system

Abstract

We provide the system. [Solution] An information input means for accepting location input, A data collection means for collecting recommended meal data based on the location, A data processing method that integrates collected data and analyzes the most cost-effective meal options, A proposal generation means that generates meal suggestions based on the analysis results, A display means for outputting the generated proposals, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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] With the recent soaring prices, consumers' food expenses have become a significant burden on household budgets. However, there is a problem that it takes a great deal of time and effort to individually check the special sale information of each store and the campaign information of restaurants to make an optimal meal choice. In such a situation, there is a demand for a system that enables consumers to efficiently suppress their daily food expenses while enjoying a rich diet.

Means for Solving the Problems

[0005] This invention relates to a system that suggests optimal meal choices based on the consumer's location. The system includes means for receiving location input, means for collecting data on recommended meals based on the location, and means for integrating the collected data and analyzing cost-effectiveness. Furthermore, it has means for generating meal suggestions based on the analysis results and displaying those suggestions. This allows consumers to efficiently obtain meal options that offer the best cost performance.

[0006] "Location input" is the process by which users enter their home or current address into the system.

[0007] An "information input means" is a hardware or software interface for receiving data from a user.

[0008] "Recommended meal data" refers to data used to guide users to the optimal meal choices based on special offer information and campaign information.

[0009] "Data collection means" refers to a process or apparatus for obtaining necessary diet-related data from the internet or other sources.

[0010] "Data processing means" refers to a process or device that analyzes and integrates collected data to derive the optimal meal selection.

[0011] A "proposal generation method" is a process or system that creates meal plans to be provided to users based on analysis results.

[0012] "Display means" refers to a device or interface for visually presenting the generated proposal to the user. [Brief explanation of the drawing]

[0013] [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings <000009 >

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] This invention is a system aimed at effectively saving on food expenses, and it uses local sale information and restaurant promotion information to suggest the best meal options to the user. Specifically, it starts with the user entering their home or current address into the system.

[0035] User-side actions

[0036] The user enters their current address into a smartphone or computer application. This prepares the system to begin location-based data processing.

[0037] Server-side processing

[0038] Based on the user's entered location, the server collects information on supermarkets and restaurants in the area from online databases and APIs. This information includes current sale prices and promotional menus from nearby restaurants. The server integrates and analyzes this information to identify the most cost-effective dining options for each user.

[0039] Proposal generation and presentation

[0040] Based on the analysis results, the server generates suggestions for different meal options. These suggestions include detailed information about each option, such as the cost-effectiveness of cooking at home using discounted items from the supermarket, or the specific cost of restaurant menus offered at promotional prices. The suggestions are later displayed to the user visually in a format that is easy to understand and compare.

[0041] Specific example

[0042] If a user enters "Shinjuku Ward, Tokyo," the server collects information on ingredients on sale at supermarkets in Shinjuku Ward and promotional menus at restaurants in the same ward. For example, if it determines that ingredients for curry using potatoes and pork are available cheaply, it will suggest, "If you buy potatoes for 100 yen and pork for 200 yen at Supermarket A and make curry, it will cost 400 yen per serving," and simultaneously suggest, "Omurice is 600 yen as part of an opening campaign at Restaurant B."

[0043] In this way, users can learn about the optimal meal choices based on their daily circumstances and effectively save money on food.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The terminal displays an address input screen to the user and accepts the user's home or current address as text input.

[0047] Step 2:

[0048] The terminal sends the address data entered by the user to the server and requests that processing begin.

[0049] Step 3:

[0050] The server analyzes the received address data to identify the data sources for supermarkets and restaurants in the relevant area.

[0051] Step 4:

[0052] The server sends API requests to identified data sources to collect sale and campaign information. It also performs web scraping as needed to retrieve the latest publicly available information.

[0053] Step 5:

[0054] The server integrates the acquired data and stores it in a database. Furthermore, it analyzes the pricing information of ingredients and menu items to calculate cost-effectiveness and creates a comparison list.

[0055] Step 6:

[0056] The server uses a generative AI algorithm to select the most cost-effective meal option for the user. This process takes into account not only price but also nutritional value and cooking time.

[0057] Step 7:

[0058] Based on the analysis results, the server generates suggested meal options as text and formats them into a data format for transmission to the terminal.

[0059] Step 8:

[0060] The terminal displays the suggested data received from the server on the user's screen, allowing them to visually confirm the details of each option.

[0061] Step 9:

[0062] Users review the presented meal options and decide on the choice they believe is best for them.

[0063] This process allows users to easily make the most economical and effective meal choices for the day.

[0064] (Example 1)

[0065] 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."

[0066] In modern times, consumers need to efficiently manage their daily living expenses, but their means of effectively saving money on food and eating out are limited. Furthermore, the sheer volume of sale and promotional information makes it difficult for individual users to quickly make the best choices. Therefore, there is a need for a system that provides individually optimized meal options for each user, enabling them to save on food costs.

[0067] 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.

[0068] In this invention, the server includes an input device for receiving location information, an information acquisition device for collecting sales information and food and beverage information based on the location information, and an information processing device for integrating the collected information and analyzing cost-effective options. This makes it possible to provide users with individually optimized meal options.

[0069] "Location information" refers to data about addresses and locations that users enter to indicate their home or current location.

[0070] "Input device" refers to a device or interface that a user uses to provide location information and other relevant information to the system.

[0071] An "information acquisition device" refers to a component that a system uses to collect target information via the internet or other data sources.

[0072] An "information processing device" refers to the part of a system that has the functionality to perform computational processing used to analyze collected data and generate effective options for the user.

[0073] A "selection generation device" refers to a component that specifically forms user-oriented choices based on the analysis results of an information processing device.

[0074] A "display device" refers to an interface or device that visually presents generated options and related information to the user.

[0075] "Sales information" refers to data regarding the prices and sales status of products and services offered in specific regions or stores.

[0076] "Food and beverage information" refers to information such as restaurant menus, prices, and promotions.

[0077] "Highly efficient options" refer to choices that maximize cost-effectiveness while selecting the most suitable products or services for the user.

[0078] This invention is a system that suggests optimal meal options to a user based on their location information. The system begins with the user inputting their current location information using a terminal such as a smartphone or computer. The location information is transmitted by the user through the input device.

[0079] Upon receiving location information, the server utilizes external databases and APIs to collect sales and food / beverage information for the relevant area. Specific APIs and databases are used to retrieve this information, and price and promotional information is collected in real time.

[0080] Next, the server uses an information processing device to integrate the collected information and analyze cost-effective meal options using algorithms. This analysis utilizes machine learning techniques and generative AI models to generate personalized options tailored to each user's needs.

[0081] The server then uses a selection generator to create specific options based on the analysis results. These options include specific product prices and restaurant promotion details, and are organized in an easy-to-understand manner.

[0082] Finally, the generated options are visually presented to the user's device via a display device. Based on the presented information, the user can make the optimal choice and efficiently save on food expenses.

[0083] For example, if a user enters "Shinjuku Ward, Tokyo," the server will collect information on special offers and restaurant promotions within Shinjuku Ward and provide detailed suggestions such as, "If you buy potatoes for 100 yen and pork for 200 yen at Supermarket A and cook curry at home, one meal will cost 400 yen."

[0084] An example of a prompt message might be, "Please suggest the best dining options in Shinjuku Ward, Tokyo, based on my current location." In this way, the present invention enables users to effectively save money.

[0085] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0086] Step 1:

[0087] The user enters location information using a smartphone or computer application. They use an input device to enter their address or current location and send it to the system. The information entered at this stage is the user's geographical location, which forms the basis for subsequent processing.

[0088] Step 2:

[0089] The server sends queries to external APIs and databases based on location information received from the user. Using an information acquisition device, it collects sales and dining information from supermarkets and restaurants in the relevant area. This includes special offer and campaign information, and data is acquired in real time. The input is location information, and the output is the collected special offer and campaign information.

[0090] Step 3:

[0091] The server integrates the collected information using an information processing device and analyzes cost-effective meal options. Data processing involves deduplication and correction of inconsistencies, and a generative AI model is used to evaluate each option. The input is the collected data, and the output is evaluation data based on the analysis.

[0092] Step 4:

[0093] The server uses a selection generator to generate personalized meal options tailored to the user based on the obtained evaluation data. For example, it considers factors such as the cost of cooking at home and promotions when dining out to construct specific choices. The input is evaluation data, and the output is specific choices.

[0094] Step 5:

[0095] The server formats the generated options into a visually easy-to-understand format and sends it to the user's terminal. The display device then shows the list of options on the terminal, allowing the user to browse and compare them. The input is a list of options, and the output is a visual representation on the user interface.

[0096] Step 6:

[0097] The user reviews the options displayed on the device and makes the optimal meal choice based on them. The user's input is the decision of the selection, and the final output is the result reflected in the user's daily meal plan. This allows the user to effectively manage their food expenses and achieve savings.

[0098] (Application Example 1)

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

[0100] In recent years, with the increasing demand for the restaurant industry and food delivery services, consumers are required to make optimal meal choices while keeping food costs down. However, it is difficult for consumers to keep track of all local sale and campaign information, and it is also difficult to utilize this information efficiently. Therefore, there is a need for an effective system that can optimize food costs based on location.

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

[0102] In this invention, the server includes an input means for acquiring location information, an information gathering means for collecting food and beverage information in the vicinity of the location, and a data processing means for analyzing deliverable meal options based on the collected information. This enables consumers to make optimal meal choices by utilizing special offers and campaigns.

[0103] "Location information" refers to data about the user's current location or a specified location, and is the foundational information for generating meal suggestions.

[0104] "Input means" refers to interfaces or devices that allow users to provide location information to a system, and are operated via smartphone or computer applications.

[0105] "Information gathering means" refers to the process and technology used to obtain information on restaurants and special offers in the surrounding area based on specified location information, from internet databases and APIs.

[0106] "Data processing means" refers to a system process for analyzing collected food and beverage information, which includes analysis to evaluate cost-effectiveness and delivery feasibility in order to determine the optimal meal provision option.

[0107] "Provision and generation means" refers to a software component that proposes available meal options to the user based on the analysis results from data processing means.

[0108] "Display means" refers to screens or devices used to visually present the generated meal selection options to the user, and the information is provided through the display of a smartphone or computer.

[0109] The system that realizes this invention consists of software that suggests the optimal meal selection based on the user's location information. It mainly operates as a smartphone or computer application and functions through the following steps.

[0110] Users enter their location information through an application on their device. The user interface is designed to be intuitive, utilizing frameworks such as React Native.

[0111] The server collects information on restaurants and special offers in the surrounding area based on the location information specified by the user. This information collection is done by obtaining location data using the Google® Maps API and then accessing the APIs of restaurants and supermarkets based on that data to obtain information.

[0112] The collected data is analyzed on the server. Here, the cost-effectiveness and delivery feasibility of each meal option are evaluated using libraries such as Python's Pandas library. This helps identify the most appropriate meal choice for the user.

[0113] Subsequently, based on the analysis results, the provision generation system works to generate a list of optimal meal options for the user. This information is formatted in JSON format and sent to the terminal.

[0114] This information is displayed in real time on the user's device. Based on this information, the user can choose the most suitable meal. The display is visually clear and presented within an app built with React Native.

[0115] For example, if a user is in Shibuya Ward, this system gathers information on ingredients on sale and menus on promotion within Shibuya Ward, and suggests to the user that "you can save 800 yen per meal by taking advantage of today's sales."

[0116] An example of a prompt for the generating AI model is: "Please enter my current location and suggest delivery options that optimize the cost of meals in that area. Please also display specific menus and prices."

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The user enters location information in an application on their device. This entered location information is then sent to the system. This information is entered via text fields provided in the user interface or through location services.

[0120] Step 2:

[0121] The server uses the Google Maps API to obtain geographical information about the surrounding area based on the location information received from the user. Based on the location information received as input, it makes an API request and obtains longitude, latitude, and geographic coordinate data within the area as output.

[0122] Step 3:

[0123] The server uses acquired geographic information to access APIs of nearby restaurants and supermarkets to collect information on special offers and promotions. The input is geographic coordinate data, which is used to gather restaurant information from multiple external databases and APIs. The output is a list of special offers and promotions.

