system

Abstract

Provide a system. 【Solution means】means for a user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the diet content, means for calculating calories and nutrients based on the diet content, means for recording and analyzing the daily nutrient intake situation, means for generating proposals for lacking nutrients and calories, means for displaying the proposal content, means for generating and providing an online purchase link for the proposed product, A system including the above.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, many people have an unhealthy diet, and problems such as obesity and malnutrition are prominent. In particular, there is a tendency to choose high-calorie and high-fat diets, and as a result, the risk of harming health is increasing. Also, in busy daily life, it is difficult to grasp one's own calorie and nutritional balance. Furthermore, there is often a lack of knowledge for making appropriate food choices and nutritional supplements. Against this background, there is a need for a means for users to easily record the content of their diet and receive specific suggestions for maintaining a healthy diet.

Means for Solving the Problems

[0005] This invention is a system that allows users to record their meal content via voice input and efficiently manage their calorie and nutrient balance. The system uses a generative AI model that converts voice data into text and identifies meal content. Furthermore, it acquires nutrient data for each food item and records and analyzes the user's daily nutrient intake. Based on this analysis, it suggests specific meals and supplements to compensate for any nutrient deficiencies and calories, and displays these suggestions to the user. It also generates and provides online purchase links for the suggested products, allowing users to easily purchase necessary items. This enables users to effectively manage their calorie and nutrient balance and lead a healthy lifestyle.

[0006] "Voice input means" refers to a device or method for recording what a user says as voice data.

[0007] "Speech-to-text conversion means" refers to technologies or devices that convert recorded speech data into text data.

[0008] "Meal content analysis means" refers to technologies and methods that analyze converted text data to identify the ingredients and contents of a meal.

[0009] "Calorie and nutrient calculation means" refers to technology or equipment for calculating calories and nutrients based on specified dietary content.

[0010] The "Nutrient Intake Recording and Analysis System" is a system that records and analyzes a user's daily nutrient intake.

[0011] A "proposal generation method" refers to a technology or device that, based on the analysis results, proposes specific meals or supplements to compensate for any deficiencies in nutrients or calories.

[0012] "Proposal content display means" refers to an interface or method for visually presenting the generated proposal content to the user.

[0013] An "online purchase link generation method" refers to a technology or device that generates and provides to users online purchase links for suggested meals or supplements. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a 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.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] This invention is a system for recording a user's meal content via voice input and managing the balance of calories and nutrients. The system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake status record analysis means, suggestion generation means, suggestion content display means, and online purchase link generation means.

[0036] First, the user operates the device and uses voice input to dictate what they are eating. For example, they might say, "Today's lunch is katsu curry." This voice data is then recorded directly on the device.

[0037] Next, the terminal sends the recorded audio data to the server. The server uses a speech-to-text conversion method to convert the received audio data into text data. By utilizing speech recognition technology, the audio can be converted into text with high accuracy.

[0038] Next, the server uses a meal content analysis tool to analyze the converted text data. To identify the contents of the meal, it extracts ingredient names and dish names from the text data and compares them with a database to identify matches. This analysis reveals, for example, that "katsu curry" is included.

[0039] The server then uses calorie and nutrient calculation tools to calculate calories and nutrients based on the identified meal. It retrieves nutrient data for each food item from the database and obtains detailed information such as "Pork cutlet curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[0040] Furthermore, the server uses a nutrient intake recording and analysis system to record and analyze the user's daily nutrient intake. This reveals any nutrient deficiencies or excess calories.

[0041] Next, the server uses a suggestion generation mechanism to provide specific suggestions for meals and supplements based on the analysis results. For example, it might generate a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended."

[0042] The generated proposals are displayed to the user via the terminal. The terminal uses a proposal display mechanism to present the proposals to the user in an easily understandable visual way.

[0043] Finally, the server uses an online purchase link generation mechanism to generate and provide the user with online purchase links for the suggested meals and supplements. By clicking on these links, the user can directly purchase the desired products from an online store (e.g., an e-commerce platform).

[0044] This process allows users to easily record their meals by voice and receive suggestions for nutritionally balanced meals and supplements. As a result, they can maintain a healthy diet. For example, a user might voice-input, "Today's lunch is katsu curry," and the server would analyze and calculate that information, suggest a suitable meal balance, and then purchase the necessary food items online.

[0045] The following describes the processing flow.

[0046] Step 1: Voice Input

[0047] The user uses their device and presses the voice input button on the app screen.

[0048] The device records the user's voice. For example, the user might say, "I'm having katsu curry for lunch today."

[0049] Step 2: Sending the audio data

[0050] The device sends the recorded audio data to the server. This data is then transferred directly to the server.

[0051] Step 3: Speech-to-text conversion

[0052] The server converts the received audio data into text data using a speech recognition engine. For example, text such as "Today's lunch is katsu curry" is generated.

[0053] Step 4: Analysis of Dietary Content

[0054] The server analyzes the converted text data. The server uses an analysis algorithm to identify the food item, such as "katsu curry," from the text.

[0055] Step 5: Calculating calories and nutrients

[0056] The server retrieves nutrient data from a database based on the identified meal. For example, it calculates the calories and nutrients of "katsu curry" (e.g., 900kcal, 40g fat, 100g carbohydrates, 25g protein).

[0057] Step 6: Recording and analyzing nutrient intake

[0058] The server records and analyzes the user's daily nutrient intake. This helps identify any nutrient deficiencies or excess calories.

[0059] Step 7: Proposal Generation

[0060] Based on the analysis results, the server generates specific suggestions for meals and supplements that are suitable for the user. For example, it might suggest, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended."

[0061] Step 8: Display the proposed content

[0062] The server sends the generated proposal to the terminal. The terminal displays the proposal on its screen, allowing the user to visually confirm it.

[0063] Step 9: Generate online purchase link

[0064] The server generates online purchase links for the suggested meals and supplements and sends them to the device. The user can then click the displayed link to directly purchase the products from the online store.

[0065] This allows users to effectively record their meals using only voice input and receive suggestions for meals and supplements to maintain proper nutritional balance. As a result, it becomes possible to support them in maintaining a healthy lifestyle.

[0066] (Example 1)

[0067] 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."

[0068] In today's busy lifestyle, it is difficult for users to effectively manage their diet and maintain a healthy eating habit. Traditional methods involve manual recording of meals and calculation of nutrients, which is time-consuming, cumbersome, and prone to errors. Furthermore, systems for analyzing meals and suggesting nutrients are limited, and the accuracy of converting voice-based input to text data is particularly low. Therefore, there is a need for a reliable system that is easy for users to continue using.

[0069] 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.

[0070] In this invention, the server includes means for accurately converting voice data into text using a voice recognition service, means for extracting ingredient names and dish names from the text data using natural language processing technology, and means for identifying meal contents by comparing them with a database. This allows users to easily input voice information, convert the content into text data, analyze the meal contents to calculate calories and nutrients, and receive suggestions for appropriate meals and supplements. By providing online purchase links, the system provides an environment where users can immediately purchase the suggested ingredients and supplements, supporting comprehensive health management.

[0071] A "user" refers to an individual who uses voice input to record their meal details and manage their nutrition.

[0072] "Voice input means" refers to a device or application for a user to input voice.

[0073] "Text data" refers to a data format obtained by converting speech input via voice input devices into text information.

[0074] "Speech recognition service" refers to a technology or platform for converting speech into text data.

[0075] "Natural language processing technology" refers to computer technology used to analyze text data and extract and identify information.

[0076] "Meal details" refers to information consisting of the ingredients and names of dishes consumed by the user.

[0077] "Calories and nutrients" refers to the amount of energy and various nutrients contained in a meal.

[0078] A "database" refers to an information system that stores nutritional data for ingredients and dishes.

[0079] "Method for recording and analyzing nutrient intake" refers to technology for recording a user's daily nutrient intake and analyzing their health status.

[0080] "Suggestion generation method" refers to technology that creates appropriate dietary and supplement suggestions for users based on analysis results.

[0081] "Proposal content display means" refers to a device or application for visually displaying the generated proposal to the user.

[0082] "Online purchase link generation method" refers to technology that generates links allowing users to purchase suggested food products or supplements online.

[0083] "API" is an abbreviation for Application Programming Interface, and refers to a means of exchanging data between different software systems.

[0084] "Past meal history" refers to a record of meals the user has consumed in the past.

[0085] This invention is a system for recording a user's meal content via voice input and managing the balance of calories and nutrients. The system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake recording and analysis means, suggestion generation means, suggestion content display means, and online purchase link generation means. Specific usage examples and operational details are as follows.

[0086] Users operate devices such as smartphones and tablets and use voice input to dictate what they are eating. For example, a user might say, "Today's lunch is katsu curry." This voice data is recorded directly on the device. The device has an internet connection and can send the recorded voice data to a server.

[0087] The server converts the received audio data into text data using a speech-to-text conversion method. This uses speech recognition services such as Google® Cloud Speech-to-Text or IBM Watson®. This ensures that the audio is transcribed into text with high accuracy.

[0088] Next, the server uses natural language processing techniques to analyze the text data and identify the contents of the meal. Specifically, it extracts ingredient names and dish names from the text data and compares them with a database to identify matches. This analysis reveals, for example, that "katsu curry" is included. Libraries such as Python's NLTK and SpaCy are used here.

[0089] The server retrieves nutritional data for each food item from the database and calculates calories and nutrients based on the meal. The database contains nutritional data for each food item and can use SQL or NoSQL databases. As a result, the user's meal is output as detailed data such as "Pork cutlet curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g".

[0090] Furthermore, the server uses nutrient intake recording and analysis tools to record and analyze the user's daily nutrient intake. This reveals any nutrient deficiencies or excess calories. Data analysis libraries such as Pandas and NumPy are used for the analysis.

[0091] Next, the server uses a suggestion generation mechanism to provide specific meal and supplement recommendations based on the analysis results. For example, it might generate a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended." The generated suggestions are then displayed to the user via the terminal. The terminal uses a suggestion display mechanism to present the suggestions visually in an easy-to-understand manner for the user.

[0092] Finally, the server uses an online purchase link generation mechanism to generate online purchase links for the suggested meals and supplements and provides them to the user. By clicking on these links, the user can directly purchase the necessary products from the online store. APIs such as the Amazon API and Rakuten API are used here.

[0093] For example, if a user voice-inputs "Today's lunch is katsu curry," the server analyzes and calculates that information, suggests a suitable meal balance, and then proceeds to purchase the necessary food items online.

[0094] Examples of prompts for a generative AI model are as follows:

[0095] "Design a system that transcribes user-inputted meal details into text, calculates calories and nutrients, and suggests the next meal."

[0096] Thus, this invention provides support for users to easily record their meal content and maintain a healthy diet.

[0097] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0098] Step 1:

[0099] The user dictates the details of their meal to the device using voice input. For example, the user might say, "Today's lunch is katsu curry." The recorded voice data is stored on the device. Specifically, the user launches the app on their smartphone, presses the voice recording button, and begins speaking.

[0100] Step 2:

[0101] The device sends the recorded audio data to the server. The input is the recorded audio data, and the output is the audio data sent to the server. Specifically, the device uploads the audio data to the cloud server using Wi-Fi or mobile data.

[0102] Step 3:

[0103] The server converts received audio data into text data using a speech-to-text conversion method. The input is the received audio data, and the output is the converted text data. The server uses a speech recognition service (e.g., Google Cloud Speech-to-Text) to generate the text "Today's lunch is katsu curry."

[0104] Step 4:

[0105] The server uses natural language processing technology to analyze the converted text data and identify the contents of the meal. The input is the converted text data, and the output is the identified meal contents. Specifically, the server uses the SpaCy library to extract the ingredient name "katsu curry" and compares it with the database to identify the match.

[0106] Step 5:

[0107] The server retrieves nutritional data for each food item from the database and calculates calories and nutrients based on the identified meal. The input is the identified meal, and the output is calorie and nutrient data. Specifically, the server retrieves information such as "Pork cutlet curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g" from the database and generates the calculation result.

[0108] Step 6:

[0109] The server uses nutrient intake recording and analysis tools to record and analyze the user's daily nutrient intake. The input is multiple meal records over the day, and the output is the analysis results. Specifically, the server uses Pandas and NumPy libraries to accumulate the user's nutrient intake history and analyze daily deficiencies and excesses.

[0110] Step 7:

[0111] The server uses a suggestion generation mechanism to generate specific meal and supplement suggestions based on the analysis results. The input is the nutrient analysis results, and the output is the specific suggestions. For example, the server generates a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended."

[0112] Step 8:

[0113] The terminal uses a suggestion display mechanism to visually display the generated suggestions to the user. The input is the suggestion content sent from the server, and the output is the suggestion displayed on the terminal screen. Specifically, a smartphone application displays the suggestion message on the user's screen.

[0114] Step 9:

[0115] The server uses an online purchase link generation mechanism to generate online purchase links for suggested meals and supplements and provides them to the user. The input is the suggested content, and the output is the associated online purchase links. Specifically, the server uses the Amazon API and Rakuten API to generate links to purchase items such as "somen" noodles and "hiyayakko" (cold tofu) and provides them to the user.

[0116] This process allows users to easily record their meal details by voice and receive suggestions for nutritionally balanced meals and supplements.

[0117] (Application Example 1)

[0118] 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."

[0119] Traditional meal management systems required users to meticulously record their meals and manage calorie and nutrient balance, and lacked a means to obtain appropriate meal suggestions. Furthermore, ordering meals based on these suggestions often required using a separate online store, which was inconvenient for users. Additionally, the lack of systems offering nutritionally balanced delivery menu suggestions and purchase links made it difficult for users to make healthy choices smoothly.

[0120] 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.

[0121] In this invention, the server includes means for the user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the contents of a meal, means for calculating calories and nutrients based on the contents of the meal, means for recording and analyzing the daily nutrient intake, means for generating suggestions for deficient nutrients and calories, means for displaying the suggestions, means for generating and providing online purchase links for the suggested products, means for suggesting the optimal delivery menu based on the user's meal history, and means for generating and providing online purchase links for the suggested delivery menu. This allows the user to easily record their meal contents by voice input and receive suggestions for healthy meal choices and delivery menus. Furthermore, since suggested meals and delivery menus can be purchased smoothly online, user convenience is improved, and it becomes possible to maintain a healthy diet.

[0122] A "user" refers to an individual who uses this system to manage their diet and receive suggestions and online purchase links.

[0123] "Means of voice input" refers to microphones and other voice input devices that allow users to communicate their meal details to the system by voice.

[0124] "Means of converting speech to text data" refers to speech recognition software or algorithms that replace input speech with textual information.

[0125] "Means of analyzing text data to identify meal contents" refers to text analysis algorithms or databases that analyze converted text data and specifically identify the meal contents described.

[0126] "Means for calculating calories and nutrients based on dietary content" refers to a calculation algorithm or nutritional database for calculating the calories and nutrients contained in a specific diet.

[0127] "Means for recording and analyzing daily nutrient intake" refers to data recording and analysis tools that accumulate data on the nutrients a user consumes in a day and evaluate their intake status based on past data.

[0128] "Means for generating suggestions for deficient nutrients and calories" refers to an algorithm that creates suggestions for nutrients and calories to be supplemented based on the user's nutrient and calorie intake balance.

[0129] "Means for displaying the proposed content" refers to displays or screens used to visually show the generated proposals to the user.

[0130] "Means for generating and providing online purchase links for proposed products" refers to programs and communication means for creating and providing links that enable online purchase of products based on the proposal.

[0131] "A means of suggesting the optimal delivery menu based on the user's meal history" refers to an algorithm that selects and suggests a delivery menu suitable for the user based on accumulated meal history data.

[0132] "Means for generating and providing online purchase links for proposed delivery menus" refers to a program that generates and provides to users a link that allows them to purchase proposed delivery menus online.

[0133] This invention is a system that takes the user's meal details as voice input, manages the balance of calories and nutrients, and suggests the optimal delivery menu. The following describes how this system is implemented in detail.

[0134] First, the user operates their smartphone and uses voice input to dictate what they are eating. For example, they might say, "Today's lunch is katsu curry." The voice data is recorded on the device. Next, the device sends the recorded voice data to a server. The server uses speech recognition software (for example, Google Speech-to-Text API) to convert the received voice data into text data. Speech recognition technology allows for highly accurate transcription of speech into text.

[0135] Next, the server analyzes the text data and uses a text analysis algorithm to identify the specific contents of the meal. During this process, it compares the data with a database to identify ingredient names and dish names. For example, it might reveal that "katsu curry" is included. The server then uses a calorie and nutrient calculation algorithm to calculate the calories and nutrients based on the identified meal. Based on the nutrient data for each ingredient obtained from the database, it obtains detailed information such as "katsu curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[0136] Furthermore, the server uses a nutrient intake recording and analysis tool to record and analyze the user's daily nutrient intake. This reveals any nutrient deficiencies or excess calories. Next, the server uses a suggestion generation algorithm to suggest healthy meals and supplements to the user. For example, it might generate a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0137] Furthermore, the service will suggest convenient food delivery menus for users. To do this, it will identify the most suitable delivery menu based on the user's past meal history data and generate online purchase links. The suggested delivery menus will include items such as "healthy salad" and "grilled chicken," and each menu item will have a corresponding online purchase link. By clicking on these links, users can directly purchase the desired items from the online store.

[0138] This system allows users to easily record their meals using voice input and receive suggestions for healthy meal choices and delivery menus. Furthermore, suggested meals and delivery menus can be smoothly purchased online, improving user convenience and enabling them to maintain a healthy diet.

[0139] As a concrete example, if a user voice-inputs "Today's lunch is katsu curry," the server analyzes and calculates that information, suggests a suitable meal balance, and generates links to purchase the necessary foods online. This system utilizes a smartphone, a server, voice recognition software, a text analysis algorithm, a nutrient database, a nutrient record analysis tool, and a suggestion generation algorithm.

[0140] Examples of prompts for generative AI models:

[0141] Create a Python program that analyzes meal information entered by the user via voice input, calculates calories and nutrients, and suggests healthy meals and delivery menus. Also, describe the overview and processing flow of the API server used in the program.

[0142] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0143] Step 1:

[0144] The user operates a smartphone and uses voice input via the microphone. For example, they might say, "Today's lunch is katsu curry." The voice data is recorded on the device.

[0145] Input: User's voice data

[0146] Output: Recorded audio data

[0147] Step 2:

[0148] The device sends the recorded audio data to the server. The server uses speech recognition software (for example, Google Speech-to-Text API) to convert the transmitted audio data into text data.

[0149] Input: Audio data

[0150] Output: Converted text data

[0151] Step 3:

[0152] The server uses a text analysis algorithm to analyze text data. During analysis, it compares the data with a database to identify the contents of the meal. For example, if the text data is "Today's lunch is katsu curry," then "katsu curry" will be identified.

[0153] Input: Text data

[0154] Output: Identified dietary content

[0155] Step 4:

[0156] The server uses an algorithm to calculate calories and nutrients based on the meal content. It retrieves the components contained in the identified meal from a database and performs the calculation. For example, in the case of "katsu curry," it calculates specific data such as "900kcal, fat: 40g, carbohydrates: 100g, protein: 25g."

[0157] Input: Identified meal details

[0158] Output: Calorie and nutrient data

[0159] Step 5:

[0160] The server uses a nutrient intake recording and analysis tool to record and analyze the user's daily nutrient intake. This data clearly identifies any nutrient deficiencies or excess calories.

[0161] Input: User's daily meal data, past meal history data

[0162] Output: Nutrient intake status and analysis results

[0163] Step 6:

[0164] The server uses a suggestion generation algorithm to generate meal and supplement recommendations based on deficient nutrients and calories. For example, it might generate specific suggestions such as, "Your diet is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0165] Input: Nutrient intake status and analysis results

[0166] Output: Proposal

[0167] Step 7:

[0168] The server uses an algorithm to suggest the most suitable delivery menu based on the user's meal history, generating delivery menus and their online purchase links. For example, it might generate delivery menus such as "Healthy Salad" and "Grilled Chicken," along with their purchase links.

[0169] Input: User's past meal history data, suggested content

[0170] Output: Delivery menu and online purchase link

[0171] Step 8:

[0172] The terminal displays suggested meal options, delivery menus, and online purchase links to the user. By clicking the links, the user can directly purchase the desired items from the online store.

[0173] Input: Proposal details, delivery menu, online purchase link

[0174] Output: Proposal content and links displayed to the user, and purchase procedure.

[0175] This concludes the detailed explanation of the processing flow of this system.

[0176] 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.

[0177] This invention is a system that records the user's meal content via voice input and manages the balance of calories and nutrients. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it can provide suggestions tailored to the user's psychological state. This system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake status record analysis means, suggestion generation means, suggestion content display means, online purchase link generation means, and an emotion engine.

[0178] First, the user operates the device, presses the voice input button on the app screen, and then dictates what they are eating. For example, "Today's lunch is katsu curry." This voice data is recorded directly on the device.

[0179] Next, the terminal sends the recorded audio data to the server. The server uses a speech-to-text conversion method to convert the received audio data into text data. Speech recognition technology is used to convert the audio into text with high accuracy.

