System

Abstract

A system is provided.SOLUTION: A system comprising: means for obtaining speech of a user; means for converting the speech into text data; means for obtaining profile data of the user; means for generating a menu based on the text data and the profile data; means for presenting the menu to the user and receiving feedback; means for re-tuning the menu based on the feedback; means for determining a final menu; means for generating a shopping list based on the menu; and means for transmitting the shopping list to an online supermarket to order food.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Creating daily meal plans is a stressful task for many families, as it requires considering a wide range of factors, including likes and dislikes, allergies, nutritional balance, and family preferences. This problem has led to an increased mental burden known as "menu hell." Creating shopping lists and sourcing ingredients is also time-consuming, especially when using online supermarkets. There is a need for a single application that can solve these issues and enable users to prepare daily meals comfortably and efficiently. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means: A system including a means for acquiring a user's voice, a means for converting the voice into text data, a means for acquiring user profile data, a means for generating a menu plan based on the text data and the profile data, a means for presenting the menu plan to the user and receiving feedback, a means for re-tuning the menu plan based on the feedback, a means for finalizing the menu, a means for generating a shopping list based on the menu, and a means for transmitting the shopping list to an online supermarket and ordering ingredients, allows a user to easily receive appropriate menu suggestions and receive consistent support right up to the shopping process.

[0006] Furthermore, by referencing the user's past data and taking this into consideration when generating a menu plan using the menu plan generation means, it is possible to propose a menu that takes into account individual preferences and the health status of family members. This will reduce the stress of creating daily menus in many households and make it easier and more efficient to procure ingredients.

[0007] "User" refers to an individual who uses this system to propose menus and order ingredients.

[0008] "Voice input" refers to a means by which a user communicates information to a system by speaking.

[0009] "Speech recognition API" refers to an application program interface for converting voice data into text data.

[0010] "Profile Data" refers to a set of information about a user, such as the user's preferences, allergy information, health status, and family composition.

[0011] "Menu plan generator" refers to the algorithms and processes used to suggest appropriate menus based on the user's preferences and profile data.

[0012] "Feedback" refers to reactions from users, including correction instructions and additional requests.

[0013] "Retuning" refers to the process of re-adjusting menu suggestions based on user feedback.

[0014] A "shopping list" refers to a list of ingredients needed based on a confirmed menu.

[0015] An "online supermarket" refers to a service that sells food and daily necessities online.

[0016] "Ordering method" refers to the process of sending a shopping list to an online supermarket and automatically purchasing the necessary ingredients. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The system according to the present invention is for supporting users in efficiently planning daily meals and shopping, and includes a series of processes operated by the user, a terminal, and a server. Specific examples will be described below.

[0039] System Overview

[0040] The system begins when the user verbally communicates their menu requests using a device such as a smartphone or smart speaker. The request is converted into text data using voice recognition technology and sent to the server. The server then references the user's profile data to generate an optimal menu plan and presents it to the user. The user can then refine the menu plan based on their feedback, and a shopping list is automatically generated based on the final menu, and ingredients are ordered via an online supermarket.

[0041] Program processing

[0042] Acquiring voice input

[0043] The user speaks into a smartphone or smart speaker, saying, "Please help me think about what to have for dinner next week. I don't like chicken, but I like fish." The device receives this speech and begins analyzing it using a speech recognition API.

[0044] Audio data conversion

[0045] The device converts the voice data into text data through a voice recognition API. The analysis results of the voice data are saved on the device, and the text data is sent to the server.

[0046] Retrieving User Data

[0047] The server then consults the user's profile database to obtain information about the user's likes and dislikes, allergies, family structure, and health status, and is then ready to generate a menu plan that takes into account the user's individual requirements.

[0048] Menu generation

[0049] Based on the text data and profile data acquired by the server, the AI ​​model generates menu suggestions. For example, it might suggest "grilled salmon," "tuna and radish salad," or "potage soup." These menu suggestions are adjusted to match the user's past data.

[0050] User Review and Feedback

[0051] The terminal displays the generated menu plan on the user's device, and the user can provide feedback via voice or text regarding further requests or corrections. For example, the user can express a request in the form of "I'd like a dish that's easier to make."

[0052] Retuning

[0053] The server receives the user's feedback and again uses the AI ​​model to fine-tune the menu plan. Based on the feedback, a new menu plan is generated and presented to the user again. This process is repeated until the user is satisfied.

[0054] Finalize the menu and generate a shopping list

[0055] Based on the final menu, the server automatically generates a shopping list, listing specific ingredients and their quantities, such as "300g of salmon, 1 daikon radish, 500ml of milk."

[0056] Collaboration with online supermarkets

[0057] The device sends the generated shopping list to the online supermarket's API, which seamlessly orders ingredients. The user can check the order details and make any necessary changes. Once the order is confirmed, the ingredients are delivered directly to the user's home from the online supermarket.

[0058] In this way, this system supports the entire process, from user voice input to menu generation, shopping list creation, and even ordering ingredients through an online supermarket, significantly reducing the stress associated with creating household menus.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user speaks to a smartphone or smart speaker, for example, saying, "Please help me come up with a menu for dinner next week. I don't like chicken, but I like fish."

[0062] Step 2:

[0063] The terminal receives the voice data. Specifically, the microphone in the device captures the user's voice and inputs it into the voice recognition application.

[0064] Step 3:

[0065] The device sends the voice data to the speech recognition API, which converts it into text data. The speech recognition API analyzes the voice signal and generates corresponding text.

[0066] Step 4:

[0067] The terminal transmits the converted text data to the server, where the text data is encoded and sent to the server via a communication network.

[0068] Step 5:

[0069] The server references the user's profile database, reliably identifying the user and extracting information such as likes and dislikes, allergies, nutritional balance, and family structure.

[0070] Step 6:

[0071] The server generates menu suggestions based on text data and profile data. The AI ​​model analyzes this data and proposes multiple menu options, such as "grilled salmon," "tuna and radish salad," and "potage soup."

[0072] Step 7:

[0073] The server generates a menu plan and sends it to the device, where it is formatted in a user-friendly format and displayed on a smartphone or smart speaker.

[0074] Step 8:

[0075] The device presents the user with a menu plan. The user can review the suggestions on screen or via voice prompts and make additional requests or corrections. For example, the user can input a voice command such as, "I'd like a dish that's easier to make."

[0076] Step 9:

[0077] The terminal receives the user's feedback, converts it back into text data, and sends it to the server.

[0078] Step 10:

[0079] The server receives the feedback and uses the AI ​​model again to fine-tune the menu plan, regenerating a new plan that reflects the user's new preferences. This cycle is repeated until the user is finally satisfied.

[0080] Step 11:

[0081] The server determines the final menu plan and generates a list of ingredients based on it. For example, it creates a specific shopping list such as "300g of salmon, 1 daikon radish, 500ml of milk."

[0082] Step 12:

[0083] The server sends the shopping list to the terminal, which displays it to the user.

[0084] Step 13:

[0085] The device sends the shopping list to the online supermarket's API, and each item on the list is checked against the online supermarket's database to confirm availability and price.

[0086] Step 14:

[0087] The user checks the order details and makes any necessary corrections. After checking, the order is confirmed via the terminal.

[0088] Step 15:

[0089] The server sends the confirmed order details to the online supermarket, completing the order process. The online supermarket confirms the order and prepares for delivery.

[0090] These steps allow the user to easily receive appropriate menu suggestions and seamlessly complete a series of operations up to the point of shopping.

[0091] Example 1

[0092] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0093] Users face the challenges of spending a great deal of time and effort on daily menu planning and shopping, and difficulty in selecting ingredients and considering nutritional balance. Furthermore, for users who dislike or have allergies to certain ingredients, selection becomes even more difficult. In addition, it is necessary to link with online supermarkets to create shopping lists and smoothly purchase ingredients. Therefore, a system that can solve all of these challenges at once is needed.

[0094] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0095] In this invention, the server includes means for acquiring a user's voice, means for converting the voice into text data, means for acquiring user profile data, means for generating a menu plan using an artificial intelligence model based on the text data and the profile data, means for presenting the menu plan to the user and receiving feedback, means for readjusting the menu plan based on the feedback, means for finalizing the menu, means for generating a shopping list based on the menu, and means for transmitting the shopping list to an online shopping service and ordering ingredients. This allows users to easily create menus that suit their preferences and needs and seamlessly perform processes from generating the shopping list to purchasing ingredients.

[0096] "User" refers to an individual or group of people who use the system and provide their profile data and feedback.

[0097] "Means for capturing audio" refers to a configuration including hardware and software for capturing audio input made by a user.

[0098] "Means for converting speech to text data" refers to a process that uses speech recognition technology to analyze captured speech input and convert it into corresponding text data.

[0099] "User profile data" is a database containing individual attribute information such as a user's likes and dislikes, allergy information, family structure, and health condition.

[0100] "Means for generating menu plans using an artificial intelligence model" refers to the process of using an AI algorithm to create appropriate menu plans based on the user's text data and profile data.

[0101] "Means for presenting menu plans to the user and receiving feedback" refers to an interface that displays the generated menu plans on the user's terminal and collects further requests and correction instructions from the user.

[0102] "Means for readjusting menu plans" refers to the process of modifying the initial menu plan based on user feedback and presenting it to the user again.

[0103] The "means for finalizing the menu" refers to the process of finally setting the menu that has been adjusted until the user is satisfied.

[0104] "Means for generating a shopping list" refers to the process of creating a list of ingredients and their quantities based on a confirmed menu.

[0105] "Means of sending to an online shopping service and ordering ingredients" refers to the process of sending the generated shopping list to an online supermarket or other online shopping platform to purchase ingredients.

[0106] The present invention relates to a system for assisting a user in efficiently planning daily meals and shopping. Specific embodiments will be described below.

[0107] System Overview

[0108] The system begins when a user uses a device such as a smartphone or smart speaker to verbally communicate their menu preferences. The preference is converted into text data using voice recognition technology and sent to a server. The server then references the user's profile data to generate an optimal menu plan and presents it to the user. The user can then refine the menu plan based on their feedback, and a shopping list is automatically generated based on the final menu, and ingredients are ordered via an online shopping service.

[0109] Acquiring voice input and converting voice data

[0110] The user speaks into a smartphone or smart speaker, saying something like, "Please help me think about what to make for dinner next week. I don't like chicken, but I like fish." The device receives this speech and begins analyzing it using a speech recognition API (for example, Google Speech-to-Text API). The speech recognition API converts the voice data into text data, and the analysis results are temporarily stored on the device.

[0111] Retrieving User Data

[0112] As soon as the server receives the text data, it consults the user's profile database, which stores information about the user's likes and dislikes, allergies, family structure, and health status, to prepare a menu plan that meets the user's individual requirements.

[0113] Menu generation

[0114] The server inputs the text data and profile data into an AI model (for example, a generative AI model) and generates a menu plan. As a specific example, if the user likes fish dishes, menu plans such as "grilled salmon," "tuna and radish salad," and "potage soup" are suggested. This generation includes preprocessing, where the text data is formatted into a prompt sentence.

[0115] User Review and Feedback

[0116] The terminal displays the generated menu plan on the user's device. Specifically, the suggestions are communicated to the user via the smartphone app screen or the voice feedback function of the smart speaker. The user can provide feedback on the displayed menu plan by voice or text. For example, the user can say, "This menu is a little difficult, so I'd prefer something simpler."

[0117] Retuning

[0118] Based on the feedback received from the user, the server uses the AI ​​model to fine-tune the menu plan. Specifically, the feedback is reflected in the prompt text and input into the AI ​​model again to generate a new menu plan. This process is repeated until the user is satisfied.

[0119] Finalize the menu and generate a shopping list

[0120] The server automatically generates a shopping list based on the final menu. For example, it might list specific ingredients and their quantities, such as "300g of salmon, 1 daikon radish, 500ml of milk." This list is stored in a database and sent to the device.

[0121] Collaboration with online supermarkets

[0122] The terminal sends the shopping list received from the server to the API of the online shopping service (for example, an online supermarket API), and seamlessly orders the ingredients. The user can check the order details and make any necessary corrections. Finally, the ingredients are delivered directly from the online shopping service to the user's home.

[0123] In this way, this system supports the entire process, from user voice input to menu generation, shopping list creation, and even ordering ingredients through online shopping services, significantly reducing the time and effort required to create household menus.

[0124] Prompt Sentence Examples

[0125] The user types:

[0126] "Can you suggest a dinner menu for next week? I'd prefer something vegetarian and as easy to prepare as possible."

[0127] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0128] Step 1: Getting voice input

[0129] A user speaks into a smartphone or smart speaker, saying, "Please help me decide what to have for dinner next week. I don't like chicken, but I like fish." The input is voice data, and the device captures this voice. The device's microphone converts the voice into a digital signal and prepares it as voice data. In the next step, this voice data is sent to a voice recognition API.

[0130] Step 2: Convert the audio data

[0131] The device uses a speech recognition API (e.g., Google Speech-to-Text API) to convert the acquired voice data into text data. The input is voice data, which the API analyzes and converts into text data. The generated text data is temporarily stored on the device and then sent to the server in the next step.

[0132] Step 3: Retrieving User Data

[0133] After the server receives the text data, it references the user's profile database. The input is the text data and the user ID. The profile database stores information about the user's likes and dislikes, allergies, family composition, and health status. The server retrieves this data and prepares it for generating a menu plan.

[0134] Step 4: Generate a menu plan

[0135] The server uses text data and profile data as input to generate a menu plan using an artificial intelligence model (e.g., a generative AI model). The input is text data from the user and user profile data. The server formats this data into prompt sentences and inputs them into the AI ​​model to generate a menu plan. The generated menu plan is presented to the user in the next step.

[0136] Step 5: User review and feedback

[0137] The terminal displays the generated menu plan on the user's device. The input is the menu plan sent from the server. Specifically, the suggestions are communicated to the user via the smartphone app screen or the voice feedback function of the smart speaker. The user provides feedback by voice or text. For example, the user may give instructions such as, "This menu is a little difficult, so I would prefer something simpler."

[0138] Step 6: Retune

[0139] Based on the feedback received from the user, the server uses the AI ​​model again to fine-tune the menu plan. The input is the user's feedback. The feedback is reflected in the prompt text and input into the AI ​​model again to generate a new menu plan. This process is repeated until the user is satisfied.

[0140] Step 7: Finalize the menu and generate a shopping list

[0141] The server automatically generates a shopping list based on the finalized menu. The input is the finalized menu plan. The server lists the necessary ingredients and their quantities, and generates the shopping list. For example, the specific ingredient information is "300g of salmon, 1 daikon radish, 500ml of milk."

[0142] Step 8: Linking with online supermarkets

[0143] The terminal receives the shopping list from the server and sends it to the API of the online shopping service, seamlessly ordering ingredients. The input is the shopping list. The user confirms the order details, makes any necessary corrections, and then confirms the order. Finally, the online shopping service delivers the ingredients directly to the user's residence.

[0144] In this way, a system is realized that starts with the user's voice input and, using the server and terminals, goes through a series of processes to efficiently support menu creation and ingredient purchasing.

[0145] (Application example 1)

[0146] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0147] Conventional menu planning systems can create menus by capturing user voice input, but they lack the functionality for real-time menu suggestions and in-store navigation in physical stores, making it difficult to streamline the user's shopping experience.There is a need for a means to provide shopping support linked to real-time menu suggestions in physical stores.

[0148] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0149] In this invention, the server includes a means for receiving menu suggestions in real time using a smartphone or smart glasses when a user is shopping in a physical store, a means for providing location information of each product in the store and performing navigation, and a means for using a generative AI model to generate menu suggestions, thereby enabling users to shop efficiently in the physical store.

[0150] "User" refers to an individual or organization that uses this system.

[0151] "Voice capture means" refers to a device such as a microphone that captures the user's spoken voice in digital form.

[0152] "Speech-to-text conversion means" refers to software or hardware that analyzes acquired voice data and converts it into text information.

[0153] The "profile data acquisition means" refers to a function or device for acquiring data such as personal information, preferences, and allergy information about a user.

[0154] "Menu plan generation means" refers to software or algorithms for suggesting dishes based on the acquired text data and profile data.

[0155] "Feedback receiving means" refers to a means for receiving additional requests or correction instructions provided by a user.

[0156] "Retuning tools" refers to algorithms or software that modify and improve existing menu suggestions based on user feedback.

[0157] "Final menu determination method" refers to the software or algorithm used to determine the final menu after receiving feedback.

[0158] "Shopping list generation means" refers to a function for generating a list of necessary ingredients based on a confirmed menu.

[0159] "Net supermarket sending means" refers to a function for sending the generated shopping list to an online supermarket.

[0160] "Real-time menu suggestion means" refers to a function that allows users to receive real-time menu suggestions via their smartphone or smart glasses while shopping in a physical store.

[0161] "In-store navigation means" refers to a system that provides location information of products in a physical store and guides users based on an automatically generated shopping list.

[0162] "Generative AI model" refers to a program that uses artificial intelligence to generate optimal menus based on a user's profile data and voice data.

[0163] A "prompt sentence" refers to an input sentence used to instruct a generative AI model on appropriate processing.

[0164] The system of the present invention supports users in planning meals and purchasing ingredients while efficiently shopping in a physical store. The system includes a user, a terminal (such as a smartphone or smart glasses), and multiple processes operated through a server.

[0165] System Overview

[0166] The system starts by having the user use their smartphone or smart glasses in a physical store to voice their menu request. For example, the user might say, "I'm busy tomorrow, so please make a simple dinner." The device receives this voice and converts it into text data using a speech recognition API.