[0124] Step 4:

[0125] The server processes data based on collected food and beverage information. Using Python and libraries such as Pandas, it performs evaluations that consider cost-effectiveness and delivery feasibility. The input is a list of food and beverage information, and the output is the cost-effectiveness analysis results for each option.

[0126] Step 5:

[0127] The server generates suggestions for the user based on the analysis results obtained. The suggestion generation mechanism works and outputs a list of meal suggestions formatted in JSON format. The input is the analysis results, and the output is a list of meal options.

[0128] Step 6:

[0129] The device receives meal suggestions sent from the server and presents them visually to the user. An application built with React Native runs on the device, accepting JSON data as input and outputting results in a user-friendly format.

[0130] Step 7:

[0131] The user selects the most suitable meal option based on the presented information and makes a purchase or order. As output, the user's selection is recorded as a history within the application.

[0132] 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.

[0133] This invention provides a system that recognizes the user's emotions and adjusts the suggested content based on those emotions. The aim is to support saving on food expenses while presenting optimal meal options that take into account the user's current emotional state. Emotion recognition is performed through the user interface.

[0134] User-side actions

[0135] Users enter their address into the application and provide data such as facial expressions and voice through an interface equipped with an emotion engine. This allows the system to recognize the user's current emotions.

[0136] Server-side processing

[0137] The server receives location and sentiment data from the user. First, the server collects information on nearby sales and restaurant promotions based on the user's location. Next, it analyzes the sentiment data and uses an AI algorithm to understand the user's emotional state. This information is a crucial element in creating meal recommendations.

[0138] Proposal generation and display

[0139] The server combines the user's emotional state with collected sale information to generate meal suggestions that take cost and mood into consideration. For example, if the user is tired or stressed, it might suggest easy-to-prepare meal options. Conversely, if the user is in a good mood, it might suggest a slightly more elaborate recipe, providing an opportunity to enjoy cooking.

[0140] As a concrete example, if a user is located in "Kita Ward, Osaka City" and the emotion engine determines they are "feeling tired," the server will suggest discounted prepared foods or easy-to-cook curry sets. At the same time, it will also provide information on cafes offering sweet desserts to lift their spirits.

[0141] This allows users to make meal choices that suit not only their economic needs but also their emotions and physical condition at the time, resulting in more satisfying savings on food expenses.

[0142] The following describes the processing flow.

[0143] Step 1:

[0144] The user enters their location into the device and provides data using the camera and microphone to recognize their current emotional state. This process includes capturing facial expressions and inputting voice.

[0145] Step 2:

[0146] The device sends address data and sentiment data obtained from the user to the server. Since the sentiment data is processed in real time, a dedicated protocol is used.

[0147] Step 3:

[0148] The server uses the received address to collect data on supermarkets and restaurants in the relevant area. This data collection may include information on special offers and details of promotions. Furthermore, the special offer information is obtained from reliable APIs.

[0149] Step 4:

[0150] The server activates the emotion engine and analyzes the user's emotional state. Specifically, it performs facial expression analysis and voice tone detection to determine the user's emotional state.

[0151] Step 5:

[0152] The server integrates the analysis results from the emotion engine with collected sale data to generate optimal meal suggestions based on the user's emotional state. These suggestions are tailored to the user's mood and may include, for example, "easy and fun meal options" or "relaxing menus."

[0153] Step 6:

[0154] The server formats the generated meal suggestions into a data format and sends them to the terminal.

[0155] Step 7:

[0156] The terminal displays meal suggestions received from the server to the user. To make it easier for the user to choose, the suggestions include the reason for the recommendation and price information at the beginning.

[0157] Step 8:

[0158] Users review the presented meal suggestions and select the option they feel is best suited to their mood and circumstances. This selection allows users to make daily meal decisions more intuitively.

[0159] (Example 2)

[0160] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0161] In modern society, users can obtain food-related information from a variety of sources, but their means of optimizing that information based on their individual emotions and current state are limited. This makes it difficult for users to effectively choose meals that suit their emotional state, which can lead to decreased satisfaction. Furthermore, users are also required to make optimal choices from an economic perspective, so a new approach is needed that provides suggestions that consider both emotions and economics simultaneously.

[0162] 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.

[0163] In this invention, the server includes an input device for receiving location information, an emotion data acquisition device for receiving facial and voice data, a data collection device for collecting food supply information based on location information, an emotion analysis device for analyzing the acquired emotion data and identifying the emotional state, a suggestion generation device using a generative AI model for creating suggestions, and a screen display device for visually outputting the generated suggestion content. This makes it possible to generate optimal meal suggestions that simultaneously consider the user's emotional state and economic options.

[0164] "Location information" refers to information about the user's current location and living area, and is used to collect information on food supply and generate appropriate suggestions.

[0165] An "input device" is an interface consisting of hardware or software that receives location information and sentiment data from a user.

[0166] An "emotional data acquisition device" is a device that collects data from a user's facial expressions and voice to identify the user's emotional state.

[0167] "Food supply information" refers to data related to food and ingredients, such as special offers and campaign information, collected based on the user's location.

[0168] A "data collection device" is a device that has the function of collecting location-based food supply information through the internet or affiliated databases.

[0169] An "emotion analysis device" is hardware or software that analyzes acquired emotional data and uses an AI algorithm to identify the user's emotional state.

[0170] A "suggestion generation device" is a device that utilizes a generation AI model to create optimal meal suggestions based on emotional states and ingredient supply information.

[0171] A "screen display device" is an interface for visually presenting generated meal suggestions to the user.

[0172] A "generative AI model" is an artificial intelligence model used to generate meal suggestions based on the user's emotional state and location information.

[0173] This invention is an information processing system that provides optimal meal suggestions by utilizing the user's emotional state and location information. This system collects and processes data using the following hardware and software.

[0174] The user uses a device with a dedicated application installed. The device is equipped with an input device for entering location information and an emotion data acquisition device for collecting the user's facial expressions and voice data. Location information is either entered manually by the user or automatically acquired through the device's location services. Emotion data is collected in real time using the device's camera and microphone. The collected data is then transmitted from the device to a server via the internet.

[0175] The server collects local food supply information through external APIs and partner databases based on the received location information. This collection is performed using data collection devices. The server also analyzes emotional data using an emotion analysis device and identifies the user's emotional state using AI algorithms. Machine learning platforms such as TENSORFLOW® and PyTorch are utilized for this purpose.

[0176] Subsequently, the server uses a generative AI model to generate meal suggestions based on the user's emotional state and location. These suggestions include discounted ingredients, easy-to-prepare menus, and information on nearby cafes, tailored to the user's emotions. The suggestions generated by the suggestion generator are sent back to the terminal and visually displayed to the user via a screen display device.

[0177] As a concrete example, if a user is located in a "specific area of ​​an urban area" and their emotional data indicates they are "feeling tired," the server will offer convenient ready-made meals on sale, easy-to-prepare curry sets, and even information on cafes suitable for a change of pace. This system helps users make optimal meal choices that consider both their emotions and their budget.

[0178] An example of a prompt for the generating AI model is to input the text, "Create recommendations that provide special offers for a specific area, taking emotions into consideration." This will cause the AI ​​to generate suggestions that take into account the user's emotions and location.

[0179] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0180] Step 1:

[0181] The user launches a dedicated application and either manually enters their location information or automatically acquires it using the device's location services. Furthermore, an emotion data acquisition device is used to capture facial expressions with a camera and record voice with a microphone. This process inputs both location information and emotion data.

[0182] Step 2:

[0183] The device bundles the acquired location information and sentiment data into packets and sends them to the server via the internet. The input data consists of the user's current location (postal code, GPS coordinates), as well as audio waveforms and image data. This prepares the server to receive the location information and sentiment data.

[0184] Step 3:

[0185] The server uses a data collection device based on the received location information to gather food supply information for that region from partner databases and external APIs. The input here is location information, and the output is a list of special offers and restaurant campaigns for that region.

[0186] Step 4:

[0187] When the server receives emotional data, it analyzes it using an emotion analysis device and identifies the user's emotional state using an AI algorithm. Inputs include audio waveforms and images, and the output is an emotional state such as "relaxed" or "tired." This process involves extracting facial features and analyzing the tone of the voice.

[0188] Step 5:

[0189] The server utilizes a generative AI model to generate meal suggestions based on collected ingredient supply information and the user's emotional state. It receives emotional state and supply information as input and creates suggested menus and cafe information as output. Here, the priority of options is adjusted according to the user's emotions.

[0190] Step 6:

[0191] The server sends the generated suggestions to the terminal, which then presents the information to the user via its on-screen display. The terminal visually displays details of special offers, recommended menu items, and cafe information in a list format, allowing the user to review each suggestion.

[0192] (Application Example 2)

[0193] 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".

[0194] There is a need for services that can improve user satisfaction while effectively saving on food costs by offering meal suggestions that take into account the user's current emotional state. However, conventional systems do not offer suggestions based on the user's emotions and lack personalized suggestions that meet individual needs, so further improvements are needed.

[0195] 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.

[0196] In this invention, the server includes an information input means for receiving location input, an emotion recognition means for recognizing the user's emotional state, and a data collection means for collecting recommended meal data based on the location and emotional state. This enables personalized suggestions of meal options that reflect the user's location and emotional state.

[0197] "Location input" is a function that allows users to input their current location into a specific system.

[0198] "Information input means" refers to a device or interface that receives data from a user.

[0199] "Emotion recognition means" refers to a device or software that has the function of determining the emotional state of a user based on data such as facial expressions and voice.

[0200] "Recommended dietary data" refers to information about specific meals or menus selected based on collected data.

[0201] "Data collection means" refers to a device or system that has the function of collecting necessary information according to specified conditions.

[0202] "Data processing means" refers to a device or software that has the function of integrating and analyzing collected data.

[0203] A "proposal generation means" is a device or system that has the function of creating suggestions for the user based on the analyzed results.

[0204] A "display means" is a device for visually presenting the generated proposal to the user.

[0205] "Stress-reducing options" are choices or services offered with the aim of alleviating user stress.

[0206] In this invention, the user first enters their location into the terminal. The terminal uses an information input means that accepts location input and obtains the user's location information. Next, the user provides facial expressions and voice data using emotion recognition means. This data is collected by a camera and microphone installed in the device. The terminal transmits this data to a server.

[0207] The server receives the user's location information and emotional data, and analyzes the user's emotional state using an emotional recognition means. It uses software such as OpenCV or TensorFlow to determine the emotion, compares it with the location, and collects corresponding recommended meal data. The data collection means obtains recommended meal information based on the user's emotional state and location, and the data processing means integrates this information.

[0208] Based on the collected data, the server generates meal options that are cost-effective and best suited to the user's mood through a suggestion generation mechanism. These suggestions are visually presented to the user via a terminal display mechanism. For example, if a user is located in Chuo Ward, Sapporo City, and their emotional state is analyzed as "slightly depressed," delivery information from a cafe including herbal tea and a matcha parfait will be presented as a menu that helps reduce stress.

[0209] In this way, a system is realized that personalizes and suggests the optimal meal options based on the user's emotions and location. A generative AI model is used, and an example of a prompt message tailored to the emotional state is: "The user's location is Sapporo City, Chuo Ward, and it has been recognized that they are feeling a little down. Please suggest a meal delivery option that will relieve stress."

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The user enters their location into the terminal. The terminal's information input method receives this location information. The entered location data is then sent directly to the server as input.

[0213] Step 2:

[0214] The user provides facial expressions and voice data to the emotion recognition system using the camera and microphone built into the device. This inputs data to determine the user's emotional state.

[0215] Step 3:

[0216] The device sends the collected location and sentiment data to the server. This becomes new input data for the server.

[0217] Step 4:

[0218] The server uses the received location data to activate data collection mechanisms to collect information on nearby food options. These data collection mechanisms access external databases or APIs to retrieve relevant food data. The output of this process is a list of food options related to the user's location.

[0219] Step 5:

[0220] The server uses emotion recognition tools to evaluate the user's facial expressions and voice data. It utilizes software such as OpenCV and TensorFlow to analyze the user's emotional state from the input data. The output is an evaluation result indicating the user's emotional state.

[0221] Step 6:

[0222] The server uses data processing tools to integrate acquired meal data with sentiment analysis results. This generates optimal meal options that take into account the user's emotions and cost-effectiveness. The output is a list of optimized meal options.