[0180] Next, the server uses a meal content analysis tool to analyze the converted text data. To identify the contents of the meal, it extracts ingredient names and dish names from the text data and compares them with a database to identify matches. This analysis reveals, for example, that "katsu curry" is included.

[0181] The server then uses calorie and nutrient calculation tools to calculate calories and nutrients based on the identified meal. It retrieves nutrient data for each food item from the database and obtains detailed information such as "Pork cutlet curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[0182] Furthermore, the server uses a nutrient intake recording and analysis means to record and analyze the user's daily nutrient intake. This helps identify any nutrient deficiencies or excess calories. Based on the analysis results, the server uses a suggestion generation means to generate specific suggestions for meals and supplements suitable for the user. For example, it might suggest, "Your intake is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0183] One of the key features of this invention is the emotion engine. The server recognizes emotions from the user's voice and can adjust the suggestions based on that emotion data. For example, if the user's voice is detected as indicating fatigue, a suggestion such as, "Let's incorporate foods that help relieve stress. Herbal tea and dark chocolate are recommended," will be generated. The emotion engine can also determine the user's stress level and suggest meals or supplements suitable for stress relief.

[0184] The generated suggestions are displayed to the user via the terminal. The terminal uses a suggestion display mechanism to present the suggestions visually in an easy-to-understand manner for the user. Furthermore, the server uses an online purchase link generation mechanism to generate online purchase links for the suggested meals and supplements and provides them to the user. By clicking on these links, the user can directly purchase the necessary products from an online store (e.g., an e-commerce platform).

[0185] This process allows users to easily record their meals by voice, receive suggestions for nutritionally balanced meals, and get recommendations for supplements tailored to their emotional state. For example, a user might voice-input, "Today's lunch is katsu curry," and the server would analyze and calculate the information, suggest a suitable meal balance, and then purchase the necessary food items online. If the system detects that the user is feeling tired, it will also suggest meals suitable for stress relief.

[0186] The following describes the processing flow.

[0187] Step 1: Voice Input

[0188] The user operates the device and presses the voice input button on the app screen. The user says, "Today's lunch is katsu curry."

[0189] Step 2: Sending the audio data

[0190] The terminal sends the recorded audio data to the server using the voice input method. The terminal temporarily stores the audio data in a buffer and then transfers the data to the server via the internet.

[0191] Step 3: Speech-to-text conversion

[0192] The server that receives the audio data activates its speech recognition engine and converts the audio data into text data. The text "Today's lunch is katsu curry" is generated.

[0193] Step 4: Analysis of Dietary Content

[0194] The server uses text data analysis tools to analyze the converted text data. For example, it identifies the keyword "katsu curry" and compares it with a food database to identify the ingredients.

[0195] Step 5: Emotion Recognition

[0196] The server activates an emotion engine and recognizes the user's emotions from the voice data. It analyzes the tone, speed, and word choice of the voice to determine if the user is tired.

[0197] Step 6: Calculating calories and nutrients

[0198] The server uses a calorie and nutrient calculation method based on the meal content to calculate the calories and nutrients of "Katsu Curry" (e.g., 900kcal, 40g fat, 100g carbohydrates, 25g protein). This data is obtained from a meal database.

[0199] Step 7: Recording and analyzing nutrient intake

[0200] The server records and analyzes the user's daily nutrient intake. It compares past meal history with newly entered data to determine which nutrients are deficient and which are in excess.

[0201] Step 8: Proposal Generation

[0202] The server uses a suggestion generation mechanism to take into account multiple factors (nutritional status and emotional state) and suggest meals and supplements suitable for the user. For example, it generates specific suggestions such as, "Your diet is high in fat, so for your next meal, choose low-fat options like somen noodles or cold tofu. Also, you seem tired, so a relaxing herbal tea is recommended."

[0203] Step 9: Display the proposed content

[0204] The server sends the generated proposal content to the terminal. The terminal uses a proposal content display mechanism to display the proposal content on the screen in a visually easy-to-understand manner for the user.

[0205] Step 10: Generate online purchase link

[0206] The server uses an online purchase link generation mechanism to generate online purchase links for the suggested food items and supplements. These links are sent to the device, and when the user clicks them, they are directed to the purchase page on the e-commerce platform.

[0207] This process allows users to easily record their meals using voice input and receive suggestions for nutritionally balanced meals and suggestions based on their emotional state. For example, if a user voice-inputs, "Today's lunch is katsu curry," the server analyzes and calculates that information to suggest a balanced meal that takes into account the user's fatigue level and helps them purchase necessary food items online.

[0208] (Example 2)

[0209] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0210] In modern society, users are often pressed for time, making it difficult to choose nutritionally balanced meals or select foods and supplements that suit their emotional state. Therefore, there is a need for a system that allows users to easily record their meals, consider nutritional balance, and receive suggestions tailored to their emotional state.

[0211] 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.

[0212] In this invention, the server includes means for the user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the contents of a meal, means for calculating calories and nutrients based on the contents of the meal, means for recording and analyzing the daily nutrient intake, means for generating suggestions for deficient nutrients and calories, means for recognizing emotions from the user's voice and adjusting the suggestions, means for displaying the suggestions, and means for generating and providing online purchase links for the suggested products. As a result, the user can easily record their meal contents using only voice input, receive appropriate suggestions that take nutritional balance into consideration, and receive suggestions tailored to their emotional state.

[0213] A "user" refers to a person who uses the system.

[0214] "Voice input means" refers to a device or software that records the user's voice.

[0215] "Speech-to-text conversion means" refers to a device or software that converts input speech data into text data.

[0216] "Meal content analysis means" refers to a device or software that analyzes text data to identify the specific contents of a meal.

[0217] "Calorie and nutrient calculation means" refers to a device or software that calculates calories and nutrients based on specified dietary content.

[0218] "Nutrient intake status recording and analysis means" refers to a device or software that records and analyzes a user's daily nutrient intake status.

[0219] "Suggestion generation means" refers to a device or software that generates suggestions tailored to the user based on the nutrients and calories the user is lacking.

[0220] "Emotion recognition means" refers to a device or software that recognizes emotions from the user's voice and adjusts the suggested content accordingly.

[0221] "Proposal content display means" refers to a device or software that visually presents the generated proposal content to the user.

[0222] "Online purchase link generation and provision means" refers to a device or software that generates an online purchase link for a proposed product and provides it to the user.

[0223] This invention is a system that records the user's meal content via voice input and manages the balance of calories and nutrients. Furthermore, by combining it with an emotion engine that recognizes the user's emotional state, the system can provide suggestions tailored to the user's psychological state. This system has the following functions:

[0224] Hardware and software to be used

[0225] Voice input methods: Input devices such as smartphones and tablets

[0226] Speech-to-text conversion method: Python speech recognition library "SpeechRecognition"

[0227] Method for analyzing meal content: Natural language processing library "spaCy"

[0228] Calorie and nutrient calculation methods: Nutrient databases (e.g., USDA Food Database)

[0229] Nutrient intake record and analysis tools: Database and statistical analysis software

[0230] Proposal generation method: Computational algorithm using a programming language such as Python

[0231] Emotion recognition method: Google's "Cloud Natural Language API"

[0232] Proposed content display method: Smartphone and tablet display screen (UI design)

[0233] Online purchase link generation and provision method: E-commerce platform API (e.g., Amazon API)

[0234] Specific functions of the system

[0235] First, the user presses the voice input button on the app screen of their device, and then dictates what they are eating. For example, they might say, "Today's lunch is katsu curry." This voice data is then recorded on the device.

[0236] The terminal sends the recorded audio data to the server. The server uses the Python speech recognition library "SpeechRecognition" to convert the received audio data into text data. Specifically, the command used is recognizer.recognize_google(audio_data).

[0237] Next, the server uses the natural language processing library "spaCy" to analyze the converted text data. Specifically, it extracts proper nouns (names of ingredients and dishes) from the text data. For example, it extracts "katsu curry" from the text data "Today's lunch is katsu curry."

[0238] Subsequently, the server retrieves nutrient data from a nutrient database (e.g., USDA Food Database) based on the identified meal (e.g., "Katsu Curry"). This allows the server to obtain specific calorie and nutrient information such as "Katsu Curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g". In this case, queries to the database are often performed using SQL (e.g., SELECT FROM food_database WHERE food_name = 'Katsu Curry').

[0239] Furthermore, the server records and analyzes the user's daily nutrient intake. Based on this analysis, it can identify any nutrient deficiencies or excess calories. Using this data, the server generates specific meal and supplement suggestions tailored to the user. For example, it might suggest, "Your intake is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0240] The system uses Google's Cloud Natural Language API for emotion recognition. The server uses commands like `response = client.analyze_sentiment(document)` to recognize emotions from the user's voice. For example, if the voice indicates "tired," the suggestions are adjusted to better suit the user's mental state. Suggestions such as, "Let's incorporate foods that help relieve stress. Herbal tea and dark chocolate are recommended," might be added.

[0241] Furthermore, the generated suggestions are visually presented to the user through their device. Users are also provided with links to purchase the suggested meals and supplements online. These links are generated using the APIs of e-commerce platforms (e.g., Amazon API or Rakuten API).

[0242] Examples of specific cases and prompt statements

[0243] As a concrete example, a user might voice-input "Today's lunch is katsu curry," and the server would analyze and calculate the information to suggest a suitable meal balance, providing links to purchase the necessary food items online. Furthermore, if the system detects that the user is feeling tired, it would also suggest meals suitable for stress relief.

[0244] Example of a prompt:

[0245] The user voice-inputted, "Today's lunch is katsu curry." This data was sent to the server and converted to text using speech recognition technology. Next, a meal content analysis tool identified "katsu curry," and the calories and nutrient content were calculated based on the nutritional data of each ingredient. Based on these results, the balance of calories and nutrients was analyzed, and meal suggestions suitable for the user were generated. Furthermore, because the emotion engine recognized the user's fatigue level, suggestions for foods that could help alleviate stress were also added. The user can review these suggestions on their device and receive links to purchase the necessary food items from an online store.

[0246] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0247] Step 1:

[0248] Input: User voice input

[0249] Specific steps: The user presses the voice input button on the app screen of their device and dictates the details of their meal.

[0250] Output: Recorded audio data

[0251] The user presses the voice input button on the app screen of their device and says, "Today's lunch is katsu curry." This voice data is recorded on the device.

[0252] Step 2:

[0253] Input: Recorded audio data

[0254] Specific operation: The device sends the recorded audio data to the server.

[0255] Output: Audio data sent to the server

[0256] The device sends the recorded audio data to the server. This data is transmitted using HTTP or WebSocket.

[0257] Step 3:

[0258] Input: Audio data sent to the server

[0259] Specific operation: The server uses the Python speech recognition library "SpeechRecognition" to convert audio data into text data.

[0260] Output: Converted text data

[0261] The server converts the received audio data into text data using the "SpeechRecognition" library. Specifically, it uses the command recognizer.recognize_google(audio_data).

[0262] Step 4:

[0263] Input: Converted text data

[0264] Specific operation: The server uses the NLP library "spaCy" to analyze text data and identify the contents of the meal.

[0265] Output: Extraction results of meal contents

[0266] The server analyzes the converted text data and extracts proper nouns (names of ingredients and dishes). Specifically, it extracts "katsu curry" from the text "Today's lunch is katsu curry."

[0267] Step 5:

[0268] Input: Extraction results of meal contents

[0269] Specific operation: The server retrieves calorie and nutrient data based on the extracted meal content from the nutrient database and calculates the calories and nutrients.

[0270] Output: Calculated calorie and nutrient data

[0271] The server retrieves data from a nutrition database (e.g., the USDA Food Database) based on the identified "katsu curry" and calculates the calories and nutrient content. This is done using the SQL query SELECT FROM food_database WHERE food_name = 'katsu curry'.

[0272] Step 6:

[0273] Input: Calculated calorie and nutrient data

[0274] Specific operation: The server records the user's daily nutrient intake status and analyzes it.

[0275] Output: Daily nutrient intake status and its analysis results

[0276] Based on the calculated calorie and nutrient data, the server records the user's daily nutrient intake status and compares it with past data to analyze the lacking nutrients and excessive calories.

[0277] Step 7:

[0278] Input: Daily nutrient intake status and its analysis results

[0279] Specific operation: The server generates proposals for lacking nutrients and calories.

[0280] Output: Proposal content

[0281] The server generates specific diet proposals and supplement proposals suitable for the user. For example, it makes proposals such as "Since there is a lot of fat, choose a low-fat diet next time".

[0282] Step 8:

[0283] Input: User's voice data

[0284] Specific operation: The server uses Google's "Cloud Natural Language API" to recognize the emotion from the user's voice and adjusts the proposal content.

[0285] Output: Adjusted proposal content

[0286] The server uses the command response = client.analyze_sentiment(document) to recognize the user's emotion from the voice. For example, if "tired" is recognized from the voice, it also makes a proposal about "ingredients helpful for stress relief".

[0287] Step 9:

[0288] Input: Adjusted proposal content

[0289] Specific operation: The terminal visually presents the proposal content to the user using the proposal content display means.

[0290] Output: Proposal content displayed on the user's terminal

[0291] The terminal visually presents the proposal content so that the user can confirm the content. It is displayed using UI components (e.g., HTML, CSS, React.js).

[0292] Step 10:

[0293] Input: Adjusted proposal content

[0294] Specific operation: The server generates an online purchase link for the proposed product and provides it to the terminal.

[0295] Output: Online purchase link

[0296] The server generates an online purchase link for the proposed diet or supplement and sends it to the terminal. Thereby, the user can access the e-commerce platform (e.g., Amazon API or Rakuten API) through the terminal and purchase the necessary products.

[0297] The above is the processing flow of the program of this system. Through these steps, the user can easily record their diet content and receive appropriate proposals based on the nutritional balance and emotional state.

[0298] (Application Example 2)

[0299] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0300] In modern society, it is difficult for busy users to record their meals and manage their calorie and nutrient balance. Furthermore, there are few systems that can suggest appropriate meals and supplements based on the user's emotional state, making health management even more challenging. Additionally, the lack of specific suggestions for meals and supplements makes it difficult for users to purchase them directly online.

[0301] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the contents of a meal, means for calculating calories and nutrients based on the contents of the meal, means for recording and analyzing the daily nutrient intake, means for generating suggestions for deficient nutrients and calories, means for recognizing the user's emotions, means for adjusting the suggested content based on the recognized emotions, means for displaying the suggested content, and means for generating and providing online purchase links for the suggested products. As a result, the user can easily record the contents of their meal and emotions by voice input, receive specific suggestions that take into account the balance of calories and nutrients, and make immediate online purchases.

[0302] "Means for user voice input" refers to a device or interface for users to input details of their meals using voice.

[0303] "Means of converting input audio into text data" refers to technologies and algorithms for converting audio data into text data.

[0304] The means for analyzing text data to identify dietary content refers to a process or system for analyzing the converted text data to recognize specific dietary content.

[0305] The means for calculating calories and nutrients based on dietary content refers to a method or device for calculating the calories and ingested nutrients based on the identified dietary content.

[0306] The means for recording and analyzing the daily nutrient intake situation refers to the technology or device for storing the data of the nutrients ingested by the user in a day and analyzing it.

[0307] The means for generating proposals for lacking nutrients and calories refers to a system for proposing methods for supplementing the lacking nutrients and calories to the user based on the recorded and analyzed nutrient data.

[0308] The means for recognizing the user's emotions refers to the technology or process for identifying the user's emotions and psychological state from voice or text data.

[0309] The means for adjusting the proposed content based on the recognized emotions refers to a system or method for considering the user's emotions and changing the proposed content of diet or supplements accordingly.

[0310] The means for displaying the proposed content refers to the device or interface for visually presenting the generated proposal to the user.

[0311] The means for generating and providing an online purchase link for the proposed product refers to the technology or method for generating a link for purchasing the proposed food or supplement online and providing it to the user.

[0312] This invention is a system that allows users to record their meal contents via voice input and manage the balance of calories and nutrients. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides suggestions tailored to the user's psychological state. This system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake status record analysis means, suggestion generation means, suggestion content display means, online purchase link generation means, and an emotion engine.

[0313] First, the user operates a device such as a smartphone, presses the voice input button on the app screen, and then dictates what they are eating. For example, "Today's lunch is katsu curry." This voice data is recorded directly on the device. The device then sends the recorded voice data to the server. The server uses the Google Speech-to-Text API to convert the received voice data into text data. Speech recognition technology is used to convert the voice into text with high accuracy.

[0314] Next, the server uses a meal content analysis tool to analyze the converted text data. This analysis identifies the contents of the meal. For example, it might reveal that "katsu curry" is included. The server then uses a calorie and nutrient calculation tool to calculate the calories and nutrients based on the identified meal. It retrieves nutrient data for each ingredient from the database and obtains detailed information such as "katsu curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[0315] Furthermore, the server uses a nutrient intake recording and analysis means to record and analyze the user's daily nutrient intake. This helps identify any nutrient deficiencies or excess calories. Based on the analysis results, the server uses a suggestion generation means to generate specific suggestions for meals and supplements suitable for the user. For example, it might suggest, "Your intake is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0316] The server further uses an emotion engine to recognize emotions from the user's voice. Based on this emotion data, it can adjust the suggestions. For example, if the user's voice is detected as tired, a suggestion such as "Let's incorporate foods that help relieve stress. Herbal tea and dark chocolate are recommended" will be generated. The emotion engine also determines the user's stress level and suggests meals and supplements suitable for stress relief. The suggestions generated through this process are displayed to the user through the terminal. The terminal uses a suggestion display means to present the information visually in an easy-to-understand manner for the user. Furthermore, the server uses an online purchase link generation means to generate online purchase links for the suggested meals and supplements and provides them to the user. By clicking on these links, the user can directly purchase the necessary products from the online store.

[0317] As a concrete example, a user might voice-input, "I'm having katsu curry for lunch today," and the server would analyze and calculate the information to suggest a healthy balance. Furthermore, considering the user's emotions, if their stress level is high, the system would suggest relaxing foods or drinks. Additionally, a link to immediately purchase the suggested items online is provided, allowing users to easily obtain the products they need.

[0318] Example of a prompt:

[0319] A user voice-inputs "Today's lunch is katsu curry. I feel tired" into their smartphone. This voice data is converted to text, analyzed to identify the food items, and the calories and nutrients are calculated. The system also recognizes the user's emotions and suggests meals and supplements based on those emotions. An example of the results displayed is "Try some herbal tea or dark chocolate to help relieve stress. Purchase link: https: / / example.com / harb_tea".

[0320] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0321] Step 1:

[0322] The user operates a device such as a smartphone and presses the voice input button on the app screen to dictate the details of their meal. For example, they might say, "Today's lunch is katsu curry." The input data is voice data, and this voice data is recorded on the device.

[0323] Step 2:

[0324] The terminal sends the recorded audio data to the server. In this case, the input is audio data, and the data sent is also audio data.

[0325] Step 3:

[0326] The server uses a speech-to-text conversion tool (Google Speech-to-Text API) to convert the received audio data into text data. The input is audio data, and the output is text data. This conversion operation converts speech into text data with high accuracy.

[0327] Step 4:

[0328] The server analyzes text data to identify the contents of a meal. The meal content analysis tool extracts ingredient names and dish names from the text data and compares them with a database. The input is text data, and the output is the identified meal content (e.g., "Katsu Curry").

[0329] Step 5:

[0330] The server calculates calories and nutrients based on the specified meal. The calorie and nutrient calculation means retrieves nutrient data for each food item from the database and performs the calculation. The input is the specified meal, and the output is calorie and nutrient information (e.g., "Pork cutlet curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g").

[0331] Step 6:

[0332] The server records and analyzes the user's daily nutrient intake. The nutrient intake recording and analysis system calculates the nutrient balance based on the user's daily diet. The input is the user's daily diet and nutrient information, and the output is the intake balance.

[0333] Step 7:

[0334] The server generates suggestions for deficient nutrients and calories. The suggestion generation mechanism generates suggestions based on recorded and analyzed results. The input is information on nutrient intake balance, and the output is the suggested content (e.g., "Your intake is high in fat, so choose a low-fat option for your next meal").

[0335] Step 8:

[0336] The server uses an emotion engine to recognize emotions from the user's voice. The input is text data, and the output is the recognized emotion information (e.g., "tired").

[0337] Step 9:

[0338] The server adjusts its suggestions based on the recognized emotions. The input is emotion information and the suggested content, and the output is the adjusted suggested content (e.g., "Let's incorporate foods that help relieve stress").

[0339] Step 10:

[0340] The server displays the adjusted proposal to the user via the terminal. The proposal is presented visually to the user through the proposal display mechanism. The input is the adjusted proposal, and the output is the proposal displayed to the user.

[0341] Step 11:

[0342] The server generates and provides online purchase links for the suggested products. The online purchase link generation mechanism generates and provides purchase links for suggested foods and supplements to the user. The input is the suggested content, and the output is the online purchase link (e.g., "Purchase link: https: / / example.com / harb_tea").

[0343] 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.

[0344] 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.

[0345] 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.

[0346] [Second Embodiment]

[0347] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0348] 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.

[0349] 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).

[0350] 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.