[0167] Hardware and software used

[0168] Audio acquisition method: Microphone built into smartphone or smart glasses

[0169] Speech-to-text conversion method: Google Cloud Speech-to-Text API

[0170] Generative AI model: OpenAI API (e.g. GPT-3, ChatGPT)

[0171] Profile data acquisition method: User information database

[0172] Menu planning method: Menu generation algorithm using generative AI model

[0173] Feedback reception method: Interface via smartphone or smart glasses

[0174] Retuning method: Retraining the algorithm based on user feedback

[0175] Shopping list generator: Menu-based list generator software

[0176] In-store navigation: Using AR features on smartphones and smart glasses

[0177] Process Details

[0178] Acquiring and converting voice input

[0179] The user speaks into a smartphone or smart glasses, and the speech is picked up by a microphone on the device and converted into text data using the Google Cloud Speech-to-Text API.

[0180] Obtaining user data and generating menu plans

[0181] The text request is sent to the server, which then references the user's profile data, which includes the user's preferences, allergy information, health status, etc. The server uses this data to generate an optimal menu plan using a generative AI model (such as GPT-3 or ChatGPT).

[0182] As an example, the following prompt sentence will be used:

[0183] User profile: Family: 4 people, Fish lover, Chicken allergy

[0184] Request: I'm busy tomorrow, so I'd like a quick dinner please.

[0185] Please suggest a proposal.

[0186] Menu suggestions and feedback

[0187] The generated menu plan is sent back to the device and presented to the user through the interface of a smartphone or smart glasses, and the user can provide feedback on the proposal via voice or text with any additional requests or corrections.

[0188] Retuning and finalizing the menu

[0189] The server receives feedback from the user and again uses the generative AI model to refine the menu plan, and this process is repeated until the user is satisfied, at which point the final menu is finalized.

[0190] Shopping list generation and in-store navigation

[0191] Based on the final menu, a list of necessary ingredients is automatically generated. The generated shopping list is linked to the location information of products in the store via a smartphone or smart glasses, providing navigation information to the user.

[0192] This allows users to efficiently purchase the ingredients they need in-store, greatly improving their shopping experience.

[0193] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0194] Step 1:

[0195] The user provides voice input. The user's request is recorded using a microphone on a smartphone or smart glasses, providing voice data as input.

[0196] Step 2:

[0197] The device converts the voice data into text using the Google Cloud Speech-to-Text API. Real-time voice analysis is performed to obtain the converted text data.

[0198] Step 3:

[0199] The device sends the textual request to the server, which then transfers the text data to the server via the Internet and retrieves the text data as input. This data is then used to generate the menu.

[0200] Step 4:

[0201] The server retrieves the user's profile data, such as preferences, allergies, and health status, from a profile database or user information database. User-specific data is collected through database query processing.

[0202] Step 5:

[0203] The server generates a menu plan using a generative AI model. For example, it uses GPT-3 or ChatGPT to generate a menu plan based on the acquired text data and profile data. The prompt sentence is input into the generative AI model, and the optimal menu plan is output.

[0204] Examples of prompts include:

[0205] User profile: Family: 4 people, Fish lover, Chicken allergy

[0206] Request: I'm busy tomorrow, so I'd like a quick dinner please.

[0207] Please suggest a proposal.

[0208] Step 6:

[0209] The server returns the generated menu plan to the terminal, which then transfers the plan to the terminal via the Internet. Specifically, the data is preprocessed and transmitted.

[0210] Step 7:

[0211] The device presents the menu plan to the user. The generated menu plan is displayed to the user through the interface of a smartphone or smart glasses. The output may include text data and image data.

[0212] Step 8:

[0213] The user provides feedback, either by voice or text, regarding further requests or corrections to the proposal. This feedback is then sent back to the server via the device. The input is voice or text data.

[0214] Step 9:

[0215] The server retunes the menu plan based on the feedback. It then uses the generative AI model again to generate a new menu plan that reflects the feedback. The adjusted menu plan is output by reapplying the model.

[0216] Step 10:

[0217] The device then presents the retuned menu plan to the user again, and the new proposal is displayed on the smartphone or smart glasses, where a final confirmation is made.

[0218] Step 11:

[0219] The server generates a shopping list based on the final menu. The server lists the ingredients and their quantities needed based on the finalized menu plan. The server retrieves ingredient information from the database and builds the shopping list.

[0220] Step 12:

[0221] Send the shopping list to the online supermarket. The generated shopping list is sent using the online supermarket's API, and ingredients are automatically ordered.

[0222] Step 13:

[0223] Users can receive real-time menu suggestions in-store via their smartphones or smart glasses, enabling efficient in-store purchasing.

[0224] Step 14:

[0225] Provide in-store navigation. Using the AR function of smartphones or smart glasses, the system provides users with location information for the ingredients they need, helping them navigate smoothly within the store. This step involves acquiring location data and providing navigation.

[0226] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0227] The system of the present invention assists users in efficiently planning daily meals and shopping, and also recognizes the user's emotions and suggests meals based on those emotions. The system includes a series of processes operated by the user, a terminal, and a server. Specific examples are described below.

[0228] System Overview

[0229] The system begins when a user uses a device such as a smartphone or smart speaker to voice their menu request. The request is converted into text data using voice recognition technology and sent to a server. The server then references the user's profile data to generate an optimal menu plan and presents it to the user. It then uses an emotion engine to analyze the user's emotions and optimize the suggestions. The user can then re-tune the menu plan based on their feedback, and a shopping list is automatically generated based on the final menu, and ingredients are ordered via an online supermarket.

[0230] Program processing

[0231] Acquiring voice input

[0232] The user speaks into a smartphone or smart speaker, saying, "Please help me think about what to have for dinner next week. I don't like chicken, but I like fish." The device receives this speech and begins analyzing it using a speech recognition API.

[0233] Audio data conversion

[0234] The device sends the voice data to the speech recognition API, which converts it into text data. The speech recognition API analyzes the voice signal and generates corresponding text.

[0235] emotion recognition

[0236] Once the voice and text data are generated, the device uses an emotion engine to recognize the user's emotions. This process analyzes voice characteristics such as tone, speed, and strength to determine whether the user is relaxed or stressed.

[0237] Retrieving User Data

[0238] The server then consults the user's profile database to obtain information about likes, dislikes, allergies, family structure, and health conditions, and is then ready to generate a menu plan that takes into account the user's individual requirements.

[0239] Menu generation

[0240] The server uses an AI model to generate menu suggestions based on the text, profile, and emotion data it acquires. For example, it might suggest "grilled salmon," "tuna and daikon salad," or "potage soup." These menu suggestions are tailored to match the user's past data.

[0241] User Review and Feedback

[0242] The terminal displays the generated menu plan on the user's device. The user can review the proposed content on the screen or via voice prompts and make additional requests or corrections. For example, the user can input a voice command such as, "I'd like a dish that's easier to make."

[0243] Retuning

[0244] The server receives the user's feedback and again uses the AI ​​model to fine-tune the menu plan. Based on the feedback, a new menu plan is generated and presented to the user again. This process is repeated until the user is satisfied.

[0245] Finalize the menu and generate a shopping list

[0246] The server automatically generates a shopping list based on the final menu. For example, it creates a specific shopping list such as "300g of salmon, 1 daikon radish, and 500ml of milk."

[0247] Collaboration with online supermarkets

[0248] The device sends the generated shopping list to the online supermarket's API, which seamlessly orders ingredients. The user can check the order details and make any necessary changes. Once the order is confirmed, the ingredients are delivered directly to the user's home from the online supermarket.

[0249] Specific examples

[0250] For example, a user might say to a smart speaker, "I'm very tired today, so please make me a simple and delicious dinner." At this time, the device converts the voice into text data, and at the same time, the emotion engine recognizes the user's level of fatigue. The server takes into account the user's profile data and level of fatigue and suggests a menu item such as "simple grilled salmon with salad." When the user responds with "That'll do," the server automatically generates a shopping list and sends it to the online supermarket.

[0251] In this way, this system proposes optimal menus based on the user's emotional state and provides easy support for shopping.

[0252] The processing flow will be explained below.

[0253] Step 1:

[0254] The user speaks to a smartphone or smart speaker, for example, saying, "Please help me come up with a menu for dinner next week. I don't like chicken, but I like fish."

[0255] Step 2:

[0256] The terminal receives the voice data. Specifically, the microphone in the device captures the user's voice and inputs it into the voice recognition application.

[0257] Step 3:

[0258] The device sends the voice data to the speech recognition API, which converts it into text data. The speech recognition API analyzes the voice signal and generates corresponding text.

[0259] Step 4:

[0260] The terminal transmits the generated text data to the server, where the text data is encoded and sent to the server via a communication network.

[0261] Step 5:

[0262] The device sends voice and text data to the emotion engine, which recognizes the user's emotions. The emotion engine analyzes features such as tone, speed, and strength of the voice to determine whether the user is relaxed or stressed.

[0263] Step 6:

[0264] The server accesses the user's profile database and retrieves data such as likes and dislikes, allergies, health status, and family composition based on the user's identification information.

[0265] Step 7:

[0266] The server uses an AI model to generate multiple menu suggestions based on the text data, profile data, and emotional data it acquires. For example, it generates "grilled salmon," "tuna and daikon salad," and "potage soup." These menu suggestions are adjusted according to the user's past data and current emotional state.

[0267] Step 8:

[0268] The server generates a menu plan and sends it to the device, where it is formatted in a user-friendly format and displayed on a smartphone or smart speaker.

[0269] Step 9:

[0270] The device presents the user with a menu plan. The user can then review the proposal on screen or by voice, and provide additional requests or corrections. For example, they can input a command such as, "I'd like a dish that's easier to make."

[0271] Step 10:

[0272] The terminal receives the user's feedback, converts it into text data, and sends it to the server.

[0273] Step 11:

[0274] The server receives the feedback and uses the AI ​​model to re-tune the menu plan, regenerating a new plan that reflects the user's new preferences. This cycle is repeated until the user is satisfied.

[0275] Step 12:

[0276] The server determines the final menu plan and generates a list of ingredients based on it. For example, it creates a list tailored to the user's needs, such as "300g of salmon, 1 daikon radish, 500ml of milk" or even "recipes with short cooking times."

[0277] Step 13:

[0278] The server sends the shopping list to the terminal, which displays it to the user.

[0279] Step 14:

[0280] The device sends the shopping list to the online supermarket's API, and each item on the list is checked against the online supermarket's database to confirm availability and price.

[0281] Step 15:

[0282] The user checks the order details and makes any necessary corrections. After checking, the order is confirmed via the terminal.

[0283] Step 16:

[0284] The server sends the confirmed order details to the online supermarket, completing the order process. The online supermarket confirms the order and prepares for delivery.

[0285] These steps allow users to easily receive appropriate menu suggestions and seamlessly complete the entire process, right up to the point of shopping. Taking the user's emotional state into consideration also enables suggestions that better fit their needs, improving the user experience.

[0286] Example 2

[0287] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0288] While conventional menu planning systems can take into account user preferences and allergy information, they have difficulty reflecting the user's emotional state or temporary requests in real time. Furthermore, they lack the ability to automatically generate shopping lists and integrate with online sales systems, leaving room for improvement in user convenience. Therefore, there was a need for a system that could recognize the user's emotional state, suggest optimal menus based on that, automatically generate shopping lists based on those menus, and order ingredients in cooperation with online sales systems, thereby reducing the burden on users.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0290] In this invention, the server includes means for acquiring a user's voice, means for converting the voice into text data, means for analyzing the text data and voice data to recognize the user's emotions, means for acquiring user profile data, means for generating a menu plan based on the text data, the profile data, and the emotion data, means for presenting the menu plan to the user and receiving feedback, means for re-tuning the menu plan based on the feedback, means for finalizing the menu, means for generating a shopping list based on the menu, and means for transmitting the shopping list to an online sales system and ordering ingredients. This makes it possible to propose an optimal menu taking into account the user's emotional state, automatically generate a shopping list based on the menu, and seamlessly link with the online sales system.

[0291] A "user" is a person who uses the system to create menu plans and order ingredients.

[0292] "Means for acquiring audio" refers to a function for capturing a user's voice using a device such as a smartphone or smart speaker.

[0293] "Means for converting speech to text data" refers to the process of using speech recognition technology to analyze speech signals and convert them into corresponding text information.

[0294] "Means for recognizing emotions" refers to a function for analyzing voice data and text data to determine the user's emotional state.

[0295] "User profile data" refers to data including the user's individual preferences, allergy information, family structure, health status, and the like.

[0296] "Means for generating menu suggestions" refers to the process of using an AI model to suggest optimal menu suggestions based on the acquired text data, profile data, and emotional data.

[0297] "Means for presenting menu plans and receiving feedback" refers to a function for displaying the generated menu plans to the user and receiving opinions and requests from the user.

[0298] "Means for retuning menu suggestions" refers to the process of readjusting suggested menu suggestions based on user feedback.

[0299] The "means for finalizing the menu" refers to the process of finalizing the menu plan after the user has made adjustments until they are satisfied.

[0300] "Means for generating a shopping list" refers to a function that automatically generates a list of necessary ingredients based on the finalized menu.

[0301] An "online sales system" is a system for ordering and purchasing ingredients and products online.

[0302] "Means for sending to the online sales system and ordering ingredients" refers to the function of sending the automatically generated shopping list to the online sales system and ordering the necessary ingredients.

[0303] The system according to the present invention assists users in efficiently planning daily meals and shopping, and also recognizes the user's emotions and suggests meals based on those emotions. The system includes a series of processes operated by the user, a terminal, and a server. Specific embodiments for implementing the present invention will be described in detail below.

[0304] System Configuration

[0305] This system is constructed mainly using the following hardware and software.

[0306] Hardware

[0307] 1. User device: A device that allows voice input, such as a smartphone or smart speaker.

[0308] 2. Server: A server for processing data and running AI models.

[0309] software

[0310] 1. Speech recognition API: Convert the user's voice into text data using Google Cloud Speech-to-Text, Amazon Transcribe, etc.

[0311] 2. Emotion recognition engine: Analyzes user emotions using IBM Watson Tone Analyzer, Microsoft Azure Emotion API, etc.

[0312] 3. Generative AI model: Menu plans are generated using OpenAI GPT series, Google BERT, etc.

[0313] 4. Online sales system API: Send a shopping list and order ingredients using the APIs of Rakuten Ichiba and Amazon Fresh.

[0314] System Operation

[0315] Acquiring voice input

[0316] The user speaks to the device, saying, "Please help me decide what to have for dinner next week. I don't like chicken, but I like fish." The device receives this speech, sends it to the speech recognition API, and begins analyzing it.

[0317] Audio data conversion

[0318] The device sends the audio data to a speech recognition API, such as Google Cloud Speech-to-Text, which converts it into text. The speech recognition API then analyzes the audio signal and generates corresponding text.

[0319] emotion recognition

[0320] Once the voice and text data is generated, the device uses an emotion engine such as IBM Watson Tone Analyzer to recognize the user's emotions. This process analyzes features such as tone, speed, and strength of the voice to determine whether the user is relaxed or stressed.

[0321] Retrieving User Data

[0322] The server then consults the user's profile database to obtain information about likes, dislikes, allergies, family structure, and health conditions, and is then ready to generate a menu plan that takes into account the user's individual requirements.

[0323] Menu generation

[0324] Based on the text data, profile data, and emotion data acquired by the server, the system uses generative AI models such as the OpenAI GPT series to generate menu suggestions, such as "grilled salmon," "tuna and radish salad," and "potage soup."

[0325] User Review and Feedback

[0326] The terminal displays the generated menu plan on the user's device. The user can review the proposed content on the screen or via voice prompts and make additional requests or corrections. For example, the user can input a voice command such as, "I'd like a dish that's easier to make."

[0327] Retuning

[0328] The server receives the user's feedback and uses the AI ​​model again to fine-tune the menu suggestion. A new menu suggestion, such as "Simmered fish and vegetable miso soup," is generated and presented to the user again. This process is repeated until the user is satisfied.

[0329] Finalize the menu and generate a shopping list

[0330] The server automatically generates a shopping list based on the final menu. For example, it creates a specific shopping list such as "300g of salmon, 1 daikon radish, and 500ml of milk."

[0331] Collaboration with online supermarkets

[0332] The device sends the generated shopping list to the API of online sales systems such as Rakuten Ichiba and Amazon Fresh, allowing for seamless ordering of ingredients. Users can check the order details and make any necessary changes. Once the order is confirmed, the ingredients are delivered directly to the user's home from the online supermarket.

[0333] Specific examples

[0334] For example, a user might say to a smart speaker, "I'm very tired today, so please make me a simple and delicious dinner." At this time, the device converts the voice into text data, and at the same time, the emotion engine recognizes the user's level of fatigue. The server takes into account the user's profile data and level of fatigue and suggests a menu item such as "simple grilled salmon with salad." When the user responds with "That'll do," the server automatically generates a shopping list and sends it to the online supermarket.

[0335] In this way, this system proposes optimal menus based on the user's emotional state and provides easy support for shopping.

[0336] Prompt Sentence Examples

[0337] "I'm very tired today, so please make me a simple and delicious dinner. I don't like chicken, but I do like fish."

[0338] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0339] Step 1:

[0340] A user speaks into a smartphone or smart speaker, for example, saying, "Please help me decide what to have for dinner next week. I don't like chicken, but I like fish." This speech is picked up through the device's microphone. The input is the user's voice data, and the output is a digital audio signal ready to be sent to a speech recognition API.

[0341] Step 2:

[0342] The device sends the captured voice data to a speech recognition API such as Google Cloud Speech-to-Text. The API analyzes the voice signal and generates corresponding text data. The input is the user's voice data, and the output is the analyzed text data. Specific operations include the API converting the voice signal into numerical information, identifying voice patterns, and converting them into text.

[0343] Step 3:

[0344] The device sends the generated text and voice data to an emotion recognition engine such as IBM Watson Tone Analyzer to analyze the user's emotions. The input is data containing features such as the tone, speed, and strength of the voice, and the output is the user's emotional state (relaxed, stressed, etc.). Specific operations include a process in which the emotion engine analyzes the features of the text and voice data to determine the type and intensity of the emotion.

[0345] Step 4:

[0346] The server references the user's profile database to retrieve data such as the user's preferences, allergy information, family structure, and health status. The input is the user's identification information, and the output is the user's detailed profile data. Specifically, the server executes a database query to retrieve the required data and prepares it for use in the next step.

