message might be: "Could you please suggest cafeteria menus that take into account the health and emotional state of our employees? Specifically, we would like menu suggestions for employees who are experiencing stress and are deficient in vitamin D."

[0531] This system not only enables employees to make healthy and emotionally satisfying meal choices, but also contributes to maintaining employee health and improving the work environment.

[0532] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0533] Step 1:

[0534] The server collects employee health and emotional information. Inputs include physical data such as weight and blood pressure, obtained from each employee's wearable devices and health management systems. Emotional information inputs include facial expression data captured by the device's camera and emotional responses from a survey system. This data is linked to each employee's ID and stored in a database. The output is a unified health and emotional profile for each employee.

[0535] Step 2:

[0536] The server inputs the collected health and emotional information into a generating AI model and an emotion engine for analysis. First, the AI ​​model generates a nutritionally balanced cafeteria menu based on the health information. For example, for an employee who is deficient in vitamin D, a menu including fish and mushrooms will be generated. Meanwhile, the emotion engine analyzes the emotional information and evaluates the employee's current emotional state. The output of this process is a customized menu proposal that takes both health and emotional states into consideration.

[0537] Step 3:

[0538] The server sends the generated customized menu proposals to the terminal. The terminal builds a user interface based on the received data and displays the information. The input is the menu proposals sent from the server, and the output is a visually organized list of menus presented to the user. Filtering by health benefits and emotional satisfaction is also possible on the terminal.

[0539] Step 4:

[0540] The user makes their selection from a menu displayed on the device. The input is the menu list displayed on the device, and the output is information about the menu selected by the user. Through this process, the user can choose a meal that is best suited to their health and emotional state.

[0541] Step 5:

[0542] The server records data on the menu selected by the user and the emotional information associated with that selection. Inputs include the user's selection data and the results of the emotional analysis. This data is stored in a database to improve the accuracy of future menu suggestions. Output is the addition of new selection and emotional records to the updated database.

[0543] (Application Example 2)

[0544] 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."

[0545] In recent years, with the advancement of automation in manufacturing, efficient operational management of robots within factories has become a critical issue. However, current robot operation management systems do not adequately provide dynamic schedule optimization based on operational data or maintenance suggestions tailored to individual operating conditions. Therefore, there is a need for an integrated management system that proactively prevents robot failures and efficiency declines.

[0546] 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.

[0547] In this invention, the server includes means for collecting employee work data, means for artificial intelligence to propose an optimal machine work schedule based on the work data, and means for providing the proposed schedule to the system and evaluating the need for maintenance when selected. This enables dynamic and effective operational management to prevent robot failures.

[0548] "Operational data" refers to information that shows the working status of equipment and robots operating within a factory, and includes data such as vibration, temperature, and efficient work time measured by sensors.

[0549] "Artificial intelligence" is a technological system that learns and analyzes collected data to automatically perform judgments and predictions similar to those made by humans.

[0550] A "machine operation schedule" is a plan that defines the timetable and sequence for efficiently performing a series of tasks or operations that equipment or robots are responsible for.

[0551] "Maintenance" refers to maintenance work such as inspections, repairs, or adjustments performed to maintain the normal operation of equipment and robots and to prevent malfunctions.

[0552] "Selection data" refers to information about when a proposed work schedule or maintenance plan is selected and put into action, and is a record used to generate future proposals.

[0553] This system combines operational data collection, analysis, scheduling suggestions, and maintenance evaluation to achieve efficient equipment operation management in a factory. The server collects operational data through sensors attached to each piece of equipment within the factory. This data includes vibration, temperature, and work efficiency during operation.

[0554] Next, the server inputs the collected operational data into the AI ​​engine, which performs pattern recognition and anomaly detection. The AI ​​engine utilizes machine learning libraries such as TensorFlow and PyTorch to generate a schedule for optimizing the equipment's operating status. This analysis helps to determine the most efficient work sequence and time allocation.

[0555] Furthermore, the server uses an emotion engine to determine maintenance needs from operational data. This allows it to proactively detect potential failures and propose appropriate maintenance work. The generated proposals are sent in real time to the supervisor's or operator's terminal to support rapid decision-making on-site.