[0351] 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.

[0352] 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).

[0353] 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.

[0354] 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.

[0355] 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.

[0356] 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.

[0357] 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.

[0358] 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".

[0359] This invention is a system for recording a user's meal content via voice input and managing the balance of calories and nutrients. The system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake status record analysis means, suggestion generation means, suggestion content display means, and online purchase link generation means.

[0360] First, the user operates the device and uses voice input to dictate what they are eating. For example, they might say, "Today's lunch is katsu curry." This voice data is then recorded directly on the device.

[0361] Next, the terminal sends the recorded audio data to the server. The server uses a speech-to-text conversion method to convert the received audio data into text data. By utilizing speech recognition technology, the audio can be converted into text with high accuracy.

[0362] Next, the server uses a meal content analysis tool to analyze the converted text data. To identify the contents of the meal, it extracts ingredient names and dish names from the text data and compares them with a database to identify matches. This analysis reveals, for example, that "katsu curry" is included.

[0363] The server then uses calorie and nutrient calculation tools to calculate calories and nutrients based on the identified meal. It retrieves nutrient data for each food item from the database and obtains detailed information such as "Pork cutlet curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[0364] Furthermore, the server uses a nutrient intake recording and analysis system to record and analyze the user's daily nutrient intake. This reveals any nutrient deficiencies or excess calories.

[0365] Next, the server uses a suggestion generation mechanism to provide specific suggestions for meals and supplements based on the analysis results. For example, it might generate a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended."

[0366] The generated proposals are displayed to the user via the terminal. The terminal uses a proposal display mechanism to present the proposals to the user in an easily understandable visual way.

[0367] Finally, the server uses an online purchase link generation mechanism to generate and provide the user with online purchase links for the suggested meals and supplements. By clicking on these links, the user can directly purchase the desired products from an online store (e.g., an e-commerce platform).

[0368] This process allows users to easily record their meals by voice and receive suggestions for nutritionally balanced meals and supplements. As a result, they can maintain a healthy diet. For example, a user might voice-input, "Today's lunch is katsu curry," and the server would analyze and calculate that information, suggest a suitable meal balance, and then purchase the necessary food items online.

[0369] The following describes the processing flow.

[0370] Step 1: Voice Input

[0371] The user uses their device and presses the voice input button on the app screen.

[0372] The device records the user's voice. For example, the user might say, "I'm having katsu curry for lunch today."

[0373] Step 2: Sending the audio data

[0374] The device sends the recorded audio data to the server. This data is then transferred directly to the server.

[0375] Step 3: Speech-to-text conversion

[0376] The server converts the received audio data into text data using a speech recognition engine. For example, text such as "Today's lunch is katsu curry" is generated.

[0377] Step 4: Analysis of Dietary Content

[0378] The server analyzes the converted text data. The server uses an analysis algorithm to identify the food item, such as "katsu curry," from the text.

[0379] Step 5: Calculating calories and nutrients

[0380] The server retrieves nutrient data from a database based on the identified meal. For example, it calculates the calories and nutrients of "katsu curry" (e.g., 900kcal, 40g fat, 100g carbohydrates, 25g protein).

[0381] Step 6: Recording and analyzing nutrient intake

[0382] The server records and analyzes the user's daily nutrient intake. This helps identify any nutrient deficiencies or excess calories.

[0383] Step 7: Proposal Generation

[0384] Based on the analysis results, the server generates specific suggestions for meals and supplements that are suitable for the user. For example, it might suggest, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended."

[0385] Step 8: Display the proposed content

[0386] The server sends the generated proposal to the terminal. The terminal displays the proposal on its screen, allowing the user to visually confirm it.

[0387] Step 9: Generate online purchase link

[0388] The server generates online purchase links for the suggested meals and supplements and sends them to the device. The user can then click the displayed link to directly purchase the products from the online store.

[0389] This allows users to effectively record their meals using only voice input and receive suggestions for meals and supplements to maintain proper nutritional balance. As a result, it becomes possible to support them in maintaining a healthy lifestyle.

[0390] (Example 1)

[0391] 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."

[0392] In today's busy lifestyle, it is difficult for users to effectively manage their diet and maintain a healthy eating habit. Traditional methods involve manual recording of meals and calculation of nutrients, which is time-consuming, cumbersome, and prone to errors. Furthermore, systems for analyzing meals and suggesting nutrients are limited, and the accuracy of converting voice-based input to text data is particularly low. Therefore, there is a need for a reliable system that is easy for users to continue using.

[0393] 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.

[0394] In this invention, the server includes means for accurately converting voice data into text using a voice recognition service, means for extracting ingredient names and dish names from the text data using natural language processing technology, and means for identifying meal contents by comparing them with a database. This allows users to easily input voice information, convert the content into text data, analyze the meal contents to calculate calories and nutrients, and receive suggestions for appropriate meals and supplements. By providing online purchase links, the system provides an environment where users can immediately purchase the suggested ingredients and supplements, supporting comprehensive health management.

[0395] A "user" refers to an individual who uses voice input to record their meal details and manage their nutrition.

[0396] "Voice input means" refers to a device or application for a user to input voice.

[0397] "Text data" refers to a data format obtained by converting speech input via voice input devices into text information.

[0398] "Speech recognition service" refers to a technology or platform for converting speech into text data.

[0399] "Natural language processing technology" refers to computer technology used to analyze text data and extract and identify information.

[0400] "Meal details" refers to information consisting of the ingredients and names of dishes consumed by the user.

[0401] "Calories and nutrients" refers to the amount of energy and various nutrients contained in a meal.

[0402] A "database" refers to an information system that stores nutritional data for ingredients and dishes.

[0403] "Method for recording and analyzing nutrient intake" refers to technology for recording a user's daily nutrient intake and analyzing their health status.

[0404] "Suggestion generation method" refers to technology that creates appropriate dietary and supplement suggestions for users based on analysis results.

[0405] "Proposal content display means" refers to a device or application for visually displaying the generated proposal to the user.

[0406] "Online purchase link generation method" refers to technology that generates links allowing users to purchase suggested food products or supplements online.

[0407] "API" is an abbreviation for Application Programming Interface, and refers to a means of exchanging data between different software systems.

[0408] "Past meal history" refers to a record of meals the user has consumed in the past.

[0409] This invention is a system for recording a user's meal content via voice input and managing the balance of calories and nutrients. The system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake recording and analysis means, suggestion generation means, suggestion content display means, and online purchase link generation means. Specific usage examples and operational details are as follows.

[0410] Users operate devices such as smartphones and tablets and use voice input to dictate what they are eating. For example, a user might say, "Today's lunch is katsu curry." This voice data is recorded directly on the device. The device has an internet connection and can send the recorded voice data to a server.

[0411] The server converts the received audio data into text data using a speech-to-text conversion method. Here, speech recognition services such as Google Cloud Speech-to-Text or IBM Watson are used. This ensures that the audio is transcribed into text with high accuracy.

[0412] Next, the server uses natural language processing techniques to analyze the text data and identify the contents of the meal. Specifically, it extracts ingredient names and dish names from the text data and compares them with a database to identify matches. This analysis reveals, for example, that "katsu curry" is included. Libraries such as Python's NLTK and SpaCy are used here.

[0413] The server retrieves nutritional data for each food item from the database and calculates calories and nutrients based on the meal. The database contains nutritional data for each food item and can use SQL or NoSQL databases. As a result, the user's meal is output as detailed data such as "Pork cutlet curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g".

[0414] Furthermore, the server uses nutrient intake recording and analysis tools to record and analyze the user's daily nutrient intake. This reveals any nutrient deficiencies or excess calories. Data analysis libraries such as Pandas and NumPy are used for the analysis.

[0415] Next, the server uses a suggestion generation mechanism to provide specific meal and supplement recommendations based on the analysis results. For example, it might generate a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended." The generated suggestions are then displayed to the user via the terminal. The terminal uses a suggestion display mechanism to present the suggestions visually in an easy-to-understand manner for the user.

[0416] Finally, the server uses an online purchase link generation mechanism to generate online purchase links for the suggested meals and supplements and provides them to the user. By clicking on these links, the user can directly purchase the necessary products from the online store. APIs such as the Amazon API and Rakuten API are used here.

[0417] For example, if a user voice-inputs "Today's lunch is katsu curry," the server analyzes and calculates that information, suggests a suitable meal balance, and then proceeds to purchase the necessary food items online.

[0418] Examples of prompts for a generative AI model are as follows:

[0419] "Design a system that transcribes user-inputted meal details into text, calculates calories and nutrients, and suggests the next meal."

[0420] Thus, this invention provides support for users to easily record their meal content and maintain a healthy diet.

[0421] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0422] Step 1:

[0423] The user dictates the details of their meal to the device using voice input. For example, the user might say, "Today's lunch is katsu curry." The recorded voice data is stored on the device. Specifically, the user launches the app on their smartphone, presses the voice recording button, and begins speaking.

[0424] Step 2:

[0425] The device sends the recorded audio data to the server. The input is the recorded audio data, and the output is the audio data sent to the server. Specifically, the device uploads the audio data to the cloud server using Wi-Fi or mobile data.

[0426] Step 3:

[0427] The server converts received audio data into text data using a speech-to-text conversion method. The input is the received audio data, and the output is the converted text data. The server uses a speech recognition service (e.g., Google Cloud Speech-to-Text) to generate the text "Today's lunch is katsu curry."

[0428] Step 4:

[0429] The server uses natural language processing technology to analyze the converted text data and identify the contents of the meal. The input is the converted text data, and the output is the identified meal contents. Specifically, the server uses the SpaCy library to extract the ingredient name "katsu curry" and compares it with the database to identify the match.

[0430] Step 5:

[0431] The server retrieves nutritional data for each food item from the database and calculates calories and nutrients based on the identified meal. The input is the identified meal, and the output is calorie and nutrient data. Specifically, the server retrieves information such as "Pork cutlet curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g" from the database and generates the calculation result.

[0432] Step 6:

[0433] The server uses nutrient intake recording and analysis tools to record and analyze the user's daily nutrient intake. The input is multiple meal records over the day, and the output is the analysis results. Specifically, the server uses Pandas and NumPy libraries to accumulate the user's nutrient intake history and analyze daily deficiencies and excesses.

[0434] Step 7:

[0435] The server uses a suggestion generation mechanism to generate specific meal and supplement suggestions based on the analysis results. The input is the nutrient analysis results, and the output is the specific suggestions. For example, the server generates a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended."

[0436] Step 8:

[0437] The terminal uses a suggestion display mechanism to visually display the generated suggestions to the user. The input is the suggestion content sent from the server, and the output is the suggestion displayed on the terminal screen. Specifically, a smartphone application displays the suggestion message on the user's screen.

[0438] Step 9:

[0439] The server uses an online purchase link generation mechanism to generate online purchase links for suggested meals and supplements and provides them to the user. The input is the suggested content, and the output is the associated online purchase links. Specifically, the server uses the Amazon API and Rakuten API to generate links to purchase items such as "somen" noodles and "hiyayakko" (cold tofu) and provides them to the user.

[0440] This process allows users to easily record their meal details by voice and receive suggestions for nutritionally balanced meals and supplements.

[0441] (Application Example 1)

[0442] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0443] Traditional meal management systems required users to meticulously record their meals and manage calorie and nutrient balance, and lacked a means to obtain appropriate meal suggestions. Furthermore, ordering meals based on these suggestions often required using a separate online store, which was inconvenient for users. Additionally, the lack of systems offering nutritionally balanced delivery menu suggestions and purchase links made it difficult for users to make healthy choices smoothly.

[0444] 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.

[0445] In this invention, the server includes means for the user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the contents of a meal, means for calculating calories and nutrients based on the contents of the meal, means for recording and analyzing the daily nutrient intake, means for generating suggestions for deficient nutrients and calories, means for displaying the suggestions, means for generating and providing online purchase links for the suggested products, means for suggesting the optimal delivery menu based on the user's meal history, and means for generating and providing online purchase links for the suggested delivery menu. This allows the user to easily record their meal contents by voice input and receive suggestions for healthy meal choices and delivery menus. Furthermore, since suggested meals and delivery menus can be purchased smoothly online, user convenience is improved, and it becomes possible to maintain a healthy diet.

[0446] A "user" refers to an individual who uses this system to manage their diet and receive suggestions and online purchase links.

[0447] "Means of voice input" refers to microphones and other voice input devices that allow users to communicate their meal details to the system by voice.

[0448] "Means of converting speech to text data" refers to speech recognition software or algorithms that replace input speech with textual information.

[0449] "Means of analyzing text data to identify meal contents" refers to text analysis algorithms or databases that analyze converted text data and specifically identify the meal contents described.

[0450] "Means for calculating calories and nutrients based on dietary content" refers to a calculation algorithm or nutritional database for calculating the calories and nutrients contained in a specific diet.

[0451] "Means for recording and analyzing daily nutrient intake" refers to data recording and analysis tools that accumulate data on the nutrients a user consumes in a day and evaluate their intake status based on past data.

[0452] "Means for generating suggestions for deficient nutrients and calories" refers to an algorithm that creates suggestions for nutrients and calories to be supplemented based on the user's nutrient and calorie intake balance.

[0453] "Means for displaying the proposed content" refers to displays or screens used to visually show the generated proposals to the user.

[0454] "Means for generating and providing online purchase links for proposed products" refers to programs and communication means for creating and providing links that enable online purchase of products based on the proposal.

[0455] "A means of suggesting the optimal delivery menu based on the user's meal history" refers to an algorithm that selects and suggests a delivery menu suitable for the user based on accumulated meal history data.

[0456] "Means for generating and providing online purchase links for proposed delivery menus" refers to a program that generates and provides to users a link that allows them to purchase proposed delivery menus online.

[0457] This invention is a system that takes the user's meal details as voice input, manages the balance of calories and nutrients, and suggests the optimal delivery menu. The following describes how this system is implemented in detail.

[0458] First, the user operates their smartphone and uses voice input to dictate what they are eating. For example, they might say, "Today's lunch is katsu curry." The voice data is recorded on the device. Next, the device sends the recorded voice data to a server. The server uses speech recognition software (for example, Google Speech-to-Text API) to convert the received voice data into text data. Speech recognition technology allows for highly accurate transcription of speech into text.

[0459] Next, the server analyzes the text data and uses a text analysis algorithm to identify the specific contents of the meal. During this process, it compares the data with a database to identify ingredient names and dish names. For example, it might reveal that "katsu curry" is included. The server then uses a calorie and nutrient calculation algorithm to calculate the calories and nutrients based on the identified meal. Based on the nutrient data for each ingredient obtained from the database, it obtains detailed information such as "katsu curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[0460] Furthermore, the server uses a nutrient intake recording and analysis tool to record and analyze the user's daily nutrient intake. This reveals any nutrient deficiencies or excess calories. Next, the server uses a suggestion generation algorithm to suggest healthy meals and supplements to the user. For example, it might generate a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0461] Furthermore, the service will suggest convenient food delivery menus for users. To do this, it will identify the most suitable delivery menu based on the user's past meal history data and generate online purchase links. The suggested delivery menus will include items such as "healthy salad" and "grilled chicken," and each menu item will have a corresponding online purchase link. By clicking on these links, users can directly purchase the desired items from the online store.

[0462] This system allows users to easily record their meals using voice input and receive suggestions for healthy meal choices and delivery menus. Furthermore, suggested meals and delivery menus can be smoothly purchased online, improving user convenience and enabling them to maintain a healthy diet.

[0463] As a concrete example, if a user voice-inputs "Today's lunch is katsu curry," the server analyzes and calculates that information, suggests a suitable meal balance, and generates links to purchase the necessary foods online. This system utilizes a smartphone, a server, voice recognition software, a text analysis algorithm, a nutrient database, a nutrient record analysis tool, and a suggestion generation algorithm.

[0464] Examples of prompts for generative AI models:

[0465] Create a Python program that analyzes meal information entered by the user via voice input, calculates calories and nutrients, and suggests healthy meals and delivery menus. Also, describe the overview and processing flow of the API server used in the program.

[0466] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0467] Step 1:

[0468] The user operates a smartphone and uses voice input via the microphone. For example, they might say, "Today's lunch is katsu curry." The voice data is recorded on the device.

[0469] Input: User's voice data

[0470] Output: Recorded audio data

[0471] Step 2:

[0472] The device sends the recorded audio data to the server. The server uses speech recognition software (for example, Google Speech-to-Text API) to convert the transmitted audio data into text data.

[0473] Input: Audio data

[0474] Output: Converted text data

[0475] Step 3:

[0476] The server uses a text analysis algorithm to analyze text data. During analysis, it compares the data with a database to identify the contents of the meal. For example, if the text data is "Today's lunch is katsu curry," then "katsu curry" will be identified.

[0477] Input: Text data

[0478] Output: Identified dietary content

[0479] Step 4:

[0480] The server uses an algorithm to calculate calories and nutrients based on the meal content. It retrieves the components contained in the identified meal from a database and performs the calculation. For example, in the case of "katsu curry," it calculates specific data such as "900kcal, fat: 40g, carbohydrates: 100g, protein: 25g."

[0481] Input: Identified meal details

[0482] Output: Calorie and nutrient data

[0483] Step 5:

[0484] The server uses a nutrient intake recording and analysis tool to record and analyze the user's daily nutrient intake. This data clearly identifies any nutrient deficiencies or excess calories.

[0485] Input: User's daily meal data, past meal history data

[0486] Output: Nutrient intake status and analysis results

[0487] Step 6:

[0488] The server uses a suggestion generation algorithm to generate meal and supplement recommendations based on deficient nutrients and calories. For example, it might generate specific suggestions such as, "Your diet is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0489] Input: Nutrient intake status and analysis results

[0490] Output: Proposal

[0491] Step 7:

[0492] The server uses an algorithm to suggest the most suitable delivery menu based on the user's meal history, generating delivery menus and their online purchase links. For example, it might generate delivery menus such as "Healthy Salad" and "Grilled Chicken," along with their purchase links.

[0493] Input: User's past meal history data, suggested content

[0494] Output: Delivery menu and online purchase link

[0495] Step 8:

[0496] The terminal displays suggested meal options, delivery menus, and online purchase links to the user. By clicking the links, the user can directly purchase the desired items from the online store.

[0497] Input: Proposal details, delivery menu, online purchase link

[0498] Output: Proposal content and links displayed to the user, and purchase procedure.

[0499] This concludes the detailed explanation of the processing flow of this system.

[0500] 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.

[0501] This invention is a system that records the user's meal content via voice input and manages the balance of calories and nutrients. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it can provide suggestions tailored to the user's psychological state. This system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake status record analysis means, suggestion generation means, suggestion content display means, online purchase link generation means, and an emotion engine.

[0502] First, the user operates the device, presses the voice input button on the app screen, and then dictates what they are eating. For example, "Today's lunch is katsu curry." This voice data is recorded directly on the device.

[0503] Next, the terminal sends the recorded audio data to the server. The server uses a speech-to-text conversion method to convert the received audio data into text data. Speech recognition technology is used to convert the audio into text with high accuracy.

[0504] Next, the server uses a meal content analysis tool to analyze the converted text data. To identify the contents of the meal, it extracts ingredient names and dish names from the text data and compares them with a database to identify matches. This analysis reveals, for example, that "katsu curry" is included.

[0505] The server then uses calorie and nutrient calculation tools to calculate calories and nutrients based on the identified meal. It retrieves nutrient data for each food item from the database and obtains detailed information such as "Pork cutlet curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[0506] Furthermore, the server uses a nutrient intake recording and analysis means to record and analyze the user's daily nutrient intake. This helps identify any nutrient deficiencies or excess calories. Based on the analysis results, the server uses a suggestion generation means to generate specific suggestions for meals and supplements suitable for the user. For example, it might suggest, "Your intake is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0507] One of the key features of this invention is the emotion engine. The server recognizes emotions from the user's voice and can adjust the suggestions based on that emotion data. For example, if the user's voice is detected as indicating fatigue, a suggestion such as, "Let's incorporate foods that help relieve stress. Herbal tea and dark chocolate are recommended," will be generated. The emotion engine can also determine the user's stress level and suggest meals or supplements suitable for stress relief.

[0508] The generated suggestions are displayed to the user via the terminal. The terminal uses a suggestion display mechanism to present the suggestions visually in an easy-to-understand manner for the user. Furthermore, the server uses an online purchase link generation mechanism to generate online purchase links for the suggested meals and supplements and provides them to the user. By clicking on these links, the user can directly purchase the necessary products from an online store (e.g., an e-commerce platform).

[0509] This process allows users to easily record their meals by voice, receive suggestions for nutritionally balanced meals, and get recommendations for supplements tailored to their emotional state. For example, a user might voice-input, "Today's lunch is katsu curry," and the server would analyze and calculate the information, suggest a suitable meal balance, and then purchase the necessary food items online. If the system detects that the user is feeling tired, it will also suggest meals suitable for stress relief.

[0510] The following describes the processing flow.

[0511] Step 1: Voice Input

[0512] The user operates the device and presses the voice input button on the app screen. The user says, "Today's lunch is katsu curry."