[0347] Step 5:

[0348] Based on the text data, profile data, and emotion data acquired by the server, a generative AI model such as the OpenAI GPT series is used to generate a menu plan. The input is text data, profile data, and emotion data, and the output is the generated text of the menu plan. Specific operations include the process in which the AI ​​model takes these data as input and creates a menu plan that meets the user's requirements.

[0349] Step 6:

[0350] The terminal displays the generated menu plan on the user's device. The user can check the suggestions on the screen or through voice prompts and make additional requests or corrections. For example, the user can input feedback such as "I'd like a dish that's easier to make" through voice input. The input is the generated menu plan, and the output is the user's feedback. Specific operations include the process of the terminal displaying the menu plan using a user interface and receiving feedback from the user through voice or text input.

[0351] Step 7:

[0352] The server receives the user's feedback and uses the generative AI model again to tune the menu plan. The input is the user's feedback and the initial menu plan, and the output is a new, tuned menu plan. Specific operations include the AI ​​model taking the feedback into account to generate a new menu plan with new parameters and presenting it to the user again.

[0353] Step 8:

[0354] The server automatically generates a shopping list based on the finalized menu. The input is the text data of the finalized menu, and the output is a shopping list containing specific ingredients and quantities. Specific operations include the server calculating the ingredients and quantities of each dish included in the menu and generating the shopping list.

[0355] Step 9:

[0356] The device sends the generated shopping list to the API of an online retail system such as Rakuten Ichiba or Amazon Fresh, allowing for seamless ordering of ingredients. The input is the generated shopping list, and the output is an order confirmation and delivery information for the online retail system. Specific operations include the device calling the API, sending the required parameters, and providing the user with order confirmation and delivery tracking information.

[0357] (Application example 2)

[0358] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0359] In today's world, users often have little time to plan their meals due to their busy daily lives, and purchasing and cooking ingredients is a time-consuming process. Furthermore, it is important for users to eat meals that suit their health and taste preferences, but it can be difficult to find the optimal menu for that purpose. There is also a need for an efficient system that can integrate with modern food delivery services to handle everything from menu suggestions to ordering and delivery of ingredients.

[0360] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0361] In this invention, the server includes means for acquiring a user's voice, means for converting the voice into text data, means for acquiring user profile data, means for generating a menu plan based on the text data and the profile data, means for presenting the menu plan to the user and receiving feedback, means for refining the menu plan based on the feedback, means for finalizing the menu, means for generating a shopping list based on the menu, means for transmitting the shopping list to an online supermarket and ordering ingredients, and means for linking the ingredient order information with a food delivery service and delivering the ingredients to the user. This not only allows the user to easily receive optimal menu suggestions through voice input, but also allows ingredients to be purchased and delivered automatically, significantly reducing time and effort.

[0362] The "means for acquiring user's voice" is a function for collecting voice uttered by the user through the device.

[0363] The "means for converting voice into text data" is a process for analyzing acquired voice data and converting it into text information.

[0364] The "means for acquiring user profile data" is a function for acquiring personal information, preferences, allergy information, health status, etc., about the user from a database.

[0365] The "means for generating a menu plan based on text data and profile data" is a process for proposing an appropriate menu based on the text data converted from speech and the user's profile data.

[0366] The "means for presenting a menu plan to a user and receiving feedback" is a function for displaying the generated menu plan to a user and collecting opinions and requests for corrections from the user.

[0367] "Means for retuning menu plans based on feedback" refers to the process of re-adjusting the initial menu plans to take into account user comments and modifications.

[0368] The "means for determining the final menu" is the process of determining the final menu based on user feedback.

[0369] The "means for generating a shopping list based on a menu" is a function that automatically creates a list of ingredients to be purchased based on the confirmed menu contents.

[0370] The "means for sending the shopping list to the online supermarket and ordering ingredients" is the process of sending the generated shopping list to the online supermarket and ordering the necessary ingredients.

[0371] "Means of linking ingredient order information with a food delivery service and delivering ingredients to the user" refers to the process of sharing ordered ingredient information with a food delivery service and delivering the ingredients to the user's desired location.

[0372] The "Smart Food Delivery Planner" system of the present invention supports a series of processes: automatically generating a menu from a user's voice input, and purchasing and delivering ingredients based on that menu. This system is mainly composed of the following means:

[0373] Hardware and software used

[0374] Hardware: Smartphones, smart speakers

[0375] Software: AWS Lambda (voice analysis), Azure Cognitive Services (emotion engine), Google Firebase (user profile management), GPT-4 API (menu generation using AI models)

[0376] Acquiring and converting voice input

[0377] A user speaks their meal request into their smartphone or smart speaker. For example, they might say, "I'm tired today, so please make me a healthy and easy dinner." AWS Lambda receives this voice input and converts the voice data into text data.

[0378] Emotion Analysis

[0379] After the voice is converted into text data, Azure Cognitive Services is used to analyze the user's emotions, allowing it to recognize their emotional state, such as whether they are tired or stressed.

[0380] Get user profile

[0381] The server retrieves the user's profile data (food preferences, allergy information, health status, etc.) from Google Firebase and combines it with text data to prepare a menu.

[0382] Menu generation

[0383] The server inputs text data, emotion data, and user profile data into the GPT-4 API to generate optimal menu suggestions, such as "salmon salad, tofu miso soup, and avocado and tomato salad."

[0384] Prompt Sentence Examples

[0385] Suggest a menu for tonight's dinner. Consider the following:

[0386] The user is now tired.

[0387] User profile information: I like fish, I don't like chicken, I have no allergies.

[0388] Easy to cook menu.

[0389] Feedback and Retuning

[0390] The generated menu plan is presented to the user's smartphone or smart speaker. The user provides feedback such as "That's it" or "I'd prefer something simpler." Based on this feedback, the server again uses the AI ​​model to fine-tune the menu plan and present it to the user again. This process is repeated until the user is satisfied.

[0391] Finalize the menu and generate a shopping list

[0392] Based on the final menu, the server automatically generates a shopping list, such as "300g of salmon, 1 daikon radish, 500ml of milk."

[0393] Collaboration with online supermarkets and food delivery services

[0394] The generated shopping list is sent to the online supermarket's API, and the necessary ingredients are automatically ordered. At the same time, this order information is linked to a food delivery service, and the ingredients are delivered to the user's home.

[0395] In this way, the present invention provides a system that proposes optimal menus based on the user's voice input and provides consistent support for the subsequent purchase and delivery of ingredients, allowing users to efficiently perform their daily cooking tasks.

[0396] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0397] Step 1: Getting User Input

[0398] The user speaks their meal request into their smartphone or smart speaker. For example, they might say, "I'm tired today, so please make me a healthy and simple dinner." This voice data is collected by the device.

[0399] Input: User's voice data

[0400] Output: Collected audio data

[0401] Step 2: Convert the audio data

[0402] The device sends the collected voice data to AWS Lambda, which converts it into text data through a speech recognition API. The device analyzes the voice signal and generates corresponding text data.

[0403] Input: Audio data

[0404] Output: Text data

[0405] Step 3: Sentiment Analysis

[0406] After the text data is generated, the device uses Azure Cognitive Services to perform sentiment analysis, which extracts data on the user's emotional state (e.g., tired, stressed) from features such as voice tone and speaking rate.

[0407] Input: Text data

[0408] Output: Emotion data

[0409] Step 4: Retrieving the user profile

[0410] The server retrieves user profile data (food preferences, allergy information, health status, etc.) from Google Firebase. This data, along with text data and emotion data, is the basis for generating menus.

[0411] Input: User identification information

[0412] Output: Profile data

[0413] Step 5: Generate a menu plan

[0414] The server inputs text data, emotion data, and profile data into the GPT-4 API to generate optimal menu suggestions.

[0415] As a specific example, we suggest a menu of "salmon salad, tofu miso soup, and avocado and tomato salad."

[0416] Input: Text data, emotion data, profile data

[0417] Output: Menu plan

[0418] Step 6: Menu presentation and feedback

[0419] The generated menu plan is presented to the user via the device, and the user provides feedback such as "That's fine" or "I'd prefer something simpler." The device collects this feedback.

[0420] Input: Menu plan

[0421] Output: User feedback

[0422] Step 7: Retune your menu

[0423] The server receives user feedback and uses the AI ​​model again to tune the menu plan, generating a new menu plan that modifies and optimizes the initial plan and presents it to the user again.

[0424] Input: User feedback

[0425] Output: Retuned menu plan

[0426] Step 8: Finalize and generate shopping list

[0427] After the user decides on a menu that satisfies them, the server automatically generates a shopping list based on that menu. For example, a specific shopping list such as "300g of salmon, 1 daikon radish, and 500ml of milk" is created.

[0428] Input: Confirmed menu plan

[0429] Output: Shopping list

[0430] Step 9: Order and deliver ingredients

[0431] The server sends the generated shopping list to the online supermarket's API, and the necessary ingredients are automatically ordered. At the same time, this order information is linked to a food delivery service, and the ingredients are delivered to the user's home.

[0432] Input: Shopping list

[0433] Output: Ingredient order and delivery information

[0434] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0435] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0436] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0437] [Second embodiment]

[0438] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0439] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0440] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0441] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0442] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0443] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0444] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0445] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0446] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0447] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0448] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0449] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0450] The system according to the present invention is for supporting users in efficiently planning daily meals and shopping, and includes a series of processes operated by the user, a terminal, and a server. Specific examples will be described below.

[0451] System Overview

[0452] The system begins when the user verbally communicates their menu requests using a device such as a smartphone or smart speaker. The request is converted into text data using voice recognition technology and sent to the server. The server then references the user's profile data to generate an optimal menu plan and presents it to the user. The user can then refine the menu plan based on their feedback, and a shopping list is automatically generated based on the final menu, and ingredients are ordered via an online supermarket.

[0453] Program processing

[0454] Acquiring voice input

[0455] The user speaks into a smartphone or smart speaker, saying, "Please help me think about what to have for dinner next week. I don't like chicken, but I like fish." The device receives this speech and begins analyzing it using a speech recognition API.

[0456] Audio data conversion

[0457] The device converts the voice data into text data through a voice recognition API. The analysis results of the voice data are saved on the device, and the text data is sent to the server.

[0458] Retrieving User Data

[0459] The server then consults the user's profile database to obtain information about the user's likes and dislikes, allergies, family structure, and health status, and is then ready to generate a menu plan that takes into account the user's individual requirements.

[0460] Menu generation

[0461] Based on the text data and profile data acquired by the server, the AI ​​model generates menu suggestions. For example, it might suggest "grilled salmon," "tuna and radish salad," or "potage soup." These menu suggestions are adjusted to match the user's past data.

[0462] User Review and Feedback

[0463] The terminal displays the generated menu plan on the user's device, and the user can provide feedback via voice or text regarding further requests or corrections. For example, the user can express a request in the form of "I'd like a dish that's easier to make."

[0464] Retuning

[0465] The server receives the user's feedback and again uses the AI ​​model to fine-tune the menu plan. Based on the feedback, a new menu plan is generated and presented to the user again. This process is repeated until the user is satisfied.

[0466] Finalize the menu and generate a shopping list

[0467] Based on the final menu, the server automatically generates a shopping list, listing specific ingredients and their quantities, such as "300g of salmon, 1 daikon radish, 500ml of milk."

[0468] Collaboration with online supermarkets

[0469] The device sends the generated shopping list to the online supermarket's API, which seamlessly orders ingredients. The user can check the order details and make any necessary changes. Once the order is confirmed, the ingredients are delivered directly to the user's home from the online supermarket.

[0470] In this way, this system supports the entire process, from user voice input to menu generation, shopping list creation, and even ordering ingredients through an online supermarket, significantly reducing the stress associated with creating household menus.

[0471] The processing flow will be explained below.

[0472] Step 1:

[0473] The user speaks to a smartphone or smart speaker, for example, saying, "Please help me come up with a menu for dinner next week. I don't like chicken, but I like fish."

[0474] Step 2:

[0475] The terminal receives the voice data. Specifically, the microphone in the device captures the user's voice and inputs it into the voice recognition application.

[0476] Step 3:

[0477] The device sends the voice data to the speech recognition API, which converts it into text data. The speech recognition API analyzes the voice signal and generates corresponding text.

[0478] Step 4:

[0479] The terminal transmits the converted text data to the server, where the text data is encoded and sent to the server via a communication network.

[0480] Step 5:

[0481] The server references the user's profile database, reliably identifying the user and extracting information such as likes and dislikes, allergies, nutritional balance, and family structure.

[0482] Step 6:

[0483] The server generates menu suggestions based on text data and profile data. The AI ​​model analyzes this data and proposes multiple menu options, such as "grilled salmon," "tuna and radish salad," and "potage soup."

[0484] Step 7:

[0485] The server generates a menu plan and sends it to the device, where it is formatted in a user-friendly format and displayed on a smartphone or smart speaker.

[0486] Step 8:

[0487] The device presents the user with a menu plan. The user can review the suggestions on screen or via voice prompts and make additional requests or corrections. For example, the user can input a voice command such as, "I'd like a dish that's easier to make."

[0488] Step 9:

[0489] The terminal receives the user's feedback, converts it back into text data, and sends it to the server.

[0490] Step 10:

[0491] The server receives the feedback and uses the AI ​​model again to fine-tune the menu plan, regenerating a new plan that reflects the user's new preferences. This cycle is repeated until the user is finally satisfied.

[0492] Step 11:

[0493] The server determines the final menu plan and generates a list of ingredients based on it. For example, it creates a specific shopping list such as "300g of salmon, 1 daikon radish, 500ml of milk."

[0494] Step 12:

[0495] The server sends the shopping list to the terminal, which displays it to the user.

[0496] Step 13:

[0497] The device sends the shopping list to the online supermarket's API, and each item on the list is checked against the online supermarket's database to confirm availability and price.

[0498] Step 14:

[0499] The user checks the order details and makes any necessary corrections. After checking, the order is confirmed via the terminal.

[0500] Step 15:

[0501] The server sends the confirmed order details to the online supermarket, completing the order process. The online supermarket confirms the order and prepares for delivery.

[0502] These steps allow the user to easily receive appropriate menu suggestions and seamlessly complete a series of operations up to the point of shopping.

[0503] Example 1

[0504] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0505] Users face the challenges of spending a great deal of time and effort on daily menu planning and shopping, and difficulty in selecting ingredients and considering nutritional balance. Furthermore, for users who dislike or have allergies to certain ingredients, selection becomes even more difficult. In addition, it is necessary to link with online supermarkets to create shopping lists and smoothly purchase ingredients. Therefore, a system that can solve all of these challenges at once is needed.

[0506] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0507] In this invention, the server includes means for acquiring a user's voice, means for converting the voice into text data, means for acquiring user profile data, means for generating a menu plan using an artificial intelligence model based on the text data and the profile data, means for presenting the menu plan to the user and receiving feedback, means for readjusting the menu plan based on the feedback, means for finalizing the menu, means for generating a shopping list based on the menu, and means for transmitting the shopping list to an online shopping service and ordering ingredients. This allows users to easily create menus that suit their preferences and needs and seamlessly perform processes from generating the shopping list to purchasing ingredients.

[0508] "User" refers to an individual or group of people who use the system and provide their profile data and feedback.

[0509] "Means for capturing audio" refers to a configuration including hardware and software for capturing audio input made by a user.

[0510] "Means for converting speech to text data" refers to a process that uses speech recognition technology to analyze captured speech input and convert it into corresponding text data.

[0511] "User profile data" is a database containing individual attribute information such as a user's likes and dislikes, allergy information, family structure, and health condition.

[0512] "Means for generating menu plans using an artificial intelligence model" refers to the process of using an AI algorithm to create appropriate menu plans based on the user's text data and profile data.

[0513] "Means for presenting menu plans to the user and receiving feedback" refers to an interface that displays the generated menu plans on the user's terminal and collects further requests and correction instructions from the user.

[0514] "Means for readjusting menu plans" refers to the process of modifying the initial menu plan based on user feedback and presenting it to the user again.

[0515] The "means for finalizing the menu" refers to the process of finally setting the menu that has been adjusted until the user is satisfied.

[0516] "Means for generating a shopping list" refers to the process of creating a list of ingredients and their quantities based on a confirmed menu.

[0517] "Means of sending to an online shopping service and ordering ingredients" refers to the process of sending the generated shopping list to an online supermarket or other online shopping platform to purchase ingredients.

[0518] The present invention relates to a system for assisting a user in efficiently planning daily meals and shopping. Specific embodiments will be described below.

[0519] System Overview

[0520] The system begins when a user uses a device such as a smartphone or smart speaker to verbally communicate their menu preferences. The preference is converted into text data using voice recognition technology and sent to a server. The server then references the user's profile data to generate an optimal menu plan and presents it to the user. The user can then refine the menu plan based on their feedback, and a shopping list is automatically generated based on the final menu, and ingredients are ordered via an online shopping service.

[0521] Acquiring voice input and converting voice data

[0522] The user speaks into a smartphone or smart speaker, saying something like, "Please help me think about what to make for dinner next week. I don't like chicken, but I like fish." The device receives this speech and begins analyzing it using a speech recognition API (for example, Google Speech-to-Text API). The speech recognition API converts the voice data into text data, and the analysis results are temporarily stored on the device.

[0523] Retrieving User Data

[0524] As soon as the server receives the text data, it consults the user's profile database, which stores information about the user's likes and dislikes, allergies, family structure, and health status, to prepare a menu plan that meets the user's individual requirements.

[0525] Menu generation

[0526] The server inputs the text data and profile data into an AI model (for example, a generative AI model) and generates a menu plan. As a specific example, if the user likes fish dishes, menu plans such as "grilled salmon," "tuna and radish salad," and "potage soup" are suggested. This generation includes preprocessing, where the text data is formatted into a prompt sentence.