[0556] As a concrete example, if equipment A on a manufacturing line exhibits higher-than-normal vibrations, the AI ​​engine will immediately detect the anomaly, and the emotion engine will suggest maintenance. This information will then be displayed on a terminal, allowing the supervisor to decide on the maintenance.

[0557] An example of a prompt message for a generated AI model is: "The vibration of equipment A in the factory has become abnormally high. Please propose maintenance based on this data."

[0558] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0559] Step 1:

[0560] The server collects operational data from sensors installed on equipment within the factory. This data includes measurements of vibration, temperature, and work efficiency. The server periodically checks this data, cleans up the collected data, and converts it into a format suitable for analysis. The input is raw data from the sensors, and the output is formatted data stored in a database.

[0561] Step 2:

[0562] The server inputs the collected operational data into the AI ​​engine. Here, the AI ​​performs pattern recognition and applies anomaly detection algorithms to evaluate the operational status of the equipment. In this process, TensorFlow is used to run the model and calculate an anomaly score for each piece of equipment. The input is formatted operational data, and the output is a list of anomaly scores.

[0563] Step 3:

[0564] The server generates an optimal machine work schedule based on the output of the AI ​​engine. Here, it takes into account the anomaly score generated by the AI ​​model to formulate the most efficient schedule. This minimizes the risk of equipment failure while proposing an efficient work sequence. The input is the anomaly score, and the output is the work schedule.

[0565] Step 4:

[0566] The server uses an emotion engine to further analyze operational data and AI analysis results to determine the need for maintenance. The emotion engine examines the equipment's operational history and current anomaly score to make maintenance recommendations. This includes early prediction of anomalies and suggestions for specific maintenance methods. The inputs are operational history and anomaly scores, and the output is the maintenance recommendation.

[0567] Step 5:

[0568] The server sends the generated work schedule and maintenance proposals to the supervisor's terminal. The terminal receives the schedule and proposals in real time, helping the supervisor make quick decisions based on them. The input is the work schedule and maintenance proposals, and the output is the information displayed on the terminal screen.

[0569] Step 6:

[0570] The user performs appropriate maintenance and schedule adjustments based on the suggestions displayed on the terminal. Once maintenance is decided, operational data and anomaly scores are updated and reflected in the next suggestion. The input is the suggestion on the terminal screen, and the output is the updated system data.

[0571] 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.

[0572] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0573] 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.

[0574] [Fourth Embodiment]

[0575] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0576] 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.

[0577] 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).

[0578] 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.

[0579] 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.

[0580] 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).

[0581] 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.

[0582] 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.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] 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.

[0587] 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".

[0588] This invention provides a system for suggesting individually customized in-house cafeteria menus based on the health status of employees. The following describes an embodiment of the system.

[0589] Data collection and integration

[0590] The server periodically collects health data for each employee. This includes company health checkup results and data from wearable devices provided voluntarily by employees. The server centrally manages this data by linking it to individual employee IDs.

[0591] AI-based data analysis

[0592] The server uses an AI engine to analyze the collected health data. The AI ​​considers the employee's current health status and past cafeteria menu selection history, and generates an optimal meal plan based on nutritional balance, individual health goals, allergy information, and more.

[0593] Menu customization and suggestions

[0594] The server customizes and delivers the generated meal plan to each employee. This information is notified to the employee's device (smartphone or computer). The device displays an interface for selecting from the menu, and certain menu items are eligible for health-promoting discounts.

[0595] Recording of selections and history

[0596] Users select their desired items from a menu suggested on their terminal. This selection information is managed on the server side and used to improve future menu suggestions. This allows for continuous improvement based on the choices of each individual employee.

[0597] Specific example

[0598] For example, if employee B's recent health checkup reveals high cholesterol, the server will use AI analysis to suggest menus such as "low-fat, high-fiber salads and steamed dishes." These menus will be displayed on B's terminal with a discount, and once B selects a menu item, the history will be recorded and used to improve future AI suggestions.

[0599] This system makes it possible to improve the utilization rate of the cafeteria while maintaining the health of employees.

[0600] The following describes the processing flow.

[0601] Step 1:

[0602] The server collects employee health checkup data and daily health data. This includes various biometric information linked to employee IDs.

[0603] Step 2:

[0604] The server integrates the collected data and creates a dataset for analysis by the AI ​​engine. This dataset includes individual health status and dietary history.