[0513] Step 2: Sending the audio data

[0514] The terminal sends the recorded audio data to the server using the voice input method. The terminal temporarily stores the audio data in a buffer and then transfers the data to the server via the internet.

[0515] Step 3: Speech-to-text conversion

[0516] The server that receives the audio data activates its speech recognition engine and converts the audio data into text data. The text "Today's lunch is katsu curry" is generated.

[0517] Step 4: Analysis of Dietary Content

[0518] The server uses text data analysis tools to analyze the converted text data. For example, it identifies the keyword "katsu curry" and compares it with a food database to identify the ingredients.

[0519] Step 5: Emotion Recognition

[0520] The server activates an emotion engine and recognizes the user's emotions from the voice data. It analyzes the tone, speed, and word choice of the voice to determine if the user is tired.

[0521] Step 6: Calculating calories and nutrients

[0522] The server uses a calorie and nutrient calculation method based on the meal content to calculate the calories and nutrients of "Katsu Curry" (e.g., 900kcal, 40g fat, 100g carbohydrates, 25g protein). This data is obtained from a meal database.

[0523] Step 7: Recording and analyzing nutrient intake

[0524] The server records and analyzes the user's daily nutrient intake. It compares past meal history with newly entered data to determine which nutrients are deficient and which are in excess.

[0525] Step 8: Proposal Generation

[0526] The server uses a suggestion generation mechanism to take into account multiple factors (nutritional status and emotional state) and suggest meals and supplements suitable for the user. For example, it generates specific suggestions such as, "Your diet is high in fat, so for your next meal, choose low-fat options like somen noodles or cold tofu. Also, you seem tired, so a relaxing herbal tea is recommended."

[0527] Step 9: Display the proposed content

[0528] The server sends the generated proposal content to the terminal. The terminal uses a proposal content display mechanism to display the proposal content on the screen in a visually easy-to-understand manner for the user.

[0529] Step 10: Generate online purchase link

[0530] The server uses an online purchase link generation mechanism to generate online purchase links for the suggested food items and supplements. These links are sent to the device, and when the user clicks them, they are directed to the purchase page on the e-commerce platform.

[0531] This process allows users to easily record their meals using voice input and receive suggestions for nutritionally balanced meals and suggestions based on their emotional state. For example, if a user voice-inputs, "Today's lunch is katsu curry," the server analyzes and calculates that information to suggest a balanced meal that takes into account the user's fatigue level and helps them purchase necessary food items online.

[0532] (Example 2)

[0533] 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".

[0534] In modern society, users are often pressed for time, making it difficult to choose nutritionally balanced meals or select foods and supplements that suit their emotional state. Therefore, there is a need for a system that allows users to easily record their meals, consider nutritional balance, and receive suggestions tailored to their emotional state.

[0535] 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.

[0536] In this invention, the server includes means for the user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the contents of a meal, means for calculating calories and nutrients based on the contents of the meal, means for recording and analyzing the daily nutrient intake, means for generating suggestions for deficient nutrients and calories, means for recognizing emotions from the user's voice and adjusting the suggestions, means for displaying the suggestions, and means for generating and providing online purchase links for the suggested products. As a result, the user can easily record their meal contents using only voice input, receive appropriate suggestions that take nutritional balance into consideration, and receive suggestions tailored to their emotional state.

[0537] A "user" refers to a person who uses the system.

[0538] "Voice input means" refers to a device or software that records the user's voice.

[0539] "Speech-to-text conversion means" refers to a device or software that converts input speech data into text data.

[0540] "Meal content analysis means" refers to a device or software that analyzes text data to identify the specific contents of a meal.

[0541] "Calorie and nutrient calculation means" refers to a device or software that calculates calories and nutrients based on specified dietary content.

[0542] "Nutrient intake status recording and analysis means" refers to a device or software that records and analyzes a user's daily nutrient intake status.

[0543] "Suggestion generation means" refers to a device or software that generates suggestions tailored to the user based on the nutrients and calories the user is lacking.

[0544] "Emotion recognition means" refers to a device or software that recognizes emotions from the user's voice and adjusts the suggested content accordingly.

[0545] "Proposal content display means" refers to a device or software that visually presents the generated proposal content to the user.

[0546] "Online purchase link generation and provision means" refers to a device or software that generates an online purchase link for a proposed product and provides it to the user.

[0547] This invention is a system that records the user's meal content via voice input and manages the balance of calories and nutrients. Furthermore, by combining it with an emotion engine that recognizes the user's emotional state, the system can provide suggestions tailored to the user's psychological state. This system has the following functions:

[0548] Hardware and software to be used

[0549] Voice input methods: Input devices such as smartphones and tablets

[0550] Speech-to-text conversion method: Python speech recognition library "SpeechRecognition"

[0551] Method for analyzing meal content: Natural language processing library "spaCy"

[0552] Calorie and nutrient calculation methods: Nutrient databases (e.g., USDA Food Database)

[0553] Nutrient intake record and analysis tools: Database and statistical analysis software

[0554] Proposal generation method: Computational algorithm using a programming language such as Python

[0555] Emotion recognition method: Google's "Cloud Natural Language API"

[0556] Proposed content display method: Smartphone and tablet display screen (UI design)

[0557] Online purchase link generation and provision method: E-commerce platform API (e.g., Amazon API)

[0558] Specific functions of the system

[0559] First, the user presses the voice input button on the app screen of their device, and then dictates what they are eating. For example, they might say, "Today's lunch is katsu curry." This voice data is then recorded on the device.

[0560] The terminal sends the recorded audio data to the server. The server uses the Python speech recognition library "SpeechRecognition" to convert the received audio data into text data. Specifically, the command used is recognizer.recognize_google(audio_data).

[0561] Next, the server uses the natural language processing library "spaCy" to analyze the converted text data. Specifically, it extracts proper nouns (names of ingredients and dishes) from the text data. For example, it extracts "katsu curry" from the text data "Today's lunch is katsu curry."

[0562] Subsequently, the server retrieves nutrient data from a nutrient database (e.g., USDA Food Database) based on the identified meal (e.g., "Katsu Curry"). This allows the server to obtain specific calorie and nutrient information such as "Katsu Curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g". In this case, queries to the database are often performed using SQL (e.g., SELECT FROM food_database WHERE food_name = 'Katsu Curry').

[0563] Furthermore, the server records and analyzes the user's daily nutrient intake. Based on this analysis, it can identify any nutrient deficiencies or excess calories. Using this data, the server generates specific meal and supplement suggestions tailored to the user. For example, it might suggest, "Your intake is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0564] The system uses Google's Cloud Natural Language API for emotion recognition. The server uses commands like `response = client.analyze_sentiment(document)` to recognize emotions from the user's voice. For example, if the voice indicates "tired," the suggestions are adjusted to better suit the user's mental state. Suggestions such as, "Let's incorporate foods that help relieve stress. Herbal tea and dark chocolate are recommended," might be added.

[0565] Furthermore, the generated suggestions are visually presented to the user through their device. Users are also provided with links to purchase the suggested meals and supplements online. These links are generated using the APIs of e-commerce platforms (e.g., Amazon API or Rakuten API).

[0566] Examples of specific cases and prompt statements

[0567] As a concrete example, a user might voice-input "Today's lunch is katsu curry," and the server would analyze and calculate the information to suggest a suitable meal balance, providing links to purchase the necessary food items online. Furthermore, if the system detects that the user is feeling tired, it would also suggest meals suitable for stress relief.

[0568] Example of a prompt:

[0569] The user voice-inputted, "Today's lunch is katsu curry." This data was sent to the server and converted to text using speech recognition technology. Next, a meal content analysis tool identified "katsu curry," and the calories and nutrient content were calculated based on the nutritional data of each ingredient. Based on these results, the balance of calories and nutrients was analyzed, and meal suggestions suitable for the user were generated. Furthermore, because the emotion engine recognized the user's fatigue level, suggestions for foods that could help alleviate stress were also added. The user can review these suggestions on their device and receive links to purchase the necessary food items from an online store.

[0570] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0571] Step 1:

[0572] Input: User voice input

[0573] Specific steps: The user presses the voice input button on the app screen of their device and dictates the details of their meal.

[0574] Output: Recorded audio data

[0575] The user presses the voice input button on the app screen of their device and says, "Today's lunch is katsu curry." This voice data is recorded on the device.

[0576] Step 2:

[0577] Input: Recorded audio data

[0578] Specific operation: The device sends the recorded audio data to the server.

[0579] Output: Audio data sent to the server

[0580] The device sends the recorded audio data to the server. This data is transmitted using HTTP or WebSocket.

[0581] Step 3:

[0582] Input: Audio data sent to the server

[0583] Specific operation: The server uses the Python speech recognition library "SpeechRecognition" to convert audio data into text data.

[0584] Output: Converted text data

[0585] The server converts the received audio data into text data using the "SpeechRecognition" library. Specifically, it uses the command recognizer.recognize_google(audio_data).

[0586] Step 4:

[0587] Input: Converted text data

[0588] Specific operation: The server uses the NLP library "spaCy" to analyze text data and identify the contents of the meal.

[0589] Output: Extraction results of meal contents

[0590] The server analyzes the converted text data and extracts proper nouns (names of ingredients and dishes). Specifically, it extracts "katsu curry" from the text "Today's lunch is katsu curry."

[0591] Step 5:

[0592] Input: Extraction results of meal contents

[0593] Specific operation: The server retrieves calorie and nutrient data based on the extracted meal content from the nutrient database and calculates the calories and nutrients.

[0594] Output: Calculated calorie and nutrient data

[0595] The server retrieves data from a nutrition database (e.g., the USDA Food Database) based on the identified "katsu curry" and calculates the calories and nutrient content. This is done using the SQL query SELECT FROM food_database WHERE food_name = 'katsu curry'.

[0596] Step 6:

[0597] Input: Calculated calorie and nutrient data

[0598] Specific operation: The server records and analyzes the user's daily nutrient intake.

[0599] Output: Daily nutrient intake status and analysis results

[0600] Based on calculated calorie and nutrient data, the server records the user's daily nutrient intake and analyzes any nutrient deficiencies or excess calories by comparing it with past data.

[0601] Step 7:

[0602] Input: Daily nutrient intake status and its analysis results

[0603] Specific operation: The server generates suggestions for deficient nutrients and calories.

[0604] Output: Proposal

[0605] The server generates specific meal and supplement suggestions tailored to the user. For example, it might suggest, "Your current meal is high in fat, so choose a low-fat option for your next meal."

[0606] Step 8:

[0607] Input: User's voice data

[0608] Specific operation: The server uses Google's "Cloud Natural Language API" to recognize emotions from the user's voice and adjust the suggested content accordingly.

[0609] Output: Revised proposal

[0610] The server uses the command `response = client.analyze_sentiment(document)` to recognize emotions from the user's voice. For example, if the voice indicates that the user is "tired," it will also suggest foods that can help relieve stress.

[0611] Step 9:

[0612] Input: Adjusted proposal

[0613] Specific operation: The terminal visually presents the suggested content to the user using a suggested content display means.

[0614] Output: Suggestions displayed on the user's device

[0615] The device visually presents the suggested content, allowing the user to review it. It uses UI components (e.g., HTML, CSS, React.js) for display.

[0616] Step 10:

[0617] Input: Adjusted proposal

[0618] Specific operation: The server generates an online purchase link for the suggested product and provides it to the terminal.

[0619] Output: Online purchase link

[0620] The server generates online purchase links for the suggested meals and supplements and sends them to the device. This allows the user to access e-commerce platforms (e.g., Amazon API or Rakuten API) through the device and purchase the desired products.

[0621] The above outlines the processing flow of this system's program. Through these steps, users can easily record their meal content and receive appropriate suggestions based on nutritional balance and emotional state.

[0622] (Application Example 2)

[0623] 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."

[0624] In modern society, it is difficult for busy users to record their meals and manage their calorie and nutrient balance. Furthermore, there are few systems that can suggest appropriate meals and supplements based on the user's emotional state, making health management even more challenging. Additionally, the lack of specific suggestions for meals and supplements makes it difficult for users to purchase them directly online.

[0625] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the contents of a meal, means for calculating calories and nutrients based on the contents of the meal, means for recording and analyzing the daily nutrient intake, means for generating suggestions for deficient nutrients and calories, means for recognizing the user's emotions, means for adjusting the suggested content based on the recognized emotions, means for displaying the suggested content, and means for generating and providing online purchase links for the suggested products. As a result, the user can easily record the contents of their meal and emotions by voice input, receive specific suggestions that take into account the balance of calories and nutrients, and make immediate online purchases.

[0626] "Means for user voice input" refers to a device or interface for users to input details of their meals using voice.

[0627] "Means of converting input audio into text data" refers to technologies and algorithms for converting audio data into text data.

[0628] "Methods for analyzing text data to identify meal contents" refers to a process or system for analyzing converted text data and recognizing specific meal contents.

[0629] "Means for calculating calories and nutrients based on dietary content" refers to a method or apparatus for calculating calories and nutrients based on specified dietary content.

[0630] "Means for recording and analyzing daily nutrient intake" refers to technologies and devices that store data on the nutrients a user consumes in a day and analyze that data.

[0631] A "means for generating suggestions for deficient nutrients and calories" is a system that, based on recorded and analyzed nutrient data, suggests to the user how to supplement deficient nutrients and calories.

[0632] "Means of recognizing user emotions" refers to technologies and processes that identify a user's emotions and psychological state from voice and text data.

[0633] "Means of adjusting suggestions based on perceived emotions" refers to a system or method that takes the user's emotions into consideration and modifies the suggested meals or supplements accordingly.

[0634] "Means for displaying proposed content" refers to devices or interfaces used to visually present generated proposals to users.

[0635] "Means for generating and providing online purchase links for suggested products" refers to technologies and methods for generating and providing users with links to purchase suggested food products or supplements online.

[0636] This invention is a system that allows users to record their meal contents via voice input and manage the balance of calories and nutrients. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides suggestions tailored to the user's psychological state. This system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake status record analysis means, suggestion generation means, suggestion content display means, online purchase link generation means, and an emotion engine.

[0637] First, the user operates a device such as a smartphone, presses the voice input button on the app screen, and then dictates what they are eating. For example, "Today's lunch is katsu curry." This voice data is recorded directly on the device. The device then sends the recorded voice data to the server. The server uses the Google Speech-to-Text API to convert the received voice data into text data. Speech recognition technology is used to convert the voice into text with high accuracy.

[0638] Next, the server uses a meal content analysis tool to analyze the converted text data. This analysis identifies the contents of the meal. For example, it might reveal that "katsu curry" is included. The server then uses a calorie and nutrient calculation tool to calculate the calories and nutrients based on the identified meal. It retrieves nutrient data for each ingredient from the database and obtains detailed information such as "katsu curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[0639] Furthermore, the server uses a nutrient intake recording and analysis means to record and analyze the user's daily nutrient intake. This helps identify any nutrient deficiencies or excess calories. Based on the analysis results, the server uses a suggestion generation means to generate specific suggestions for meals and supplements suitable for the user. For example, it might suggest, "Your intake is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0640] The server further uses an emotion engine to recognize emotions from the user's voice. Based on this emotion data, it can adjust the suggestions. For example, if the user's voice is detected as tired, a suggestion such as "Let's incorporate foods that help relieve stress. Herbal tea and dark chocolate are recommended" will be generated. The emotion engine also determines the user's stress level and suggests meals and supplements suitable for stress relief. The suggestions generated through this process are displayed to the user through the terminal. The terminal uses a suggestion display means to present the information visually in an easy-to-understand manner for the user. Furthermore, the server uses an online purchase link generation means to generate online purchase links for the suggested meals and supplements and provides them to the user. By clicking on these links, the user can directly purchase the necessary products from the online store.

[0641] As a concrete example, a user might voice-input, "I'm having katsu curry for lunch today," and the server would analyze and calculate the information to suggest a healthy balance. Furthermore, considering the user's emotions, if their stress level is high, the system would suggest relaxing foods or drinks. Additionally, a link to immediately purchase the suggested items online is provided, allowing users to easily obtain the products they need.

[0642] Example of a prompt:

[0643] A user voice-inputs "Today's lunch is katsu curry. I feel tired" into their smartphone. This voice data is converted to text, analyzed to identify the food items, and the calories and nutrients are calculated. The system also recognizes the user's emotions and suggests meals and supplements based on those emotions. An example of the results displayed is "Try some herbal tea or dark chocolate to help relieve stress. Purchase link: https: / / example.com / harb_tea".

[0644] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0645] Step 1:

[0646] The user operates a device such as a smartphone and presses the voice input button on the app screen to dictate the details of their meal. For example, they might say, "Today's lunch is katsu curry." The input data is voice data, and this voice data is recorded on the device.

[0647] Step 2:

[0648] The terminal sends the recorded audio data to the server. In this case, the input is audio data, and the data sent is also audio data.

[0649] Step 3:

[0650] The server uses a speech-to-text conversion tool (Google Speech-to-Text API) to convert the received audio data into text data. The input is audio data, and the output is text data. This conversion operation converts speech into text data with high accuracy.

[0651] Step 4:

[0652] The server analyzes text data to identify the contents of a meal. The meal content analysis tool extracts ingredient names and dish names from the text data and compares them with a database. The input is text data, and the output is the identified meal content (e.g., "Katsu Curry").

[0653] Step 5:

[0654] The server calculates calories and nutrients based on the specified meal. The calorie and nutrient calculation means retrieves nutrient data for each food item from the database and performs the calculation. The input is the specified meal, and the output is calorie and nutrient information (e.g., "Pork cutlet curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g").

[0655] Step 6:

[0656] The server records and analyzes the user's daily nutrient intake. The nutrient intake recording and analysis system calculates the nutrient balance based on the user's daily diet. The input is the user's daily diet and nutrient information, and the output is the intake balance.

[0657] Step 7:

[0658] The server generates suggestions for deficient nutrients and calories. The suggestion generation mechanism generates suggestions based on recorded and analyzed results. The input is information on nutrient intake balance, and the output is the suggested content (e.g., "Your intake is high in fat, so choose a low-fat option for your next meal").

[0659] Step 8:

[0660] The server uses an emotion engine to recognize emotions from the user's voice. The input is text data, and the output is the recognized emotion information (e.g., "tired").

[0661] Step 9:

[0662] The server adjusts its suggestions based on the recognized emotions. The input is emotion information and the suggested content, and the output is the adjusted suggested content (e.g., "Let's incorporate foods that help relieve stress").

[0663] Step 10:

[0664] The server displays the adjusted proposal to the user via the terminal. The proposal is presented visually to the user through the proposal display mechanism. The input is the adjusted proposal, and the output is the proposal displayed to the user.

[0665] Step 11:

[0666] The server generates and provides online purchase links for the suggested products. The online purchase link generation mechanism generates and provides purchase links for suggested foods and supplements to the user. The input is the suggested content, and the output is the online purchase link (e.g., "Purchase link: https: / / example.com / harb_tea").

[0667] 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.

[0668] 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.

[0669] 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.

[0670] [Third Embodiment]

[0671] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0672] 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.

[0673] 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).

[0674] 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.

[0675] 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.

[0676] 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).

[0677] 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.

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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".

[0683] This invention is a system for recording a user's meal content via voice input and managing the balance of calories and nutrients. The system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake status record analysis means, suggestion generation means, suggestion content display means, and online purchase link generation means.

[0684] First, the user operates the device and uses voice input to dictate what they are eating. For example, they might say, "Today's lunch is katsu curry." This voice data is then recorded directly on the device.

[0685] Next, the terminal sends the recorded audio data to the server. The server uses a speech-to-text conversion method to convert the received audio data into text data. By utilizing speech recognition technology, the audio can be converted into text with high accuracy.

[0686] Next, the server uses a meal content analysis tool to analyze the converted text data. To identify the contents of the meal, it extracts ingredient names and dish names from the text data and compares them with a database to identify matches. This analysis reveals, for example, that "katsu curry" is included.

[0687] The server then uses calorie and nutrient calculation tools to calculate calories and nutrients based on the identified meal. It retrieves nutrient data for each food item from the database and obtains detailed information such as "Pork cutlet curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[0688] Furthermore, the server uses a nutrient intake recording and analysis system to record and analyze the user's daily nutrient intake. This reveals any nutrient deficiencies or excess calories.

[0689] Next, the server uses a suggestion generation mechanism to provide specific suggestions for meals and supplements based on the analysis results. For example, it might generate a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended."

[0690] The generated proposals are displayed to the user via the terminal. The terminal uses a proposal display mechanism to present the proposals to the user in an easily understandable visual way.

[0691] Finally, the server uses an online purchase link generation mechanism to generate and provide the user with online purchase links for the suggested meals and supplements. By clicking on these links, the user can directly purchase the desired products from an online store (e.g., an e-commerce platform).

[0692] This process allows users to easily record their meals by voice and receive suggestions for nutritionally balanced meals and supplements. As a result, they can maintain a healthy diet. For example, a user might voice-input, "Today's lunch is katsu curry," and the server would analyze and calculate that information, suggest a suitable meal balance, and then purchase the necessary food items online.

[0693] The following describes the processing flow.

[0694] Step 1: Voice Input

[0695] The user uses their device and presses the voice input button on the app screen.