[0527] User Review and Feedback

[0528] The terminal displays the generated menu plan on the user's device. Specifically, the suggestions are communicated to the user via the smartphone app screen or the voice feedback function of the smart speaker. The user can provide feedback on the displayed menu plan by voice or text. For example, the user can say, "This menu is a little difficult, so I'd prefer something simpler."

[0529] Retuning

[0530] Based on the feedback received from the user, the server uses the AI ​​model to fine-tune the menu plan. Specifically, the feedback is reflected in the prompt text and input into the AI ​​model again to generate a new menu plan. This process is repeated until the user is satisfied.

[0531] Finalize the menu and generate a shopping list

[0532] The server automatically generates a shopping list based on the final menu. For example, it might list specific ingredients and their quantities, such as "300g of salmon, 1 daikon radish, 500ml of milk." This list is stored in a database and sent to the device.

[0533] Collaboration with online supermarkets

[0534] The terminal sends the shopping list received from the server to the API of the online shopping service (for example, an online supermarket API), and seamlessly orders the ingredients. The user can check the order details and make any necessary corrections. Finally, the ingredients are delivered directly from the online shopping service to the user's home.

[0535] In this way, this system supports the entire process, from user voice input to menu generation, shopping list creation, and even ordering ingredients through online shopping services, significantly reducing the time and effort required to create household menus.

[0536] Prompt Sentence Examples

[0537] The user types:

[0538] "Can you suggest a dinner menu for next week? I'd prefer something vegetarian and as easy to prepare as possible."

[0539] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0540] Step 1: Getting voice input

[0541] A user speaks into a smartphone or smart speaker, saying, "Please help me decide what to have for dinner next week. I don't like chicken, but I like fish." The input is voice data, and the device captures this voice. The device's microphone converts the voice into a digital signal and prepares it as voice data. In the next step, this voice data is sent to a voice recognition API.

[0542] Step 2: Convert the audio data

[0543] The device uses a speech recognition API (e.g., Google Speech-to-Text API) to convert the acquired voice data into text data. The input is voice data, which the API analyzes and converts into text data. The generated text data is temporarily stored on the device and then sent to the server in the next step.

[0544] Step 3: Retrieving User Data

[0545] After the server receives the text data, it references the user's profile database. The input is the text data and the user ID. The profile database stores information about the user's likes and dislikes, allergies, family composition, and health status. The server retrieves this data and prepares it for generating a menu plan.

[0546] Step 4: Generate a menu plan

[0547] The server uses text data and profile data as input to generate a menu plan using an artificial intelligence model (e.g., a generative AI model). The input is text data from the user and user profile data. The server formats this data into prompt sentences and inputs them into the AI ​​model to generate a menu plan. The generated menu plan is presented to the user in the next step.

[0548] Step 5: User review and feedback

[0549] The terminal displays the generated menu plan on the user's device. The input is the menu plan sent from the server. Specifically, the suggestions are communicated to the user via the smartphone app screen or the voice feedback function of the smart speaker. The user provides feedback by voice or text. For example, the user may give instructions such as, "This menu is a little difficult, so I would prefer something simpler."

[0550] Step 6: Retune

[0551] Based on the feedback received from the user, the server uses the AI ​​model again to fine-tune the menu plan. The input is the user's feedback. The feedback is reflected in the prompt text and input into the AI ​​model again to generate a new menu plan. This process is repeated until the user is satisfied.

[0552] Step 7: Finalize the menu and generate a shopping list

[0553] The server automatically generates a shopping list based on the finalized menu. The input is the finalized menu plan. The server lists the necessary ingredients and their quantities, and generates the shopping list. For example, the specific ingredient information is "300g of salmon, 1 daikon radish, 500ml of milk."

[0554] Step 8: Linking with online supermarkets

[0555] The terminal receives the shopping list from the server and sends it to the API of the online shopping service, seamlessly ordering ingredients. The input is the shopping list. The user confirms the order details, makes any necessary corrections, and then confirms the order. Finally, the online shopping service delivers the ingredients directly to the user's residence.

[0556] In this way, a system is realized that starts with the user's voice input and, using the server and terminals, goes through a series of processes to efficiently support menu creation and ingredient purchasing.

[0557] (Application example 1)

[0558] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0559] Conventional menu planning systems can create menus by capturing user voice input, but they lack the functionality for real-time menu suggestions and in-store navigation in physical stores, making it difficult to streamline the user's shopping experience.There is a need for a means to provide shopping support linked to real-time menu suggestions in physical stores.

[0560] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0561] In this invention, the server includes a means for receiving menu suggestions in real time using a smartphone or smart glasses when a user is shopping in a physical store, a means for providing location information of each product in the store and performing navigation, and a means for using a generative AI model to generate menu suggestions, thereby enabling users to shop efficiently in the physical store.

[0562] "User" refers to an individual or organization that uses this system.

[0563] "Voice capture means" refers to a device such as a microphone that captures the user's spoken voice in digital form.

[0564] "Speech-to-text conversion means" refers to software or hardware that analyzes acquired voice data and converts it into text information.

[0565] The "profile data acquisition means" refers to a function or device for acquiring data such as personal information, preferences, and allergy information about a user.

[0566] "Menu plan generation means" refers to software or algorithms for suggesting dishes based on the acquired text data and profile data.

[0567] "Feedback receiving means" refers to a means for receiving additional requests or correction instructions provided by a user.

[0568] "Retuning tools" refers to algorithms or software that modify and improve existing menu suggestions based on user feedback.

[0569] "Final menu determination method" refers to the software or algorithm used to determine the final menu after receiving feedback.

[0570] "Shopping list generation means" refers to a function for generating a list of necessary ingredients based on a confirmed menu.

[0571] "Net supermarket sending means" refers to a function for sending the generated shopping list to an online supermarket.

[0572] "Real-time menu suggestion means" refers to a function that allows users to receive real-time menu suggestions via their smartphone or smart glasses while shopping in a physical store.

[0573] "In-store navigation means" refers to a system that provides location information of products in a physical store and guides users based on an automatically generated shopping list.

[0574] "Generative AI model" refers to a program that uses artificial intelligence to generate optimal menus based on a user's profile data and voice data.

[0575] A "prompt sentence" refers to an input sentence used to instruct a generative AI model on appropriate processing.

[0576] The system of the present invention supports users in planning meals and purchasing ingredients while efficiently shopping in a physical store. The system includes a user, a terminal (such as a smartphone or smart glasses), and multiple processes operated through a server.

[0577] System Overview

[0578] The system starts by having the user use their smartphone or smart glasses in a physical store to voice their menu request. For example, the user might say, "I'm busy tomorrow, so please make a simple dinner." The device receives this voice and converts it into text data using a speech recognition API.

[0579] Hardware and software used

[0580] Audio acquisition method: Microphone built into smartphone or smart glasses

[0581] Speech-to-text conversion method: Google Cloud Speech-to-Text API

[0582] Generative AI model: OpenAI API (e.g. GPT-3, ChatGPT)

[0583] Profile data acquisition method: User information database

[0584] Menu planning method: Menu generation algorithm using generative AI model

[0585] Feedback reception method: Interface via smartphone or smart glasses

[0586] Retuning method: Retraining the algorithm based on user feedback

[0587] Shopping list generator: Menu-based list generator software

[0588] In-store navigation: Using AR features on smartphones and smart glasses

[0589] Process Details

[0590] Acquiring and converting voice input

[0591] The user speaks into a smartphone or smart glasses, and the speech is picked up by a microphone on the device and converted into text data using the Google Cloud Speech-to-Text API.

[0592] Obtaining user data and generating menu plans

[0593] The text request is sent to the server, which then references the user's profile data, which includes the user's preferences, allergy information, health status, etc. The server uses this data to generate an optimal menu plan using a generative AI model (such as GPT-3 or ChatGPT).

[0594] As an example, the following prompt sentence will be used:

[0595] User profile: Family: 4 people, Fish lover, Chicken allergy

[0596] Request: I'm busy tomorrow, so I'd like a quick dinner please.

[0597] Please suggest a proposal.

[0598] Menu suggestions and feedback

[0599] The generated menu plan is sent back to the device and presented to the user through the interface of a smartphone or smart glasses, and the user can provide feedback on the proposal via voice or text with any additional requests or corrections.

[0600] Retuning and finalizing the menu

[0601] The server receives feedback from the user and again uses the generative AI model to refine the menu plan, and this process is repeated until the user is satisfied, at which point the final menu is finalized.

[0602] Shopping list generation and in-store navigation

[0603] Based on the final menu, a list of necessary ingredients is automatically generated. The generated shopping list is linked to the location information of products in the store via a smartphone or smart glasses, providing navigation information to the user.

[0604] This allows users to efficiently purchase the ingredients they need in-store, greatly improving their shopping experience.

[0605] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0606] Step 1:

[0607] The user provides voice input. The user's request is recorded using a microphone on a smartphone or smart glasses, providing voice data as input.

[0608] Step 2:

[0609] The device converts the voice data into text using the Google Cloud Speech-to-Text API. Real-time voice analysis is performed to obtain the converted text data.

[0610] Step 3:

[0611] The device sends the textual request to the server, which then transfers the text data to the server via the Internet and retrieves the text data as input. This data is then used to generate the menu.

[0612] Step 4:

[0613] The server retrieves the user's profile data, such as preferences, allergies, and health status, from a profile database or user information database. User-specific data is collected through database query processing.

[0614] Step 5:

[0615] The server generates a menu plan using a generative AI model. For example, it uses GPT-3 or ChatGPT to generate a menu plan based on the acquired text data and profile data. The prompt sentence is input into the generative AI model, and the optimal menu plan is output.

[0616] Examples of prompts include:

[0617] User profile: Family: 4 people, Fish lover, Chicken allergy

[0618] Request: I'm busy tomorrow, so I'd like a quick dinner please.

[0619] Please suggest a proposal.

[0620] Step 6:

[0621] The server returns the generated menu plan to the terminal, which then transfers the plan to the terminal via the Internet. Specifically, the data is preprocessed and transmitted.

[0622] Step 7:

[0623] The device presents the menu plan to the user. The generated menu plan is displayed to the user through the interface of a smartphone or smart glasses. The output may include text data and image data.

[0624] Step 8:

[0625] The user provides feedback, either by voice or text, regarding further requests or corrections to the proposal. This feedback is then sent back to the server via the device. The input is voice or text data.

[0626] Step 9:

[0627] The server retunes the menu plan based on the feedback. It then uses the generative AI model again to generate a new menu plan that reflects the feedback. The adjusted menu plan is output by reapplying the model.

[0628] Step 10:

[0629] The device then presents the retuned menu plan to the user again, and the new proposal is displayed on the smartphone or smart glasses, where a final confirmation is made.

[0630] Step 11:

[0631] The server generates a shopping list based on the final menu. The server lists the ingredients and their quantities needed based on the finalized menu plan. The server retrieves ingredient information from the database and builds the shopping list.

[0632] Step 12:

[0633] Send the shopping list to the online supermarket. The generated shopping list is sent using the online supermarket's API, and ingredients are automatically ordered.

[0634] Step 13:

[0635] Users can receive real-time menu suggestions in-store via their smartphones or smart glasses, enabling efficient in-store purchasing.

[0636] Step 14:

[0637] Provide in-store navigation. Using the AR function of smartphones or smart glasses, the system provides users with location information for the ingredients they need, helping them navigate smoothly within the store. This step involves acquiring location data and providing navigation.

[0638] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0639] The system of the present invention assists users in efficiently planning daily meals and shopping, and also recognizes the user's emotions and suggests meals based on those emotions. The system includes a series of processes operated by the user, a terminal, and a server. Specific examples are described below.

[0640] System Overview

[0641] The system begins when a user uses a device such as a smartphone or smart speaker to voice their menu request. The request is converted into text data using voice recognition technology and sent to a server. The server then references the user's profile data to generate an optimal menu plan and presents it to the user. It then uses an emotion engine to analyze the user's emotions and optimize the suggestions. The user can then re-tune the menu plan based on their feedback, and a shopping list is automatically generated based on the final menu, and ingredients are ordered via an online supermarket.

[0642] Program processing

[0643] Acquiring voice input

[0644] The user speaks into a smartphone or smart speaker, saying, "Please help me think about what to have for dinner next week. I don't like chicken, but I like fish." The device receives this speech and begins analyzing it using a speech recognition API.

[0645] Audio data conversion

[0646] The device sends the voice data to the speech recognition API, which converts it into text data. The speech recognition API analyzes the voice signal and generates corresponding text.

[0647] emotion recognition

[0648] Once the voice and text data are generated, the device uses an emotion engine to recognize the user's emotions. This process analyzes voice characteristics such as tone, speed, and strength to determine whether the user is relaxed or stressed.

[0649] Retrieving User Data

[0650] The server then consults the user's profile database to obtain information about likes, dislikes, allergies, family structure, and health conditions, and is then ready to generate a menu plan that takes into account the user's individual requirements.

[0651] Menu generation

[0652] The server uses an AI model to generate menu suggestions based on the text, profile, and emotion data it acquires. For example, it might suggest "grilled salmon," "tuna and daikon salad," or "potage soup." These menu suggestions are tailored to match the user's past data.

[0653] User Review and Feedback

[0654] The terminal displays the generated menu plan on the user's device. The user can review the proposed content on the screen or via voice prompts and make additional requests or corrections. For example, the user can input a voice command such as, "I'd like a dish that's easier to make."

[0655] Retuning

[0656] The server receives the user's feedback and again uses the AI ​​model to fine-tune the menu plan. Based on the feedback, a new menu plan is generated and presented to the user again. This process is repeated until the user is satisfied.

[0657] Finalize the menu and generate a shopping list

[0658] The server automatically generates a shopping list based on the final menu. For example, it creates a specific shopping list such as "300g of salmon, 1 daikon radish, and 500ml of milk."

[0659] Collaboration with online supermarkets

[0660] The device sends the generated shopping list to the online supermarket's API, which seamlessly orders ingredients. The user can check the order details and make any necessary changes. Once the order is confirmed, the ingredients are delivered directly to the user's home from the online supermarket.

[0661] Specific examples

[0662] For example, a user might say to a smart speaker, "I'm very tired today, so please make me a simple and delicious dinner." At this time, the device converts the voice into text data, and at the same time, the emotion engine recognizes the user's level of fatigue. The server takes into account the user's profile data and level of fatigue and suggests a menu item such as "simple grilled salmon with salad." When the user responds with "That'll do," the server automatically generates a shopping list and sends it to the online supermarket.

[0663] In this way, this system proposes optimal menus based on the user's emotional state and provides easy support for shopping.

[0664] The processing flow will be explained below.

[0665] Step 1:

[0666] The user speaks to a smartphone or smart speaker, for example, saying, "Please help me come up with a menu for dinner next week. I don't like chicken, but I like fish."

[0667] Step 2:

[0668] The terminal receives the voice data. Specifically, the microphone in the device captures the user's voice and inputs it into the voice recognition application.

[0669] Step 3:

[0670] The device sends the voice data to the speech recognition API, which converts it into text data. The speech recognition API analyzes the voice signal and generates corresponding text.

[0671] Step 4:

[0672] The terminal transmits the generated text data to the server, where the text data is encoded and sent to the server via a communication network.

[0673] Step 5:

[0674] The device sends voice and text data to the emotion engine, which recognizes the user's emotions. The emotion engine analyzes features such as tone, speed, and strength of the voice to determine whether the user is relaxed or stressed.

[0675] Step 6:

[0676] The server accesses the user's profile database and retrieves data such as likes and dislikes, allergies, health status, and family composition based on the user's identification information.

[0677] Step 7:

[0678] The server uses an AI model to generate multiple menu suggestions based on the text data, profile data, and emotional data it acquires. For example, it generates "grilled salmon," "tuna and daikon salad," and "potage soup." These menu suggestions are adjusted according to the user's past data and current emotional state.

[0679] Step 8:

[0680] The server generates a menu plan and sends it to the device, where it is formatted in a user-friendly format and displayed on a smartphone or smart speaker.

[0681] Step 9:

[0682] The device presents the user with a menu plan. The user can then review the proposal on screen or by voice, and provide additional requests or corrections. For example, they can input a command such as, "I'd like a dish that's easier to make."

[0683] Step 10:

[0684] The terminal receives the user's feedback, converts it into text data, and sends it to the server.

[0685] Step 11:

[0686] The server receives the feedback and uses the AI ​​model to re-tune the menu plan, regenerating a new plan that reflects the user's new preferences. This cycle is repeated until the user is satisfied.

[0687] Step 12:

[0688] The server determines the final menu plan and generates a list of ingredients based on it. For example, it creates a list tailored to the user's needs, such as "300g of salmon, 1 daikon radish, 500ml of milk" or even "recipes with short cooking times."

[0689] Step 13:

[0690] The server sends the shopping list to the terminal, which displays it to the user.

[0691] Step 14:

[0692] The device sends the shopping list to the online supermarket's API, and each item on the list is checked against the online supermarket's database to confirm availability and price.

[0693] Step 15:

[0694] The user checks the order details and makes any necessary corrections. After checking, the order is confirmed via the terminal.

[0695] Step 16:

[0696] The server sends the confirmed order details to the online supermarket, completing the order process. The online supermarket confirms the order and prepares for delivery.

[0697] These steps allow users to easily receive appropriate menu suggestions and seamlessly complete the entire process, right up to the point of shopping. Taking the user's emotional state into consideration also enables suggestions that better fit their needs, improving the user experience.

[0698] Example 2

[0699] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0700] While conventional menu planning systems can take into account user preferences and allergy information, they have difficulty reflecting the user's emotional state or temporary requests in real time. Furthermore, they lack the ability to automatically generate shopping lists and integrate with online sales systems, leaving room for improvement in user convenience. Therefore, there was a need for a system that could recognize the user's emotional state, suggest optimal menus based on that, automatically generate shopping lists based on those menus, and order ingredients in cooperation with online sales systems, thereby reducing the burden on users.