[0223] Step 7:

[0224] The suggestion generation system creates meal suggestions based on these optimized meal options. The output is a specific meal plan to be presented to the user.

[0225] Step 8:

[0226] The terminal uses a display means to visually display the generated meal suggestions to the user. The output of this step is the meal suggestions on a visual interface provided to the user.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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).

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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".

[0243] This invention is a system aimed at effectively saving on food expenses, and it uses local sale information and restaurant promotion information to suggest the best meal options to the user. Specifically, it starts with the user entering their home or current address into the system.

[0244] User-side actions

[0245] The user enters their current address into a smartphone or computer application. This prepares the system to begin location-based data processing.

[0246] Server-side processing

[0247] Based on the user's entered location, the server collects information on supermarkets and restaurants in the area from online databases and APIs. This information includes current sale prices and promotional menus from nearby restaurants. The server integrates and analyzes this information to identify the most cost-effective dining options for each user.

[0248] Proposal generation and presentation

[0249] Based on the analysis results, the server generates suggestions for different meal options. These suggestions include detailed information about each option, such as the cost-effectiveness of cooking at home using discounted items from the supermarket, or the specific cost of restaurant menus offered at promotional prices. The suggestions are later displayed to the user visually in a format that is easy to understand and compare.

[0250] Specific example

[0251] If a user enters "Shinjuku Ward, Tokyo," the server collects information on ingredients on sale at supermarkets in Shinjuku Ward and promotional menus at restaurants in the same ward. For example, if it determines that ingredients for curry using potatoes and pork are available cheaply, it will suggest, "If you buy potatoes for 100 yen and pork for 200 yen at Supermarket A and make curry, it will cost 400 yen per serving," and simultaneously suggest, "Omurice is 600 yen as part of an opening campaign at Restaurant B."

[0252] In this way, users can learn about the optimal meal choices based on their daily circumstances and effectively save money on food.

[0253] The following describes the processing flow.

[0254] Step 1:

[0255] The terminal displays an address input screen to the user and accepts the user's home or current address as text input.

[0256] Step 2:

[0257] The terminal sends the address data entered by the user to the server and requests that processing begin.

[0258] Step 3:

[0259] The server analyzes the received address data to identify the data sources for supermarkets and restaurants in the relevant area.

[0260] Step 4:

[0261] The server sends API requests to identified data sources to collect sale and campaign information. It also performs web scraping as needed to retrieve the latest publicly available information.

[0262] Step 5:

[0263] The server integrates the acquired data and stores it in a database. Furthermore, it analyzes the pricing information of ingredients and menu items to calculate cost-effectiveness and creates a comparison list.

[0264] Step 6:

[0265] The server uses a generative AI algorithm to select the most cost-effective meal option for the user. This process takes into account not only price but also nutritional value and cooking time.

[0266] Step 7:

[0267] Based on the analysis results, the server generates suggested meal options as text and formats them into a data format for transmission to the terminal.

[0268] Step 8:

[0269] The terminal displays the suggested data received from the server on the user's screen, allowing them to visually confirm the details of each option.

[0270] Step 9:

[0271] Users review the presented meal options and decide on the choice they believe is best for them.

[0272] This process allows users to easily make the most economical and effective meal choices for the day.

[0273] (Example 1)

[0274] 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."

[0275] In modern times, consumers need to efficiently manage their daily living expenses, but their means of effectively saving money on food and eating out are limited. Furthermore, the sheer volume of sale and promotional information makes it difficult for individual users to quickly make the best choices. Therefore, there is a need for a system that provides individually optimized meal options for each user, enabling them to save on food costs.

[0276] 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.

[0277] In this invention, the server includes an input device for receiving location information, an information acquisition device for collecting sales information and food and beverage information based on the location information, and an information processing device for integrating the collected information and analyzing cost-effective options. This makes it possible to provide users with individually optimized meal options.

[0278] "Location information" refers to data related to the address or location entered by the user to indicate their home or current location.

[0279] "Input device" refers to a device or interface used by the user to provide location information and other related information to the system.

[0280] "Information acquisition device" refers to a component for the system to collect target information via the Internet or other data sources.

[0281] "Information processing device" refers to the part with the function of computational processing used to analyze the collected data and generate effective options for the user.

[0282] "Option generation device" refers to a component for specifically forming options for the user from the analysis results of the information processing device.

[0283] "Display device" refers to an interface or device for visually presenting the generated options and related information to the user.

[0284] "Sales information" refers to data related to the prices and sales status of products and services offered in a specific region or store.

[0285] ​​​​​​​​​​​​ Upon receiving location information, the server utilizes external databases and APIs to collect sales and food / beverage information for the relevant area. Specific APIs and databases are used to retrieve this information, and price and promotional information is collected in real time.

[0289] Next, the server uses an information processing device to integrate the collected information and analyze cost-effective meal options using algorithms. This analysis utilizes machine learning techniques and generative AI models to generate personalized options tailored to each user's needs.

[0290] The server then uses a selection generator to create specific options based on the analysis results. These options include specific product prices and restaurant promotion details, and are organized in an easy-to-understand manner.

[0291] Finally, the generated options are visually presented to the user's device via a display device. Based on the presented information, the user can make the optimal choice and efficiently save on food expenses.

[0292] For example, if a user enters "Shinjuku Ward, Tokyo," the server will collect information on special offers and restaurant promotions within Shinjuku Ward and provide detailed suggestions such as, "If you buy potatoes for 100 yen and pork for 200 yen at Supermarket A and cook curry at home, one meal will cost 400 yen."

[0293] An example of a prompt message might be, "Please suggest the best dining options in Shinjuku Ward, Tokyo, based on my current location." In this way, the present invention enables users to effectively save money.

[0294] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0295] Step 1:

[0296] The user enters location information using a smartphone or computer application. They use an input device to enter their address or current location and send it to the system. The information entered at this stage is the user's geographical location, which forms the basis for subsequent processing.

[0297] Step 2:

[0298] The server sends queries to external APIs and databases based on location information received from the user. Using an information acquisition device, it collects sales and dining information from supermarkets and restaurants in the relevant area. This includes special offer and campaign information, and data is acquired in real time. The input is location information, and the output is the collected special offer and campaign information.

[0299] Step 3:

[0300] The server integrates the collected information using an information processing device and analyzes cost-effective meal options. Data processing involves deduplication and correction of inconsistencies, and a generative AI model is used to evaluate each option. The input is the collected data, and the output is evaluation data based on the analysis.

[0301] Step 4:

[0302] The server uses a selection generator to generate personalized meal options tailored to the user based on the obtained evaluation data. For example, it considers factors such as the cost of cooking at home and promotions when dining out to construct specific choices. The input is evaluation data, and the output is specific choices.

[0303] Step 5:

[0304] The server formats the generated options into a visually easy-to-understand format and sends it to the user's terminal. The display device then shows the list of options on the terminal, allowing the user to browse and compare them. The input is a list of options, and the output is a visual representation on the user interface.

[0305] Step 6:

[0306] The user checks the options displayed on the terminal and makes an optimal meal choice based on them. The user's input is the determination of the selection content, and the final output is the result reflected in the user's daily meal plan. In this way, the user can effectively manage food expenses and achieve savings.

[0307] (Application Example 1)

[0308] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0309] In recent years, with the increasing demand for the eating-out industry and food delivery services, consumers are required to make optimal meal choices while suppressing food expenses. However, it is limited for consumers themselves to grasp all the special sale information and campaign information in the neighborhood, and it is difficult to utilize this information efficiently. Therefore, an effective system that can optimize food expenses based on the location is required.

[0310] 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.

[0311] In this invention, the server includes an input means for acquiring location information, an information collection means for collecting food and beverage information around the location, and a data processing means for analyzing deliverable meal choices based on the collected information. Thereby, consumers can make optimal meal choices using special sale information and campaigns.

[0312] The "location information" is data related to the user's current location or the designated location, and is the information that serves as the basis for generating meal proposals.

[0313] "Input means" refers to interfaces or devices that allow users to provide location information to a system, and are operated via smartphone or computer applications.

[0314] "Information gathering means" refers to the process and technology used to obtain information on restaurants and special offers in the surrounding area based on specified location information, from internet databases and APIs.

[0315] "Data processing means" refers to a system process for analyzing collected food and beverage information, which includes analysis to evaluate cost-effectiveness and delivery feasibility in order to determine the optimal meal provision option.

[0316] "Provision and generation means" refers to a software component that proposes available meal options to the user based on the analysis results from data processing means.

[0317] "Display means" refers to screens or devices used to visually present the generated meal selection options to the user, and the information is provided through the display of a smartphone or computer.

[0318] The system that realizes this invention consists of software that suggests the optimal meal selection based on the user's location information. It mainly operates as a smartphone or computer application and functions through the following steps.

[0319] Users enter their location information through an application on their device. The user interface is designed to be intuitive, utilizing frameworks such as React Native.

[0320] The server collects information on restaurants and special offers in the surrounding area based on the location information specified by the user. This information collection is done by obtaining location data using the Google Maps API and then accessing the APIs of restaurants and supermarkets based on that data to retrieve information.

[0321] The collected data is analyzed on the server. Here, the cost-effectiveness and delivery feasibility of each meal option are evaluated using libraries such as Python's Pandas library. This helps identify the most appropriate meal choice for the user.

[0322] Subsequently, based on the analysis results, the provision generation system works to generate a list of optimal meal options for the user. This information is formatted in JSON format and sent to the terminal.

[0323] This information is displayed in real time on the user's device. Based on this information, the user can choose the most suitable meal. The display is visually clear and presented within an app built with React Native.

[0324] For example, if a user is in Shibuya Ward, this system gathers information on ingredients on sale and menus on promotion within Shibuya Ward, and suggests to the user that "you can save 800 yen per meal by taking advantage of today's sales."

[0325] An example of a prompt for the generating AI model is: "Please enter my current location and suggest delivery options that optimize the cost of meals in that area. Please also display specific menus and prices."

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] The user enters location information in an application on their device. This entered location information is then sent to the system. This information is entered via text fields provided in the user interface or through location services.

[0329] Step 2:

[0330] The server uses the Google Maps API to obtain geographical information about the surrounding area based on the location information received from the user. Based on the location information received as input, it makes an API request and obtains longitude, latitude, and geographic coordinate data within the area as output.

[0331] Step 3:

[0332] The server uses acquired geographic information to access APIs of nearby restaurants and supermarkets to collect information on special offers and promotions. The input is geographic coordinate data, which is used to gather restaurant information from multiple external databases and APIs. The output is a list of special offers and promotions.

[0333] Step 4:

[0334] The server processes data based on collected food and beverage information. Using Python and libraries such as Pandas, it performs evaluations that consider cost-effectiveness and delivery feasibility. The input is a list of food and beverage information, and the output is the cost-effectiveness analysis results for each option.

[0335] Step 5:

[0336] The server generates suggestions for the user based on the analysis results obtained. The suggestion generation mechanism works and outputs a list of meal suggestions formatted in JSON format. The input is the analysis results, and the output is a list of meal options.

[0337] Step 6:

[0338] The device receives meal suggestions sent from the server and presents them visually to the user. An application built with React Native runs on the device, accepting JSON data as input and outputting results in a user-friendly format.

[0339] Step 7:

[0340] The user selects the most suitable meal option based on the presented information and makes a purchase or order. As output, the user's selection is recorded as a history within the application.

[0341] 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.

[0342] This invention provides a system that recognizes the user's emotions and adjusts the suggested content based on those emotions. The aim is to support saving on food expenses while presenting optimal meal options that take into account the user's current emotional state. Emotion recognition is performed through the user interface.

[0343] User-side actions

[0344] Users enter their address into the application and provide data such as facial expressions and voice through an interface equipped with an emotion engine. This allows the system to recognize the user's current emotions.

[0345] Server-side processing

[0346] The server receives location and sentiment data from the user. First, the server collects information on nearby sales and restaurant promotions based on the user's location. Next, it analyzes the sentiment data and uses an AI algorithm to understand the user's emotional state. This information is a crucial element in creating meal recommendations.

[0347] Proposal generation and display

[0348] The server combines the user's emotional state with collected sale information to generate meal suggestions that take cost and mood into consideration. For example, if the user is tired or stressed, it might suggest easy-to-prepare meal options. Conversely, if the user is in a good mood, it might suggest a slightly more elaborate recipe, providing an opportunity to enjoy cooking.