[0696] The device records the user's voice. For example, the user might say, "I'm having katsu curry for lunch today."

[0697] Step 2: Sending the audio data

[0698] The device sends the recorded audio data to the server. This data is then transferred directly to the server.

[0699] Step 3: Speech-to-text conversion

[0700] The server converts the received audio data into text data using a speech recognition engine. For example, text such as "Today's lunch is katsu curry" is generated.

[0701] Step 4: Analysis of Dietary Content

[0702] The server analyzes the converted text data. The server uses an analysis algorithm to identify the food item, such as "katsu curry," from the text.

[0703] Step 5: Calculating calories and nutrients

[0704] The server retrieves nutrient data from a database based on the identified meal. For example, it calculates the calories and nutrients of "katsu curry" (e.g., 900kcal, 40g fat, 100g carbohydrates, 25g protein).

[0705] Step 6: Recording and analyzing nutrient intake

[0706] The server records and analyzes the user's daily nutrient intake. This helps identify any nutrient deficiencies or excess calories.

[0707] Step 7: Proposal Generation

[0708] Based on the analysis results, the server generates specific suggestions for meals and supplements that are suitable for the user. For example, it might suggest, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended."

[0709] Step 8: Display the proposed content

[0710] The server sends the generated proposal to the terminal. The terminal displays the proposal on its screen, allowing the user to visually confirm it.

[0711] Step 9: Generate online purchase link

[0712] The server generates online purchase links for the suggested meals and supplements and sends them to the device. The user can then click the displayed link to directly purchase the products from the online store.

[0713] This allows users to effectively record their meals using only voice input and receive suggestions for meals and supplements to maintain proper nutritional balance. As a result, it becomes possible to support them in maintaining a healthy lifestyle.

[0714] (Example 1)

[0715] 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."

[0716] In today's busy lifestyle, it is difficult for users to effectively manage their diet and maintain a healthy eating habit. Traditional methods involve manual recording of meals and calculation of nutrients, which is time-consuming, cumbersome, and prone to errors. Furthermore, systems for analyzing meals and suggesting nutrients are limited, and the accuracy of converting voice-based input to text data is particularly low. Therefore, there is a need for a reliable system that is easy for users to continue using.

[0717] 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.

[0718] In this invention, the server includes means for accurately converting voice data into text using a voice recognition service, means for extracting ingredient names and dish names from the text data using natural language processing technology, and means for identifying meal contents by comparing them with a database. This allows users to easily input voice information, convert the content into text data, analyze the meal contents to calculate calories and nutrients, and receive suggestions for appropriate meals and supplements. By providing online purchase links, the system provides an environment where users can immediately purchase the suggested ingredients and supplements, supporting comprehensive health management.

[0719] A "user" refers to an individual who uses voice input to record their meal details and manage their nutrition.

[0720] "Voice input means" refers to a device or application for a user to input voice.

[0721] "Text data" refers to a data format obtained by converting speech input via voice input devices into text information.

[0722] "Speech recognition service" refers to a technology or platform for converting speech into text data.

[0723] "Natural language processing technology" refers to computer technology used to analyze text data and extract and identify information.

[0724] "Meal details" refers to information consisting of the ingredients and names of dishes consumed by the user.

[0725] "Calories and nutrients" refers to the amount of energy and various nutrients contained in a meal.

[0726] A "database" refers to an information system that stores nutritional data for ingredients and dishes.

[0727] "Method for recording and analyzing nutrient intake" refers to technology for recording a user's daily nutrient intake and analyzing their health status.

[0728] "Suggestion generation method" refers to technology that creates appropriate dietary and supplement suggestions for users based on analysis results.

[0729] "Proposal content display means" refers to a device or application for visually displaying the generated proposal to the user.

[0730] "Online purchase link generation method" refers to technology that generates links allowing users to purchase suggested food products or supplements online.

[0731] "API" is an abbreviation for Application Programming Interface, and refers to a means of exchanging data between different software systems.

[0732] "Past meal history" refers to a record of meals the user has consumed in the past.

[0733] This invention is a system for recording a user's meal content via voice input and managing the balance of calories and nutrients. The system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake recording and analysis means, suggestion generation means, suggestion content display means, and online purchase link generation means. Specific usage examples and operational details are as follows.

[0734] Users operate devices such as smartphones and tablets and use voice input to dictate what they are eating. For example, a user might say, "Today's lunch is katsu curry." This voice data is recorded directly on the device. The device has an internet connection and can send the recorded voice data to a server.

[0735] The server converts the received audio data into text data using a speech-to-text conversion method. Here, speech recognition services such as Google Cloud Speech-to-Text or IBM Watson are used. This ensures that the audio is transcribed into text with high accuracy.

[0736] Next, the server uses natural language processing techniques to analyze the text data and identify the contents of the meal. Specifically, it extracts ingredient names and dish names from the text data and compares them with a database to identify matches. This analysis reveals, for example, that "katsu curry" is included. Libraries such as Python's NLTK and SpaCy are used here.

[0737] The server retrieves nutritional data for each food item from the database and calculates calories and nutrients based on the meal. The database contains nutritional data for each food item and can use SQL or NoSQL databases. As a result, the user's meal is output as detailed data such as "Pork cutlet curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g".

[0738] Furthermore, the server uses nutrient intake recording and analysis tools to record and analyze the user's daily nutrient intake. This reveals any nutrient deficiencies or excess calories. Data analysis libraries such as Pandas and NumPy are used for the analysis.

[0739] Next, the server uses a suggestion generation mechanism to provide specific meal and supplement recommendations based on the analysis results. For example, it might generate a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended." The generated suggestions are then displayed to the user via the terminal. The terminal uses a suggestion display mechanism to present the suggestions visually in an easy-to-understand manner for the user.

[0740] Finally, the server uses an online purchase link generation mechanism to generate online purchase links for the suggested meals and supplements and provides them to the user. By clicking on these links, the user can directly purchase the necessary products from the online store. APIs such as the Amazon API and Rakuten API are used here.

[0741] For example, if a user voice-inputs "Today's lunch is katsu curry," the server analyzes and calculates that information, suggests a suitable meal balance, and then proceeds to purchase the necessary food items online.

[0742] Examples of prompts for a generative AI model are as follows:

[0743] "Design a system that transcribes user-inputted meal details into text, calculates calories and nutrients, and suggests the next meal."

[0744] Thus, this invention provides support for users to easily record their meal content and maintain a healthy diet.

[0745] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0746] Step 1:

[0747] The user dictates the details of their meal to the device using voice input. For example, the user might say, "Today's lunch is katsu curry." The recorded voice data is stored on the device. Specifically, the user launches the app on their smartphone, presses the voice recording button, and begins speaking.

[0748] Step 2:

[0749] The device sends the recorded audio data to the server. The input is the recorded audio data, and the output is the audio data sent to the server. Specifically, the device uploads the audio data to the cloud server using Wi-Fi or mobile data.

[0750] Step 3:

[0751] The server converts received audio data into text data using a speech-to-text conversion method. The input is the received audio data, and the output is the converted text data. The server uses a speech recognition service (e.g., Google Cloud Speech-to-Text) to generate the text "Today's lunch is katsu curry."

[0752] Step 4:

[0753] The server uses natural language processing technology to analyze the converted text data and identify the contents of the meal. The input is the converted text data, and the output is the identified meal contents. Specifically, the server uses the SpaCy library to extract the ingredient name "katsu curry" and compares it with the database to identify the match.

[0754] Step 5:

[0755] The server retrieves nutritional data for each food item from the database and calculates calories and nutrients based on the identified meal. The input is the identified meal, and the output is calorie and nutrient data. Specifically, the server retrieves information such as "Pork cutlet curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g" from the database and generates the calculation result.

[0756] Step 6:

[0757] The server uses nutrient intake recording and analysis tools to record and analyze the user's daily nutrient intake. The input is multiple meal records over the day, and the output is the analysis results. Specifically, the server uses Pandas and NumPy libraries to accumulate the user's nutrient intake history and analyze daily deficiencies and excesses.

[0758] Step 7:

[0759] The server uses a suggestion generation mechanism to generate specific meal and supplement suggestions based on the analysis results. The input is the nutrient analysis results, and the output is the specific suggestions. For example, the server generates a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended."

[0760] Step 8:

[0761] The terminal uses a suggestion display mechanism to visually display the generated suggestions to the user. The input is the suggestion content sent from the server, and the output is the suggestion displayed on the terminal screen. Specifically, a smartphone application displays the suggestion message on the user's screen.

[0762] Step 9:

[0763] The server uses an online purchase link generation mechanism to generate online purchase links for suggested meals and supplements and provides them to the user. The input is the suggested content, and the output is the associated online purchase links. Specifically, the server uses the Amazon API and Rakuten API to generate links to purchase items such as "somen" noodles and "hiyayakko" (cold tofu) and provides them to the user.

[0764] This process allows users to easily record their meal details by voice and receive suggestions for nutritionally balanced meals and supplements.

[0765] (Application Example 1)

[0766] 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."

[0767] Traditional meal management systems required users to meticulously record their meals and manage calorie and nutrient balance, and lacked a means to obtain appropriate meal suggestions. Furthermore, ordering meals based on these suggestions often required using a separate online store, which was inconvenient for users. Additionally, the lack of systems offering nutritionally balanced delivery menu suggestions and purchase links made it difficult for users to make healthy choices smoothly.

[0768] 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.

[0769] In this invention, the server includes means for the user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the contents of a meal, means for calculating calories and nutrients based on the contents of the meal, means for recording and analyzing the daily nutrient intake, means for generating suggestions for deficient nutrients and calories, means for displaying the suggestions, means for generating and providing online purchase links for the suggested products, means for suggesting the optimal delivery menu based on the user's meal history, and means for generating and providing online purchase links for the suggested delivery menu. This allows the user to easily record their meal contents by voice input and receive suggestions for healthy meal choices and delivery menus. Furthermore, since suggested meals and delivery menus can be purchased smoothly online, user convenience is improved, and it becomes possible to maintain a healthy diet.

[0770] A "user" refers to an individual who uses this system to manage their diet and receive suggestions and online purchase links.

[0771] "Means of voice input" refers to microphones and other voice input devices that allow users to communicate their meal details to the system by voice.

[0772] "Means of converting speech to text data" refers to speech recognition software or algorithms that replace input speech with textual information.

[0773] "Means of analyzing text data to identify meal contents" refers to text analysis algorithms or databases that analyze converted text data and specifically identify the meal contents described.

[0774] "Means for calculating calories and nutrients based on dietary content" refers to a calculation algorithm or nutritional database for calculating the calories and nutrients contained in a specific diet.

[0775] "Means for recording and analyzing daily nutrient intake" refers to data recording and analysis tools that accumulate data on the nutrients a user consumes in a day and evaluate their intake status based on past data.

[0776] "Means for generating suggestions for deficient nutrients and calories" refers to an algorithm that creates suggestions for nutrients and calories to be supplemented based on the user's nutrient and calorie intake balance.

[0777] "Means for displaying the proposed content" refers to displays or screens used to visually show the generated proposals to the user.

[0778] "Means for generating and providing online purchase links for proposed products" refers to programs and communication means for creating and providing links that enable online purchase of products based on the proposal.

[0779] "A means of suggesting the optimal delivery menu based on the user's meal history" refers to an algorithm that selects and suggests a delivery menu suitable for the user based on accumulated meal history data.

[0780] "Means for generating and providing online purchase links for proposed delivery menus" refers to a program that generates and provides to users a link that allows them to purchase proposed delivery menus online.

[0781] This invention is a system that takes the user's meal details as voice input, manages the balance of calories and nutrients, and suggests the optimal delivery menu. The following describes how this system is implemented in detail.

[0782] First, the user operates their smartphone and uses voice input to dictate what they are eating. For example, they might say, "Today's lunch is katsu curry." The voice data is recorded on the device. Next, the device sends the recorded voice data to a server. The server uses speech recognition software (for example, Google Speech-to-Text API) to convert the received voice data into text data. Speech recognition technology allows for highly accurate transcription of speech into text.

[0783] Next, the server analyzes the text data and uses a text analysis algorithm to identify the specific contents of the meal. During this process, it compares the data with a database to identify ingredient names and dish names. For example, it might reveal that "katsu curry" is included. The server then uses a calorie and nutrient calculation algorithm to calculate the calories and nutrients based on the identified meal. Based on the nutrient data for each ingredient obtained from the database, it obtains detailed information such as "katsu curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[0784] Furthermore, the server uses a nutrient intake recording and analysis tool to record and analyze the user's daily nutrient intake. This reveals any nutrient deficiencies or excess calories. Next, the server uses a suggestion generation algorithm to suggest healthy meals and supplements to the user. For example, it might generate a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0785] Furthermore, the service will suggest convenient food delivery menus for users. To do this, it will identify the most suitable delivery menu based on the user's past meal history data and generate online purchase links. The suggested delivery menus will include items such as "healthy salad" and "grilled chicken," and each menu item will have a corresponding online purchase link. By clicking on these links, users can directly purchase the desired items from the online store.

[0786] This system allows users to easily record their meals using voice input and receive suggestions for healthy meal choices and delivery menus. Furthermore, suggested meals and delivery menus can be smoothly purchased online, improving user convenience and enabling them to maintain a healthy diet.

[0787] As a concrete example, if a user voice-inputs "Today's lunch is katsu curry," the server analyzes and calculates that information, suggests a suitable meal balance, and generates links to purchase the necessary foods online. This system utilizes a smartphone, a server, voice recognition software, a text analysis algorithm, a nutrient database, a nutrient record analysis tool, and a suggestion generation algorithm.

[0788] Examples of prompts for generative AI models:

[0789] Create a Python program that analyzes meal information entered by the user via voice input, calculates calories and nutrients, and suggests healthy meals and delivery menus. Also, describe the overview and processing flow of the API server used in the program.

[0790] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0791] Step 1:

[0792] The user operates a smartphone and uses voice input via the microphone. For example, they might say, "Today's lunch is katsu curry." The voice data is recorded on the device.

[0793] Input: User's voice data

[0794] Output: Recorded audio data

[0795] Step 2:

[0796] The device sends the recorded audio data to the server. The server uses speech recognition software (for example, Google Speech-to-Text API) to convert the transmitted audio data into text data.

[0797] Input: Audio data

[0798] Output: Converted text data

[0799] Step 3:

[0800] The server uses a text analysis algorithm to analyze text data. During analysis, it compares the data with a database to identify the contents of the meal. For example, if the text data is "Today's lunch is katsu curry," then "katsu curry" will be identified.

[0801] Input: Text data

[0802] Output: Identified dietary content

[0803] Step 4:

[0804] The server uses an algorithm to calculate calories and nutrients based on the meal content. It retrieves the components contained in the identified meal from a database and performs the calculation. For example, in the case of "katsu curry," it calculates specific data such as "900kcal, fat: 40g, carbohydrates: 100g, protein: 25g."

[0805] Input: Identified meal details

[0806] Output: Calorie and nutrient data

[0807] Step 5:

[0808] The server uses a nutrient intake recording and analysis tool to record and analyze the user's daily nutrient intake. This data clearly identifies any nutrient deficiencies or excess calories.

[0809] Input: User's daily meal data, past meal history data

[0810] Output: Nutrient intake status and analysis results

[0811] Step 6:

[0812] The server uses a suggestion generation algorithm to generate meal and supplement recommendations based on deficient nutrients and calories. For example, it might generate specific suggestions such as, "Your diet is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0813] Input: Nutrient intake status and analysis results

[0814] Output: Proposal

[0815] Step 7:

[0816] The server uses an algorithm to suggest the most suitable delivery menu based on the user's meal history, generating delivery menus and their online purchase links. For example, it might generate delivery menus such as "Healthy Salad" and "Grilled Chicken," along with their purchase links.

[0817] Input: User's past meal history data, suggested content

[0818] Output: Delivery menu and online purchase link

[0819] Step 8:

[0820] The terminal displays suggested meal options, delivery menus, and online purchase links to the user. By clicking the links, the user can directly purchase the desired items from the online store.

[0821] Input: Proposal details, delivery menu, online purchase link

[0822] Output: Proposal content and links displayed to the user, and purchase procedure.

[0823] This concludes the detailed explanation of the processing flow of this system.

[0824] 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.

[0825] This invention is a system that records the user's meal content via voice input and manages the balance of calories and nutrients. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it can provide suggestions tailored to the user's psychological state. This system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake status record analysis means, suggestion generation means, suggestion content display means, online purchase link generation means, and an emotion engine.

[0826] First, the user operates the device, presses the voice input button on the app screen, and then dictates what they are eating. For example, "Today's lunch is katsu curry." This voice data is recorded directly on the device.

[0827] Next, the terminal sends the recorded audio data to the server. The server uses a speech-to-text conversion method to convert the received audio data into text data. Speech recognition technology is used to convert the audio into text with high accuracy.

[0828] Next, the server uses a meal content analysis tool to analyze the converted text data. To identify the contents of the meal, it extracts ingredient names and dish names from the text data and compares them with a database to identify matches. This analysis reveals, for example, that "katsu curry" is included.

[0829] The server then uses calorie and nutrient calculation tools to calculate calories and nutrients based on the identified meal. It retrieves nutrient data for each food item from the database and obtains detailed information such as "Pork cutlet curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[0830] Furthermore, the server uses a nutrient intake recording and analysis means to record and analyze the user's daily nutrient intake. This helps identify any nutrient deficiencies or excess calories. Based on the analysis results, the server uses a suggestion generation means to generate specific suggestions for meals and supplements suitable for the user. For example, it might suggest, "Your intake is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0831] One of the key features of this invention is the emotion engine. The server recognizes emotions from the user's voice and can adjust the suggestions based on that emotion data. For example, if the user's voice is detected as indicating fatigue, a suggestion such as, "Let's incorporate foods that help relieve stress. Herbal tea and dark chocolate are recommended," will be generated. The emotion engine can also determine the user's stress level and suggest meals or supplements suitable for stress relief.

[0832] The generated suggestions are displayed to the user via the terminal. The terminal uses a suggestion display mechanism to present the suggestions visually in an easy-to-understand manner for the user. Furthermore, the server uses an online purchase link generation mechanism to generate online purchase links for the suggested meals and supplements and provides them to the user. By clicking on these links, the user can directly purchase the necessary products from an online store (e.g., an e-commerce platform).

[0833] This process allows users to easily record their meals by voice, receive suggestions for nutritionally balanced meals, and get recommendations for supplements tailored to their emotional state. For example, a user might voice-input, "Today's lunch is katsu curry," and the server would analyze and calculate the information, suggest a suitable meal balance, and then purchase the necessary food items online. If the system detects that the user is feeling tired, it will also suggest meals suitable for stress relief.

[0834] The following describes the processing flow.

[0835] Step 1: Voice Input

[0836] The user operates the device and presses the voice input button on the app screen. The user says, "Today's lunch is katsu curry."

[0837] Step 2: Sending the audio data

[0838] The terminal sends the recorded audio data to the server using the voice input method. The terminal temporarily stores the audio data in a buffer and then transfers the data to the server via the internet.

[0839] Step 3: Speech-to-text conversion

[0840] The server that receives the audio data activates its speech recognition engine and converts the audio data into text data. The text "Today's lunch is katsu curry" is generated.

[0841] Step 4: Analysis of Dietary Content

[0842] The server uses text data analysis tools to analyze the converted text data. For example, it identifies the keyword "katsu curry" and compares it with a food database to identify the ingredients.

[0843] Step 5: Emotion Recognition

[0844] The server activates an emotion engine and recognizes the user's emotions from the voice data. It analyzes the tone, speed, and word choice of the voice to determine if the user is tired.

[0845] Step 6: Calculating calories and nutrients

[0846] The server uses a calorie and nutrient calculation method based on the meal content to calculate the calories and nutrients of "Katsu Curry" (e.g., 900kcal, 40g fat, 100g carbohydrates, 25g protein). This data is obtained from a meal database.

[0847] Step 7: Recording and analyzing nutrient intake

[0848] The server records and analyzes the user's daily nutrient intake. It compares past meal history with newly entered data to determine which nutrients are deficient and which are in excess.

[0849] Step 8: Proposal Generation

[0850] The server uses a suggestion generation mechanism to take into account multiple factors (nutritional status and emotional state) and suggest meals and supplements suitable for the user. For example, it generates specific suggestions such as, "Your diet is high in fat, so for your next meal, choose low-fat options like somen noodles or cold tofu. Also, you seem tired, so a relaxing herbal tea is recommended."

[0851] Step 9: Display the proposed content

[0852] The server sends the generated proposal content to the terminal. The terminal uses a proposal content display mechanism to display the proposal content on the screen in a visually easy-to-understand manner for the user.

[0853] Step 10: Generate online purchase link

[0854] The server uses an online purchase link generation mechanism to generate online purchase links for the suggested food items and supplements. These links are sent to the device, and when the user clicks them, they are directed to the purchase page on the e-commerce platform.

[0855] This process allows users to easily record their meals using voice input and receive suggestions for nutritionally balanced meals and suggestions based on their emotional state. For example, if a user voice-inputs, "Today's lunch is katsu curry," the server analyzes and calculates that information to suggest a balanced meal that takes into account the user's fatigue level and helps them purchase necessary food items online.