[0701] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0702] In this invention, the server includes means for acquiring a user's voice, means for converting the voice into text data, means for analyzing the text data and voice data to recognize the user's emotions, means for acquiring user profile data, means for generating a menu plan based on the text data, the profile data, and the emotion data, means for presenting the menu plan to the user and receiving feedback, means for re-tuning the menu plan based on the feedback, means for finalizing the menu, means for generating a shopping list based on the menu, and means for transmitting the shopping list to an online sales system and ordering ingredients. This makes it possible to propose an optimal menu taking into account the user's emotional state, automatically generate a shopping list based on the menu, and seamlessly link with the online sales system.

[0703] A "user" is a person who uses the system to create menu plans and order ingredients.

[0704] "Means for acquiring audio" refers to a function for capturing a user's voice using a device such as a smartphone or smart speaker.

[0705] "Means for converting speech to text data" refers to the process of using speech recognition technology to analyze speech signals and convert them into corresponding text information.

[0706] "Means for recognizing emotions" refers to a function for analyzing voice data and text data to determine the user's emotional state.

[0707] "User profile data" refers to data including the user's individual preferences, allergy information, family structure, health status, and the like.

[0708] "Means for generating menu suggestions" refers to the process of using an AI model to suggest optimal menu suggestions based on the acquired text data, profile data, and emotional data.

[0709] "Means for presenting menu plans and receiving feedback" refers to a function for displaying the generated menu plans to the user and receiving opinions and requests from the user.

[0710] "Means for retuning menu suggestions" refers to the process of readjusting suggested menu suggestions based on user feedback.

[0711] The "means for finalizing the menu" refers to the process of finalizing the menu plan after the user has made adjustments until they are satisfied.

[0712] "Means for generating a shopping list" refers to a function that automatically generates a list of necessary ingredients based on the finalized menu.

[0713] An "online sales system" is a system for ordering and purchasing ingredients and products online.

[0714] "Means for sending to the online sales system and ordering ingredients" refers to the function of sending the automatically generated shopping list to the online sales system and ordering the necessary ingredients.

[0715] The system according to the present invention assists users in efficiently planning daily meals and shopping, and also recognizes the user's emotions and suggests meals based on those emotions. The system includes a series of processes operated by the user, a terminal, and a server. Specific embodiments for implementing the present invention will be described in detail below.

[0716] System Configuration

[0717] This system is constructed mainly using the following hardware and software.

[0718] Hardware

[0719] 1. User device: A device that allows voice input, such as a smartphone or smart speaker.

[0720] 2. Server: A server for processing data and running AI models.

[0721] software

[0722] 1. Speech recognition API: Convert the user's voice into text data using Google Cloud Speech-to-Text, Amazon Transcribe, etc.

[0723] 2. Emotion recognition engine: Analyzes user emotions using IBM Watson Tone Analyzer, Microsoft Azure Emotion API, etc.

[0724] 3. Generative AI model: Menu plans are generated using OpenAI GPT series, Google BERT, etc.

[0725] 4. Online sales system API: Send a shopping list and order ingredients using the APIs of Rakuten Ichiba and Amazon Fresh.

[0726] System Operation

[0727] Acquiring voice input

[0728] The user speaks to the device, saying, "Please help me decide what to have for dinner next week. I don't like chicken, but I like fish." The device receives this speech, sends it to the speech recognition API, and begins analyzing it.

[0729] Audio data conversion

[0730] The device sends the audio data to a speech recognition API, such as Google Cloud Speech-to-Text, which converts it into text. The speech recognition API then analyzes the audio signal and generates corresponding text.

[0731] emotion recognition

[0732] Once the voice and text data is generated, the device uses an emotion engine such as IBM Watson Tone Analyzer to recognize the user's emotions. This process analyzes features such as tone, speed, and strength of the voice to determine whether the user is relaxed or stressed.

[0733] Retrieving User Data

[0734] The server then consults the user's profile database to obtain information about likes, dislikes, allergies, family structure, and health conditions, and is then ready to generate a menu plan that takes into account the user's individual requirements.

[0735] Menu generation

[0736] Based on the text data, profile data, and emotion data acquired by the server, the system uses generative AI models such as the OpenAI GPT series to generate menu suggestions, such as "grilled salmon," "tuna and radish salad," and "potage soup."

[0737] User Review and Feedback

[0738] The terminal displays the generated menu plan on the user's device. The user can review the proposed content on the screen or via voice prompts and make additional requests or corrections. For example, the user can input a voice command such as, "I'd like a dish that's easier to make."

[0739] Retuning

[0740] The server receives the user's feedback and uses the AI ​​model again to fine-tune the menu suggestion. A new menu suggestion, such as "Simmered fish and vegetable miso soup," is generated and presented to the user again. This process is repeated until the user is satisfied.

[0741] Finalize the menu and generate a shopping list

[0742] The server automatically generates a shopping list based on the final menu. For example, it creates a specific shopping list such as "300g of salmon, 1 daikon radish, and 500ml of milk."

[0743] Collaboration with online supermarkets

[0744] The device sends the generated shopping list to the API of online sales systems such as Rakuten Ichiba and Amazon Fresh, allowing for seamless ordering of ingredients. Users can check the order details and make any necessary changes. Once the order is confirmed, the ingredients are delivered directly to the user's home from the online supermarket.

[0745] Specific examples

[0746] For example, a user might say to a smart speaker, "I'm very tired today, so please make me a simple and delicious dinner." At this time, the device converts the voice into text data, and at the same time, the emotion engine recognizes the user's level of fatigue. The server takes into account the user's profile data and level of fatigue and suggests a menu item such as "simple grilled salmon with salad." When the user responds with "That'll do," the server automatically generates a shopping list and sends it to the online supermarket.

[0747] In this way, this system proposes optimal menus based on the user's emotional state and provides easy support for shopping.

[0748] Prompt Sentence Examples

[0749] "I'm very tired today, so please make me a simple and delicious dinner. I don't like chicken, but I do like fish."

[0750] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0751] Step 1:

[0752] A user speaks into a smartphone or smart speaker, for example, saying, "Please help me decide what to have for dinner next week. I don't like chicken, but I like fish." This speech is picked up through the device's microphone. The input is the user's voice data, and the output is a digital audio signal ready to be sent to a speech recognition API.

[0753] Step 2:

[0754] The device sends the captured voice data to a speech recognition API such as Google Cloud Speech-to-Text. The API analyzes the voice signal and generates corresponding text data. The input is the user's voice data, and the output is the analyzed text data. Specific operations include the API converting the voice signal into numerical information, identifying voice patterns, and converting them into text.

[0755] Step 3:

[0756] The device sends the generated text and voice data to an emotion recognition engine such as IBM Watson Tone Analyzer to analyze the user's emotions. The input is data containing features such as the tone, speed, and strength of the voice, and the output is the user's emotional state (relaxed, stressed, etc.). Specific operations include a process in which the emotion engine analyzes the features of the text and voice data to determine the type and intensity of the emotion.

[0757] Step 4:

[0758] The server references the user's profile database to retrieve data such as the user's preferences, allergy information, family structure, and health status. The input is the user's identification information, and the output is the user's detailed profile data. Specifically, the server executes a database query to retrieve the required data and prepares it for use in the next step.

[0759] Step 5:

[0760] Based on the text data, profile data, and emotion data acquired by the server, a generative AI model such as the OpenAI GPT series is used to generate a menu plan. The input is text data, profile data, and emotion data, and the output is the generated text of the menu plan. Specific operations include the process in which the AI ​​model takes these data as input and creates a menu plan that meets the user's requirements.

[0761] Step 6:

[0762] The terminal displays the generated menu plan on the user's device. The user can check the suggestions on the screen or through voice prompts and make additional requests or corrections. For example, the user can input feedback such as "I'd like a dish that's easier to make" through voice input. The input is the generated menu plan, and the output is the user's feedback. Specific operations include the process of the terminal displaying the menu plan using a user interface and receiving feedback from the user through voice or text input.

[0763] Step 7:

[0764] The server receives the user's feedback and uses the generative AI model again to tune the menu plan. The input is the user's feedback and the initial menu plan, and the output is a new, tuned menu plan. Specific operations include the AI ​​model taking the feedback into account to generate a new menu plan with new parameters and presenting it to the user again.

[0765] Step 8:

[0766] The server automatically generates a shopping list based on the finalized menu. The input is the text data of the finalized menu, and the output is a shopping list containing specific ingredients and quantities. Specific operations include the server calculating the ingredients and quantities of each dish included in the menu and generating the shopping list.

[0767] Step 9:

[0768] The device sends the generated shopping list to the API of an online retail system such as Rakuten Ichiba or Amazon Fresh, allowing for seamless ordering of ingredients. The input is the generated shopping list, and the output is an order confirmation and delivery information for the online retail system. Specific operations include the device calling the API, sending the required parameters, and providing the user with order confirmation and delivery tracking information.

[0769] (Application example 2)

[0770] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0771] In today's world, users often have little time to plan their meals due to their busy daily lives, and purchasing and cooking ingredients is a time-consuming process. Furthermore, it is important for users to eat meals that suit their health and taste preferences, but it can be difficult to find the optimal menu for that purpose. There is also a need for an efficient system that can integrate with modern food delivery services to handle everything from menu suggestions to ordering and delivery of ingredients.

[0772] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0773] In this invention, the server includes means for acquiring a user's voice, means for converting the voice into text data, means for acquiring user profile data, means for generating a menu plan based on the text data and the profile data, means for presenting the menu plan to the user and receiving feedback, means for refining the menu plan based on the feedback, means for finalizing the menu, means for generating a shopping list based on the menu, means for transmitting the shopping list to an online supermarket and ordering ingredients, and means for linking the ingredient order information with a food delivery service and delivering the ingredients to the user. This not only allows the user to easily receive optimal menu suggestions through voice input, but also allows ingredients to be purchased and delivered automatically, significantly reducing time and effort.

[0774] The "means for acquiring user's voice" is a function for collecting voice uttered by the user through the device.

[0775] The "means for converting voice into text data" is a process for analyzing acquired voice data and converting it into text information.

[0776] The "means for acquiring user profile data" is a function for acquiring personal information, preferences, allergy information, health status, etc., about the user from a database.

[0777] The "means for generating a menu plan based on text data and profile data" is a process for proposing an appropriate menu based on the text data converted from speech and the user's profile data.

[0778] The "means for presenting a menu plan to a user and receiving feedback" is a function for displaying the generated menu plan to a user and collecting opinions and requests for corrections from the user.

[0779] "Means for retuning menu plans based on feedback" refers to the process of re-adjusting the initial menu plans to take into account user comments and modifications.

[0780] The "means for determining the final menu" is the process of determining the final menu based on user feedback.

[0781] The "means for generating a shopping list based on a menu" is a function that automatically creates a list of ingredients to be purchased based on the confirmed menu contents.

[0782] The "means for sending the shopping list to the online supermarket and ordering ingredients" is the process of sending the generated shopping list to the online supermarket and ordering the necessary ingredients.

[0783] "Means of linking ingredient order information with a food delivery service and delivering ingredients to the user" refers to the process of sharing ordered ingredient information with a food delivery service and delivering the ingredients to the user's desired location.

[0784] The "Smart Food Delivery Planner" system of the present invention supports a series of processes: automatically generating a menu from a user's voice input, and purchasing and delivering ingredients based on that menu. This system is mainly composed of the following means:

[0785] Hardware and software used

[0786] Hardware: Smartphones, smart speakers

[0787] Software: AWS Lambda (voice analysis), Azure Cognitive Services (emotion engine), Google Firebase (user profile management), GPT-4 API (menu generation using AI models)

[0788] Acquiring and converting voice input

[0789] A user speaks their meal request into their smartphone or smart speaker. For example, they might say, "I'm tired today, so please make me a healthy and easy dinner." AWS Lambda receives this voice input and converts the voice data into text data.

[0790] Emotion Analysis

[0791] After the voice is converted into text data, Azure Cognitive Services is used to analyze the user's emotions, allowing it to recognize their emotional state, such as whether they are tired or stressed.

[0792] Get user profile

[0793] The server retrieves the user's profile data (food preferences, allergy information, health status, etc.) from Google Firebase and combines it with text data to prepare a menu.

[0794] Menu generation

[0795] The server inputs text data, emotion data, and user profile data into the GPT-4 API to generate optimal menu suggestions, such as "salmon salad, tofu miso soup, and avocado and tomato salad."

[0796] Prompt Sentence Examples

[0797] Suggest a menu for tonight's dinner. Consider the following:

[0798] The user is now tired.

[0799] User profile information: I like fish, I don't like chicken, I have no allergies.

[0800] Easy to cook menu.

[0801] Feedback and Retuning

[0802] The generated menu plan is presented to the user's smartphone or smart speaker. The user provides feedback such as "That's it" or "I'd prefer something simpler." Based on this feedback, the server again uses the AI ​​model to fine-tune the menu plan and present it to the user again. This process is repeated until the user is satisfied.

[0803] Finalize the menu and generate a shopping list

[0804] Based on the final menu, the server automatically generates a shopping list, such as "300g of salmon, 1 daikon radish, 500ml of milk."

[0805] Collaboration with online supermarkets and food delivery services

[0806] The generated shopping list is sent to the online supermarket's API, and the necessary ingredients are automatically ordered. At the same time, this order information is linked to a food delivery service, and the ingredients are delivered to the user's home.

[0807] In this way, the present invention provides a system that proposes optimal menus based on the user's voice input and provides consistent support for the subsequent purchase and delivery of ingredients, allowing users to efficiently perform their daily cooking tasks.

[0808] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0809] Step 1: Getting User Input

[0810] The user speaks their meal request into their smartphone or smart speaker. For example, they might say, "I'm tired today, so please make me a healthy and simple dinner." This voice data is collected by the device.

[0811] Input: User's voice data

[0812] Output: Collected audio data

[0813] Step 2: Convert the audio data

[0814] The device sends the collected voice data to AWS Lambda, which converts it into text data through a speech recognition API. The device analyzes the voice signal and generates corresponding text data.

[0815] Input: Audio data

[0816] Output: Text data

[0817] Step 3: Sentiment Analysis

[0818] After the text data is generated, the device uses Azure Cognitive Services to perform sentiment analysis, which extracts data on the user's emotional state (e.g., tired, stressed) from features such as voice tone and speaking rate.

[0819] Input: Text data

[0820] Output: Emotion data

[0821] Step 4: Retrieving the user profile

[0822] The server retrieves user profile data (food preferences, allergy information, health status, etc.) from Google Firebase. This data, along with text data and emotion data, is the basis for generating menus.

[0823] Input: User identification information

[0824] Output: Profile data

[0825] Step 5: Generate a menu plan

[0826] The server inputs text data, emotion data, and profile data into the GPT-4 API to generate optimal menu suggestions.

[0827] As a specific example, we suggest a menu of "salmon salad, tofu miso soup, and avocado and tomato salad."

[0828] Input: Text data, emotion data, profile data

[0829] Output: Menu plan

[0830] Step 6: Menu presentation and feedback

[0831] The generated menu plan is presented to the user via the device, and the user provides feedback such as "That's fine" or "I'd prefer something simpler." The device collects this feedback.

[0832] Input: Menu plan

[0833] Output: User feedback

[0834] Step 7: Retune your menu

[0835] The server receives user feedback and uses the AI ​​model again to tune the menu plan, generating a new menu plan that modifies and optimizes the initial plan and presents it to the user again.

[0836] Input: User feedback

[0837] Output: Retuned menu plan

[0838] Step 8: Finalize and generate shopping list

[0839] After the user decides on a menu that satisfies them, the server automatically generates a shopping list based on that menu. For example, a specific shopping list such as "300g of salmon, 1 daikon radish, and 500ml of milk" is created.

[0840] Input: Confirmed menu plan

[0841] Output: Shopping list

[0842] Step 9: Order and deliver ingredients

[0843] The server sends the generated shopping list to the online supermarket's API, and the necessary ingredients are automatically ordered. At the same time, this order information is linked to a food delivery service, and the ingredients are delivered to the user's home.

[0844] Input: Shopping list

[0845] Output: Ingredient order and delivery information

[0846] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0847] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0848] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0849] [Third embodiment]

[0850] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0851] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0852] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0853] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0854] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0855] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0856] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0857] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0858] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0859] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0860] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0861] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0862] The system according to the present invention is for supporting users in efficiently planning daily meals and shopping, and includes a series of processes operated by the user, a terminal, and a server. Specific examples will be described below.

[0863] System Overview

[0864] The system begins when the user verbally communicates their menu requests using a device such as a smartphone or smart speaker. The request is converted into text data using voice recognition technology and sent to the server. The server then references the user's profile data to generate an optimal menu plan and presents it to the user. The user can then refine the menu plan based on their feedback, and a shopping list is automatically generated based on the final menu, and ingredients are ordered via an online supermarket.

[0865] Program processing

[0866] Acquiring voice input

[0867] The user speaks into a smartphone or smart speaker, saying, "Please help me think about what to have for dinner next week. I don't like chicken, but I like fish." The device receives this speech and begins analyzing it using a speech recognition API.

[0868] Audio data conversion

[0869] The device converts the voice data into text data through a voice recognition API. The analysis results of the voice data are saved on the device, and the text data is sent to the server.

[0870] Retrieving User Data

[0871] The server then consults the user's profile database to obtain information about the user's likes and dislikes, allergies, family structure, and health status, and is then ready to generate a menu plan that takes into account the user's individual requirements.

[0872] Menu generation

[0873] Based on the text data and profile data acquired by the server, the AI ​​model generates menu suggestions. For example, it might suggest "grilled salmon," "tuna and radish salad," or "potage soup." These menu suggestions are adjusted to match the user's past data.

[0874] User Review and Feedback

[0875] The terminal displays the generated menu plan on the user's device, and the user can provide feedback via voice or text regarding further requests or corrections. For example, the user can express a request in the form of "I'd like a dish that's easier to make."

[0876] Retuning

[0877] The server receives the user's feedback and again uses the AI ​​model to fine-tune the menu plan. Based on the feedback, a new menu plan is generated and presented to the user again. This process is repeated until the user is satisfied.