[0349] As a concrete example, if a user is located in "Kita Ward, Osaka City" and the emotion engine determines they are "feeling tired," the server will suggest discounted prepared foods or easy-to-cook curry sets. At the same time, it will also provide information on cafes offering sweet desserts to lift their spirits.

[0350] This allows users to make meal choices that suit not only their economic needs but also their emotions and physical condition at the time, resulting in more satisfying savings on food expenses.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The user enters their location into the device and provides data using the camera and microphone to recognize their current emotional state. This process includes capturing facial expressions and inputting voice.

[0354] Step 2:

[0355] The device sends address data and sentiment data obtained from the user to the server. Since the sentiment data is processed in real time, a dedicated protocol is used.

[0356] Step 3:

[0357] The server uses the received address to collect data on supermarkets and restaurants in the relevant area. This data collection may include information on special offers and details of promotions. Furthermore, the special offer information is obtained from reliable APIs.

[0358] Step 4:

[0359] The server activates the emotion engine and analyzes the user's emotional state. Specifically, it performs facial expression analysis and voice tone detection to determine the user's emotional state.

[0360] Step 5:

[0361] The server integrates the analysis results from the emotion engine with collected sale data to generate optimal meal suggestions based on the user's emotional state. These suggestions are tailored to the user's mood and may include, for example, "easy and fun meal options" or "relaxing menus."

[0362] Step 6:

[0363] The server formats the generated meal suggestions into a data format and sends them to the terminal.

[0364] Step 7:

[0365] The terminal displays meal suggestions received from the server to the user. To make it easier for the user to choose, the suggestions include the reason for the recommendation and price information at the beginning.

[0366] Step 8:

[0367] Users review the presented meal suggestions and select the option they feel is best suited to their mood and circumstances. This selection allows users to make daily meal decisions more intuitively.

[0368] (Example 2)

[0369] 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".

[0370] In modern society, users can obtain food-related information from a variety of sources, but their means of optimizing that information based on their individual emotions and current state are limited. This makes it difficult for users to effectively choose meals that suit their emotional state, which can lead to decreased satisfaction. Furthermore, users are also required to make optimal choices from an economic perspective, so a new approach is needed that provides suggestions that consider both emotions and economics simultaneously.

[0371] 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.

[0372] In this invention, the server includes an input device for receiving location information, an emotion data acquisition device for receiving facial and voice data, a data collection device for collecting food supply information based on location information, an emotion analysis device for analyzing the acquired emotion data and identifying the emotional state, a suggestion generation device using a generative AI model for creating suggestions, and a screen display device for visually outputting the generated suggestion content. This makes it possible to generate optimal meal suggestions that simultaneously consider the user's emotional state and economic options.

[0373] "Location information" refers to information about the user's current location and living area, and is used to collect information on food supply and generate appropriate suggestions.

[0374] An "input device" is an interface consisting of hardware or software that receives location information and sentiment data from a user.

[0375] An "emotional data acquisition device" is a device that collects data from a user's facial expressions and voice to identify the user's emotional state.

[0376] "Food supply information" refers to data related to food and ingredients, such as special offers and campaign information, collected based on the user's location.

[0377] A "data collection device" is a device that has the function of collecting location-based food supply information through the internet or affiliated databases.

[0378] An "emotion analysis device" is hardware or software that analyzes acquired emotional data and uses an AI algorithm to identify the user's emotional state.

[0379] A "suggestion generation device" is a device that utilizes a generation AI model to create optimal meal suggestions based on emotional states and ingredient supply information.

[0380] A "screen display device" is an interface for visually presenting generated meal suggestions to the user.

[0381] A "generative AI model" is an artificial intelligence model used to generate meal suggestions based on the user's emotional state and location information.

[0382] This invention is an information processing system that provides optimal meal suggestions by utilizing the user's emotional state and location information. This system collects and processes data using the following hardware and software.

[0383] The user uses a device with a dedicated application installed. The device is equipped with an input device for entering location information and an emotion data acquisition device for collecting the user's facial expressions and voice data. Location information is either entered manually by the user or automatically acquired through the device's location services. Emotion data is collected in real time using the device's camera and microphone. The collected data is then transmitted from the device to a server via the internet.

[0384] The server collects local food supply information through external APIs and partner databases based on the received location information. This collection is performed using data collection devices. The server also analyzes emotional data using an emotion analysis device and identifies the user's emotional state using AI algorithms. Machine learning platforms such as TensorFlow and PyTorch are used for this purpose.

[0385] Subsequently, the server uses a generative AI model to generate meal suggestions based on the user's emotional state and location. These suggestions include discounted ingredients, easy-to-prepare menus, and information on nearby cafes, tailored to the user's emotions. The suggestions generated by the suggestion generator are sent back to the terminal and visually displayed to the user via a screen display device.

[0386] As a concrete example, if a user is located in a "specific area of ​​an urban area" and their emotional data indicates they are "feeling tired," the server will offer convenient ready-made meals on sale, easy-to-prepare curry sets, and even information on cafes suitable for a change of pace. This system helps users make optimal meal choices that consider both their emotions and their budget.

[0387] An example of a prompt for the generating AI model is to input the text, "Create recommendations that provide special offers for a specific area, taking emotions into consideration." This will cause the AI ​​to generate suggestions that take into account the user's emotions and location.

[0388] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0389] Step 1:

[0390] The user launches a dedicated application and either manually enters their location information or automatically acquires it using the device's location services. Furthermore, an emotion data acquisition device is used to capture facial expressions with a camera and record voice with a microphone. This process inputs both location information and emotion data.

[0391] Step 2:

[0392] The device bundles the acquired location information and sentiment data into packets and sends them to the server via the internet. The input data consists of the user's current location (postal code, GPS coordinates), as well as audio waveforms and image data. This prepares the server to receive the location information and sentiment data.

[0393] Step 3:

[0394] The server uses a data collection device based on the received location information to gather food supply information for that region from partner databases and external APIs. The input here is location information, and the output is a list of special offers and restaurant campaigns for that region.

[0395] Step 4:

[0396] When the server receives emotional data, it analyzes it using an emotion analysis device and identifies the user's emotional state using an AI algorithm. Inputs include audio waveforms and images, and the output is an emotional state such as "relaxed" or "tired." This process involves extracting facial features and analyzing the tone of the voice.

[0397] Step 5:

[0398] The server utilizes a generative AI model to generate meal suggestions based on collected ingredient supply information and the user's emotional state. It receives emotional state and supply information as input and creates suggested menus and cafe information as output. Here, the priority of options is adjusted according to the user's emotions.

[0399] Step 6:

[0400] The server sends the generated suggestions to the terminal, which then presents the information to the user via its on-screen display. The terminal visually displays details of special offers, recommended menu items, and cafe information in a list format, allowing the user to review each suggestion.

[0401] (Application Example 2)

[0402] 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."

[0403] There is a need for services that can improve user satisfaction while effectively saving on food costs by offering meal suggestions that take into account the user's current emotional state. However, conventional systems do not offer suggestions based on the user's emotions and lack personalized suggestions that meet individual needs, so further improvements are needed.

[0404] 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.

[0405] In this invention, the server includes an information input means for receiving location input, an emotion recognition means for recognizing the user's emotional state, and a data collection means for collecting recommended meal data based on the location and emotional state. This enables personalized suggestions of meal options that reflect the user's location and emotional state.

[0406] "Location input" is a function that allows users to input their current location into a specific system.

[0407] "Information input means" refers to a device or interface that receives data from a user.

[0408] "Emotion recognition means" refers to a device or software that has the function of determining the emotional state of a user based on data such as facial expressions and voice.

[0409] "Recommended dietary data" refers to information about specific meals or menus selected based on collected data.

[0410] "Data collection means" refers to a device or system that has the function of collecting necessary information according to specified conditions.

[0411] "Data processing means" refers to a device or software that has the function of integrating and analyzing collected data.

[0412] A "proposal generation means" is a device or system that has the function of creating suggestions for the user based on the analyzed results.

[0413] A "display means" is a device for visually presenting the generated proposal to the user.

[0414] "Stress-reducing options" are choices or services offered with the aim of alleviating user stress.

[0415] In this invention, the user first enters their location into the terminal. The terminal uses an information input means that accepts location input and obtains the user's location information. Next, the user provides facial expressions and voice data using emotion recognition means. This data is collected by a camera and microphone installed in the device. The terminal transmits this data to a server.

[0416] The server receives the user's location information and emotional data, and analyzes the user's emotional state using an emotional recognition means. It uses software such as OpenCV or TensorFlow to determine the emotion, compares it with the location, and collects corresponding recommended meal data. The data collection means obtains recommended meal information based on the user's emotional state and location, and the data processing means integrates this information.

[0417] Based on the collected data, the server generates meal options that are cost-effective and best suited to the user's mood through a suggestion generation mechanism. These suggestions are visually presented to the user via a terminal display mechanism. For example, if a user is located in Chuo Ward, Sapporo City, and their emotional state is analyzed as "slightly depressed," delivery information from a cafe including herbal tea and a matcha parfait will be presented as a menu that helps reduce stress.

[0418] In this way, a system is realized that personalizes and suggests the optimal meal options based on the user's emotions and location. A generative AI model is used, and an example of a prompt message tailored to the emotional state is: "The user's location is Sapporo City, Chuo Ward, and it has been recognized that they are feeling a little down. Please suggest a meal delivery option that will relieve stress."

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

[0420] Step 1:

[0421] The user enters their location into the terminal. The terminal's information input method receives this location information. The entered location data is then sent directly to the server as input.

[0422] Step 2:

[0423] The user provides facial expressions and voice data to the emotion recognition system using the camera and microphone built into the device. This inputs data to determine the user's emotional state.

[0424] Step 3:

[0425] The device sends the collected location and sentiment data to the server. This becomes new input data for the server.

[0426] Step 4:

[0427] The server uses the received location data to activate data collection mechanisms to collect information on nearby food options. These data collection mechanisms access external databases or APIs to retrieve relevant food data. The output of this process is a list of food options related to the user's location.

[0428] Step 5:

[0429] The server uses emotion recognition tools to evaluate the user's facial expressions and voice data. It utilizes software such as OpenCV and TensorFlow to analyze the user's emotional state from the input data. The output is an evaluation result indicating the user's emotional state.

[0430] Step 6:

[0431] The server uses data processing tools to integrate acquired meal data with sentiment analysis results. This generates optimal meal options that take into account the user's emotions and cost-effectiveness. The output is a list of optimized meal options.

[0432] Step 7:

[0433] The suggestion generation system creates meal suggestions based on these optimized meal options. The output is a specific meal plan to be presented to the user.

[0434] Step 8:

[0435] The terminal uses a display means to visually display the generated meal suggestions to the user. The output of this step is the meal suggestions on a visual interface provided to the user.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] [Third Embodiment]

[0440] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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".

[0452] This invention is a system aimed at effectively saving on food expenses, and it uses local sale information and restaurant promotion information to suggest the best meal options to the user. Specifically, it starts with the user entering their home or current address into the system.

[0453] User-side actions

[0454] The user enters their current address into a smartphone or computer application. This prepares the system to begin location-based data processing.

[0455] Server-side processing

[0456] Based on the user's entered location, the server collects information on supermarkets and restaurants in the area from online databases and APIs. This information includes current sale prices and promotional menus from nearby restaurants. The server integrates and analyzes this information to identify the most cost-effective dining options for each user.

[0457] Proposal generation and presentation

[0458] Based on the analysis results, the server generates suggestions for different meal options. These suggestions include detailed information about each option, such as the cost-effectiveness of cooking at home using discounted items from the supermarket, or the specific cost of restaurant menus offered at promotional prices. The suggestions are later displayed to the user visually in a format that is easy to understand and compare.

[0459] Specific example

[0460] If a user enters "Shinjuku Ward, Tokyo," the server collects information on ingredients on sale at supermarkets in Shinjuku Ward and promotional menus at restaurants in the same ward. For example, if it determines that ingredients for curry using potatoes and pork are available cheaply, it will suggest, "If you buy potatoes for 100 yen and pork for 200 yen at Supermarket A and make curry, it will cost 400 yen per serving," and simultaneously suggest, "Omurice is 600 yen as part of an opening campaign at Restaurant B."