[0605] Step 3:

[0606] The server uses an AI engine to analyze each employee's health status and eating history, and generates an optimal meal plan that takes into account nutritional balance and health goals.

[0607] Step 4:

[0608] The server sends the generated meal plan to the employee's terminal, which displays a customized menu. This menu includes discount information for each option.

[0609] Step 5:

[0610] The user makes a selection from a menu presented on the terminal. This selection has been verified by the server to be appropriate for the user's current health condition.

[0611] Step 6:

[0612] The server records the user's selection data and uses it for future analysis. This accumulated data contributes to improving the AI ​​model for future suggestions.

[0613] (Example 1)

[0614] 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".

[0615] The aim is to effectively improve employees' dietary habits and manage their health according to their individual health conditions, as well as to make the company cafeteria more accessible to employees and promote health maintenance. However, the conventional system made it difficult to suggest individually customized menus based on each employee's health condition and past preferences.

[0616] 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.

[0617] In this invention, the server includes means for acquiring employee biometric information, means for a machine learning engine to generate an optimal meal plan based on the biometric information, means for transferring the generated meal plan to the employee's computer and providing preferential treatment if selected, and means for recording the selection results and using them for future suggestions. This makes it possible to propose menus optimized for each employee's health condition, enabling the provision of a system that simultaneously achieves health maintenance and increases cafeteria utilization.

[0618] The term "employee" refers to an individual who belongs to a company or organization and performs duties within that organization.

[0619] "Biometric information" refers to data that indicates an individual's health status, including, for example, heart rate, steps taken, and sleep patterns.

[0620] A "machine learning engine" refers to a program that analyzes collected data, finds patterns, and executes algorithms to perform predictions and optimizations.

[0621] A "meal plan" refers to a set of menus proposed based on each employee's health condition and preferences.

[0622] "Computer equipment" refers to electronic devices used to receive, display, and process information, and includes personal computers and smartphones.

[0623] "Privilege" refers to discounts or benefits offered when certain conditions are met.

[0624] "Selection results" refers to the history of menus that employees actually chose from the proposed meal plans.

[0625] An "information processing system" refers to a system that includes a series of devices and software that solve problems through data input, processing, and output.

[0626] This invention is an information processing system for suggesting individually customized meal menus to employees in the company cafeteria based on their health status. The following describes a specific implementation of this system.

[0627] The server periodically collects employees' biometric information. This collection utilizes data from the company's health checkup database and data from wearable devices that employees voluntarily connect to. Data from wearable devices is retrieved via an API and securely stored in the database. This data collection can be automated using a task scheduler.

[0628] The server then uses the collected data to run a machine learning engine, such as TensorFlow. The program on the server generates an optimized meal plan for each employee based on their health indicators and past eating history. This generating AI model considers biometric information and past history to analyze the content and nutritional balance of meals to promote the health of each individual employee.

[0629] The generated meal plan is sent via push notification from the server to the employee's device, such as a smartphone or PC. On the device, a user interface is generated where the suggested meal menu can be viewed, and in some cases, a discount may be applied to the selected menu.

[0630] Users, i.e., employees, select their desired items from the menus suggested through the provided interface. The user's selection is recorded on the server and used to generate future meal plans.

[0631] As a concrete example, if an employee's health checkup reveals that they need to limit their salt intake, the server uses a machine learning engine to generate a "low-salt snack and vegetable-centered menu" and notifies the employee of this plan on their device. If the employee selects the plan, a special offer is provided to encourage healthy choices.

[0632] Examples of prompts for the generating AI model include sentences that include specific instructions, such as, "Suggest an appropriate lunch menu based on the employee's health indicators. The reference biometric information should be the most up-to-date data."

[0633] In this way, this invention aims to make it easier for employees to choose a healthy diet, thereby improving health awareness throughout the company and promoting the use of the company cafeteria.

[0634] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0635] Step 1:

[0636] The server collects employee biometric information. It receives data from the company's health checkup database and API data obtained from wearable devices as input. During data collection, the data (e.g., heart rate, steps, blood pressure) is stored in the database in a format that identifies each employee. This collection process is automated by a task scheduler and runs periodically.