[0878] Finalize the menu and generate a shopping list

[0879] Based on the final menu, the server automatically generates a shopping list, listing specific ingredients and their quantities, such as "300g of salmon, 1 daikon radish, 500ml of milk."

[0880] Collaboration with online supermarkets

[0881] The device sends the generated shopping list to the online supermarket's API, which seamlessly orders ingredients. The user can check the order details and make any necessary changes. Once the order is confirmed, the ingredients are delivered directly to the user's home from the online supermarket.

[0882] In this way, this system supports the entire process, from user voice input to menu generation, shopping list creation, and even ordering ingredients through an online supermarket, significantly reducing the stress associated with creating household menus.

[0883] The processing flow will be explained below.

[0884] Step 1:

[0885] The user speaks to a smartphone or smart speaker, for example, saying, "Please help me come up with a menu for dinner next week. I don't like chicken, but I like fish."

[0886] Step 2:

[0887] The terminal receives the voice data. Specifically, the microphone in the device captures the user's voice and inputs it into the voice recognition application.

[0888] Step 3:

[0889] The device sends the voice data to the speech recognition API, which converts it into text data. The speech recognition API analyzes the voice signal and generates corresponding text.

[0890] Step 4:

[0891] The terminal transmits the converted text data to the server, where the text data is encoded and sent to the server via a communication network.

[0892] Step 5:

[0893] The server references the user's profile database, reliably identifying the user and extracting information such as likes and dislikes, allergies, nutritional balance, and family structure.

[0894] Step 6:

[0895] The server generates menu suggestions based on text data and profile data. The AI ​​model analyzes this data and proposes multiple menu options, such as "grilled salmon," "tuna and radish salad," and "potage soup."

[0896] Step 7:

[0897] The server generates a menu plan and sends it to the device, where it is formatted in a user-friendly format and displayed on a smartphone or smart speaker.

[0898] Step 8:

[0899] The device presents the user with a menu plan. The user can review the suggestions on screen or via voice prompts and make additional requests or corrections. For example, the user can input a voice command such as, "I'd like a dish that's easier to make."

[0900] Step 9:

[0901] The terminal receives the user's feedback, converts it back into text data, and sends it to the server.

[0902] Step 10:

[0903] The server receives the feedback and uses the AI ​​model again to fine-tune the menu plan, regenerating a new plan that reflects the user's new preferences. This cycle is repeated until the user is finally satisfied.

[0904] Step 11:

[0905] The server determines the final menu plan and generates a list of ingredients based on it. For example, it creates a specific shopping list such as "300g of salmon, 1 daikon radish, 500ml of milk."

[0906] Step 12:

[0907] The server sends the shopping list to the terminal, which displays it to the user.

[0908] Step 13:

[0909] The device sends the shopping list to the online supermarket's API, and each item on the list is checked against the online supermarket's database to confirm availability and price.

[0910] Step 14:

[0911] The user checks the order details and makes any necessary corrections. After checking, the order is confirmed via the terminal.

[0912] Step 15:

[0913] The server sends the confirmed order details to the online supermarket, completing the order process. The online supermarket confirms the order and prepares for delivery.

[0914] These steps allow the user to easily receive appropriate menu suggestions and seamlessly complete a series of operations up to the point of shopping.

[0915] Example 1

[0916] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0917] Users face the challenges of spending a great deal of time and effort on daily menu planning and shopping, and difficulty in selecting ingredients and considering nutritional balance. Furthermore, for users who dislike or have allergies to certain ingredients, selection becomes even more difficult. In addition, it is necessary to link with online supermarkets to create shopping lists and smoothly purchase ingredients. Therefore, a system that can solve all of these challenges at once is needed.

[0918] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0919] In this invention, the server includes means for acquiring a user's voice, means for converting the voice into text data, means for acquiring user profile data, means for generating a menu plan using an artificial intelligence model based on the text data and the profile data, means for presenting the menu plan to the user and receiving feedback, means for readjusting the menu plan based on the feedback, means for finalizing the menu, means for generating a shopping list based on the menu, and means for transmitting the shopping list to an online shopping service and ordering ingredients. This allows users to easily create menus that suit their preferences and needs and seamlessly perform processes from generating the shopping list to purchasing ingredients.

[0920] "User" refers to an individual or group of people who use the system and provide their profile data and feedback.

[0921] "Means for capturing audio" refers to a configuration including hardware and software for capturing audio input made by a user.

[0922] "Means for converting speech to text data" refers to a process that uses speech recognition technology to analyze captured speech input and convert it into corresponding text data.

[0923] "User profile data" is a database containing individual attribute information such as a user's likes and dislikes, allergy information, family structure, and health condition.

[0924] "Means for generating menu plans using an artificial intelligence model" refers to the process of using an AI algorithm to create appropriate menu plans based on the user's text data and profile data.

[0925] "Means for presenting menu plans to the user and receiving feedback" refers to an interface that displays the generated menu plans on the user's terminal and collects further requests and correction instructions from the user.

[0926] "Means for readjusting menu plans" refers to the process of modifying the initial menu plan based on user feedback and presenting it to the user again.

[0927] The "means for finalizing the menu" refers to the process of finally setting the menu that has been adjusted until the user is satisfied.

[0928] "Means for generating a shopping list" refers to the process of creating a list of ingredients and their quantities based on a confirmed menu.

[0929] "Means of sending to an online shopping service and ordering ingredients" refers to the process of sending the generated shopping list to an online supermarket or other online shopping platform to purchase ingredients.

[0930] The present invention relates to a system for assisting a user in efficiently planning daily meals and shopping. Specific embodiments will be described below.

[0931] System Overview

[0932] The system begins when a user uses a device such as a smartphone or smart speaker to verbally communicate their menu preferences. The preference is converted into text data using voice recognition technology and sent to a server. The server then references the user's profile data to generate an optimal menu plan and presents it to the user. The user can then refine the menu plan based on their feedback, and a shopping list is automatically generated based on the final menu, and ingredients are ordered via an online shopping service.

[0933] Acquiring voice input and converting voice data

[0934] The user speaks into a smartphone or smart speaker, saying something like, "Please help me think about what to make for dinner next week. I don't like chicken, but I like fish." The device receives this speech and begins analyzing it using a speech recognition API (for example, Google Speech-to-Text API). The speech recognition API converts the voice data into text data, and the analysis results are temporarily stored on the device.

[0935] Retrieving User Data

[0936] As soon as the server receives the text data, it consults the user's profile database, which stores information about the user's likes and dislikes, allergies, family structure, and health status, to prepare a menu plan that meets the user's individual requirements.

[0937] Menu generation

[0938] The server inputs the text data and profile data into an AI model (for example, a generative AI model) and generates a menu plan. As a specific example, if the user likes fish dishes, menu plans such as "grilled salmon," "tuna and radish salad," and "potage soup" are suggested. This generation includes preprocessing, where the text data is formatted into a prompt sentence.

[0939] User Review and Feedback

[0940] The terminal displays the generated menu plan on the user's device. Specifically, the suggestions are communicated to the user via the smartphone app screen or the voice feedback function of the smart speaker. The user can provide feedback on the displayed menu plan by voice or text. For example, the user can say, "This menu is a little difficult, so I'd prefer something simpler."

[0941] Retuning

[0942] Based on the feedback received from the user, the server uses the AI ​​model to fine-tune the menu plan. Specifically, the feedback is reflected in the prompt text and input into the AI ​​model again to generate a new menu plan. This process is repeated until the user is satisfied.

[0943] Finalize the menu and generate a shopping list

[0944] The server automatically generates a shopping list based on the final menu. For example, it might list specific ingredients and their quantities, such as "300g of salmon, 1 daikon radish, 500ml of milk." This list is stored in a database and sent to the device.

[0945] Collaboration with online supermarkets

[0946] The terminal sends the shopping list received from the server to the API of the online shopping service (for example, an online supermarket API), and seamlessly orders the ingredients. The user can check the order details and make any necessary corrections. Finally, the ingredients are delivered directly from the online shopping service to the user's home.

[0947] In this way, this system supports the entire process, from user voice input to menu generation, shopping list creation, and even ordering ingredients through online shopping services, significantly reducing the time and effort required to create household menus.

[0948] Prompt Sentence Examples

[0949] The user types:

[0950] "Can you suggest a dinner menu for next week? I'd prefer something vegetarian and as easy to prepare as possible."

[0951] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0952] Step 1: Getting voice input

[0953] A user speaks into a smartphone or smart speaker, saying, "Please help me decide what to have for dinner next week. I don't like chicken, but I like fish." The input is voice data, and the device captures this voice. The device's microphone converts the voice into a digital signal and prepares it as voice data. In the next step, this voice data is sent to a voice recognition API.

[0954] Step 2: Convert the audio data

[0955] The device uses a speech recognition API (e.g., Google Speech-to-Text API) to convert the acquired voice data into text data. The input is voice data, which the API analyzes and converts into text data. The generated text data is temporarily stored on the device and then sent to the server in the next step.

[0956] Step 3: Retrieving User Data

[0957] After the server receives the text data, it references the user's profile database. The input is the text data and the user ID. The profile database stores information about the user's likes and dislikes, allergies, family composition, and health status. The server retrieves this data and prepares it for generating a menu plan.

[0958] Step 4: Generate a menu plan

[0959] The server uses text data and profile data as input to generate a menu plan using an artificial intelligence model (e.g., a generative AI model). The input is text data from the user and user profile data. The server formats this data into prompt sentences and inputs them into the AI ​​model to generate a menu plan. The generated menu plan is presented to the user in the next step.

[0960] Step 5: User review and feedback

[0961] The terminal displays the generated menu plan on the user's device. The input is the menu plan sent from the server. Specifically, the suggestions are communicated to the user via the smartphone app screen or the voice feedback function of the smart speaker. The user provides feedback by voice or text. For example, the user may give instructions such as, "This menu is a little difficult, so I would prefer something simpler."

[0962] Step 6: Retune

[0963] Based on the feedback received from the user, the server uses the AI ​​model again to fine-tune the menu plan. The input is the user's feedback. The feedback is reflected in the prompt text and input into the AI ​​model again to generate a new menu plan. This process is repeated until the user is satisfied.

[0964] Step 7: Finalize the menu and generate a shopping list

[0965] The server automatically generates a shopping list based on the finalized menu. The input is the finalized menu plan. The server lists the necessary ingredients and their quantities, and generates the shopping list. For example, the specific ingredient information is "300g of salmon, 1 daikon radish, 500ml of milk."

[0966] Step 8: Linking with online supermarkets

[0967] The terminal receives the shopping list from the server and sends it to the API of the online shopping service, seamlessly ordering ingredients. The input is the shopping list. The user confirms the order details, makes any necessary corrections, and then confirms the order. Finally, the online shopping service delivers the ingredients directly to the user's residence.

[0968] In this way, a system is realized that starts with the user's voice input and, using the server and terminals, goes through a series of processes to efficiently support menu creation and ingredient purchasing.

[0969] (Application example 1)

[0970] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0971] Conventional menu planning systems can create menus by capturing user voice input, but they lack the functionality for real-time menu suggestions and in-store navigation in physical stores, making it difficult to streamline the user's shopping experience.There is a need for a means to provide shopping support linked to real-time menu suggestions in physical stores.

[0972] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0973] In this invention, the server includes a means for receiving menu suggestions in real time using a smartphone or smart glasses when a user is shopping in a physical store, a means for providing location information of each product in the store and performing navigation, and a means for using a generative AI model to generate menu suggestions, thereby enabling users to shop efficiently in the physical store.

[0974] "User" refers to an individual or organization that uses this system.

[0975] "Voice capture means" refers to a device such as a microphone that captures the user's spoken voice in digital form.

[0976] "Speech-to-text conversion means" refers to software or hardware that analyzes acquired voice data and converts it into text information.

[0977] The "profile data acquisition means" refers to a function or device for acquiring data such as personal information, preferences, and allergy information about a user.

[0978] "Menu plan generation means" refers to software or algorithms for suggesting dishes based on the acquired text data and profile data.

[0979] "Feedback receiving means" refers to a means for receiving additional requests or correction instructions provided by a user.

[0980] "Retuning tools" refers to algorithms or software that modify and improve existing menu suggestions based on user feedback.

[0981] "Final menu determination method" refers to the software or algorithm used to determine the final menu after receiving feedback.

[0982] "Shopping list generation means" refers to a function for generating a list of necessary ingredients based on a confirmed menu.

[0983] "Net supermarket sending means" refers to a function for sending the generated shopping list to an online supermarket.

[0984] "Real-time menu suggestion means" refers to a function that allows users to receive real-time menu suggestions via their smartphone or smart glasses while shopping in a physical store.

[0985] "In-store navigation means" refers to a system that provides location information of products in a physical store and guides users based on an automatically generated shopping list.

[0986] "Generative AI model" refers to a program that uses artificial intelligence to generate optimal menus based on a user's profile data and voice data.

[0987] A "prompt sentence" refers to an input sentence used to instruct a generative AI model on appropriate processing.

[0988] The system of the present invention supports users in planning meals and purchasing ingredients while efficiently shopping in a physical store. The system includes a user, a terminal (such as a smartphone or smart glasses), and multiple processes operated through a server.

[0989] System Overview

[0990] The system starts by having the user use their smartphone or smart glasses in a physical store to voice their menu request. For example, the user might say, "I'm busy tomorrow, so please make a simple dinner." The device receives this voice and converts it into text data using a speech recognition API.

[0991] Hardware and software used

[0992] Audio acquisition method: Microphone built into smartphone or smart glasses

[0993] Speech-to-text conversion method: Google Cloud Speech-to-Text API

[0994] Generative AI model: OpenAI API (e.g. GPT-3, ChatGPT)

[0995] Profile data acquisition method: User information database

[0996] Menu planning method: Menu generation algorithm using generative AI model

[0997] Feedback reception method: Interface via smartphone or smart glasses

[0998] Retuning method: Retraining the algorithm based on user feedback

[0999] Shopping list generator: Menu-based list generator software

[1000] In-store navigation: Using AR features on smartphones and smart glasses

[1001] Process Details

[1002] Acquiring and converting voice input

[1003] The user speaks into a smartphone or smart glasses, and the speech is picked up by a microphone on the device and converted into text data using the Google Cloud Speech-to-Text API.

[1004] Obtaining user data and generating menu plans

[1005] The text request is sent to the server, which then references the user's profile data, which includes the user's preferences, allergy information, health status, etc. The server uses this data to generate an optimal menu plan using a generative AI model (such as GPT-3 or ChatGPT).

[1006] As an example, the following prompt sentence will be used:

[1007] User profile: Family: 4 people, Fish lover, Chicken allergy

[1008] Request: I'm busy tomorrow, so I'd like a quick dinner please.

[1009] Please suggest a proposal.

[1010] Menu suggestions and feedback

[1011] The generated menu plan is sent back to the device and presented to the user through the interface of a smartphone or smart glasses, and the user can provide feedback on the proposal via voice or text with any additional requests or corrections.

[1012] Retuning and finalizing the menu

[1013] The server receives feedback from the user and again uses the generative AI model to refine the menu plan, and this process is repeated until the user is satisfied, at which point the final menu is finalized.

[1014] Shopping list generation and in-store navigation

[1015] Based on the final menu, a list of necessary ingredients is automatically generated. The generated shopping list is linked to the location information of products in the store via a smartphone or smart glasses, providing navigation information to the user.

[1016] This allows users to efficiently purchase the ingredients they need in-store, greatly improving their shopping experience.

[1017] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1018] Step 1:

[1019] The user provides voice input. The user's request is recorded using a microphone on a smartphone or smart glasses, providing voice data as input.

[1020] Step 2:

[1021] The device converts the voice data into text using the Google Cloud Speech-to-Text API. Real-time voice analysis is performed to obtain the converted text data.

[1022] Step 3:

[1023] The device sends the textual request to the server, which then transfers the text data to the server via the Internet and retrieves the text data as input. This data is then used to generate the menu.

[1024] Step 4:

[1025] The server retrieves the user's profile data, such as preferences, allergies, and health status, from a profile database or user information database. User-specific data is collected through database query processing.

[1026] Step 5:

[1027] The server generates a menu plan using a generative AI model. For example, it uses GPT-3 or ChatGPT to generate a menu plan based on the acquired text data and profile data. The prompt sentence is input into the generative AI model, and the optimal menu plan is output.

[1028] Examples of prompts include:

[1029] User profile: Family: 4 people, Fish lover, Chicken allergy

[1030] Request: I'm busy tomorrow, so I'd like a quick dinner please.

[1031] Please suggest a proposal.

[1032] Step 6:

[1033] The server returns the generated menu plan to the terminal, which then transfers the plan to the terminal via the Internet. Specifically, the data is preprocessed and transmitted.

[1034] Step 7:

[1035] The device presents the menu plan to the user. The generated menu plan is displayed to the user through the interface of a smartphone or smart glasses. The output may include text data and image data.

[1036] Step 8:

[1037] The user provides feedback, either by voice or text, regarding further requests or corrections to the proposal. This feedback is then sent back to the server via the device. The input is voice or text data.

[1038] Step 9:

[1039] The server retunes the menu plan based on the feedback. It then uses the generative AI model again to generate a new menu plan that reflects the feedback. The adjusted menu plan is output by reapplying the model.

[1040] Step 10:

[1041] The device then presents the retuned menu plan to the user again, and the new proposal is displayed on the smartphone or smart glasses, where a final confirmation is made.

[1042] Step 11:

[1043] The server generates a shopping list based on the final menu. The server lists the ingredients and their quantities needed based on the finalized menu plan. The server retrieves ingredient information from the database and builds the shopping list.

[1044] Step 12:

[1045] Send the shopping list to the online supermarket. The generated shopping list is sent using the online supermarket's API, and ingredients are automatically ordered.

[1046] Step 13:

[1047] Users can receive real-time menu suggestions in-store via their smartphones or smart glasses, enabling efficient in-store purchasing.