[0461] In this way, users can learn about the optimal meal choices based on their daily circumstances and effectively save money on food.

[0462] The following describes the processing flow.

[0463] Step 1:

[0464] The terminal displays an address input screen to the user and accepts the user's home or current address as text input.

[0465] Step 2:

[0466] The terminal sends the address data entered by the user to the server and requests that processing begin.

[0467] Step 3:

[0468] The server analyzes the received address data to identify the data sources for supermarkets and restaurants in the relevant area.

[0469] Step 4:

[0470] The server sends API requests to identified data sources to collect sale and campaign information. It also performs web scraping as needed to retrieve the latest publicly available information.

[0471] Step 5:

[0472] The server integrates the acquired data and stores it in a database. Furthermore, it analyzes the pricing information of ingredients and menu items to calculate cost-effectiveness and creates a comparison list.

[0473] Step 6:

[0474] The server uses a generative AI algorithm to select the most cost-effective meal option for the user. This process takes into account not only price but also nutritional value and cooking time.

[0475] Step 7:

[0476] Based on the analysis results, the server generates suggested meal options as text and formats them into a data format for transmission to the terminal.

[0477] Step 8:

[0478] The terminal displays the suggested data received from the server on the user's screen, allowing them to visually confirm the details of each option.

[0479] Step 9:

[0480] Users review the presented meal options and decide on the choice they believe is best for them.

[0481] This process allows users to easily make the most economical and effective meal choices for the day.

[0482] (Example 1)

[0483] 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."

[0484] In modern times, consumers need to efficiently manage their daily living expenses, but their means of effectively saving money on food and eating out are limited. Furthermore, the sheer volume of sale and promotional information makes it difficult for individual users to quickly make the best choices. Therefore, there is a need for a system that provides individually optimized meal options for each user, enabling them to save on food costs.

[0485] 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.

[0486] In this invention, the server includes an input device for receiving location information, an information acquisition device for collecting sales information and food and beverage information based on the location information, and an information processing device for integrating the collected information and analyzing cost-effective options. This makes it possible to provide users with individually optimized meal options.

[0487] "Location information" refers to data about addresses and locations that users enter to indicate their home or current location.

[0488] "Input device" refers to a device or interface that a user uses to provide location information and other relevant information to the system.

[0489] An "information acquisition device" refers to a component that a system uses to collect target information via the internet or other data sources.

[0490] An "information processing device" refers to the part of a system that has the functionality to perform computational processing used to analyze collected data and generate effective options for the user.

[0491] A "selection generation device" refers to a component that specifically forms user-oriented choices based on the analysis results of an information processing device.

[0492] A "display device" refers to an interface or device that visually presents generated options and related information to the user.

[0493] "Sales information" refers to data regarding the prices and sales status of products and services offered in specific regions or stores.

[0494] "Food and beverage information" refers to information such as restaurant menus, prices, and promotions.

[0495] "Highly efficient options" refer to choices that maximize cost-effectiveness while selecting the most suitable products or services for the user.

[0496] This invention is a system that suggests optimal meal options to a user based on their location information. The system begins with the user inputting their current location information using a terminal such as a smartphone or computer. The location information is transmitted by the user through the input device.

[0497] Upon receiving location information, the server utilizes external databases and APIs to collect sales and food / beverage information for the relevant area. Specific APIs and databases are used to retrieve this information, and price and promotional information is collected in real time.

[0498] Next, the server uses an information processing device to integrate the collected information and analyze cost-effective meal options using algorithms. This analysis utilizes machine learning techniques and generative AI models to generate personalized options tailored to each user's needs.

[0499] The server then uses a selection generator to create specific options based on the analysis results. These options include specific product prices and restaurant promotion details, and are organized in an easy-to-understand manner.

[0500] Finally, the generated options are visually presented to the user's device via a display device. Based on the presented information, the user can make the optimal choice and efficiently save on food expenses.

[0501] For example, if a user enters "Shinjuku Ward, Tokyo," the server will collect information on special offers and restaurant promotions within Shinjuku Ward and provide detailed suggestions such as, "If you buy potatoes for 100 yen and pork for 200 yen at Supermarket A and cook curry at home, one meal will cost 400 yen."

[0502] An example of a prompt message might be, "Please suggest the best dining options in Shinjuku Ward, Tokyo, based on my current location." In this way, the present invention enables users to effectively save money.

[0503] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0504] Step 1:

[0505] The user enters location information using a smartphone or computer application. They use an input device to enter their address or current location and send it to the system. The information entered at this stage is the user's geographical location, which forms the basis for subsequent processing.

[0506] Step 2:

[0507] The server sends queries to external APIs and databases based on location information received from the user. Using an information acquisition device, it collects sales and dining information from supermarkets and restaurants in the relevant area. This includes special offer and campaign information, and data is acquired in real time. The input is location information, and the output is the collected special offer and campaign information.

[0508] Step 3:

[0509] The server integrates the collected information using an information processing device and analyzes cost-effective meal options. Data processing involves deduplication and correction of inconsistencies, and a generative AI model is used to evaluate each option. The input is the collected data, and the output is evaluation data based on the analysis.

[0510] Step 4:

[0511] The server uses a selection generator to generate personalized meal options tailored to the user based on the obtained evaluation data. For example, it considers factors such as the cost of cooking at home and promotions when dining out to construct specific choices. The input is evaluation data, and the output is specific choices.

[0512] Step 5:

[0513] The server formats the generated options into a visually easy-to-understand format and sends it to the user's terminal. The display device then shows the list of options on the terminal, allowing the user to browse and compare them. The input is a list of options, and the output is a visual representation on the user interface.

[0514] Step 6:

[0515] The user reviews the options displayed on the device and makes the optimal meal choice based on them. The user's input is the decision of the selection, and the final output is the result reflected in the user's daily meal plan. This allows the user to effectively manage their food expenses and achieve savings.

[0516] (Application Example 1)

[0517] 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."

[0518] In recent years, with the increasing demand for the restaurant industry and food delivery services, consumers are required to make optimal meal choices while keeping food costs down. However, it is difficult for consumers to keep track of all local sale and campaign information, and it is also difficult to utilize this information efficiently. Therefore, there is a need for an effective system that can optimize food costs based on location.

[0519] 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.

[0520] In this invention, the server includes an input means for acquiring location information, an information gathering means for collecting food and beverage information in the vicinity of the location, and a data processing means for analyzing deliverable meal options based on the collected information. This enables consumers to make optimal meal choices by utilizing special offers and campaigns.

[0521] "Location information" refers to data about the user's current location or a specified location, and is the foundational information for generating meal suggestions.

[0522] "Input means" refers to interfaces or devices that allow users to provide location information to a system, and are operated via smartphone or computer applications.

[0523] "Information gathering means" refers to the process and technology used to obtain information on restaurants and special offers in the surrounding area based on specified location information, from internet databases and APIs.

[0524] "Data processing means" refers to a system process for analyzing collected food and beverage information, which includes analysis to evaluate cost-effectiveness and delivery feasibility in order to determine the optimal meal provision option.

[0525] "Provision and generation means" refers to a software component that proposes available meal options to the user based on the analysis results from data processing means.

[0526] "Display means" refers to screens or devices used to visually present the generated meal selection options to the user, and the information is provided through the display of a smartphone or computer.

[0527] The system that realizes this invention consists of software that suggests the optimal meal selection based on the user's location information. It mainly operates as a smartphone or computer application and functions through the following steps.

[0528] Users enter their location information through an application on their device. The user interface is designed to be intuitive, utilizing frameworks such as React Native.

[0529] The server collects information on restaurants and special offers in the surrounding area based on the location information specified by the user. This information collection is done by obtaining location data using the Google Maps API and then accessing the APIs of restaurants and supermarkets based on that data to retrieve information.

[0530] The collected data is analyzed on the server. Here, the cost-effectiveness and delivery feasibility of each meal option are evaluated using libraries such as Python's Pandas library. This helps identify the most appropriate meal choice for the user.

[0531] Subsequently, based on the analysis results, the provision generation system works to generate a list of optimal meal options for the user. This information is formatted in JSON format and sent to the terminal.

[0532] This information is displayed in real time on the user's device. Based on this information, the user can choose the most suitable meal. The display is visually clear and presented within an app built with React Native.

[0533] For example, if a user is in Shibuya Ward, this system gathers information on ingredients on sale and menus on promotion within Shibuya Ward, and suggests to the user that "you can save 800 yen per meal by taking advantage of today's sales."

[0534] An example of a prompt for the generating AI model is: "Please enter my current location and suggest delivery options that optimize the cost of meals in that area. Please also display specific menus and prices."

[0535] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0536] Step 1:

[0537] The user enters location information in an application on their device. This entered location information is then sent to the system. This information is entered via text fields provided in the user interface or through location services.

[0538] Step 2:

[0539] The server uses the Google Maps API to obtain geographical information about the surrounding area based on the location information received from the user. Based on the location information received as input, it makes an API request and obtains longitude, latitude, and geographic coordinate data within the area as output.

[0540] Step 3:

[0541] The server uses acquired geographic information to access APIs of nearby restaurants and supermarkets to collect information on special offers and promotions. The input is geographic coordinate data, which is used to gather restaurant information from multiple external databases and APIs. The output is a list of special offers and promotions.

[0542] Step 4:

[0543] The server processes data based on collected food and beverage information. Using Python and libraries such as Pandas, it performs evaluations that consider cost-effectiveness and delivery feasibility. The input is a list of food and beverage information, and the output is the cost-effectiveness analysis results for each option.

[0544] Step 5:

[0545] The server generates suggestions for the user based on the analysis results obtained. The suggestion generation mechanism works and outputs a list of meal suggestions formatted in JSON format. The input is the analysis results, and the output is a list of meal options.

[0546] Step 6:

[0547] The device receives meal suggestions sent from the server and presents them visually to the user. An application built with React Native runs on the device, accepting JSON data as input and outputting results in a user-friendly format.

[0548] Step 7:

[0549] The user selects the most suitable meal option based on the presented information and makes a purchase or order. As output, the user's selection is recorded as a history within the application.

[0550] 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.

[0551] This invention provides a system that recognizes the user's emotions and adjusts the suggested content based on those emotions. The aim is to support saving on food expenses while presenting optimal meal options that take into account the user's current emotional state. Emotion recognition is performed through the user interface.

[0552] User-side actions

[0553] Users enter their address into the application and provide data such as facial expressions and voice through an interface equipped with an emotion engine. This allows the system to recognize the user's current emotions.

[0554] Server-side processing

[0555] The server receives location and sentiment data from the user. First, the server collects information on nearby sales and restaurant promotions based on the user's location. Next, it analyzes the sentiment data and uses an AI algorithm to understand the user's emotional state. This information is a crucial element in creating meal recommendations.

[0556] Proposal generation and display

[0557] The server combines the user's emotional state with collected sale information to generate meal suggestions that take cost and mood into consideration. For example, if the user is tired or stressed, it might suggest easy-to-prepare meal options. Conversely, if the user is in a good mood, it might suggest a slightly more elaborate recipe, providing an opportunity to enjoy cooking.

[0558] As a concrete example, if a user is located in "Kita Ward, Osaka City" and the emotion engine determines they are "feeling tired," the server will suggest discounted prepared foods or easy-to-cook curry sets. At the same time, it will also provide information on cafes offering sweet desserts to lift their spirits.

[0559] This allows users to make meal choices that suit not only their economic needs but also their emotions and physical condition at the time, resulting in more satisfying savings on food expenses.

[0560] The following describes the processing flow.

[0561] Step 1:

[0562] The user enters their location into the device and provides data using the camera and microphone to recognize their current emotional state. This process includes capturing facial expressions and inputting voice.

[0563] Step 2:

[0564] The device sends address data and sentiment data obtained from the user to the server. Since the sentiment data is processed in real time, a dedicated protocol is used.

[0565] Step 3:

[0566] The server uses the received address to collect data on supermarkets and restaurants in the relevant area. This data collection may include information on special offers and details of promotions. Furthermore, the special offer information is obtained from reliable APIs.

[0567] Step 4:

[0568] The server activates the emotion engine and analyzes the user's emotional state. Specifically, it performs facial expression analysis and voice tone detection to determine the user's emotional state.