[0637] Step 2:

[0638] The server feeds the collected biometric information into a machine learning engine. The set of data collected in step 1 is used as input. This data is processed by the AI ​​model to generate meal plans based on each employee's health status and past eating history. Data analysis is performed using Python scripts and the TensorFlow library, and optimized menu suggestions are output.

[0639] Step 3:

[0640] The server sends the generated meal plan to the employee's device. The input uses the meal plan obtained from the AI ​​model in step 2. Menu information is sent to the device via a push notification service and displayed on the device. This notification includes suggested menu items and special offers, allowing the employee to make a selection.

[0641] Step 4:

[0642] The user selects their desired meal from a menu displayed on the terminal. The terminal uses a visual interface to make the selection based on the menu information provided as input. The selection action is recognized by touch or click, and the result is sent to the server.

[0643] Step 5:

[0644] The server records the user's selections and updates the database for future suggestions. The selection information obtained in step 4 is used as input. The selection history is added to the database and used when generating the next meal plan to provide even more personalized suggestions. This process allows for the optimization of menus to better suit individual health conditions and preferences.

[0645] (Application Example 1)

[0646] 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".

[0647] Providing appropriate nutrition tailored to each employee's health condition is challenging, and individualized support is needed to maintain employee health and improve work efficiency. In this situation, there is a need for a method that efficiently presents meal menus tailored to each employee's health condition and preferences, and continuously optimizes them by utilizing their choices.

[0648] 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.

[0649] In this invention, the server includes means for acquiring information on the health of employees, means for an AI to present an optimal nutritional menu based on the health information, and means for providing the presented menu to the employee through an information display device and applying a value discount when selected. This enables the efficient presentation of personalized nutritional menus to employees and continuous optimization based on their selection history.

[0650] An "employee" is a person who works and performs duties in a factory, workplace, or similar location.

[0651] "Health-related information" refers to data related to an employee's health status, including their physical condition and health checkup results.

[0652] "AI" refers to artificial intelligence, a technology that analyzes large amounts of data to derive optimal results.

[0653] A "nutritional menu" refers to a meal plan proposed based on the health status of employees.

[0654] An "information display device" is a device used to visually present information to employees.

[0655] "Value discount" refers to a discount or reduction from the quoted price.

[0656] "Selection information" refers to data that records the choices made by employees from a presented menu.

[0657] This invention is a system that proposes individually customized nutritional menus based on employees' health information. The server first acquires information about the employees' health. This is done using data collected from wearable devices and the results of health checkups.

[0658] Next, the server uses a generative AI model to analyze the collected health information and create nutritional menus optimized for each employee's health condition. Python is used for processing this AI engine, and machine learning libraries are utilized for data analysis.

[0659] The terminal displays menus sent from the server on an information display device, such as a monitor, allowing employees to select their desired menu items. The selections are sent to and recorded by the server. The server uses this selection information to optimize menu suggestions for future visits.

[0660] This system allows for the suggestion of nutritious meals based on employees' health conditions, and enables the effective use of their selection information. For example, if employee C is identified as having a vitamin D deficiency, the server can suggest a meal rich in vitamin D. By selecting this suggestion, the employee can improve their health.

[0661] Examples of prompts for the generative AI model include: "Please suggest a meal plan for employee C based on recent health data," and "Please create a menu suitable for an employee with a vitamin D deficiency."

[0662] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0663] Step 1:

[0664] The server retrieves health information from employees' wearable devices and health checkup results. This input data includes heart rate, body temperature, blood pressure, and health checkup results. The server stores this data in a database, linked to the employee ID. This information retrieval is performed in real time or at regular intervals and is also managed as a history.

[0665] Step 2:

[0666] The server takes health information obtained from the database as input and performs data analysis using a generative AI model. Specifically, it analyzes employees' health status using Python and machine learning libraries and outputs an optimal nutritional menu that takes into account nutritional balance, allergy information, and health goals. In this process, it calculates the recommended amount of nutrients according to health status and evaluates the nutritional value of each menu.

[0667] Step 3:

[0668] The server sends a nutrition menu generated by AI to the terminal. The terminal displays the menu to the employee via an information display device. At this time, the user interface displays menu details and value information for promoting health. The user selects what they want from the displayed menu and sends that information as input.

[0669] Step 4:

[0670] Menu information selected on the terminal is sent to the server, which records this selection information in a database. This record includes information such as selection history and frequency, as it is used for data analysis to improve subsequent menu suggestions. The server analyzes the user's selection patterns and performs calculations to further improve future suggestions.