[1048] Step 14:

[1049] Provide in-store navigation. Using the AR function of smartphones or smart glasses, the system provides users with location information for the ingredients they need, helping them navigate smoothly within the store. This step involves acquiring location data and providing navigation.

[1050] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1051] The system of the present invention assists users in efficiently planning daily meals and shopping, and also recognizes the user's emotions and suggests meals based on those emotions. The system includes a series of processes operated by the user, a terminal, and a server. Specific examples are described below.

[1052] System Overview

[1053] The system begins when a user uses a device such as a smartphone or smart speaker to voice their menu request. The request is converted into text data using voice recognition technology and sent to a server. The server then references the user's profile data to generate an optimal menu plan and presents it to the user. It then uses an emotion engine to analyze the user's emotions and optimize the suggestions. The user can then re-tune the menu plan based on their feedback, and a shopping list is automatically generated based on the final menu, and ingredients are ordered via an online supermarket.

[1054] Program processing

[1055] Acquiring voice input

[1056] The user speaks into a smartphone or smart speaker, saying, "Please help me think about what to have for dinner next week. I don't like chicken, but I like fish." The device receives this speech and begins analyzing it using a speech recognition API.

[1057] Audio data conversion

[1058] The device sends the voice data to the speech recognition API, which converts it into text data. The speech recognition API analyzes the voice signal and generates corresponding text.

[1059] emotion recognition

[1060] Once the voice and text data are generated, the device uses an emotion engine to recognize the user's emotions. This process analyzes voice characteristics such as tone, speed, and strength to determine whether the user is relaxed or stressed.

[1061] Retrieving User Data

[1062] The server then consults the user's profile database to obtain information about likes, dislikes, allergies, family structure, and health conditions, and is then ready to generate a menu plan that takes into account the user's individual requirements.

[1063] Menu generation

[1064] The server uses an AI model to generate menu suggestions based on the text, profile, and emotion data it acquires. For example, it might suggest "grilled salmon," "tuna and daikon salad," or "potage soup." These menu suggestions are tailored to match the user's past data.

[1065] User Review and Feedback

[1066] The terminal displays the generated menu plan on the user's device. The user can review the proposed content on the screen or via voice prompts and make additional requests or corrections. For example, the user can input a voice command such as, "I'd like a dish that's easier to make."

[1067] Retuning

[1068] The server receives the user's feedback and again uses the AI ​​model to fine-tune the menu plan. Based on the feedback, a new menu plan is generated and presented to the user again. This process is repeated until the user is satisfied.

[1069] Finalize the menu and generate a shopping list

[1070] The server automatically generates a shopping list based on the final menu. For example, it creates a specific shopping list such as "300g of salmon, 1 daikon radish, and 500ml of milk."

[1071] Collaboration with online supermarkets

[1072] The device sends the generated shopping list to the online supermarket's API, which seamlessly orders ingredients. The user can check the order details and make any necessary changes. Once the order is confirmed, the ingredients are delivered directly to the user's home from the online supermarket.

[1073] Specific examples

[1074] For example, a user might say to a smart speaker, "I'm very tired today, so please make me a simple and delicious dinner." At this time, the device converts the voice into text data, and at the same time, the emotion engine recognizes the user's level of fatigue. The server takes into account the user's profile data and level of fatigue and suggests a menu item such as "simple grilled salmon with salad." When the user responds with "That'll do," the server automatically generates a shopping list and sends it to the online supermarket.

[1075] In this way, this system proposes optimal menus based on the user's emotional state and provides easy support for shopping.

[1076] The processing flow will be explained below.

[1077] Step 1:

[1078] The user speaks to a smartphone or smart speaker, for example, saying, "Please help me come up with a menu for dinner next week. I don't like chicken, but I like fish."

[1079] Step 2:

[1080] The terminal receives the voice data. Specifically, the microphone in the device captures the user's voice and inputs it into the voice recognition application.

[1081] Step 3:

[1082] The device sends the voice data to the speech recognition API, which converts it into text data. The speech recognition API analyzes the voice signal and generates corresponding text.

[1083] Step 4:

[1084] The terminal transmits the generated text data to the server, where the text data is encoded and sent to the server via a communication network.

[1085] Step 5:

[1086] The device sends voice and text data to the emotion engine, which recognizes the user's emotions. The emotion engine analyzes features such as tone, speed, and strength of the voice to determine whether the user is relaxed or stressed.

[1087] Step 6:

[1088] The server accesses the user's profile database and retrieves data such as likes and dislikes, allergies, health status, and family composition based on the user's identification information.

[1089] Step 7:

[1090] The server uses an AI model to generate multiple menu suggestions based on the text data, profile data, and emotional data it acquires. For example, it generates "grilled salmon," "tuna and daikon salad," and "potage soup." These menu suggestions are adjusted according to the user's past data and current emotional state.

[1091] Step 8:

[1092] The server generates a menu plan and sends it to the device, where it is formatted in a user-friendly format and displayed on a smartphone or smart speaker.

[1093] Step 9:

[1094] The device presents the user with a menu plan. The user can then review the proposal on screen or by voice, and provide additional requests or corrections. For example, they can input a command such as, "I'd like a dish that's easier to make."

[1095] Step 10:

[1096] The terminal receives the user's feedback, converts it into text data, and sends it to the server.

[1097] Step 11:

[1098] The server receives the feedback and uses the AI ​​model to re-tune the menu plan, regenerating a new plan that reflects the user's new preferences. This cycle is repeated until the user is satisfied.

[1099] Step 12:

[1100] The server determines the final menu plan and generates a list of ingredients based on it. For example, it creates a list tailored to the user's needs, such as "300g of salmon, 1 daikon radish, 500ml of milk" or even "recipes with short cooking times."

[1101] Step 13:

[1102] The server sends the shopping list to the terminal, which displays it to the user.

[1103] Step 14:

[1104] The device sends the shopping list to the online supermarket's API, and each item on the list is checked against the online supermarket's database to confirm availability and price.

[1105] Step 15:

[1106] The user checks the order details and makes any necessary corrections. After checking, the order is confirmed via the terminal.

[1107] Step 16:

[1108] The server sends the confirmed order details to the online supermarket, completing the order process. The online supermarket confirms the order and prepares for delivery.

[1109] These steps allow users to easily receive appropriate menu suggestions and seamlessly complete the entire process, right up to the point of shopping. Taking the user's emotional state into consideration also enables suggestions that better fit their needs, improving the user experience.

[1110] Example 2

[1111] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1112] While conventional menu planning systems can take into account user preferences and allergy information, they have difficulty reflecting the user's emotional state or temporary requests in real time. Furthermore, they lack the ability to automatically generate shopping lists and integrate with online sales systems, leaving room for improvement in user convenience. Therefore, there was a need for a system that could recognize the user's emotional state, suggest optimal menus based on that, automatically generate shopping lists based on those menus, and order ingredients in cooperation with online sales systems, thereby reducing the burden on users.

[1113] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1114] In this invention, the server includes means for acquiring a user's voice, means for converting the voice into text data, means for analyzing the text data and voice data to recognize the user's emotions, means for acquiring user profile data, means for generating a menu plan based on the text data, the profile data, and the emotion data, means for presenting the menu plan to the user and receiving feedback, means for re-tuning the menu plan based on the feedback, means for finalizing the menu, means for generating a shopping list based on the menu, and means for transmitting the shopping list to an online sales system and ordering ingredients. This makes it possible to propose an optimal menu taking into account the user's emotional state, automatically generate a shopping list based on the menu, and seamlessly link with the online sales system.

[1115] A "user" is a person who uses the system to create menu plans and order ingredients.

[1116] "Means for acquiring audio" refers to a function for capturing a user's voice using a device such as a smartphone or smart speaker.

[1117] "Means for converting speech to text data" refers to the process of using speech recognition technology to analyze speech signals and convert them into corresponding text information.

[1118] "Means for recognizing emotions" refers to a function for analyzing voice data and text data to determine the user's emotional state.

[1119] "User profile data" refers to data including the user's individual preferences, allergy information, family structure, health status, and the like.

[1120] "Means for generating menu suggestions" refers to the process of using an AI model to suggest optimal menu suggestions based on the acquired text data, profile data, and emotional data.

[1121] "Means for presenting menu plans and receiving feedback" refers to a function for displaying the generated menu plans to the user and receiving opinions and requests from the user.

[1122] "Means for retuning menu suggestions" refers to the process of readjusting suggested menu suggestions based on user feedback.

[1123] The "means for finalizing the menu" refers to the process of finalizing the menu plan after the user has made adjustments until they are satisfied.

[1124] "Means for generating a shopping list" refers to a function that automatically generates a list of necessary ingredients based on the finalized menu.

[1125] An "online sales system" is a system for ordering and purchasing ingredients and products online.

[1126] "Means for sending to the online sales system and ordering ingredients" refers to the function of sending the automatically generated shopping list to the online sales system and ordering the necessary ingredients.

[1127] The system according to the present invention assists users in efficiently planning daily meals and shopping, and also recognizes the user's emotions and suggests meals based on those emotions. The system includes a series of processes operated by the user, a terminal, and a server. Specific embodiments for implementing the present invention will be described in detail below.

[1128] System Configuration

[1129] This system is constructed mainly using the following hardware and software.

[1130] Hardware

[1131] 1. User device: A device that allows voice input, such as a smartphone or smart speaker.

[1132] 2. Server: A server for processing data and running AI models.

[1133] software

[1134] 1. Speech recognition API: Convert the user's voice into text data using Google Cloud Speech-to-Text, Amazon Transcribe, etc.

[1135] 2. Emotion recognition engine: Analyzes user emotions using IBM Watson Tone Analyzer, Microsoft Azure Emotion API, etc.

[1136] 3. Generative AI model: Menu plans are generated using OpenAI GPT series, Google BERT, etc.

[1137] 4. Online sales system API: Send a shopping list and order ingredients using the APIs of Rakuten Ichiba and Amazon Fresh.

[1138] System Operation

[1139] Acquiring voice input

[1140] The user speaks to the device, saying, "Please help me decide what to have for dinner next week. I don't like chicken, but I like fish." The device receives this speech, sends it to the speech recognition API, and begins analyzing it.

[1141] Audio data conversion

[1142] The device sends the audio data to a speech recognition API, such as Google Cloud Speech-to-Text, which converts it into text. The speech recognition API then analyzes the audio signal and generates corresponding text.

[1143] emotion recognition

[1144] Once the voice and text data is generated, the device uses an emotion engine such as IBM Watson Tone Analyzer to recognize the user's emotions. This process analyzes features such as tone, speed, and strength of the voice to determine whether the user is relaxed or stressed.

[1145] Retrieving User Data

[1146] The server then consults the user's profile database to obtain information about likes, dislikes, allergies, family structure, and health conditions, and is then ready to generate a menu plan that takes into account the user's individual requirements.

[1147] Menu generation

[1148] Based on the text data, profile data, and emotion data acquired by the server, the system uses generative AI models such as the OpenAI GPT series to generate menu suggestions, such as "grilled salmon," "tuna and radish salad," and "potage soup."

[1149] User Review and Feedback

[1150] The terminal displays the generated menu plan on the user's device. The user can review the proposed content on the screen or via voice prompts and make additional requests or corrections. For example, the user can input a voice command such as, "I'd like a dish that's easier to make."

[1151] Retuning

[1152] The server receives the user's feedback and uses the AI ​​model again to fine-tune the menu suggestion. A new menu suggestion, such as "Simmered fish and vegetable miso soup," is generated and presented to the user again. This process is repeated until the user is satisfied.

[1153] Finalize the menu and generate a shopping list

[1154] The server automatically generates a shopping list based on the final menu. For example, it creates a specific shopping list such as "300g of salmon, 1 daikon radish, and 500ml of milk."

[1155] Collaboration with online supermarkets

[1156] The device sends the generated shopping list to the API of online sales systems such as Rakuten Ichiba and Amazon Fresh, allowing for seamless ordering of ingredients. Users can check the order details and make any necessary changes. Once the order is confirmed, the ingredients are delivered directly to the user's home from the online supermarket.

[1157] Specific examples

[1158] For example, a user might say to a smart speaker, "I'm very tired today, so please make me a simple and delicious dinner." At this time, the device converts the voice into text data, and at the same time, the emotion engine recognizes the user's level of fatigue. The server takes into account the user's profile data and level of fatigue and suggests a menu item such as "simple grilled salmon with salad." When the user responds with "That'll do," the server automatically generates a shopping list and sends it to the online supermarket.

[1159] In this way, this system proposes optimal menus based on the user's emotional state and provides easy support for shopping.

[1160] Prompt Sentence Examples

[1161] "I'm very tired today, so please make me a simple and delicious dinner. I don't like chicken, but I do like fish."

[1162] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1163] Step 1:

[1164] A user speaks into a smartphone or smart speaker, for example, saying, "Please help me decide what to have for dinner next week. I don't like chicken, but I like fish." This speech is picked up through the device's microphone. The input is the user's voice data, and the output is a digital audio signal ready to be sent to a speech recognition API.

[1165] Step 2:

[1166] The device sends the captured voice data to a speech recognition API such as Google Cloud Speech-to-Text. The API analyzes the voice signal and generates corresponding text data. The input is the user's voice data, and the output is the analyzed text data. Specific operations include the API converting the voice signal into numerical information, identifying voice patterns, and converting them into text.

[1167] Step 3:

[1168] The device sends the generated text and voice data to an emotion recognition engine such as IBM Watson Tone Analyzer to analyze the user's emotions. The input is data containing features such as the tone, speed, and strength of the voice, and the output is the user's emotional state (relaxed, stressed, etc.). Specific operations include a process in which the emotion engine analyzes the features of the text and voice data to determine the type and intensity of the emotion.

[1169] Step 4:

[1170] The server references the user's profile database to retrieve data such as the user's preferences, allergy information, family structure, and health status. The input is the user's identification information, and the output is the user's detailed profile data. Specifically, the server executes a database query to retrieve the required data and prepares it for use in the next step.

[1171] Step 5:

[1172] Based on the text data, profile data, and emotion data acquired by the server, a generative AI model such as the OpenAI GPT series is used to generate a menu plan. The input is text data, profile data, and emotion data, and the output is the generated text of the menu plan. Specific operations include the process in which the AI ​​model takes these data as input and creates a menu plan that meets the user's requirements.

[1173] Step 6:

[1174] The terminal displays the generated menu plan on the user's device. The user can check the suggestions on the screen or through voice prompts and make additional requests or corrections. For example, the user can input feedback such as "I'd like a dish that's easier to make" through voice input. The input is the generated menu plan, and the output is the user's feedback. Specific operations include the process of the terminal displaying the menu plan using a user interface and receiving feedback from the user through voice or text input.

[1175] Step 7:

[1176] The server receives the user's feedback and uses the generative AI model again to tune the menu plan. The input is the user's feedback and the initial menu plan, and the output is a new, tuned menu plan. Specific operations include the AI ​​model taking the feedback into account to generate a new menu plan with new parameters and presenting it to the user again.

[1177] Step 8:

[1178] The server automatically generates a shopping list based on the finalized menu. The input is the text data of the finalized menu, and the output is a shopping list containing specific ingredients and quantities. Specific operations include the server calculating the ingredients and quantities of each dish included in the menu and generating the shopping list.

[1179] Step 9:

[1180] The device sends the generated shopping list to the API of an online retail system such as Rakuten Ichiba or Amazon Fresh, allowing for seamless ordering of ingredients. The input is the generated shopping list, and the output is an order confirmation and delivery information for the online retail system. Specific operations include the device calling the API, sending the required parameters, and providing the user with order confirmation and delivery tracking information.

[1181] (Application example 2)

[1182] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1183] In today's world, users often have little time to plan their meals due to their busy daily lives, and purchasing and cooking ingredients is a time-consuming process. Furthermore, it is important for users to eat meals that suit their health and taste preferences, but it can be difficult to find the optimal menu for that purpose. There is also a need for an efficient system that can integrate with modern food delivery services to handle everything from menu suggestions to ordering and delivery of ingredients.

[1184] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1185] In this invention, the server includes means for acquiring a user's voice, means for converting the voice into text data, means for acquiring user profile data, means for generating a menu plan based on the text data and the profile data, means for presenting the menu plan to the user and receiving feedback, means for refining the menu plan based on the feedback, means for finalizing the menu, means for generating a shopping list based on the menu, means for transmitting the shopping list to an online supermarket and ordering ingredients, and means for linking the ingredient order information with a food delivery service and delivering the ingredients to the user. This not only allows the user to easily receive optimal menu suggestions through voice input, but also allows ingredients to be purchased and delivered automatically, significantly reducing time and effort.

[1186] The "means for acquiring user's voice" is a function for collecting voice uttered by the user through the device.

[1187] The "means for converting voice into text data" is a process for analyzing acquired voice data and converting it into text information.

[1188] The "means for acquiring user profile data" is a function for acquiring personal information, preferences, allergy information, health status, etc., about the user from a database.

[1189] The "means for generating a menu plan based on text data and profile data" is a process for proposing an appropriate menu based on the text data converted from speech and the user's profile data.

[1190] The "means for presenting a menu plan to a user and receiving feedback" is a function for displaying the generated menu plan to a user and collecting opinions and requests for corrections from the user.

[1191] "Means for retuning menu plans based on feedback" refers to the process of re-adjusting the initial menu plans to take into account user comments and modifications.

[1192] The "means for determining the final menu" is the process of determining the final menu based on user feedback.

[1193] The "means for generating a shopping list based on a menu" is a function that automatically creates a list of ingredients to be purchased based on the confirmed menu contents.

[1194] The "means for sending the shopping list to the online supermarket and ordering ingredients" is the process of sending the generated shopping list to the online supermarket and ordering the necessary ingredients.

[1195] "Means of linking ingredient order information with a food delivery service and delivering ingredients to the user" refers to the process of sharing ordered ingredient information with a food delivery service and delivering the ingredients to the user's desired location.