[0856] (Example 2)

[0857] 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."

[0858] In modern society, users are often pressed for time, making it difficult to choose nutritionally balanced meals or select foods and supplements that suit their emotional state. Therefore, there is a need for a system that allows users to easily record their meals, consider nutritional balance, and receive suggestions tailored to their emotional state.

[0859] 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.

[0860] In this invention, the server includes means for the user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the contents of a meal, means for calculating calories and nutrients based on the contents of the meal, means for recording and analyzing the daily nutrient intake, means for generating suggestions for deficient nutrients and calories, means for recognizing emotions from the user's voice and adjusting the suggestions, means for displaying the suggestions, and means for generating and providing online purchase links for the suggested products. As a result, the user can easily record their meal contents using only voice input, receive appropriate suggestions that take nutritional balance into consideration, and receive suggestions tailored to their emotional state.

[0861] A "user" refers to a person who uses the system.

[0862] "Voice input means" refers to a device or software that records the user's voice.

[0863] "Speech-to-text conversion means" refers to a device or software that converts input speech data into text data.

[0864] "Meal content analysis means" refers to a device or software that analyzes text data to identify the specific contents of a meal.

[0865] "Calorie and nutrient calculation means" refers to a device or software that calculates calories and nutrients based on specified dietary content.

[0866] "Nutrient intake status recording and analysis means" refers to a device or software that records and analyzes a user's daily nutrient intake status.

[0867] "Suggestion generation means" refers to a device or software that generates suggestions tailored to the user based on the nutrients and calories the user is lacking.

[0868] "Emotion recognition means" refers to a device or software that recognizes emotions from the user's voice and adjusts the suggested content accordingly.

[0869] "Proposal content display means" refers to a device or software that visually presents the generated proposal content to the user.

[0870] "Online purchase link generation and provision means" refers to a device or software that generates an online purchase link for a proposed product and provides it to the user.

[0871] This invention is a system that records the user's meal content via voice input and manages the balance of calories and nutrients. Furthermore, by combining it with an emotion engine that recognizes the user's emotional state, the system can provide suggestions tailored to the user's psychological state. This system has the following functions:

[0872] Hardware and software to be used

[0873] Voice input methods: Input devices such as smartphones and tablets

[0874] Speech-to-text conversion method: Python speech recognition library "SpeechRecognition"

[0875] Method for analyzing meal content: Natural language processing library "spaCy"

[0876] Calorie and nutrient calculation methods: Nutrient databases (e.g., USDA Food Database)

[0877] Nutrient intake record and analysis tools: Database and statistical analysis software

[0878] Proposal generation method: Computational algorithm using a programming language such as Python

[0879] Emotion recognition method: Google's "Cloud Natural Language API"

[0880] Proposed content display method: Smartphone and tablet display screen (UI design)

[0881] Online purchase link generation and provision method: E-commerce platform API (e.g., Amazon API)

[0882] Specific functions of the system

[0883] First, the user presses the voice input button on the app screen of their device, and then dictates what they are eating. For example, they might say, "Today's lunch is katsu curry." This voice data is then recorded on the device.

[0884] The terminal sends the recorded audio data to the server. The server uses the Python speech recognition library "SpeechRecognition" to convert the received audio data into text data. Specifically, the command used is recognizer.recognize_google(audio_data).

[0885] Next, the server uses the natural language processing library "spaCy" to analyze the converted text data. Specifically, it extracts proper nouns (names of ingredients and dishes) from the text data. For example, it extracts "katsu curry" from the text data "Today's lunch is katsu curry."

[0886] Subsequently, the server retrieves nutrient data from a nutrient database (e.g., USDA Food Database) based on the identified meal (e.g., "Katsu Curry"). This allows the server to obtain specific calorie and nutrient information such as "Katsu Curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g". In this case, queries to the database are often performed using SQL (e.g., SELECT FROM food_database WHERE food_name = 'Katsu Curry').

[0887] Furthermore, the server records and analyzes the user's daily nutrient intake. Based on this analysis, it can identify any nutrient deficiencies or excess calories. Using this data, the server generates specific meal and supplement suggestions tailored to the user. For example, it might suggest, "Your intake is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0888] The system uses Google's Cloud Natural Language API for emotion recognition. The server uses commands like `response = client.analyze_sentiment(document)` to recognize emotions from the user's voice. For example, if the voice indicates "tired," the suggestions are adjusted to better suit the user's mental state. Suggestions such as, "Let's incorporate foods that help relieve stress. Herbal tea and dark chocolate are recommended," might be added.

[0889] Furthermore, the generated suggestions are visually presented to the user through their device. Users are also provided with links to purchase the suggested meals and supplements online. These links are generated using the APIs of e-commerce platforms (e.g., Amazon API or Rakuten API).

[0890] Examples of specific cases and prompt statements

[0891] As a concrete example, a user might voice-input "Today's lunch is katsu curry," and the server would analyze and calculate the information to suggest a suitable meal balance, providing links to purchase the necessary food items online. Furthermore, if the system detects that the user is feeling tired, it would also suggest meals suitable for stress relief.

[0892] Example of a prompt:

[0893] The user voice-inputted, "Today's lunch is katsu curry." This data was sent to the server and converted to text using speech recognition technology. Next, a meal content analysis tool identified "katsu curry," and the calories and nutrient content were calculated based on the nutritional data of each ingredient. Based on these results, the balance of calories and nutrients was analyzed, and meal suggestions suitable for the user were generated. Furthermore, because the emotion engine recognized the user's fatigue level, suggestions for foods that could help alleviate stress were also added. The user can review these suggestions on their device and receive links to purchase the necessary food items from an online store.

[0894] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0895] Step 1:

[0896] Input: User voice input

[0897] Specific steps: The user presses the voice input button on the app screen of their device and dictates the details of their meal.

[0898] Output: Recorded audio data

[0899] The user presses the voice input button on the app screen of their device and says, "Today's lunch is katsu curry." This voice data is recorded on the device.

[0900] Step 2:

[0901] Input: Recorded audio data

[0902] Specific operation: The device sends the recorded audio data to the server.

[0903] Output: Audio data sent to the server

[0904] The device sends the recorded audio data to the server. This data is transmitted using HTTP or WebSocket.

[0905] Step 3:

[0906] Input: Audio data sent to the server

[0907] Specific operation: The server uses the Python speech recognition library "SpeechRecognition" to convert audio data into text data.

[0908] Output: Converted text data

[0909] The server converts the received audio data into text data using the "SpeechRecognition" library. Specifically, it uses the command recognizer.recognize_google(audio_data).

[0910] Step 4:

[0911] Input: Converted text data

[0912] Specific operation: The server uses the NLP library "spaCy" to analyze text data and identify the contents of the meal.

[0913] Output: Extraction results of meal contents

[0914] The server analyzes the converted text data and extracts proper nouns (names of ingredients and dishes). Specifically, it extracts "katsu curry" from the text "Today's lunch is katsu curry."

[0915] Step 5:

[0916] Input: Extraction results of meal contents

[0917] Specific operation: The server retrieves calorie and nutrient data based on the extracted meal content from the nutrient database and calculates the calories and nutrients.

[0918] Output: Calculated calorie and nutrient data

[0919] The server retrieves data from a nutrition database (e.g., the USDA Food Database) based on the identified "katsu curry" and calculates the calories and nutrient content. This is done using the SQL query SELECT FROM food_database WHERE food_name = 'katsu curry'.

[0920] Step 6:

[0921] Input: Calculated calorie and nutrient data

[0922] Specific operation: The server records and analyzes the user's daily nutrient intake.

[0923] Output: Daily nutrient intake status and analysis results

[0924] Based on calculated calorie and nutrient data, the server records the user's daily nutrient intake and analyzes any nutrient deficiencies or excess calories by comparing it with past data.

[0925] Step 7:

[0926] Input: Daily nutrient intake status and its analysis results

[0927] Specific operation: The server generates suggestions for deficient nutrients and calories.

[0928] Output: Proposal

[0929] The server generates specific meal and supplement suggestions tailored to the user. For example, it might suggest, "Your current meal is high in fat, so choose a low-fat option for your next meal."

[0930] Step 8:

[0931] Input: User's voice data

[0932] Specific operation: The server uses Google's "Cloud Natural Language API" to recognize emotions from the user's voice and adjust the suggested content accordingly.

[0933] Output: Revised proposal

[0934] The server uses the command `response = client.analyze_sentiment(document)` to recognize emotions from the user's voice. For example, if the voice indicates that the user is "tired," it will also suggest foods that can help relieve stress.

[0935] Step 9:

[0936] Input: Adjusted proposal

[0937] Specific operation: The terminal visually presents the suggested content to the user using a suggested content display means.

[0938] Output: Suggestions displayed on the user's device

[0939] The device visually presents the suggested content, allowing the user to review it. It uses UI components (e.g., HTML, CSS, React.js) for display.

[0940] Step 10:

[0941] Input: Adjusted proposal

[0942] Specific operation: The server generates an online purchase link for the suggested product and provides it to the terminal.

[0943] Output: Online purchase link

[0944] The server generates online purchase links for the suggested meals and supplements and sends them to the device. This allows the user to access e-commerce platforms (e.g., Amazon API or Rakuten API) through the device and purchase the desired products.

[0945] The above outlines the processing flow of this system's program. Through these steps, users can easily record their meal content and receive appropriate suggestions based on nutritional balance and emotional state.

[0946] (Application Example 2)

[0947] 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."

[0948] In modern society, it is difficult for busy users to record their meals and manage their calorie and nutrient balance. Furthermore, there are few systems that can suggest appropriate meals and supplements based on the user's emotional state, making health management even more challenging. Additionally, the lack of specific suggestions for meals and supplements makes it difficult for users to purchase them directly online.

[0949] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the contents of a meal, means for calculating calories and nutrients based on the contents of the meal, means for recording and analyzing the daily nutrient intake, means for generating suggestions for deficient nutrients and calories, means for recognizing the user's emotions, means for adjusting the suggested content based on the recognized emotions, means for displaying the suggested content, and means for generating and providing online purchase links for the suggested products. As a result, the user can easily record the contents of their meal and emotions by voice input, receive specific suggestions that take into account the balance of calories and nutrients, and make immediate online purchases.

[0950] "Means for user voice input" refers to a device or interface for users to input details of their meals using voice.

[0951] "Means of converting input audio into text data" refers to technologies and algorithms for converting audio data into text data.

[0952] "Methods for analyzing text data to identify meal contents" refers to a process or system for analyzing converted text data and recognizing specific meal contents.

[0953] "Means for calculating calories and nutrients based on dietary content" refers to a method or apparatus for calculating calories and nutrients based on specified dietary content.

[0954] "Means for recording and analyzing daily nutrient intake" refers to technologies and devices that store data on the nutrients a user consumes in a day and analyze that data.

[0955] A "means for generating suggestions for deficient nutrients and calories" is a system that, based on recorded and analyzed nutrient data, suggests to the user how to supplement deficient nutrients and calories.

[0956] "Means of recognizing user emotions" refers to technologies and processes that identify a user's emotions and psychological state from voice and text data.

[0957] "Means of adjusting suggestions based on perceived emotions" refers to a system or method that takes the user's emotions into consideration and modifies the suggested meals or supplements accordingly.

[0958] "Means for displaying proposed content" refers to devices or interfaces used to visually present generated proposals to users.

[0959] "Means for generating and providing online purchase links for suggested products" refers to technologies and methods for generating and providing users with links to purchase suggested food products or supplements online.

[0960] This invention is a system that allows users to record their meal contents via voice input and manage the balance of calories and nutrients. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides suggestions tailored to the user's psychological state. This system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake status record analysis means, suggestion generation means, suggestion content display means, online purchase link generation means, and an emotion engine.

[0961] First, the user operates a device such as a smartphone, presses the voice input button on the app screen, and then dictates what they are eating. For example, "Today's lunch is katsu curry." This voice data is recorded directly on the device. The device then sends the recorded voice data to the server. The server uses the Google Speech-to-Text API to convert the received voice data into text data. Speech recognition technology is used to convert the voice into text with high accuracy.

[0962] Next, the server uses a meal content analysis tool to analyze the converted text data. This analysis identifies the contents of the meal. For example, it might reveal that "katsu curry" is included. The server then uses a calorie and nutrient calculation tool to calculate the calories and nutrients based on the identified meal. It retrieves nutrient data for each ingredient from the database and obtains detailed information such as "katsu curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[0963] Furthermore, the server uses a nutrient intake recording and analysis means to record and analyze the user's daily nutrient intake. This helps identify any nutrient deficiencies or excess calories. Based on the analysis results, the server uses a suggestion generation means to generate specific suggestions for meals and supplements suitable for the user. For example, it might suggest, "Your intake is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[0964] The server further uses an emotion engine to recognize emotions from the user's voice. Based on this emotion data, it can adjust the suggestions. For example, if the user's voice is detected as tired, a suggestion such as "Let's incorporate foods that help relieve stress. Herbal tea and dark chocolate are recommended" will be generated. The emotion engine also determines the user's stress level and suggests meals and supplements suitable for stress relief. The suggestions generated through this process are displayed to the user through the terminal. The terminal uses a suggestion display means to present the information visually in an easy-to-understand manner for the user. Furthermore, the server uses an online purchase link generation means to generate online purchase links for the suggested meals and supplements and provides them to the user. By clicking on these links, the user can directly purchase the necessary products from the online store.

[0965] As a concrete example, a user might voice-input, "I'm having katsu curry for lunch today," and the server would analyze and calculate the information to suggest a healthy balance. Furthermore, considering the user's emotions, if their stress level is high, the system would suggest relaxing foods or drinks. Additionally, a link to immediately purchase the suggested items online is provided, allowing users to easily obtain the products they need.

[0966] Example of a prompt:

[0967] A user voice-inputs "Today's lunch is katsu curry. I feel tired" into their smartphone. This voice data is converted to text, analyzed to identify the food items, and the calories and nutrients are calculated. The system also recognizes the user's emotions and suggests meals and supplements based on those emotions. An example of the results displayed is "Try some herbal tea or dark chocolate to help relieve stress. Purchase link: https: / / example.com / harb_tea".

[0968] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0969] Step 1:

[0970] The user operates a device such as a smartphone and presses the voice input button on the app screen to dictate the details of their meal. For example, they might say, "Today's lunch is katsu curry." The input data is voice data, and this voice data is recorded on the device.

[0971] Step 2:

[0972] The terminal sends the recorded audio data to the server. In this case, the input is audio data, and the data sent is also audio data.

[0973] Step 3:

[0974] The server uses a speech-to-text conversion tool (Google Speech-to-Text API) to convert the received audio data into text data. The input is audio data, and the output is text data. This conversion operation converts speech into text data with high accuracy.

[0975] Step 4:

[0976] The server analyzes text data to identify the contents of a meal. The meal content analysis tool extracts ingredient names and dish names from the text data and compares them with a database. The input is text data, and the output is the identified meal content (e.g., "Katsu Curry").

[0977] Step 5:

[0978] The server calculates calories and nutrients based on the specified meal. The calorie and nutrient calculation means retrieves nutrient data for each food item from the database and performs the calculation. The input is the specified meal, and the output is calorie and nutrient information (e.g., "Pork cutlet curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g").

[0979] Step 6:

[0980] The server records and analyzes the user's daily nutrient intake. The nutrient intake recording and analysis system calculates the nutrient balance based on the user's daily diet. The input is the user's daily diet and nutrient information, and the output is the intake balance.

[0981] Step 7:

[0982] The server generates suggestions for deficient nutrients and calories. The suggestion generation mechanism generates suggestions based on recorded and analyzed results. The input is information on nutrient intake balance, and the output is the suggested content (e.g., "Your intake is high in fat, so choose a low-fat option for your next meal").

[0983] Step 8:

[0984] The server uses an emotion engine to recognize emotions from the user's voice. The input is text data, and the output is the recognized emotion information (e.g., "tired").

[0985] Step 9:

[0986] The server adjusts its suggestions based on the recognized emotions. The input is emotion information and the suggested content, and the output is the adjusted suggested content (e.g., "Let's incorporate foods that help relieve stress").

[0987] Step 10:

[0988] The server displays the adjusted proposal to the user via the terminal. The proposal is presented visually to the user through the proposal display mechanism. The input is the adjusted proposal, and the output is the proposal displayed to the user.

[0989] Step 11:

[0990] The server generates and provides online purchase links for the suggested products. The online purchase link generation mechanism generates and provides purchase links for suggested foods and supplements to the user. The input is the suggested content, and the output is the online purchase link (e.g., "Purchase link: https: / / example.com / harb_tea").

[0991] 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.

[0992] 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.

[0993] 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.

[0994] [Fourth Embodiment]

[0995] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0996] 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.

[0997] 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).

[0998] 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.

[0999] 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.

[1000] 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).

[1001] 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.

[1002] 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.

[1003] 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.

[1004] 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.

[1005] 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.

[1006] 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.

[1007] 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".

[1008] This invention is a system for recording a user's meal content via voice input and managing the balance of calories and nutrients. The system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake status record analysis means, suggestion generation means, suggestion content display means, and online purchase link generation means.

[1009] First, the user operates the device and uses voice input to dictate what they are eating. For example, they might say, "Today's lunch is katsu curry." This voice data is then recorded directly on the device.

[1010] Next, the terminal sends the recorded audio data to the server. The server uses a speech-to-text conversion method to convert the received audio data into text data. By utilizing speech recognition technology, the audio can be converted into text with high accuracy.

[1011] Next, the server uses a meal content analysis tool to analyze the converted text data. To identify the contents of the meal, it extracts ingredient names and dish names from the text data and compares them with a database to identify matches. This analysis reveals, for example, that "katsu curry" is included.

[1012] The server then uses calorie and nutrient calculation tools to calculate calories and nutrients based on the identified meal. It retrieves nutrient data for each food item from the database and obtains detailed information such as "Pork cutlet curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[1013] Furthermore, the server uses a nutrient intake recording and analysis system to record and analyze the user's daily nutrient intake. This reveals any nutrient deficiencies or excess calories.

[1014] Next, the server uses a suggestion generation mechanism to provide specific suggestions for meals and supplements based on the analysis results. For example, it might generate a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended."

[1015] The generated proposals are displayed to the user via the terminal. The terminal uses a proposal display mechanism to present the proposals to the user in an easily understandable visual way.

[1016] Finally, the server uses an online purchase link generation mechanism to generate and provide the user with online purchase links for the suggested meals and supplements. By clicking on these links, the user can directly purchase the desired products from an online store (e.g., an e-commerce platform).

[1017] This process allows users to easily record their meals by voice and receive suggestions for nutritionally balanced meals and supplements. As a result, they can maintain a healthy diet. For example, a user might voice-input, "Today's lunch is katsu curry," and the server would analyze and calculate that information, suggest a suitable meal balance, and then purchase the necessary food items online.

[1018] The following describes the processing flow.

[1019] Step 1: Voice Input

[1020] The user uses their device and presses the voice input button on the app screen.

[1021] The device records the user's voice. For example, the user might say, "I'm having katsu curry for lunch today."

[1022] Step 2: Sending the audio data

[1023] The device sends the recorded audio data to the server. This data is then transferred directly to the server.

[1024] Step 3: Speech-to-text conversion

[1025] The server converts the received audio data into text data using a speech recognition engine. For example, text such as "Today's lunch is katsu curry" is generated.

[1026] Step 4: Analysis of Dietary Content

[1027] The server analyzes the converted text data. The server uses an analysis algorithm to identify the food item, such as "katsu curry," from the text.

[1028] Step 5: Calculating calories and nutrients

[1029] The server retrieves nutrient data from a database based on the identified meal. For example, it calculates the calories and nutrients of "katsu curry" (e.g., 900kcal, 40g fat, 100g carbohydrates, 25g protein).

[1030] Step 6: Recording and analyzing nutrient intake

[1031] The server records and analyzes the user's daily nutrient intake. This helps identify any nutrient deficiencies or excess calories.

[1032] Step 7: Proposal Generation

[1033] Based on the analysis results, the server generates specific suggestions for meals and supplements that are suitable for the user. For example, it might suggest, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended."

[1034] Step 8: Display the proposed content

[1035] The server sends the generated proposal to the terminal. The terminal displays the proposal on its screen, allowing the user to visually confirm it.

[1036] Step 9: Generate online purchase link

[1037] The server generates online purchase links for the suggested meals and supplements and sends them to the device. The user can then click the displayed link to directly purchase the products from the online store.

[1038] This allows users to effectively record their meals using only voice input and receive suggestions for meals and supplements to maintain proper nutritional balance. As a result, it becomes possible to support them in maintaining a healthy lifestyle.

[1039] (Example 1)

[1040] 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".

[1041] In today's busy lifestyle, it is difficult for users to effectively manage their diet and maintain a healthy eating habit. Traditional methods involve manual recording of meals and calculation of nutrients, which is time-consuming, cumbersome, and prone to errors. Furthermore, systems for analyzing meals and suggesting nutrients are limited, and the accuracy of converting voice-based input to text data is particularly low. Therefore, there is a need for a reliable system that is easy for users to continue using.