[0671] Step 5:

[0672] The server repeatedly learns health information and menu selection history based on the history and selection information recorded up to this point, in preparation for the next suggestion. By updating the generating AI model, the accuracy of the next menu suggestion is improved. Through this feedback loop, menu suggestions become dynamic and personalized, contributing to improved employee health.

[0673] 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.

[0674] This invention provides a system that enables the suggestion of cafeteria menus that simultaneously consider the health status and emotions of employees. The system incorporates an emotion engine that analyzes employee emotional data to support more appropriate menu selections.

[0675] Data collection and integration

[0676] The server collects health and emotional data for each employee. Health data includes physical information such as weight and blood pressure, while emotional data includes results from facial expression analysis during menu selections and automated questionnaires. The server manages this data based on the employee ID.

[0677] Data analysis using AI and emotion engines

[0678] The server analyzes collected health and emotional data using an AI engine and an emotion engine. The AI ​​engine generates menus that consider appropriate nutritional balance based on the user's health status, while the emotion engine optimizes menu suggestions from an emotional perspective, using the user's emotions at the time of selection and their past emotional history.

[0679] Menu suggestions and emotion-based customization

[0680] The server sends the generated menu to the employee's terminal. The terminal displays a customized menu based on the results of both the AI ​​and the emotion engine, presenting the user with the best options. These suggestions may include health promotion discounts and options designed to improve emotional satisfaction.

[0681] Recording of selections and history

[0682] Users make selections from a menu presented on their terminal. The server records these selections to ensure they are appropriate for the employee's current health and emotional state. Similarly, the results of the emotion engine's analysis are also recorded and used to improve subsequent menu suggestions.

[0683] Specific example

[0684] For example, if employee C is found to be deficient in vitamin D during a health checkup while experiencing a period of high stress, the server uses its AI engine to suggest vitamin-rich menus effective in reducing stress, and its emotion engine to present options that take employee C's stress level into consideration. This allows C to choose a menu that is emotionally satisfying, thus improving both their physical and emotional well-being.

[0685] This system enables support for employees' health and emotional well-being, leading to more effective promotion of cafeteria use.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] The server collects employee health and emotional data. Health data comes from regular health checkup results and information from wearable devices, while emotional data is obtained from facial recognition software and automated questionnaires.

[0689] Step 2:

[0690] The server integrates the collected data and prepares it for analysis by the AI ​​engine and emotion engine. Health data and emotion data are linked to individual employee profiles.

[0691] Step 3:

[0692] The server uses an AI engine to generate a meal plan that considers nutritional balance based on health data. At the same time, an emotion engine analyzes the user's emotional data and customizes the menu according to their current emotional state.

[0693] Step 4:

[0694] The server sends the generated menu suggestions to the employee's terminal. The terminal receives this information and presents the suggested menu through a user-facing interface. Here, discount information and elements that enhance emotional satisfaction are specially displayed.

[0695] Step 5:

[0696] The user selects from a menu presented on the terminal. The selection matches the user's health and emotional state, and the server records the received selection in the system's database.

[0697] Step 6:

[0698] The server uses recorded selection and sentiment data to adjust its model for future menu suggestions. This ensures that subsequent suggestions are more personalized and emotionally and healthily appropriate.

[0699] (Example 2)

[0700] 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".

[0701] In employee health management, there is a need for a system that can propose meal plans that not only aim to improve physical health but also consider emotional satisfaction. Therefore, it is necessary to analyze health and emotional information in real time and provide the optimal cafeteria menu for each individual employee to increase corporate productivity.

[0702] 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.

[0703] In this invention, the server includes means for collecting employee health information and emotional information, means for a generative AI model to propose an optimal cafeteria menu based on the health information and emotional information, and means for displaying the proposed menu on a terminal and customizing it using the results of the emotional engine. This enables employees to make healthy and emotionally satisfying meal choices, thereby improving the overall vitality and productivity of the company.

[0704] "Employee health information" refers to physical data such as weight and blood pressure, and is information used to assess an employee's health status.

[0705] "Emotional information" refers to data that represents the emotional state of employees, and is collected from sources such as facial expression analysis and survey results.