[0569] Step 5:

[0570] The server integrates the analysis results from the emotion engine with collected sale data to generate optimal meal suggestions based on the user's emotional state. These suggestions are tailored to the user's mood and may include, for example, "easy and fun meal options" or "relaxing menus."

[0571] Step 6:

[0572] The server formats the generated meal suggestions into a data format and sends them to the terminal.

[0573] Step 7:

[0574] The terminal displays meal suggestions received from the server to the user. To make it easier for the user to choose, the suggestions include the reason for the recommendation and price information at the beginning.

[0575] Step 8:

[0576] Users review the presented meal suggestions and select the option they feel is best suited to their mood and circumstances. This selection allows users to make daily meal decisions more intuitively.

[0577] (Example 2)

[0578] 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."

[0579] In modern society, users can obtain food-related information from a variety of sources, but their means of optimizing that information based on their individual emotions and current state are limited. This makes it difficult for users to effectively choose meals that suit their emotional state, which can lead to decreased satisfaction. Furthermore, users are also required to make optimal choices from an economic perspective, so a new approach is needed that provides suggestions that consider both emotions and economics simultaneously.

[0580] 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.

[0581] In this invention, the server includes an input device for receiving location information, an emotion data acquisition device for receiving facial and voice data, a data collection device for collecting food supply information based on location information, an emotion analysis device for analyzing the acquired emotion data and identifying the emotional state, a suggestion generation device using a generative AI model for creating suggestions, and a screen display device for visually outputting the generated suggestion content. This makes it possible to generate optimal meal suggestions that simultaneously consider the user's emotional state and economic options.

[0582] "Location information" refers to information about the user's current location and living area, and is used to collect information on food supply and generate appropriate suggestions.

[0583] An "input device" is an interface consisting of hardware or software that receives location information and sentiment data from a user.

[0584] An "emotional data acquisition device" is a device that collects data from a user's facial expressions and voice to identify the user's emotional state.

[0585] "Food supply information" refers to data related to food and ingredients, such as special offers and campaign information, collected based on the user's location.

[0586] A "data collection device" is a device that has the function of collecting location-based food supply information through the internet or affiliated databases.

[0587] An "emotion analysis device" is hardware or software that analyzes acquired emotional data and uses an AI algorithm to identify the user's emotional state.

[0588] A "suggestion generation device" is a device that utilizes a generation AI model to create optimal meal suggestions based on emotional states and ingredient supply information.

[0589] A "screen display device" is an interface for visually presenting generated meal suggestions to the user.

[0590] A "generative AI model" is an artificial intelligence model used to generate meal suggestions based on the user's emotional state and location information.

[0591] This invention is an information processing system that provides optimal meal suggestions by utilizing the user's emotional state and location information. This system collects and processes data using the following hardware and software.

[0592] The user uses a device with a dedicated application installed. The device is equipped with an input device for entering location information and an emotion data acquisition device for collecting the user's facial expressions and voice data. Location information is either entered manually by the user or automatically acquired through the device's location services. Emotion data is collected in real time using the device's camera and microphone. The collected data is then transmitted from the device to a server via the internet.

[0593] The server collects local food supply information through external APIs and partner databases based on the received location information. This collection is performed using data collection devices. The server also analyzes emotional data using an emotion analysis device and identifies the user's emotional state using AI algorithms. Machine learning platforms such as TensorFlow and PyTorch are used for this purpose.

[0594] Subsequently, the server uses a generative AI model to generate meal suggestions based on the user's emotional state and location. These suggestions include discounted ingredients, easy-to-prepare menus, and information on nearby cafes, tailored to the user's emotions. The suggestions generated by the suggestion generator are sent back to the terminal and visually displayed to the user via a screen display device.

[0595] As a concrete example, if a user is located in a "specific area of ​​an urban area" and their emotional data indicates they are "feeling tired," the server will offer convenient ready-made meals on sale, easy-to-prepare curry sets, and even information on cafes suitable for a change of pace. This system helps users make optimal meal choices that consider both their emotions and their budget.

[0596] An example of a prompt for the generating AI model is to input the text, "Create recommendations that provide special offers for a specific area, taking emotions into consideration." This will cause the AI ​​to generate suggestions that take into account the user's emotions and location.

[0597] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0598] Step 1:

[0599] The user launches a dedicated application and either manually enters their location information or automatically acquires it using the device's location services. Furthermore, an emotion data acquisition device is used to capture facial expressions with a camera and record voice with a microphone. This process inputs both location information and emotion data.

[0600] Step 2:

[0601] The device bundles the acquired location information and sentiment data into packets and sends them to the server via the internet. The input data consists of the user's current location (postal code, GPS coordinates), as well as audio waveforms and image data. This prepares the server to receive the location information and sentiment data.

[0602] Step 3:

[0603] The server uses a data collection device based on the received location information to gather food supply information for that region from partner databases and external APIs. The input here is location information, and the output is a list of special offers and restaurant campaigns for that region.

[0604] Step 4:

[0605] When the server receives emotional data, it analyzes it using an emotion analysis device and identifies the user's emotional state using an AI algorithm. Inputs include audio waveforms and images, and the output is an emotional state such as "relaxed" or "tired." This process involves extracting facial features and analyzing the tone of the voice.

[0606] Step 5:

[0607] The server utilizes a generative AI model to generate meal suggestions based on collected ingredient supply information and the user's emotional state. It receives emotional state and supply information as input and creates suggested menus and cafe information as output. Here, the priority of options is adjusted according to the user's emotions.

[0608] Step 6:

[0609] The server sends the generated suggestions to the terminal, which then presents the information to the user via its on-screen display. The terminal visually displays details of special offers, recommended menu items, and cafe information in a list format, allowing the user to review each suggestion.

[0610] (Application Example 2)

[0611] 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."

[0612] There is a need for services that can improve user satisfaction while effectively saving on food costs by offering meal suggestions that take into account the user's current emotional state. However, conventional systems do not offer suggestions based on the user's emotions and lack personalized suggestions that meet individual needs, so further improvements are needed.

[0613] 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.

[0614] In this invention, the server includes an information input means for receiving location input, an emotion recognition means for recognizing the user's emotional state, and a data collection means for collecting recommended meal data based on the location and emotional state. This enables personalized suggestions of meal options that reflect the user's location and emotional state.

[0615] "Location input" is a function that allows users to input their current location into a specific system.

[0616] "Information input means" refers to a device or interface that receives data from a user.

[0617] "Emotion recognition means" refers to a device or software that has the function of determining the emotional state of a user based on data such as facial expressions and voice.

[0618] "Recommended dietary data" refers to information about specific meals or menus selected based on collected data.

[0619] "Data collection means" refers to a device or system that has the function of collecting necessary information according to specified conditions.

[0620] "Data processing means" refers to a device or software that has the function of integrating and analyzing collected data.

[0621] A "proposal generation means" is a device or system that has the function of creating suggestions for the user based on the analyzed results.

[0622] A "display means" is a device for visually presenting the generated proposal to the user.

[0623] "Stress-reducing options" are choices or services offered with the aim of alleviating user stress.

[0624] In this invention, the user first enters their location into the terminal. The terminal uses an information input means that accepts location input and obtains the user's location information. Next, the user provides facial expressions and voice data using emotion recognition means. This data is collected by a camera and microphone installed in the device. The terminal transmits this data to a server.

[0625] The server receives the user's location information and emotional data, and analyzes the user's emotional state using an emotional recognition means. It uses software such as OpenCV or TensorFlow to determine the emotion, compares it with the location, and collects corresponding recommended meal data. The data collection means obtains recommended meal information based on the user's emotional state and location, and the data processing means integrates this information.

[0626] Based on the collected data, the server generates meal options that are cost-effective and best suited to the user's mood through a suggestion generation mechanism. These suggestions are visually presented to the user via a terminal display mechanism. For example, if a user is located in Chuo Ward, Sapporo City, and their emotional state is analyzed as "slightly depressed," delivery information from a cafe including herbal tea and a matcha parfait will be presented as a menu that helps reduce stress.

[0627] In this way, a system is realized that personalizes and suggests the optimal meal options based on the user's emotions and location. A generative AI model is used, and an example of a prompt message tailored to the emotional state is: "The user's location is Sapporo City, Chuo Ward, and it has been recognized that they are feeling a little down. Please suggest a meal delivery option that will relieve stress."

[0628] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0629] Step 1:

[0630] The user enters their location into the terminal. The terminal's information input method receives this location information. The entered location data is then sent directly to the server as input.

[0631] Step 2:

[0632] The user provides facial expressions and voice data to the emotion recognition system using the camera and microphone built into the device. This inputs data to determine the user's emotional state.

[0633] Step 3:

[0634] The device sends the collected location and sentiment data to the server. This becomes new input data for the server.

[0635] Step 4:

[0636] The server uses the received location data to activate data collection mechanisms to collect information on nearby food options. These data collection mechanisms access external databases or APIs to retrieve relevant food data. The output of this process is a list of food options related to the user's location.

[0637] Step 5:

[0638] The server uses emotion recognition tools to evaluate the user's facial expressions and voice data. It utilizes software such as OpenCV and TensorFlow to analyze the user's emotional state from the input data. The output is an evaluation result indicating the user's emotional state.

[0639] Step 6:

[0640] The server uses data processing tools to integrate acquired meal data with sentiment analysis results. This generates optimal meal options that take into account the user's emotions and cost-effectiveness. The output is a list of optimized meal options.

[0641] Step 7:

[0642] The suggestion generation system creates meal suggestions based on these optimized meal options. The output is a specific meal plan to be presented to the user.

[0643] Step 8:

[0644] The terminal uses a display means to visually display the generated meal suggestions to the user. The output of this step is the meal suggestions on a visual interface provided to the user.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] [Fourth Embodiment]

[0649] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0650] 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.

[0651] 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).

[0652] 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.

[0653] 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.

[0654] 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).

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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.

[0661] 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".

[0662] This invention is a system aimed at effectively saving on food expenses, and it uses local sale information and restaurant promotion information to suggest the best meal options to the user. Specifically, it starts with the user entering their home or current address into the system.

[0663] User-side actions

[0664] The user enters their current address into a smartphone or computer application. This prepares the system to begin location-based data processing.

[0665] Server-side processing

[0666] Based on the user's entered location, the server collects information on supermarkets and restaurants in the area from online databases and APIs. This information includes current sale prices and promotional menus from nearby restaurants. The server integrates and analyzes this information to identify the most cost-effective dining options for each user.

[0667] Proposal generation and presentation

[0668] Based on the analysis results, the server generates suggestions for different meal options. These suggestions include detailed information about each option, such as the cost-effectiveness of cooking at home using discounted items from the supermarket, or the specific cost of restaurant menus offered at promotional prices. The suggestions are later displayed to the user visually in a format that is easy to understand and compare.

[0669] Specific example

[0670] If a user enters "Shinjuku Ward, Tokyo," the server collects information on ingredients on sale at supermarkets in Shinjuku Ward and promotional menus at restaurants in the same ward. For example, if it determines that ingredients for curry using potatoes and pork are available cheaply, it will suggest, "If you buy potatoes for 100 yen and pork for 200 yen at Supermarket A and make curry, it will cost 400 yen per serving," and simultaneously suggest, "Omurice is 600 yen as part of an opening campaign at Restaurant B."

[0671] In this way, users can learn about the optimal meal choices based on their daily circumstances and effectively save money on food.

[0672] The following describes the processing flow.

[0673] Step 1:

[0674] The terminal displays an address input screen to the user and accepts the user's home or current address as text input.

[0675] Step 2:

[0676] The terminal sends the address data entered by the user to the server and requests that processing begin.

[0677] Step 3:

[0678] The server analyzes the received address data to identify the data sources for supermarkets and restaurants in the relevant area.

[0679] Step 4:

[0680] The server sends API requests to identified data sources to collect sale and campaign information. It also performs web scraping as needed to retrieve the latest publicly available information.

[0681] Step 5:

[0682] The server integrates the acquired data and stores it in a database. Furthermore, it analyzes the pricing information of ingredients and menu items to calculate cost-effectiveness and creates a comparison list.

[0683] Step 6:

[0684] The server uses a generative AI algorithm to select the most cost-effective meal option for the user. This process takes into account not only price but also nutritional value and cooking time.

[0685] Step 7:

[0686] Based on the analysis results, the server generates suggested meal options as text and formats them into a data format for transmission to the terminal.