[1042] 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.

[1043] In this invention, the server includes means for accurately converting voice data into text using a voice recognition service, means for extracting ingredient names and dish names from the text data using natural language processing technology, and means for identifying meal contents by comparing them with a database. This allows users to easily input voice information, convert the content into text data, analyze the meal contents to calculate calories and nutrients, and receive suggestions for appropriate meals and supplements. By providing online purchase links, the system provides an environment where users can immediately purchase the suggested ingredients and supplements, supporting comprehensive health management.

[1044] A "user" refers to an individual who uses voice input to record their meal details and manage their nutrition.

[1045] "Voice input means" refers to a device or application for a user to input voice.

[1046] "Text data" refers to a data format obtained by converting speech input via voice input devices into text information.

[1047] "Speech recognition service" refers to a technology or platform for converting speech into text data.

[1048] "Natural language processing technology" refers to computer technology used to analyze text data and extract and identify information.

[1049] "Meal details" refers to information consisting of the ingredients and names of dishes consumed by the user.

[1050] "Calories and nutrients" refers to the amount of energy and various nutrients contained in a meal.

[1051] A "database" refers to an information system that stores nutritional data for ingredients and dishes.

[1052] "Method for recording and analyzing nutrient intake" refers to technology for recording a user's daily nutrient intake and analyzing their health status.

[1053] "Suggestion generation method" refers to technology that creates appropriate dietary and supplement suggestions for users based on analysis results.

[1054] "Proposal content display means" refers to a device or application for visually displaying the generated proposal to the user.

[1055] "Online purchase link generation method" refers to technology that generates links allowing users to purchase suggested food products or supplements online.

[1056] "API" is an abbreviation for Application Programming Interface, and refers to a means of exchanging data between different software systems.

[1057] "Past meal history" refers to a record of meals the user has consumed in the past.

[1058] This invention is a system for recording a user's meal content via voice input and managing the balance of calories and nutrients. The system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake recording and analysis means, suggestion generation means, suggestion content display means, and online purchase link generation means. Specific usage examples and operational details are as follows.

[1059] Users operate devices such as smartphones and tablets and use voice input to dictate what they are eating. For example, a user might say, "Today's lunch is katsu curry." This voice data is recorded directly on the device. The device has an internet connection and can send the recorded voice data to a server.

[1060] The server converts the received audio data into text data using a speech-to-text conversion method. Here, speech recognition services such as Google Cloud Speech-to-Text or IBM Watson are used. This ensures that the audio is transcribed into text with high accuracy.

[1061] Next, the server uses natural language processing techniques to analyze the text data and identify the contents of the meal. Specifically, it extracts ingredient names and dish names from the text data and compares them with a database to identify matches. This analysis reveals, for example, that "katsu curry" is included. Libraries such as Python's NLTK and SpaCy are used here.

[1062] The server retrieves nutritional data for each food item from the database and calculates calories and nutrients based on the meal. The database contains nutritional data for each food item and can use SQL or NoSQL databases. As a result, the user's meal is output as detailed data such as "Pork cutlet curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g".

[1063] Furthermore, the server uses nutrient intake recording and analysis tools to record and analyze the user's daily nutrient intake. This reveals any nutrient deficiencies or excess calories. Data analysis libraries such as Pandas and NumPy are used for the analysis.

[1064] Next, the server uses a suggestion generation mechanism to provide specific meal and supplement recommendations based on the analysis results. For example, it might generate a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended." The generated suggestions are then displayed to the user via the terminal. The terminal uses a suggestion display mechanism to present the suggestions visually in an easy-to-understand manner for the user.

[1065] Finally, the server uses an online purchase link generation mechanism to generate online purchase links for the suggested meals and supplements and provides them to the user. By clicking on these links, the user can directly purchase the necessary products from the online store. APIs such as the Amazon API and Rakuten API are used here.

[1066] For example, if a user voice-inputs "Today's lunch is katsu curry," the server analyzes and calculates that information, suggests a suitable meal balance, and then proceeds to purchase the necessary food items online.

[1067] Examples of prompts for a generative AI model are as follows:

[1068] "Design a system that transcribes user-inputted meal details into text, calculates calories and nutrients, and suggests the next meal."

[1069] Thus, this invention provides support for users to easily record their meal content and maintain a healthy diet.

[1070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1071] Step 1:

[1072] The user dictates the details of their meal to the device using voice input. For example, the user might say, "Today's lunch is katsu curry." The recorded voice data is stored on the device. Specifically, the user launches the app on their smartphone, presses the voice recording button, and begins speaking.

[1073] Step 2:

[1074] The device sends the recorded audio data to the server. The input is the recorded audio data, and the output is the audio data sent to the server. Specifically, the device uploads the audio data to the cloud server using Wi-Fi or mobile data.

[1075] Step 3:

[1076] The server converts received audio data into text data using a speech-to-text conversion method. The input is the received audio data, and the output is the converted text data. The server uses a speech recognition service (e.g., Google Cloud Speech-to-Text) to generate the text "Today's lunch is katsu curry."

[1077] Step 4:

[1078] The server uses natural language processing technology to analyze the converted text data and identify the contents of the meal. The input is the converted text data, and the output is the identified meal contents. Specifically, the server uses the SpaCy library to extract the ingredient name "katsu curry" and compares it with the database to identify the match.

[1079] Step 5:

[1080] The server retrieves nutritional data for each food item from the database and calculates calories and nutrients based on the identified meal. The input is the identified meal, and the output is calorie and nutrient data. Specifically, the server retrieves information such as "Pork cutlet curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g" from the database and generates the calculation result.

[1081] Step 6:

[1082] The server uses nutrient intake recording and analysis tools to record and analyze the user's daily nutrient intake. The input is multiple meal records over the day, and the output is the analysis results. Specifically, the server uses Pandas and NumPy libraries to accumulate the user's nutrient intake history and analyze daily deficiencies and excesses.

[1083] Step 7:

[1084] The server uses a suggestion generation mechanism to generate specific meal and supplement suggestions based on the analysis results. The input is the nutrient analysis results, and the output is the specific suggestions. For example, the server generates a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. For example, somen noodles or chilled tofu are recommended."

[1085] Step 8:

[1086] The terminal uses a suggestion display mechanism to visually display the generated suggestions to the user. The input is the suggestion content sent from the server, and the output is the suggestion displayed on the terminal screen. Specifically, a smartphone application displays the suggestion message on the user's screen.

[1087] Step 9:

[1088] The server uses an online purchase link generation mechanism to generate online purchase links for suggested meals and supplements and provides them to the user. The input is the suggested content, and the output is the associated online purchase links. Specifically, the server uses the Amazon API and Rakuten API to generate links to purchase items such as "somen" noodles and "hiyayakko" (cold tofu) and provides them to the user.

[1089] This process allows users to easily record their meal details by voice and receive suggestions for nutritionally balanced meals and supplements.

[1090] (Application Example 1)

[1091] 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".

[1092] Traditional meal management systems required users to meticulously record their meals and manage calorie and nutrient balance, and lacked a means to obtain appropriate meal suggestions. Furthermore, ordering meals based on these suggestions often required using a separate online store, which was inconvenient for users. Additionally, the lack of systems offering nutritionally balanced delivery menu suggestions and purchase links made it difficult for users to make healthy choices smoothly.

[1093] 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.

[1094] In this invention, the server includes means for the user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the contents of a meal, means for calculating calories and nutrients based on the contents of the meal, means for recording and analyzing the daily nutrient intake, means for generating suggestions for deficient nutrients and calories, means for displaying the suggestions, means for generating and providing online purchase links for the suggested products, means for suggesting the optimal delivery menu based on the user's meal history, and means for generating and providing online purchase links for the suggested delivery menu. This allows the user to easily record their meal contents by voice input and receive suggestions for healthy meal choices and delivery menus. Furthermore, since suggested meals and delivery menus can be purchased smoothly online, user convenience is improved, and it becomes possible to maintain a healthy diet.

[1095] A "user" refers to an individual who uses this system to manage their diet and receive suggestions and online purchase links.

[1096] "Means of voice input" refers to microphones and other voice input devices that allow users to communicate their meal details to the system by voice.

[1097] "Means of converting speech to text data" refers to speech recognition software or algorithms that replace input speech with textual information.

[1098] "Means of analyzing text data to identify meal contents" refers to text analysis algorithms or databases that analyze converted text data and specifically identify the meal contents described.

[1099] "Means for calculating calories and nutrients based on dietary content" refers to a calculation algorithm or nutritional database for calculating the calories and nutrients contained in a specific diet.

[1100] "Means for recording and analyzing daily nutrient intake" refers to data recording and analysis tools that accumulate data on the nutrients a user consumes in a day and evaluate their intake status based on past data.

[1101] "Means for generating suggestions for deficient nutrients and calories" refers to an algorithm that creates suggestions for nutrients and calories to be supplemented based on the user's nutrient and calorie intake balance.

[1102] "Means for displaying the proposed content" refers to displays or screens used to visually show the generated proposals to the user.

[1103] "Means for generating and providing online purchase links for proposed products" refers to programs and communication means for creating and providing links that enable online purchase of products based on the proposal.

[1104] "A means of suggesting the optimal delivery menu based on the user's meal history" refers to an algorithm that selects and suggests a delivery menu suitable for the user based on accumulated meal history data.

[1105] "Means for generating and providing online purchase links for proposed delivery menus" refers to a program that generates and provides to users a link that allows them to purchase proposed delivery menus online.

[1106] This invention is a system that takes the user's meal details as voice input, manages the balance of calories and nutrients, and suggests the optimal delivery menu. The following describes how this system is implemented in detail.

[1107] First, the user operates their smartphone and uses voice input to dictate what they are eating. For example, they might say, "Today's lunch is katsu curry." The voice data is recorded on the device. Next, the device sends the recorded voice data to a server. The server uses speech recognition software (for example, Google Speech-to-Text API) to convert the received voice data into text data. Speech recognition technology allows for highly accurate transcription of speech into text.

[1108] Next, the server analyzes the text data and uses a text analysis algorithm to identify the specific contents of the meal. During this process, it compares the data with a database to identify ingredient names and dish names. For example, it might reveal that "katsu curry" is included. The server then uses a calorie and nutrient calculation algorithm to calculate the calories and nutrients based on the identified meal. Based on the nutrient data for each ingredient obtained from the database, it obtains detailed information such as "katsu curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[1109] Furthermore, the server uses a nutrient intake recording and analysis tool to record and analyze the user's daily nutrient intake. This reveals any nutrient deficiencies or excess calories. Next, the server uses a suggestion generation algorithm to suggest healthy meals and supplements to the user. For example, it might generate a suggestion such as, "Your diet is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[1110] Furthermore, the service will suggest convenient food delivery menus for users. To do this, it will identify the most suitable delivery menu based on the user's past meal history data and generate online purchase links. The suggested delivery menus will include items such as "healthy salad" and "grilled chicken," and each menu item will have a corresponding online purchase link. By clicking on these links, users can directly purchase the desired items from the online store.

[1111] This system allows users to easily record their meals using voice input and receive suggestions for healthy meal choices and delivery menus. Furthermore, suggested meals and delivery menus can be smoothly purchased online, improving user convenience and enabling them to maintain a healthy diet.

[1112] As a concrete example, if a user voice-inputs "Today's lunch is katsu curry," the server analyzes and calculates that information, suggests a suitable meal balance, and generates links to purchase the necessary foods online. This system utilizes a smartphone, a server, voice recognition software, a text analysis algorithm, a nutrient database, a nutrient record analysis tool, and a suggestion generation algorithm.

[1113] Examples of prompts for generative AI models:

[1114] Create a Python program that analyzes meal information entered by the user via voice input, calculates calories and nutrients, and suggests healthy meals and delivery menus. Also, describe the overview and processing flow of the API server used in the program.

[1115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1116] Step 1:

[1117] The user operates a smartphone and uses voice input via the microphone. For example, they might say, "Today's lunch is katsu curry." The voice data is recorded on the device.

[1118] Input: User's voice data

[1119] Output: Recorded audio data

[1120] Step 2:

[1121] The device sends the recorded audio data to the server. The server uses speech recognition software (for example, Google Speech-to-Text API) to convert the transmitted audio data into text data.

[1122] Input: Audio data

[1123] Output: Converted text data

[1124] Step 3:

[1125] The server uses a text analysis algorithm to analyze text data. During analysis, it compares the data with a database to identify the contents of the meal. For example, if the text data is "Today's lunch is katsu curry," then "katsu curry" will be identified.

[1126] Input: Text data

[1127] Output: Identified dietary content

[1128] Step 4:

[1129] The server uses an algorithm to calculate calories and nutrients based on the meal content. It retrieves the components contained in the identified meal from a database and performs the calculation. For example, in the case of "katsu curry," it calculates specific data such as "900kcal, fat: 40g, carbohydrates: 100g, protein: 25g."

[1130] Input: Identified meal details

[1131] Output: Calorie and nutrient data

[1132] Step 5:

[1133] The server uses a nutrient intake recording and analysis tool to record and analyze the user's daily nutrient intake. This data clearly identifies any nutrient deficiencies or excess calories.

[1134] Input: User's daily meal data, past meal history data

[1135] Output: Nutrient intake status and analysis results

[1136] Step 6:

[1137] The server uses a suggestion generation algorithm to generate meal and supplement recommendations based on deficient nutrients and calories. For example, it might generate specific suggestions such as, "Your diet is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[1138] Input: Nutrient intake status and analysis results

[1139] Output: Proposal

[1140] Step 7:

[1141] The server uses an algorithm to suggest the most suitable delivery menu based on the user's meal history, generating delivery menus and their online purchase links. For example, it might generate delivery menus such as "Healthy Salad" and "Grilled Chicken," along with their purchase links.

[1142] Input: User's past meal history data, suggested content

[1143] Output: Delivery menu and online purchase link

[1144] Step 8:

[1145] The terminal displays suggested meal options, delivery menus, and online purchase links to the user. By clicking the links, the user can directly purchase the desired items from the online store.

[1146] Input: Proposal details, delivery menu, online purchase link

[1147] Output: Proposal content and links displayed to the user, and purchase procedure.

[1148] This concludes the detailed explanation of the processing flow of this system.

[1149] 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.

[1150] This invention is a system that records the user's meal content via voice input and manages the balance of calories and nutrients. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it can provide suggestions tailored to the user's psychological state. This system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake status record analysis means, suggestion generation means, suggestion content display means, online purchase link generation means, and an emotion engine.

[1151] First, the user operates the device, presses the voice input button on the app screen, and then dictates what they are eating. For example, "Today's lunch is katsu curry." This voice data is recorded directly on the device.

[1152] Next, the terminal sends the recorded audio data to the server. The server uses a speech-to-text conversion method to convert the received audio data into text data. Speech recognition technology is used to convert the audio into text with high accuracy.

[1153] Next, the server uses a meal content analysis tool to analyze the converted text data. To identify the contents of the meal, it extracts ingredient names and dish names from the text data and compares them with a database to identify matches. This analysis reveals, for example, that "katsu curry" is included.

[1154] The server then uses calorie and nutrient calculation tools to calculate calories and nutrients based on the identified meal. It retrieves nutrient data for each food item from the database and obtains detailed information such as "Pork cutlet curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[1155] Furthermore, the server uses a nutrient intake recording and analysis means to record and analyze the user's daily nutrient intake. This helps identify any nutrient deficiencies or excess calories. Based on the analysis results, the server uses a suggestion generation means to generate specific suggestions for meals and supplements suitable for the user. For example, it might suggest, "Your intake is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[1156] One of the key features of this invention is the emotion engine. The server recognizes emotions from the user's voice and can adjust the suggestions based on that emotion data. For example, if the user's voice is detected as indicating fatigue, a suggestion such as, "Let's incorporate foods that help relieve stress. Herbal tea and dark chocolate are recommended," will be generated. The emotion engine can also determine the user's stress level and suggest meals or supplements suitable for stress relief.

[1157] The generated suggestions are displayed to the user via the terminal. The terminal uses a suggestion display mechanism to present the suggestions visually in an easy-to-understand manner for the user. Furthermore, the server uses an online purchase link generation mechanism to generate online purchase links for the suggested meals and supplements and provides them to the user. By clicking on these links, the user can directly purchase the necessary products from an online store (e.g., an e-commerce platform).

[1158] This process allows users to easily record their meals by voice, receive suggestions for nutritionally balanced meals, and get recommendations for supplements tailored to their emotional state. For example, a user might voice-input, "Today's lunch is katsu curry," and the server would analyze and calculate the information, suggest a suitable meal balance, and then purchase the necessary food items online. If the system detects that the user is feeling tired, it will also suggest meals suitable for stress relief.

[1159] The following describes the processing flow.

[1160] Step 1: Voice Input

[1161] The user operates the device and presses the voice input button on the app screen. The user says, "Today's lunch is katsu curry."

[1162] Step 2: Sending the audio data

[1163] The terminal sends the recorded audio data to the server using the voice input method. The terminal temporarily stores the audio data in a buffer and then transfers the data to the server via the internet.

[1164] Step 3: Speech-to-text conversion

[1165] The server that receives the audio data activates its speech recognition engine and converts the audio data into text data. The text "Today's lunch is katsu curry" is generated.

[1166] Step 4: Analysis of Dietary Content

[1167] The server uses text data analysis tools to analyze the converted text data. For example, it identifies the keyword "katsu curry" and compares it with a food database to identify the ingredients.

[1168] Step 5: Emotion Recognition

[1169] The server activates an emotion engine and recognizes the user's emotions from the voice data. It analyzes the tone, speed, and word choice of the voice to determine if the user is tired.

[1170] Step 6: Calculating calories and nutrients

[1171] The server uses a calorie and nutrient calculation method based on the meal content to calculate the calories and nutrients of "Katsu Curry" (e.g., 900kcal, 40g fat, 100g carbohydrates, 25g protein). This data is obtained from a meal database.

[1172] Step 7: Recording and analyzing nutrient intake

[1173] The server records and analyzes the user's daily nutrient intake. It compares past meal history with newly entered data to determine which nutrients are deficient and which are in excess.

[1174] Step 8: Proposal Generation

[1175] The server uses a suggestion generation mechanism to take into account multiple factors (nutritional status and emotional state) and suggest meals and supplements suitable for the user. For example, it generates specific suggestions such as, "Your diet is high in fat, so for your next meal, choose low-fat options like somen noodles or cold tofu. Also, you seem tired, so a relaxing herbal tea is recommended."

[1176] Step 9: Display the proposed content

[1177] The server sends the generated proposal content to the terminal. The terminal uses a proposal content display mechanism to display the proposal content on the screen in a visually easy-to-understand manner for the user.

[1178] Step 10: Generate online purchase link

[1179] The server uses an online purchase link generation mechanism to generate online purchase links for the suggested food items and supplements. These links are sent to the device, and when the user clicks them, they are directed to the purchase page on the e-commerce platform.

[1180] This process allows users to easily record their meals using voice input and receive suggestions for nutritionally balanced meals and suggestions based on their emotional state. For example, if a user voice-inputs, "Today's lunch is katsu curry," the server analyzes and calculates that information to suggest a balanced meal that takes into account the user's fatigue level and helps them purchase necessary food items online.

[1181] (Example 2)

[1182] 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".

[1183] In modern society, users are often pressed for time, making it difficult to choose nutritionally balanced meals or select foods and supplements that suit their emotional state. Therefore, there is a need for a system that allows users to easily record their meals, consider nutritional balance, and receive suggestions tailored to their emotional state.

[1184] 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.

[1185] In this invention, the server includes means for the user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the contents of a meal, means for calculating calories and nutrients based on the contents of the meal, means for recording and analyzing the daily nutrient intake, means for generating suggestions for deficient nutrients and calories, means for recognizing emotions from the user's voice and adjusting the suggestions, means for displaying the suggestions, and means for generating and providing online purchase links for the suggested products. As a result, the user can easily record their meal contents using only voice input, receive appropriate suggestions that take nutritional balance into consideration, and receive suggestions tailored to their emotional state.

[1186] A "user" refers to a person who uses the system.

[1187] "Voice input means" refers to a device or software that records the user's voice.

[1188] "Speech-to-text conversion means" refers to a device or software that converts input speech data into text data.

[1189] "Meal content analysis means" refers to a device or software that analyzes text data to identify the specific contents of a meal.

[1190] "Calorie and nutrient calculation means" refers to a device or software that calculates calories and nutrients based on specified dietary content.

[1191] "Nutrient intake status recording and analysis means" refers to a device or software that records and analyzes a user's daily nutrient intake status.

[1192] "Suggestion generation means" refers to a device or software that generates suggestions tailored to the user based on the nutrients and calories the user is lacking.

[1193] "Emotion recognition means" refers to a device or software that recognizes emotions from the user's voice and adjusts the suggested content accordingly.

[1194] "Proposal content display means" refers to a device or software that visually presents the generated proposal content to the user.

[1195] "Online purchase link generation and provision means" refers to a device or software that generates an online purchase link for a proposed product and provides it to the user.