[0706] A "generative AI model" refers to an algorithm that uses artificial intelligence technology to generate output based on a specific task, and in this context, it is used to suggest the optimal cafeteria menu.

[0707] "Methods for suggesting cafeteria menus" refers to a function that utilizes collected health and emotional information to present the most suitable cafeteria menu.

[0708] An "emotional engine" refers to software or algorithms that analyze the emotional state of employees and utilize the results for various suggestions and customizations.

[0709] "Terminal" refers to a device used by employees to access information, and includes personal computers and tablets.

[0710] In order to implement the invention, it is important to properly implement the various means of this system. This invention primarily operates around three main entities: a server, a terminal, and a user.

[0711] The server is responsible for collecting and managing employee health and emotional information. Health information is obtained through wearable devices and a database of regular health checkups. Emotional information is obtained through facial expression analysis of facial images captured using the terminal's camera and from questionnaires answered on the terminal. This allows the server to understand each employee's individual state in real time.

[0712] Next, the server uses the collected information to activate a generative AI model, which then suggests the optimal cafeteria menu for each employee. The generative AI model analyzes the data and generates a variety of menus that take into account nutritional balance and emotional satisfaction. The emotion engine further optimizes the impact of the suggested menus on employees' emotional well-being and incorporates this into the suggestions.

[0713] The terminal is responsible for displaying customized menus sent from the server to the user. The terminal provides an interface that visually presents the received menus in an easy-to-understand manner and allows sorting according to health-promoting effects and emotional satisfaction.

[0714] The user selects the option best suited to their situation from the menu displayed on the device. The selected menu is sent to the server, and the selection history and sentiment information are recorded in a data store for subsequent suggestions.

[0715] For example, if an employee is experiencing a period of high stress and is diagnosed with a vitamin D deficiency, the server uses a generative AI model to suggest a menu that includes fish dishes rich in vitamin D and herbal teas with stress-reducing effects. The emotion engine takes the employee's stress level into consideration to provide options that offer greater emotional satisfaction.

[0716] An example of a prompt message might be: "Could you please suggest cafeteria menus that take into account the health and emotional state of our employees? Specifically, we would like menu suggestions for employees who are experiencing stress and are deficient in vitamin D."

[0717] This system not only enables employees to make healthy and emotionally satisfying meal choices, but also contributes to maintaining employee health and improving the work environment.

[0718] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0719] Step 1:

[0720] The server collects employee health and emotional information. Inputs include physical data such as weight and blood pressure, obtained from each employee's wearable devices and health management systems. Emotional information inputs include facial expression data captured by the device's camera and emotional responses from a survey system. This data is linked to each employee's ID and stored in a database. The output is a unified health and emotional profile for each employee.

[0721] Step 2:

[0722] The server inputs the collected health and emotional information into a generating AI model and an emotion engine for analysis. First, the AI ​​model generates a nutritionally balanced cafeteria menu based on the health information. For example, for an employee who is deficient in vitamin D, a menu including fish and mushrooms will be generated. Meanwhile, the emotion engine analyzes the emotional information and evaluates the employee's current emotional state. The output of this process is a customized menu proposal that takes both health and emotional states into consideration.

[0723] Step 3:

[0724] The server sends the generated customized menu proposals to the terminal. The terminal builds a user interface based on the received data and displays the information. The input is the menu proposals sent from the server, and the output is a visually organized list of menus presented to the user. Filtering by health benefits and emotional satisfaction is also possible on the terminal.

[0725] Step 4:

[0726] The user makes their selection from a menu displayed on the device. The input is the menu list displayed on the device, and the output is information about the menu selected by the user. Through this process, the user can choose a meal that is best suited to their health and emotional state.

[0727] Step 5:

[0728] The server records data on the menu selected by the user and the emotional information associated with that selection. Inputs include the user's selection data and the results of the emotional analysis. This data is stored in a database to improve the accuracy of future menu suggestions. Output is the addition of new selection and emotional records to the updated database.

[0729] (Application Example 2)

[0730] 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".

[0731] In recent years, with the advancement of automation in manufacturing, efficient operational management of robots within factories has become a critical issue. However, current robot operation management systems do not adequately provide dynamic schedule optimization based on operational data or maintenance suggestions tailored to individual operating conditions. Therefore, there is a need for an integrated management system that proactively prevents robot failures and efficiency declines.