[0687] Step 8:

[0688] The terminal displays the suggested data received from the server on the user's screen, allowing them to visually confirm the details of each option.

[0689] Step 9:

[0690] Users review the presented meal options and decide on the choice they believe is best for them.

[0691] This process allows users to easily make the most economical and effective meal choices for the day.

[0692] (Example 1)

[0693] 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".

[0694] In modern times, consumers need to efficiently manage their daily living expenses, but their means of effectively saving money on food and eating out are limited. Furthermore, the sheer volume of sale and promotional information makes it difficult for individual users to quickly make the best choices. Therefore, there is a need for a system that provides individually optimized meal options for each user, enabling them to save on food costs.

[0695] 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.

[0696] In this invention, the server includes an input device for receiving location information, an information acquisition device for collecting sales information and food and beverage information based on the location information, and an information processing device for integrating the collected information and analyzing cost-effective options. This makes it possible to provide users with individually optimized meal options.

[0697] "Location information" refers to data about addresses and locations that users enter to indicate their home or current location.

[0698] "Input device" refers to a device or interface that a user uses to provide location information and other relevant information to the system.

[0699] An "information acquisition device" refers to a component that a system uses to collect target information via the internet or other data sources.

[0700] An "information processing device" refers to the part of a system that has the functionality to perform computational processing used to analyze collected data and generate effective options for the user.

[0701] A "selection generation device" refers to a component that specifically forms user-oriented choices based on the analysis results of an information processing device.

[0702] A "display device" refers to an interface or device that visually presents generated options and related information to the user.

[0703] "Sales information" refers to data regarding the prices and sales status of products and services offered in specific regions or stores.

[0704] "Food and beverage information" refers to information such as restaurant menus, prices, and promotions.

[0705] "Highly efficient options" refer to choices that maximize cost-effectiveness while selecting the most suitable products or services for the user.

[0706] This invention is a system that suggests optimal meal options to a user based on their location information. The system begins with the user inputting their current location information using a terminal such as a smartphone or computer. The location information is transmitted by the user through the input device.

[0707] Upon receiving location information, the server utilizes external databases and APIs to collect sales and food / beverage information for the relevant area. Specific APIs and databases are used to retrieve this information, and price and promotional information is collected in real time.

[0708] Next, the server uses an information processing device to integrate the collected information and analyze cost-effective meal options using algorithms. This analysis utilizes machine learning techniques and generative AI models to generate personalized options tailored to each user's needs.

[0709] The server then uses a selection generator to create specific options based on the analysis results. These options include specific product prices and restaurant promotion details, and are organized in an easy-to-understand manner.

[0710] Finally, the generated options are visually presented to the user's device via a display device. Based on the presented information, the user can make the optimal choice and efficiently save on food expenses.

[0711] For example, if a user enters "Shinjuku Ward, Tokyo," the server will collect information on special offers and restaurant promotions within Shinjuku Ward and provide detailed suggestions such as, "If you buy potatoes for 100 yen and pork for 200 yen at Supermarket A and cook curry at home, one meal will cost 400 yen."

[0712] An example of a prompt message might be, "Please suggest the best dining options in Shinjuku Ward, Tokyo, based on my current location." In this way, the present invention enables users to effectively save money.

[0713] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0714] Step 1:

[0715] The user enters location information using a smartphone or computer application. They use an input device to enter their address or current location and send it to the system. The information entered at this stage is the user's geographical location, which forms the basis for subsequent processing.

[0716] Step 2:

[0717] The server sends queries to external APIs and databases based on location information received from the user. Using an information acquisition device, it collects sales and dining information from supermarkets and restaurants in the relevant area. This includes special offer and campaign information, and data is acquired in real time. The input is location information, and the output is the collected special offer and campaign information.

[0718] Step 3:

[0719] The server integrates the collected information using an information processing device and analyzes cost-effective meal options. Data processing involves deduplication and correction of inconsistencies, and a generative AI model is used to evaluate each option. The input is the collected data, and the output is evaluation data based on the analysis.

[0720] Step 4:

[0721] The server uses a selection generator to generate personalized meal options tailored to the user based on the obtained evaluation data. For example, it considers factors such as the cost of cooking at home and promotions when dining out to construct specific choices. The input is evaluation data, and the output is specific choices.

[0722] Step 5:

[0723] The server formats the generated options into a visually easy-to-understand format and sends it to the user's terminal. The display device then shows the list of options on the terminal, allowing the user to browse and compare them. The input is a list of options, and the output is a visual representation on the user interface.

[0724] Step 6:

[0725] The user reviews the options displayed on the device and makes the optimal meal choice based on them. The user's input is the decision of the selection, and the final output is the result reflected in the user's daily meal plan. This allows the user to effectively manage their food expenses and achieve savings.

[0726] (Application Example 1)

[0727] 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".

[0728] In recent years, with the increasing demand for the restaurant industry and food delivery services, consumers are required to make optimal meal choices while keeping food costs down. However, it is difficult for consumers to keep track of all local sale and campaign information, and it is also difficult to utilize this information efficiently. Therefore, there is a need for an effective system that can optimize food costs based on location.

[0729] 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.

[0730] In this invention, the server includes an input means for acquiring location information, an information gathering means for collecting food and beverage information in the vicinity of the location, and a data processing means for analyzing deliverable meal options based on the collected information. This enables consumers to make optimal meal choices by utilizing special offers and campaigns.

[0731] "Location information" refers to data about the user's current location or a specified location, and is the foundational information for generating meal suggestions.

[0732] "Input means" refers to interfaces or devices that allow users to provide location information to a system, and are operated via smartphone or computer applications.

[0733] "Information gathering means" refers to the process and technology used to obtain information on restaurants and special offers in the surrounding area based on specified location information, from internet databases and APIs.

[0734] "Data processing means" refers to a system process for analyzing collected food and beverage information, which includes analysis to evaluate cost-effectiveness and delivery feasibility in order to determine the optimal meal provision option.

[0735] "Provision and generation means" refers to a software component that proposes available meal options to the user based on the analysis results from data processing means.

[0736] "Display means" refers to screens or devices used to visually present the generated meal selection options to the user, and the information is provided through the display of a smartphone or computer.

[0737] The system that realizes this invention consists of software that suggests the optimal meal selection based on the user's location information. It mainly operates as a smartphone or computer application and functions through the following steps.

[0738] Users enter their location information through an application on their device. The user interface is designed to be intuitive, utilizing frameworks such as React Native.

[0739] The server collects information on restaurants and special offers in the surrounding area based on the location information specified by the user. This information collection is done by obtaining location data using the Google Maps API and then accessing the APIs of restaurants and supermarkets based on that data to retrieve information.

[0740] The collected data is analyzed on the server. Here, the cost-effectiveness and delivery feasibility of each meal option are evaluated using libraries such as Python's Pandas library. This helps identify the most appropriate meal choice for the user.

[0741] Subsequently, based on the analysis results, the provision generation system works to generate a list of optimal meal options for the user. This information is formatted in JSON format and sent to the terminal.

[0742] This information is displayed in real time on the user's device. Based on this information, the user can choose the most suitable meal. The display is visually clear and presented within an app built with React Native.

[0743] For example, if a user is in Shibuya Ward, this system gathers information on ingredients on sale and menus on promotion within Shibuya Ward, and suggests to the user that "you can save 800 yen per meal by taking advantage of today's sales."

[0744] An example of a prompt for the generating AI model is: "Please enter my current location and suggest delivery options that optimize the cost of meals in that area. Please also display specific menus and prices."

[0745] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0746] Step 1:

[0747] The user enters location information in an application on their device. This entered location information is then sent to the system. This information is entered via text fields provided in the user interface or through location services.

[0748] Step 2:

[0749] The server uses the Google Maps API to obtain geographical information about the surrounding area based on the location information received from the user. Based on the location information received as input, it makes an API request and obtains longitude, latitude, and geographic coordinate data within the area as output.

[0750] Step 3:

[0751] The server uses acquired geographic information to access APIs of nearby restaurants and supermarkets to collect information on special offers and promotions. The input is geographic coordinate data, which is used to gather restaurant information from multiple external databases and APIs. The output is a list of special offers and promotions.

[0752] Step 4:

[0753] The server processes data based on collected food and beverage information. Using Python and libraries such as Pandas, it performs evaluations that consider cost-effectiveness and delivery feasibility. The input is a list of food and beverage information, and the output is the cost-effectiveness analysis results for each option.

[0754] Step 5:

[0755] The server generates suggestions for the user based on the analysis results obtained. The suggestion generation mechanism works and outputs a list of meal suggestions formatted in JSON format. The input is the analysis results, and the output is a list of meal options.

[0756] Step 6:

[0757] The device receives meal suggestions sent from the server and presents them visually to the user. An application built with React Native runs on the device, accepting JSON data as input and outputting results in a user-friendly format.

[0758] Step 7:

[0759] The user selects the most suitable meal option based on the presented information and makes a purchase or order. As output, the user's selection is recorded as a history within the application.

[0760] 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.

[0761] This invention provides a system that recognizes the user's emotions and adjusts the suggested content based on those emotions. The aim is to support saving on food expenses while presenting optimal meal options that take into account the user's current emotional state. Emotion recognition is performed through the user interface.

[0762] User-side actions

[0763] Users enter their address into the application and provide data such as facial expressions and voice through an interface equipped with an emotion engine. This allows the system to recognize the user's current emotions.

[0764] Server-side processing

[0765] The server receives location and sentiment data from the user. First, the server collects information on nearby sales and restaurant promotions based on the user's location. Next, it analyzes the sentiment data and uses an AI algorithm to understand the user's emotional state. This information is a crucial element in creating meal recommendations.

[0766] Proposal generation and display

[0767] The server combines the user's emotional state with collected sale information to generate meal suggestions that take cost and mood into consideration. For example, if the user is tired or stressed, it might suggest easy-to-prepare meal options. Conversely, if the user is in a good mood, it might suggest a slightly more elaborate recipe, providing an opportunity to enjoy cooking.

[0768] As a concrete example, if a user is located in "Kita Ward, Osaka City" and the emotion engine determines they are "feeling tired," the server will suggest discounted prepared foods or easy-to-cook curry sets. At the same time, it will also provide information on cafes offering sweet desserts to lift their spirits.

[0769] This allows users to make meal choices that suit not only their economic needs but also their emotions and physical condition at the time, resulting in more satisfying savings on food expenses.

[0770] The following describes the processing flow.

[0771] Step 1:

[0772] The user enters their location into the device and provides data using the camera and microphone to recognize their current emotional state. This process includes capturing facial expressions and inputting voice.

[0773] Step 2:

[0774] The device sends address data and sentiment data obtained from the user to the server. Since the sentiment data is processed in real time, a dedicated protocol is used.

[0775] Step 3:

[0776] The server uses the received address to collect data on supermarkets and restaurants in the relevant area. This data collection may include information on special offers and details of promotions. Furthermore, the special offer information is obtained from reliable APIs.

[0777] Step 4:

[0778] The server activates the emotion engine and analyzes the user's emotional state. Specifically, it performs facial expression analysis and voice tone detection to determine the user's emotional state.

[0779] Step 5:

[0780] The server integrates the analysis results from the emotion engine with collected sale data to generate optimal meal suggestions based on the user's emotional state. These suggestions are tailored to the user's mood and may include, for example, "easy and fun meal options" or "relaxing menus."

[0781] Step 6:

[0782] The server formats the generated meal suggestions into a data format and sends them to the terminal.

[0783] Step 7:

[0784] The terminal displays meal suggestions received from the server to the user. To make it easier for the user to choose, the suggestions include the reason for the recommendation and price information at the beginning.

[0785] Step 8:

[0786] Users review the presented meal suggestions and select the option they feel is best suited to their mood and circumstances. This selection allows users to make daily meal decisions more intuitively.

[0787] (Example 2)

[0788] 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".

[0789] In modern society, users can obtain food-related information from a variety of sources, but their means of optimizing that information based on their individual emotions and current state are limited. This makes it difficult for users to effectively choose meals that suit their emotional state, which can lead to decreased satisfaction. Furthermore, users are also required to make optimal choices from an economic perspective, so a new approach is needed that provides suggestions that consider both emotions and economics simultaneously.

[0790] 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.