[1196] This invention is a system that records the user's meal content via voice input and manages the balance of calories and nutrients. Furthermore, by combining it with an emotion engine that recognizes the user's emotional state, the system can provide suggestions tailored to the user's psychological state. This system has the following functions:

[1197] Hardware and software to be used

[1198] Voice input methods: Input devices such as smartphones and tablets

[1199] Speech-to-text conversion method: Python speech recognition library "SpeechRecognition"

[1200] Method for analyzing meal content: Natural language processing library "spaCy"

[1201] Calorie and nutrient calculation methods: Nutrient databases (e.g., USDA Food Database)

[1202] Nutrient intake record and analysis tools: Database and statistical analysis software

[1203] Proposal generation method: Computational algorithm using a programming language such as Python

[1204] Emotion recognition method: Google's "Cloud Natural Language API"

[1205] Proposed content display method: Smartphone and tablet display screen (UI design)

[1206] Online purchase link generation and provision method: E-commerce platform API (e.g., Amazon API)

[1207] Specific functions of the system

[1208] First, the user presses the voice input button on the app screen of their device, and then dictates what they are eating. For example, they might say, "Today's lunch is katsu curry." This voice data is then recorded on the device.

[1209] The terminal sends the recorded audio data to the server. The server uses the Python speech recognition library "SpeechRecognition" to convert the received audio data into text data. Specifically, the command used is recognizer.recognize_google(audio_data).

[1210] Next, the server uses the natural language processing library "spaCy" to analyze the converted text data. Specifically, it extracts proper nouns (names of ingredients and dishes) from the text data. For example, it extracts "katsu curry" from the text data "Today's lunch is katsu curry."

[1211] Subsequently, the server retrieves nutrient data from a nutrient database (e.g., USDA Food Database) based on the identified meal (e.g., "Katsu Curry"). This allows the server to obtain specific calorie and nutrient information such as "Katsu Curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g". In this case, queries to the database are often performed using SQL (e.g., SELECT FROM food_database WHERE food_name = 'Katsu Curry').

[1212] Furthermore, the server records and analyzes the user's daily nutrient intake. Based on this analysis, it can identify any nutrient deficiencies or excess calories. Using this data, the server generates specific meal and supplement suggestions tailored to the user. For example, it might suggest, "Your intake is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[1213] The system uses Google's Cloud Natural Language API for emotion recognition. The server uses commands like `response = client.analyze_sentiment(document)` to recognize emotions from the user's voice. For example, if the voice indicates "tired," the suggestions are adjusted to better suit the user's mental state. Suggestions such as, "Let's incorporate foods that help relieve stress. Herbal tea and dark chocolate are recommended," might be added.

[1214] Furthermore, the generated suggestions are visually presented to the user through their device. Users are also provided with links to purchase the suggested meals and supplements online. These links are generated using the APIs of e-commerce platforms (e.g., Amazon API or Rakuten API).

[1215] Examples of specific cases and prompt statements

[1216] As a concrete example, a user might voice-input "Today's lunch is katsu curry," and the server would analyze and calculate the information to suggest a suitable meal balance, providing links to purchase the necessary food items online. Furthermore, if the system detects that the user is feeling tired, it would also suggest meals suitable for stress relief.

[1217] Example of a prompt:

[1218] The user voice-inputted, "Today's lunch is katsu curry." This data was sent to the server and converted to text using speech recognition technology. Next, a meal content analysis tool identified "katsu curry," and the calories and nutrient content were calculated based on the nutritional data of each ingredient. Based on these results, the balance of calories and nutrients was analyzed, and meal suggestions suitable for the user were generated. Furthermore, because the emotion engine recognized the user's fatigue level, suggestions for foods that could help alleviate stress were also added. The user can review these suggestions on their device and receive links to purchase the necessary food items from an online store.

[1219] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1220] Step 1:

[1221] Input: User voice input

[1222] Specific steps: The user presses the voice input button on the app screen of their device and dictates the details of their meal.

[1223] Output: Recorded audio data

[1224] The user presses the voice input button on the app screen of their device and says, "Today's lunch is katsu curry." This voice data is recorded on the device.

[1225] Step 2:

[1226] Input: Recorded audio data

[1227] Specific operation: The device sends the recorded audio data to the server.

[1228] Output: Audio data sent to the server

[1229] The device sends the recorded audio data to the server. This data is transmitted using HTTP or WebSocket.

[1230] Step 3:

[1231] Input: Audio data sent to the server

[1232] Specific operation: The server uses the Python speech recognition library "SpeechRecognition" to convert audio data into text data.

[1233] Output: Converted text data

[1234] The server converts the received audio data into text data using the "SpeechRecognition" library. Specifically, it uses the command recognizer.recognize_google(audio_data).

[1235] Step 4:

[1236] Input: Converted text data

[1237] Specific operation: The server uses the NLP library "spaCy" to analyze text data and identify the contents of the meal.

[1238] Output: Extraction results of meal contents

[1239] The server analyzes the converted text data and extracts proper nouns (names of ingredients and dishes). Specifically, it extracts "katsu curry" from the text "Today's lunch is katsu curry."

[1240] Step 5:

[1241] Input: Extraction results of meal contents

[1242] Specific operation: The server retrieves calorie and nutrient data based on the extracted meal content from the nutrient database and calculates the calories and nutrients.

[1243] Output: Calculated calorie and nutrient data

[1244] The server retrieves data from a nutrition database (e.g., the USDA Food Database) based on the identified "katsu curry" and calculates the calories and nutrient content. This is done using the SQL query SELECT FROM food_database WHERE food_name = 'katsu curry'.

[1245] Step 6:

[1246] Input: Calculated calorie and nutrient data

[1247] Specific operation: The server records and analyzes the user's daily nutrient intake.

[1248] Output: Daily nutrient intake status and analysis results

[1249] Based on calculated calorie and nutrient data, the server records the user's daily nutrient intake and analyzes any nutrient deficiencies or excess calories by comparing it with past data.

[1250] Step 7:

[1251] Input: Daily nutrient intake status and its analysis results

[1252] Specific operation: The server generates suggestions for deficient nutrients and calories.

[1253] Output: Proposal

[1254] The server generates specific meal and supplement suggestions tailored to the user. For example, it might suggest, "Your current meal is high in fat, so choose a low-fat option for your next meal."

[1255] Step 8:

[1256] Input: User's voice data

[1257] Specific operation: The server uses Google's "Cloud Natural Language API" to recognize emotions from the user's voice and adjust the suggested content accordingly.

[1258] Output: Revised proposal

[1259] The server uses the command `response = client.analyze_sentiment(document)` to recognize emotions from the user's voice. For example, if the voice indicates that the user is "tired," it will also suggest foods that can help relieve stress.

[1260] Step 9:

[1261] Input: Adjusted proposal

[1262] Specific operation: The terminal visually presents the suggested content to the user using a suggested content display means.

[1263] Output: Suggestions displayed on the user's device

[1264] The device visually presents the suggested content, allowing the user to review it. It uses UI components (e.g., HTML, CSS, React.js) for display.

[1265] Step 10:

[1266] Input: Adjusted proposal

[1267] Specific operation: The server generates an online purchase link for the suggested product and provides it to the terminal.

[1268] Output: Online purchase link

[1269] The server generates online purchase links for the suggested meals and supplements and sends them to the device. This allows the user to access e-commerce platforms (e.g., Amazon API or Rakuten API) through the device and purchase the desired products.

[1270] The above outlines the processing flow of this system's program. Through these steps, users can easily record their meal content and receive appropriate suggestions based on nutritional balance and emotional state.

[1271] (Application Example 2)

[1272] 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".

[1273] In modern society, it is difficult for busy users to record their meals and manage their calorie and nutrient balance. Furthermore, there are few systems that can suggest appropriate meals and supplements based on the user's emotional state, making health management even more challenging. Additionally, the lack of specific suggestions for meals and supplements makes it difficult for users to purchase them directly online.

[1274] In Application Example 2, the identification processing by the identification processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes means for the user to input voice, means for converting the input voice into text data, means for analyzing the text data to identify the contents of a meal, means for calculating calories and nutrients based on the contents of the meal, means for recording and analyzing the daily nutrient intake, means for generating suggestions for deficient nutrients and calories, means for recognizing the user's emotions, means for adjusting the suggested content based on the recognized emotions, means for displaying the suggested content, and means for generating and providing online purchase links for the suggested products. As a result, the user can easily record the contents of their meal and emotions by voice input, receive specific suggestions that take into account the balance of calories and nutrients, and make immediate online purchases.

[1275] "Means for user voice input" refers to a device or interface for users to input details of their meals using voice.

[1276] "Means of converting input audio into text data" refers to technologies and algorithms for converting audio data into text data.

[1277] "Methods for analyzing text data to identify meal contents" refers to a process or system for analyzing converted text data and recognizing specific meal contents.

[1278] "Means for calculating calories and nutrients based on dietary content" refers to a method or apparatus for calculating calories and nutrients based on specified dietary content.

[1279] "Means for recording and analyzing daily nutrient intake" refers to technologies and devices that store data on the nutrients a user consumes in a day and analyze that data.

[1280] A "means for generating suggestions for deficient nutrients and calories" is a system that, based on recorded and analyzed nutrient data, suggests to the user how to supplement deficient nutrients and calories.

[1281] "Means of recognizing user emotions" refers to technologies and processes that identify a user's emotions and psychological state from voice and text data.

[1282] "Means of adjusting suggestions based on perceived emotions" refers to a system or method that takes the user's emotions into consideration and modifies the suggested meals or supplements accordingly.

[1283] "Means for displaying proposed content" refers to devices or interfaces used to visually present generated proposals to users.

[1284] "Means for generating and providing online purchase links for suggested products" refers to technologies and methods for generating and providing users with links to purchase suggested food products or supplements online.

[1285] This invention is a system that allows users to record their meal contents via voice input and manage the balance of calories and nutrients. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it provides suggestions tailored to the user's psychological state. This system includes voice input means, voice-to-text conversion means, meal content analysis means, calorie and nutrient calculation means, nutrient intake status record analysis means, suggestion generation means, suggestion content display means, online purchase link generation means, and an emotion engine.

[1286] First, the user operates a device such as a smartphone, presses the voice input button on the app screen, and then dictates what they are eating. For example, "Today's lunch is katsu curry." This voice data is recorded directly on the device. The device then sends the recorded voice data to the server. The server uses the Google Speech-to-Text API to convert the received voice data into text data. Speech recognition technology is used to convert the voice into text with high accuracy.

[1287] Next, the server uses a meal content analysis tool to analyze the converted text data. This analysis identifies the contents of the meal. For example, it might reveal that "katsu curry" is included. The server then uses a calorie and nutrient calculation tool to calculate the calories and nutrients based on the identified meal. It retrieves nutrient data for each ingredient from the database and obtains detailed information such as "katsu curry: 900kcal, fat: 40g, carbohydrates: 100g, protein: 25g".

[1288] Furthermore, the server uses a nutrient intake recording and analysis means to record and analyze the user's daily nutrient intake. This helps identify any nutrient deficiencies or excess calories. Based on the analysis results, the server uses a suggestion generation means to generate specific suggestions for meals and supplements suitable for the user. For example, it might suggest, "Your intake is high in fat, so choose a low-fat option for your next meal. Somen noodles or chilled tofu are good choices."

[1289] The server further uses an emotion engine to recognize emotions from the user's voice. Based on this emotion data, it can adjust the suggestions. For example, if the user's voice is detected as tired, a suggestion such as "Let's incorporate foods that help relieve stress. Herbal tea and dark chocolate are recommended" will be generated. The emotion engine also determines the user's stress level and suggests meals and supplements suitable for stress relief. The suggestions generated through this process are displayed to the user through the terminal. The terminal uses a suggestion display means to present the information visually in an easy-to-understand manner for the user. Furthermore, the server uses an online purchase link generation means to generate online purchase links for the suggested meals and supplements and provides them to the user. By clicking on these links, the user can directly purchase the necessary products from the online store.

[1290] As a concrete example, a user might voice-input, "I'm having katsu curry for lunch today," and the server would analyze and calculate the information to suggest a healthy balance. Furthermore, considering the user's emotions, if their stress level is high, the system would suggest relaxing foods or drinks. Additionally, a link to immediately purchase the suggested items online is provided, allowing users to easily obtain the products they need.

[1291] Example of a prompt:

[1292] A user voice-inputs "Today's lunch is katsu curry. I feel tired" into their smartphone. This voice data is converted to text, analyzed to identify the food items, and the calories and nutrients are calculated. The system also recognizes the user's emotions and suggests meals and supplements based on those emotions. An example of the results displayed is "Try some herbal tea or dark chocolate to help relieve stress. Purchase link: https: / / example.com / harb_tea".

[1293] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1294] Step 1:

[1295] The user operates a device such as a smartphone and presses the voice input button on the app screen to dictate the details of their meal. For example, they might say, "Today's lunch is katsu curry." The input data is voice data, and this voice data is recorded on the device.

[1296] Step 2:

[1297] The terminal sends the recorded audio data to the server. In this case, the input is audio data, and the data sent is also audio data.

[1298] Step 3:

[1299] The server uses a speech-to-text conversion tool (Google Speech-to-Text API) to convert the received audio data into text data. The input is audio data, and the output is text data. This conversion operation converts speech into text data with high accuracy.

[1300] Step 4:

[1301] The server analyzes text data to identify the contents of a meal. The meal content analysis tool extracts ingredient names and dish names from the text data and compares them with a database. The input is text data, and the output is the identified meal content (e.g., "Katsu Curry").

[1302] Step 5:

[1303] The server calculates calories and nutrients based on the specified meal. The calorie and nutrient calculation means retrieves nutrient data for each food item from the database and performs the calculation. The input is the specified meal, and the output is calorie and nutrient information (e.g., "Pork cutlet curry: 900kcal, Fat: 40g, Carbohydrates: 100g, Protein: 25g").

[1304] Step 6:

[1305] The server records and analyzes the user's daily nutrient intake. The nutrient intake recording and analysis system calculates the nutrient balance based on the user's daily diet. The input is the user's daily diet and nutrient information, and the output is the intake balance.

[1306] Step 7:

[1307] The server generates suggestions for deficient nutrients and calories. The suggestion generation mechanism generates suggestions based on recorded and analyzed results. The input is information on nutrient intake balance, and the output is the suggested content (e.g., "Your intake is high in fat, so choose a low-fat option for your next meal").

[1308] Step 8:

[1309] The server uses an emotion engine to recognize emotions from the user's voice. The input is text data, and the output is the recognized emotion information (e.g., "tired").

[1310] Step 9:

[1311] The server adjusts its suggestions based on the recognized emotions. The input is emotion information and the suggested content, and the output is the adjusted suggested content (e.g., "Let's incorporate foods that help relieve stress").

[1312] Step 10:

[1313] The server displays the adjusted proposal to the user via the terminal. The proposal is presented visually to the user through the proposal display mechanism. The input is the adjusted proposal, and the output is the proposal displayed to the user.

[1314] Step 11:

[1315] The server generates and provides online purchase links for the suggested products. The online purchase link generation mechanism generates and provides purchase links for suggested foods and supplements to the user. The input is the suggested content, and the output is the online purchase link (e.g., "Purchase link: https: / / example.com / harb_tea").

[1316] 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.

[1317] 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.

[1318] 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 robot 414.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] 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.

[1323] 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.

[1324] 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."

[1325] 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.

[1326] 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.

[1327] 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.

[1328] 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.

[1329] 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.

[1330] 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.

[1331] 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.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] 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.

[1336] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1337] The following is further disclosed regarding the embodiments described above.

[1338] (Claim 1)

[1339] A means for the user to input voice,

[1340] A means of converting input audio into text data,

[1341] A method for identifying meal contents by analyzing text data,

[1342] A means of calculating calories and nutrients based on the contents of a meal,

[1343] A means of recording and analyzing daily nutrient intake,

[1344] A means for generating suggestions for deficient nutrients and calories,

[1345] A means of displaying the proposed content,

[1346] Means for generating and providing online purchase links for proposed products,

[1347] A system that includes this.

[1348] (Claim 2)

[1349] The system according to claim 1, wherein the means for calculating calories and nutrients based on the contents of a meal obtains nutrient data from a database.

[1350] (Claim 3)

[1351] The system according to claim 1, wherein the means for recording and analyzing the daily nutrient intake is to calculate the balance of nutrient intake based on the user's past dietary history.

[1352] "Example 1"

[1353] (Claim 1)

[1354] A means for the user to input voice,

[1355] A means of converting input audio into text data,

[1356] A method for identifying meal contents by analyzing text data,

[1357] A means of calculating calories and nutrients based on the contents of a meal,

[1358] A means of recording and analyzing daily nutrient intake,

[1359] A means for generating suggestions for deficient nutrients and calories,

[1360] A means of displaying the proposed content,

[1361] Means for generating and providing online purchase links for proposed products,

[1362] A method for accurately converting speech data into text using a speech recognition service,

[1363] A method for extracting ingredient names and dish names from text data using natural language processing technology,

[1364] A means of identifying meal contents by cross-referencing with a database,

[1365] A means of obtaining nutritional data for each food ingredient from a database and calculating the corresponding nutrients,

[1366] A means of accumulating and analyzing daily nutrient intake,

[1367] A means of generating specific dietary and supplement suggestions,

[1368] A means of visually presenting the relevant proposal to the user,

[1369] Methods of using APIs to provide online purchase links,

[1370] A system that includes this.

[1371] (Claim 2)

[1372] The system according to claim 1, which obtains nutrient data from a database and calculates calories and nutrients based on the identified diet.

[1373] (Claim 3)

[1374] The system according to claim 1, which calculates the balance of nutrient intake based on the user's past meal history, and includes means for accumulating and analyzing the daily nutrient intake status.

[1375] "Application Example 1"

[1376] (Claim 1)

[1377] A means for the user to input voice,

[1378] A means of converting input audio into text data,

[1379] A method for identifying meal contents by analyzing text data,

[1380] A means of calculating calories and nutrients based on the contents of a meal,

[1381] A means of recording and analyzing daily nutrient intake,

[1382] A means for generating suggestions for deficient nutrients and calories,

[1383] A means of displaying the proposed content,

[1384] Means for generating and providing online purchase links for proposed products,

[1385] A means of suggesting the optimal delivery menu based on the user's meal history,

[1386] A means for generating and providing online purchase links for proposed delivery menus,

[1387] A system that includes this.

[1388] (Claim 2)

[1389] The system according to claim 1, wherein the means for calculating calories and nutrients based on the contents of a meal obtains nutrient data from a database.

[1390] (Claim 3)

[1391] The system according to claim 1, wherein the means for recording and analyzing the daily nutrient intake is to calculate the balance of nutrient intake based on the user's past dietary history.

[1392] "Example 2 of combining an emotion engine"

[1393] (Claim 1)

[1394] A means for the user to input voice,

[1395] A means of converting input audio into text data,

[1396] A method for identifying meal contents by analyzing text data,

[1397] A means of calculating calories and nutrients based on the contents of a meal,

[1398] A means of recording and analyzing daily nutrient intake,

[1399] A means for generating suggestions for deficient nutrients and calories,

[1400] A means of recognizing emotions from the user's voice and adjusting the suggested content,

[1401] A means of displaying the proposed content,

[1402] Means for generating and providing online purchase links for proposed products,

[1403] A system that includes this.

[1404] (Claim 2)

[1405] The system according to claim 1, wherein the means for calculating calories and nutrients based on the contents of a meal obtains nutrient data from a database.

[1406] (Claim 3)

[1407] The system according to claim 1, wherein the means for recording and analyzing the daily nutrient intake is to calculate the balance of nutrient intake based on the user's past dietary history.

[1408] "Application example 2 when combining with an emotional engine"

[1409] (Claim 1)

[1410] A means for the user to input voice,

[1411] A means of converting input audio into text data,

[1412] A method for identifying meal contents by analyzing text data,

[1413] A means of calculating calories and nutrients based on the contents of a meal,

[1414] A means of recording and analyzing daily nutrient intake,

[1415] A means for generating suggestions for deficient nutrients and calories,

[1416] Means of recognizing user emotions,

[1417] A means of adjusting the content of the proposal based on recognized emotions,

[1418] A means of displaying the proposed content,

[1419] Means for generating and providing online purchase links for proposed products,

[1420] A system that includes this.

[1421] (Claim 2)

[1422] The system according to claim 1, wherein the means for calculating calories and nutrients based on the contents of a meal obtains nutrient data from a database.

[1423] (Claim 3)

[1424] The system according to claim 1, wherein the means for recording and analyzing the daily nutrient intake is to calculate the balance of nutrient intake based on the user's past dietary history. [Explanation of Symbols]

[1425] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for the user to input voice, A means of converting input audio into text data, A method for identifying meal contents by analyzing text data, A means of calculating calories and nutrients based on the contents of a meal, A means of recording and analyzing daily nutrient intake, A means for generating suggestions for deficient nutrients and calories, A means of displaying the proposed content, Means for generating and providing online purchase links for proposed products, A system that includes this.

2. The system according to claim 1, wherein the means for calculating calories and nutrients based on the contents of a meal obtains nutrient data from a database.

3. The system according to claim 1, wherein the means for recording and analyzing the daily nutrient intake is to calculate the balance of nutrient intake based on the user's past meal history.

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