[0732] 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.

[0733] In this invention, the server includes means for collecting employee work data, means for artificial intelligence to propose an optimal machine work schedule based on the work data, and means for providing the proposed schedule to the system and evaluating the need for maintenance when selected. This enables dynamic and effective operational management to prevent robot failures.

[0734] "Operational data" refers to information that shows the working status of equipment and robots operating within a factory, and includes data such as vibration, temperature, and efficient work time measured by sensors.

[0735] "Artificial intelligence" is a technological system that learns and analyzes collected data to automatically perform judgments and predictions similar to those made by humans.

[0736] A "machine operation schedule" is a plan that defines the timetable and sequence for efficiently performing a series of tasks or operations that equipment or robots are responsible for.

[0737] "Maintenance" refers to maintenance work such as inspections, repairs, or adjustments performed to maintain the normal operation of equipment and robots and to prevent malfunctions.

[0738] "Selection data" refers to information about when a proposed work schedule or maintenance plan is selected and put into action, and is a record used to generate future proposals.

[0739] This system combines operational data collection, analysis, scheduling suggestions, and maintenance evaluation to achieve efficient equipment operation management in a factory. The server collects operational data through sensors attached to each piece of equipment within the factory. This data includes vibration, temperature, and work efficiency during operation.

[0740] Next, the server inputs the collected operational data into the AI ​​engine, which performs pattern recognition and anomaly detection. The AI ​​engine utilizes machine learning libraries such as TensorFlow and PyTorch to generate a schedule for optimizing the equipment's operating status. This analysis helps to determine the most efficient work sequence and time allocation.

[0741] Furthermore, the server uses an emotion engine to determine maintenance needs from operational data. This allows it to proactively detect potential failures and propose appropriate maintenance work. The generated proposals are sent in real time to the supervisor's or operator's terminal to support rapid decision-making on-site.

[0742] As a concrete example, if equipment A on a manufacturing line exhibits higher-than-normal vibrations, the AI ​​engine will immediately detect the anomaly, and the emotion engine will suggest maintenance. This information will then be displayed on a terminal, allowing the supervisor to decide on the maintenance.

[0743] An example of a prompt message for a generated AI model is: "The vibration of equipment A in the factory has become abnormally high. Please propose maintenance based on this data."

[0744] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0745] Step 1:

[0746] The server collects operational data from sensors installed on equipment within the factory. This data includes measurements of vibration, temperature, and work efficiency. The server periodically checks this data, cleans up the collected data, and converts it into a format suitable for analysis. The input is raw data from the sensors, and the output is formatted data stored in a database.

[0747] Step 2:

[0748] The server inputs the collected operational data into the AI ​​engine. Here, the AI ​​performs pattern recognition and applies anomaly detection algorithms to evaluate the operational status of the equipment. In this process, TensorFlow is used to run the model and calculate an anomaly score for each piece of equipment. The input is formatted operational data, and the output is a list of anomaly scores.

[0749] Step 3:

[0750] The server generates an optimal machine work schedule based on the output of the AI ​​engine. Here, it takes into account the anomaly score generated by the AI ​​model to formulate the most efficient schedule. This minimizes the risk of equipment failure while proposing an efficient work sequence. The input is the anomaly score, and the output is the work schedule.

[0751] Step 4:

[0752] The server uses an emotion engine to further analyze operational data and AI analysis results to determine the need for maintenance. The emotion engine examines the equipment's operational history and current anomaly score to make maintenance recommendations. This includes early prediction of anomalies and suggestions for specific maintenance methods. The inputs are operational history and anomaly scores, and the output is the maintenance recommendation.

[0753] Step 5:

[0754] The server sends the generated work schedule and maintenance proposals to the supervisor's terminal. The terminal receives the schedule and proposals in real time, helping the supervisor make quick decisions based on them. The input is the work schedule and maintenance proposals, and the output is the information displayed on the terminal screen.

[0755] Step 6:

[0756] The user performs appropriate maintenance and schedule adjustments based on the suggestions displayed on the terminal. Once maintenance is decided, operational data and anomaly scores are updated and reflected in the next suggestion. The input is the suggestion on the terminal screen, and the output is the updated system data.

[0757] 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.

[0758] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] 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."