[0791] In this invention, the server includes an input device for receiving location information, an emotion data acquisition device for receiving facial and voice data, a data collection device for collecting food supply information based on location information, an emotion analysis device for analyzing the acquired emotion data and identifying the emotional state, a suggestion generation device using a generative AI model for creating suggestions, and a screen display device for visually outputting the generated suggestion content. This makes it possible to generate optimal meal suggestions that simultaneously consider the user's emotional state and economic options.

[0792] "Location information" refers to information about the user's current location and living area, and is used to collect information on food supply and generate appropriate suggestions.

[0793] An "input device" is an interface consisting of hardware or software that receives location information and sentiment data from a user.

[0794] An "emotional data acquisition device" is a device that collects data from a user's facial expressions and voice to identify the user's emotional state.

[0795] "Food supply information" refers to data related to food and ingredients, such as special offers and campaign information, collected based on the user's location.

[0796] A "data collection device" is a device that has the function of collecting location-based food supply information through the internet or affiliated databases.

[0797] An "emotion analysis device" is hardware or software that analyzes acquired emotional data and uses an AI algorithm to identify the user's emotional state.

[0798] A "suggestion generation device" is a device that utilizes a generation AI model to create optimal meal suggestions based on emotional states and ingredient supply information.

[0799] A "screen display device" is an interface for visually presenting generated meal suggestions to the user.

[0800] A "generative AI model" is an artificial intelligence model used to generate meal suggestions based on the user's emotional state and location information.

[0801] This invention is an information processing system that provides optimal meal suggestions by utilizing the user's emotional state and location information. This system collects and processes data using the following hardware and software.

[0802] The user uses a device with a dedicated application installed. The device is equipped with an input device for entering location information and an emotion data acquisition device for collecting the user's facial expressions and voice data. Location information is either entered manually by the user or automatically acquired through the device's location services. Emotion data is collected in real time using the device's camera and microphone. The collected data is then transmitted from the device to a server via the internet.

[0803] The server collects local food supply information through external APIs and partner databases based on the received location information. This collection is performed using data collection devices. The server also analyzes emotional data using an emotion analysis device and identifies the user's emotional state using AI algorithms. Machine learning platforms such as TensorFlow and PyTorch are used for this purpose.

[0804] Subsequently, the server uses a generative AI model to generate meal suggestions based on the user's emotional state and location. These suggestions include discounted ingredients, easy-to-prepare menus, and information on nearby cafes, tailored to the user's emotions. The suggestions generated by the suggestion generator are sent back to the terminal and visually displayed to the user via a screen display device.

[0805] As a concrete example, if a user is located in a "specific area of ​​an urban area" and their emotional data indicates they are "feeling tired," the server will offer convenient ready-made meals on sale, easy-to-prepare curry sets, and even information on cafes suitable for a change of pace. This system helps users make optimal meal choices that consider both their emotions and their budget.

[0806] An example of a prompt for the generating AI model is to input the text, "Create recommendations that provide special offers for a specific area, taking emotions into consideration." This will cause the AI ​​to generate suggestions that take into account the user's emotions and location.

[0807] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0808] Step 1:

[0809] The user launches a dedicated application and either manually enters their location information or automatically acquires it using the device's location services. Furthermore, an emotion data acquisition device is used to capture facial expressions with a camera and record voice with a microphone. This process inputs both location information and emotion data.

[0810] Step 2:

[0811] The device bundles the acquired location information and sentiment data into packets and sends them to the server via the internet. The input data consists of the user's current location (postal code, GPS coordinates), as well as audio waveforms and image data. This prepares the server to receive the location information and sentiment data.

[0812] Step 3:

[0813] The server uses a data collection device based on the received location information to gather food supply information for that region from partner databases and external APIs. The input here is location information, and the output is a list of special offers and restaurant campaigns for that region.

[0814] Step 4:

[0815] When the server receives emotional data, it analyzes it using an emotion analysis device and identifies the user's emotional state using an AI algorithm. Inputs include audio waveforms and images, and the output is an emotional state such as "relaxed" or "tired." This process involves extracting facial features and analyzing the tone of the voice.

[0816] Step 5:

[0817] The server utilizes a generative AI model to generate meal suggestions based on collected ingredient supply information and the user's emotional state. It receives emotional state and supply information as input and creates suggested menus and cafe information as output. Here, the priority of options is adjusted according to the user's emotions.

[0818] Step 6:

[0819] The server sends the generated suggestions to the terminal, which then presents the information to the user via its on-screen display. The terminal visually displays details of special offers, recommended menu items, and cafe information in a list format, allowing the user to review each suggestion.

[0820] (Application Example 2)

[0821] 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".

[0822] There is a need for services that can improve user satisfaction while effectively saving on food costs by offering meal suggestions that take into account the user's current emotional state. However, conventional systems do not offer suggestions based on the user's emotions and lack personalized suggestions that meet individual needs, so further improvements are needed.

[0823] 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.

[0824] In this invention, the server includes an information input means for receiving location input, an emotion recognition means for recognizing the user's emotional state, and a data collection means for collecting recommended meal data based on the location and emotional state. This enables personalized suggestions of meal options that reflect the user's location and emotional state.

[0825] "Location input" is a function that allows users to input their current location into a specific system.

[0826] "Information input means" refers to a device or interface that receives data from a user.

[0827] "Emotion recognition means" refers to a device or software that has the function of determining the emotional state of a user based on data such as facial expressions and voice.

[0828] "Recommended dietary data" refers to information about specific meals or menus selected based on collected data.

[0829] "Data collection means" refers to a device or system that has the function of collecting necessary information according to specified conditions.

[0830] "Data processing means" refers to a device or software that has the function of integrating and analyzing collected data.

[0831] A "proposal generation means" is a device or system that has the function of creating suggestions for the user based on the analyzed results.

[0832] A "display means" is a device for visually presenting the generated proposal to the user.

[0833] "Stress-reducing options" are choices or services offered with the aim of alleviating user stress.

[0834] In this invention, the user first enters their location into the terminal. The terminal uses an information input means that accepts location input and obtains the user's location information. Next, the user provides facial expressions and voice data using emotion recognition means. This data is collected by a camera and microphone installed in the device. The terminal transmits this data to a server.

[0835] The server receives the user's location information and emotional data, and analyzes the user's emotional state using an emotional recognition means. It uses software such as OpenCV or TensorFlow to determine the emotion, compares it with the location, and collects corresponding recommended meal data. The data collection means obtains recommended meal information based on the user's emotional state and location, and the data processing means integrates this information.

[0836] Based on the collected data, the server generates meal options that are cost-effective and best suited to the user's mood through a suggestion generation mechanism. These suggestions are visually presented to the user via a terminal display mechanism. For example, if a user is located in Chuo Ward, Sapporo City, and their emotional state is analyzed as "slightly depressed," delivery information from a cafe including herbal tea and a matcha parfait will be presented as a menu that helps reduce stress.

[0837] In this way, a system is realized that personalizes and suggests the optimal meal options based on the user's emotions and location. A generative AI model is used, and an example of a prompt message tailored to the emotional state is: "The user's location is Sapporo City, Chuo Ward, and it has been recognized that they are feeling a little down. Please suggest a meal delivery option that will relieve stress."

[0838] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0839] Step 1:

[0840] The user enters their location into the terminal. The terminal's information input method receives this location information. The entered location data is then sent directly to the server as input.

[0841] Step 2:

[0842] The user provides facial expressions and voice data to the emotion recognition system using the camera and microphone built into the device. This inputs data to determine the user's emotional state.

[0843] Step 3:

[0844] The device sends the collected location and sentiment data to the server. This becomes new input data for the server.

[0845] Step 4:

[0846] The server uses the received location data to activate data collection mechanisms to collect information on nearby food options. These data collection mechanisms access external databases or APIs to retrieve relevant food data. The output of this process is a list of food options related to the user's location.

[0847] Step 5:

[0848] The server uses emotion recognition tools to evaluate the user's facial expressions and voice data. It utilizes software such as OpenCV and TensorFlow to analyze the user's emotional state from the input data. The output is an evaluation result indicating the user's emotional state.

[0849] Step 6:

[0850] The server uses data processing tools to integrate acquired meal data with sentiment analysis results. This generates optimal meal options that take into account the user's emotions and cost-effectiveness. The output is a list of optimized meal options.

[0851] Step 7:

[0852] The suggestion generation system creates meal suggestions based on these optimized meal options. The output is a specific meal plan to be presented to the user.

[0853] Step 8:

[0854] The terminal uses a display means to visually display the generated meal suggestions to the user. The output of this step is the meal suggestions on a visual interface provided to the user.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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."

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0876] The following is further disclosed regarding the embodiments described above.

[0877] (Claim 1)

[0878] An information input means for accepting location input,

[0879] A data collection means for collecting recommended meal data based on the location,

[0880] A data processing method that integrates collected data and analyzes the most cost-effective meal options,

[0881] A proposal generation means that generates meal suggestions based on the analysis results,

[0882] A display means for outputting the generated proposals,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, characterized in that it modifies the proposed content according to user-specified conditions.

[0886] (Claim 3)

[0887] The system described in item 1, characterized by acquiring special sale information and campaign information in real time.

[0888] "Example 1"

[0889] (Claim 1)

[0890] An input device that accepts location information,

[0891] An information acquisition device that collects sales information and food and beverage information based on the location information,

[0892] An information processing device that integrates collected information and analyzes cost-effective options,

[0893] A selection generation device that generates meal options based on analysis results,

[0894] A display device that visually outputs the generated options,

[0895] A system that includes this.

[0896] (Claim 2)

[0897] The system according to claim 1, characterized in that it adjusts the selection based on conditions specified by the user.

[0898] (Claim 3)

[0899] The system according to claim 1, characterized by instantly obtaining price information and promotional information.

[0900] "Application Example 1"

[0901] (Claim 1)

[0902] An input means for obtaining location information,

[0903] Information gathering means for collecting information on restaurants and bars in the vicinity of the said location,

[0904] A data processing means that analyzes the selection of deliverable meals based on the collected information,

[0905] A means for generating meal service options based on analysis,

[0906] A display means for presenting the generated selection of offerings,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, characterized in that it adjusts the content offered based on the user's preferences.

[0910] (Claim 3)

[0911] The system according to claim 1, characterized by collecting special sale information and promotional information in a timely manner.

[0912] "Example 2 of combining an emotion engine"

[0913] (Claim 1)

[0914] An input device that accepts location information,

[0915] An emotion data acquisition device that accepts facial expression and voice data,

[0916] A data collection device that collects food supply information based on location information,

[0917] An emotion analysis device that analyzes acquired emotional data to identify emotional states,

[0918] A suggestion generation device that integrates acquired regional data and emotional states, and creates meal suggestions using a generative AI model,

[0919] A screen display device that visually outputs the generated proposal content,

[0920] An information processing system that includes this.

[0921] (Claim 2)

[0922] The information processing system according to claim 1, characterized in that it adjusts the content of the suggestions based on the user's emotional state.

[0923] (Claim 3)

[0924] The information processing system according to claim 1, characterized by acquiring information on the supply of ingredients in the surrounding area in real time.

[0925] "Application example 2 when combining with an emotional engine"

[0926] (Claim 1)

[0927] An information input means for accepting location input,

[0928] A means of recognizing the emotional state of a user,

[0929] A data collection means for collecting recommended meal data based on the location and emotional state,

[0930] A data processing method that integrates collected data and analyzes the most cost-effective and mood-optimized meal options,

[0931] A proposal generation means that generates meal suggestions based on the analysis results,

[0932] A display means for outputting the generated proposals,

[0933] A system that includes this.

[0934] (Claim 2)

[0935] The system according to claim 1, characterized in that it changes the content of the suggestions according to the user's emotional state.

[0936] (Claim 3)

[0937] The system according to claim 1, characterized by suggesting options that help reduce stress based on emotional state. [Explanation of symbols]

[0938] 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. An information input means for accepting location input, A data collection means for collecting recommended meal data based on the location, A data processing method that integrates collected data and analyzes the most cost-effective meal options, A proposal generation means that generates meal suggestions based on the analysis results, A display means for outputting the generated proposals, A system that includes this.

2. The system according to claim 1, characterized in that the proposed content is modified according to user-specified conditions.

3. The system described in item 1, characterized by acquiring special sale information and campaign information in real time.

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