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0778] The following is further disclosed regarding the embodiments described above.

[0779] (Claim 1)

[0780] Means of collecting employee health data,

[0781] A means by which AI proposes the optimal company cafeteria menu based on the aforementioned health data,

[0782] A means of providing the aforementioned proposed menu to employees and applying a discount when selected,

[0783] A means for recording the aforementioned selection data and reflecting it in the next proposal,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, wherein the AI ​​dynamically optimizes the menu in consideration of daily changes in the user's health condition.

[0787] (Claim 3)

[0788] The system according to claim 1, which analyzes the past cafeteria usage history of employees to make suggestions based on individual preferences in the aforementioned menu suggestion.

[0789] "Example 1"

[0790] (Claim 1)

[0791] Means of acquiring employees' biometric information,

[0792] A means for a machine learning engine to generate an optimal meal plan based on the aforementioned biometric information,

[0793] A means of transferring the generated meal plan to an employee's computer and providing preferential treatment if selected,

[0794] A means of recording the aforementioned selection results and using them for future proposals,

[0795] An information processing system that includes this.

[0796] (Claim 2)

[0797] The information processing system according to claim 1, wherein the machine learning engine adaptively generates a meal plan taking into account fluctuations in health status.

[0798] (Claim 3)

[0799] The information processing system according to claim 1, which analyzes an employee's past eating and drinking history to generate a meal plan and makes suggestions based on individual preferences.

[0800] "Application Example 1"

[0801] (Claim 1)

[0802] Means of obtaining information on employee health,

[0803] A means by which AI presents an optimal nutritional menu based on the aforementioned health information,

[0804] A means of providing the aforementioned menu to employees via an information display device and applying a value discount when selected,

[0805] A means for recording the aforementioned selection information and reflecting it in the next presentation,

[0806] A system that includes this.

[0807] (Claim 2)

[0808] The system according to claim 1, wherein the AI ​​dynamically optimizes the menu in consideration of daily changes in the health condition.

[0809] (Claim 3)

[0810] The system according to claim 1, which, in presenting the menu, analyzes the past usage history of employees and makes suggestions based on their individual preferences.

[0811] "Example 2 of combining an emotion engine"

[0812] (Claim 1)

[0813] Means for collecting employee health information and emotional information,

[0814] A means by which a generative AI model proposes the optimal cafeteria menu based on the aforementioned health information and emotional information,

[0815] A means of displaying the proposed menu on a terminal and customizing it using the results of the emotion engine,

[0816] A means of recording the selected menu and analysis results and using them for future proposals,

[0817] A system that includes this.

[0818] (Claim 2)

[0819] The system according to claim 1, wherein the generating AI model dynamically optimizes the menu, taking into account daily changes in health and emotional state.

[0820] (Claim 3)

[0821] The system according to claim 1, which analyzes the past cafeteria usage history of employees and makes suggestions based on their individual preferences and emotional states in the menu suggestion.

[0822] "Application example 2 when combining with an emotional engine"

[0823] (Claim 1)

[0824] A means of collecting employee work data,

[0825] A means by which artificial intelligence proposes an optimal machine work schedule based on the aforementioned operational data,

[0826] A means of providing the proposed schedule to the system and evaluating the need for maintenance when selected,

[0827] A means for recording the aforementioned selection data and reflecting it in the next proposal,

[0828] A system that includes this.

[0829] (Claim 2)

[0830] The system according to claim 1, wherein the artificial intelligence dynamically optimizes the work schedule in consideration of daily changes in the operating state.

[0831] (Claim 3)

[0832] The system according to claim 1, which analyzes the past operating history of the equipment and makes proposals based on individual efficiency in the aforementioned schedule proposal. [Explanation of Symbols]

[0833] 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. Means of collecting employee health data, A means by which AI proposes the optimal company cafeteria menu based on the aforementioned health data, A means of providing the aforementioned proposed menu to employees and applying a discount when selected, A means for recording the aforementioned selection data and reflecting it in the next proposal, A system that includes this.

2. The system according to claim 1, wherein the AI ​​dynamically optimizes the menu in consideration of daily changes in the user's health condition.

3. The system according to claim 1, which analyzes employees' past cafeteria usage history to make suggestions based on individual preferences in the aforementioned menu suggestion.

Images