[1196] The "Smart Food Delivery Planner" system of the present invention supports a series of processes: automatically generating a menu from a user's voice input, and purchasing and delivering ingredients based on that menu. This system is mainly composed of the following means:

[1197] Hardware and software used

[1198] Hardware: Smartphones, smart speakers

[1199] Software: AWS Lambda (voice analysis), Azure Cognitive Services (emotion engine), Google Firebase (user profile management), GPT-4 API (menu generation using AI models)

[1200] Acquiring and converting voice input

[1201] A user speaks their meal request into their smartphone or smart speaker. For example, they might say, "I'm tired today, so please make me a healthy and easy dinner." AWS Lambda receives this voice input and converts the voice data into text data.

[1202] Emotion Analysis

[1203] After the voice is converted into text data, Azure Cognitive Services is used to analyze the user's emotions, allowing it to recognize their emotional state, such as whether they are tired or stressed.

[1204] Get user profile

[1205] The server retrieves the user's profile data (food preferences, allergy information, health status, etc.) from Google Firebase and combines it with text data to prepare a menu.

[1206] Menu generation

[1207] The server inputs text data, emotion data, and user profile data into the GPT-4 API to generate optimal menu suggestions, such as "salmon salad, tofu miso soup, and avocado and tomato salad."

[1208] Prompt Sentence Examples

[1209] Suggest a menu for tonight's dinner. Consider the following:

[1210] The user is now tired.

[1211] User profile information: I like fish, I don't like chicken, I have no allergies.

[1212] Easy to cook menu.

[1213] Feedback and Retuning

[1214] The generated menu plan is presented to the user's smartphone or smart speaker. The user provides feedback such as "That's it" or "I'd prefer something simpler." Based on this feedback, the server again uses the AI ​​model to fine-tune the menu plan and present it to the user again. This process is repeated until the user is satisfied.

[1215] Finalize the menu and generate a shopping list

[1216] Based on the final menu, the server automatically generates a shopping list, such as "300g of salmon, 1 daikon radish, 500ml of milk."

[1217] Collaboration with online supermarkets and food delivery services

[1218] The generated shopping list is sent to the online supermarket's API, and the necessary ingredients are automatically ordered. At the same time, this order information is linked to a food delivery service, and the ingredients are delivered to the user's home.

[1219] In this way, the present invention provides a system that proposes optimal menus based on the user's voice input and provides consistent support for the subsequent purchase and delivery of ingredients, allowing users to efficiently perform their daily cooking tasks.

[1220] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1221] Step 1: Getting User Input

[1222] The user speaks their meal request into their smartphone or smart speaker. For example, they might say, "I'm tired today, so please make me a healthy and simple dinner." This voice data is collected by the device.

[1223] Input: User's voice data

[1224] Output: Collected audio data

[1225] Step 2: Convert the audio data

[1226] The device sends the collected voice data to AWS Lambda, which converts it into text data through a speech recognition API. The device analyzes the voice signal and generates corresponding text data.

[1227] Input: Audio data

[1228] Output: Text data

[1229] Step 3: Sentiment Analysis

[1230] After the text data is generated, the device uses Azure Cognitive Services to perform sentiment analysis, which extracts data on the user's emotional state (e.g., tired, stressed) from features such as voice tone and speaking rate.

[1231] Input: Text data

[1232] Output: Emotion data

[1233] Step 4: Retrieving the user profile

[1234] The server retrieves user profile data (food preferences, allergy information, health status, etc.) from Google Firebase. This data, along with text data and emotion data, is the basis for generating menus.

[1235] Input: User identification information

[1236] Output: Profile data

[1237] Step 5: Generate a menu plan

[1238] The server inputs text data, emotion data, and profile data into the GPT-4 API to generate optimal menu suggestions.

[1239] As a specific example, we suggest a menu of "salmon salad, tofu miso soup, and avocado and tomato salad."

[1240] Input: Text data, emotion data, profile data

[1241] Output: Menu plan

[1242] Step 6: Menu presentation and feedback

[1243] The generated menu plan is presented to the user via the device, and the user provides feedback such as "That's fine" or "I'd prefer something simpler." The device collects this feedback.

[1244] Input: Menu plan

[1245] Output: User feedback

[1246] Step 7: Retune your menu

[1247] The server receives user feedback and uses the AI ​​model again to tune the menu plan, generating a new menu plan that modifies and optimizes the initial plan and presents it to the user again.

[1248] Input: User feedback

[1249] Output: Retuned menu plan

[1250] Step 8: Finalize and generate shopping list

[1251] After the user decides on a menu that satisfies them, the server automatically generates a shopping list based on that menu. For example, a specific shopping list such as "300g of salmon, 1 daikon radish, and 500ml of milk" is created.

[1252] Input: Confirmed menu plan

[1253] Output: Shopping list

[1254] Step 9: Order and deliver ingredients

[1255] The server sends the generated shopping list to the online supermarket's API, and the necessary ingredients are automatically ordered. At the same time, this order information is linked to a food delivery service, and the ingredients are delivered to the user's home.

[1256] Input: Shopping list

[1257] Output: Ingredient order and delivery information

[1258] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1259] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1260] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1261] [Fourth embodiment]

[1262] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1263] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1264] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1265] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1266] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1267] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1268] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1269] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1270] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1271] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1272] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1273] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1274] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1275] The system according to the present invention is for supporting users in efficiently planning daily meals and shopping, and includes a series of processes operated by the user, a terminal, and a server. Specific examples will be described below.

[1276] System Overview

[1277] The system begins when the user verbally communicates their menu requests using a device such as a smartphone or smart speaker. The request is converted into text data using voice recognition technology and sent to the server. The server then references the user's profile data to generate an optimal menu plan and presents it to the user. The user can then refine the menu plan based on their feedback, and a shopping list is automatically generated based on the final menu, and ingredients are ordered via an online supermarket.

[1278] Program processing

[1279] Acquiring voice input

[1280] The user speaks into a smartphone or smart speaker, saying, "Please help me think about what to have for dinner next week. I don't like chicken, but I like fish." The device receives this speech and begins analyzing it using a speech recognition API.

[1281] Audio data conversion

[1282] The device converts the voice data into text data through a voice recognition API. The analysis results of the voice data are saved on the device, and the text data is sent to the server.

[1283] Retrieving User Data

[1284] The server then consults the user's profile database to obtain information about the user's likes and dislikes, allergies, family structure, and health status, and is then ready to generate a menu plan that takes into account the user's individual requirements.

[1285] Menu generation

[1286] Based on the text data and profile data acquired by the server, the AI ​​model generates menu suggestions. For example, it might suggest "grilled salmon," "tuna and radish salad," or "potage soup." These menu suggestions are adjusted to match the user's past data.

[1287] User Review and Feedback

[1288] The terminal displays the generated menu plan on the user's device, and the user can provide feedback via voice or text regarding further requests or corrections. For example, the user can express a request in the form of "I'd like a dish that's easier to make."

[1289] Retuning

[1290] The server receives the user's feedback and again uses the AI ​​model to fine-tune the menu plan. Based on the feedback, a new menu plan is generated and presented to the user again. This process is repeated until the user is satisfied.

[1291] Finalize the menu and generate a shopping list

[1292] Based on the final menu, the server automatically generates a shopping list, listing specific ingredients and their quantities, such as "300g of salmon, 1 daikon radish, 500ml of milk."

[1293] Collaboration with online supermarkets

[1294] The device sends the generated shopping list to the online supermarket's API, which seamlessly orders ingredients. The user can check the order details and make any necessary changes. Once the order is confirmed, the ingredients are delivered directly to the user's home from the online supermarket.

[1295] In this way, this system supports the entire process, from user voice input to menu generation, shopping list creation, and even ordering ingredients through an online supermarket, significantly reducing the stress associated with creating household menus.

[1296] The processing flow will be explained below.

[1297] Step 1:

[1298] The user speaks to a smartphone or smart speaker, for example, saying, "Please help me come up with a menu for dinner next week. I don't like chicken, but I like fish."

[1299] Step 2:

[1300] The terminal receives the voice data. Specifically, the microphone in the device captures the user's voice and inputs it into the voice recognition application.

[1301] Step 3:

[1302] The device sends the voice data to the speech recognition API, which converts it into text data. The speech recognition API analyzes the voice signal and generates corresponding text.

[1303] Step 4:

[1304] The terminal transmits the converted text data to the server, where the text data is encoded and sent to the server via a communication network.

[1305] Step 5:

[1306] The server references the user's profile database, reliably identifying the user and extracting information such as likes and dislikes, allergies, nutritional balance, and family structure.

[1307] Step 6:

[1308] The server generates menu suggestions based on text data and profile data. The AI ​​model analyzes this data and proposes multiple menu options, such as "grilled salmon," "tuna and radish salad," and "potage soup."

[1309] Step 7:

[1310] The server generates a menu plan and sends it to the device, where it is formatted in a user-friendly format and displayed on a smartphone or smart speaker.

[1311] Step 8:

[1312] The device presents the user with a menu plan. The user can review the suggestions on screen or via voice prompts and make additional requests or corrections. For example, the user can input a voice command such as, "I'd like a dish that's easier to make."

[1313] Step 9:

[1314] The terminal receives the user's feedback, converts it back into text data, and sends it to the server.

[1315] Step 10:

[1316] The server receives the feedback and uses the AI ​​model again to fine-tune the menu plan, regenerating a new plan that reflects the user's new preferences. This cycle is repeated until the user is finally satisfied.

[1317] Step 11:

[1318] The server determines the final menu plan and generates a list of ingredients based on it. For example, it creates a specific shopping list such as "300g of salmon, 1 daikon radish, 500ml of milk."

[1319] Step 12:

[1320] The server sends the shopping list to the terminal, which displays it to the user.

[1321] Step 13:

[1322] The device sends the shopping list to the online supermarket's API, and each item on the list is checked against the online supermarket's database to confirm availability and price.

[1323] Step 14:

[1324] The user checks the order details and makes any necessary corrections. After checking, the order is confirmed via the terminal.

[1325] Step 15:

[1326] The server sends the confirmed order details to the online supermarket, completing the order process. The online supermarket confirms the order and prepares for delivery.

[1327] These steps allow the user to easily receive appropriate menu suggestions and seamlessly complete a series of operations up to the point of shopping.

[1328] Example 1

[1329] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1330] Users face the challenges of spending a great deal of time and effort on daily menu planning and shopping, and difficulty in selecting ingredients and considering nutritional balance. Furthermore, for users who dislike or have allergies to certain ingredients, selection becomes even more difficult. In addition, it is necessary to link with online supermarkets to create shopping lists and smoothly purchase ingredients. Therefore, a system that can solve all of these challenges at once is needed.

[1331] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1332] In this invention, the server includes means for acquiring a user's voice, means for converting the voice into text data, means for acquiring user profile data, means for generating a menu plan using an artificial intelligence model based on the text data and the profile data, means for presenting the menu plan to the user and receiving feedback, means for readjusting the menu plan based on the feedback, means for finalizing the menu, means for generating a shopping list based on the menu, and means for transmitting the shopping list to an online shopping service and ordering ingredients. This allows users to easily create menus that suit their preferences and needs and seamlessly perform processes from generating the shopping list to purchasing ingredients.

[1333] "User" refers to an individual or group of people who use the system and provide their profile data and feedback.

[1334] "Means for capturing audio" refers to a configuration including hardware and software for capturing audio input made by a user.

[1335] "Means for converting speech to text data" refers to a process that uses speech recognition technology to analyze captured speech input and convert it into corresponding text data.

[1336] "User profile data" is a database containing individual attribute information such as a user's likes and dislikes, allergy information, family structure, and health condition.

[1337] "Means for generating menu plans using an artificial intelligence model" refers to the process of using an AI algorithm to create appropriate menu plans based on the user's text data and profile data.

[1338] "Means for presenting menu plans to the user and receiving feedback" refers to an interface that displays the generated menu plans on the user's terminal and collects further requests and correction instructions from the user.

[1339] "Means for readjusting menu plans" refers to the process of modifying the initial menu plan based on user feedback and presenting it to the user again.

[1340] The "means for finalizing the menu" refers to the process of finally setting the menu that has been adjusted until the user is satisfied.

[1341] "Means for generating a shopping list" refers to the process of creating a list of ingredients and their quantities based on a confirmed menu.

[1342] "Means of sending to an online shopping service and ordering ingredients" refers to the process of sending the generated shopping list to an online supermarket or other online shopping platform to purchase ingredients.

[1343] The present invention relates to a system for assisting a user in efficiently planning daily meals and shopping. Specific embodiments will be described below.

[1344] System Overview

[1345] The system begins when a user uses a device such as a smartphone or smart speaker to verbally communicate their menu preferences. The preference is converted into text data using voice recognition technology and sent to a server. The server then references the user's profile data to generate an optimal menu plan and presents it to the user. The user can then refine the menu plan based on their feedback, and a shopping list is automatically generated based on the final menu, and ingredients are ordered via an online shopping service.

[1346] Acquiring voice input and converting voice data

[1347] The user speaks into a smartphone or smart speaker, saying something like, "Please help me think about what to make for dinner next week. I don't like chicken, but I like fish." The device receives this speech and begins analyzing it using a speech recognition API (for example, Google Speech-to-Text API). The speech recognition API converts the voice data into text data, and the analysis results are temporarily stored on the device.

[1348] Retrieving User Data

[1349] As soon as the server receives the text data, it consults the user's profile database, which stores information about the user's likes and dislikes, allergies, family structure, and health status, to prepare a menu plan that meets the user's individual requirements.

[1350] Menu generation

[1351] The server inputs the text data and profile data into an AI model (for example, a generative AI model) and generates a menu plan. As a specific example, if the user likes fish dishes, menu plans such as "grilled salmon," "tuna and radish salad," and "potage soup" are suggested. This generation includes preprocessing, where the text data is formatted into a prompt sentence.

[1352] User Review and Feedback

[1353] The terminal displays the generated menu plan on the user's device. Specifically, the suggestions are communicated to the user via the smartphone app screen or the voice feedback function of the smart speaker. The user can provide feedback on the displayed menu plan by voice or text. For example, the user can say, "This menu is a little difficult, so I'd prefer something simpler."

[1354] Retuning

[1355] Based on the feedback received from the user, the server uses the AI ​​model to fine-tune the menu plan. Specifically, the feedback is reflected in the prompt text and input into the AI ​​model again to generate a new menu plan. This process is repeated until the user is satisfied.

[1356] Finalize the menu and generate a shopping list

[1357] The server automatically generates a shopping list based on the final menu. For example, it might list specific ingredients and their quantities, such as "300g of salmon, 1 daikon radish, 500ml of milk." This list is stored in a database and sent to the device.

[1358] Collaboration with online supermarkets

[1359] The terminal sends the shopping list received from the server to the API of the online shopping service (for example, an online supermarket API), and seamlessly orders the ingredients. The user can check the order details and make any necessary corrections. Finally, the ingredients are delivered directly from the online shopping service to the user's home.

[1360] In this way, this system supports the entire process, from user voice input to menu generation, shopping list creation, and even ordering ingredients through online shopping services, significantly reducing the time and effort required to create household menus.

[1361] Prompt Sentence Examples

[1362] The user types:

[1363] "Can you suggest a dinner menu for next week? I'd prefer something vegetarian and as easy to prepare as possible."

[1364] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1365] Step 1: Getting voice input

[1366] A user speaks into a smartphone or smart speaker, saying, "Please help me decide what to have for dinner next week. I don't like chicken, but I like fish." The input is voice data, and the device captures this voice. The device's microphone converts the voice into a digital signal and prepares it as voice data. In the next step, this voice data is sent to a voice recognition API.

[1367] Step 2: Convert the audio data

[1368] The device uses a speech recognition API (e.g., Google Speech-to-Text API) to convert the acquired voice data into text data. The input is voice data, which the API analyzes and converts into text data. The generated text data is temporarily stored on the device and then sent to the server in the next step.

[1369] Step 3: Retrieving User Data

[1370] After the server receives the text data, it references the user's profile database. The input is the text data and the user ID. The profile database stores information about the user's likes and dislikes, allergies, family composition, and health status. The server retrieves this data and prepares it for generating a menu plan.

[1371] Step 4: Generate a menu plan

[1372] The server uses text data and profile data as input to generate a menu plan using an artificial intelligence model (e.g., a generative AI model). The input is text data from the user and user profile data. The server formats this data into prompt sentences and inputs them into the AI ​​model to generate a menu plan. The generated menu plan is presented to the user in the next step.

[1373] Step 5: User review and feedback

[1374] The terminal displays the generated menu plan on the user's device. The input is the menu plan sent from the server. Specifically, the suggestions are communicated to the user via the smartphone app screen or the voice feedback function of the smart speaker. The user provides feedback by voice or text. For example, the user may give instructions such as, "This menu is a little difficult, so I would prefer something simpler."

[1375] Step 6: Retune

[1376] Based on the feedback received from the user, the server uses the AI ​​model again to fine-tune the menu plan. The input is the user's feedback. The feedback is reflected in the prompt text and input into the AI ​​model again to generate a new menu plan. This process is repeated until the user is satisfied.

[1377] Step 7: Finalize the menu and generate a shopping list

[1378] The server automatically generates a shopping list based on the finalized menu. The input is the finalized menu plan. The server lists the necessary ingredients and their quantities, and generates the shopping list. For example, the specific ingredient information is "300g of salmon, 1 daikon radish, 500ml of milk."

[1379] Step 8: Linking with online supermarkets

[1380] The terminal receives the shopping list from the server and sends it to the API of the online shopping service, seamlessly ordering ingredients. The input is the shopping list. The user confirms the order details, makes any necessary corrections, and then confirms the order. Finally, the online shopping service delivers the ingredients directly to the user's residence.

[1381] In this way, a system is realized that starts with the user's voice input and, using the server and terminals, goes through a series of processes to efficiently support menu creation and ingredient purchasing.

[1382] (Application example 1)

[1383] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1384] Conventional menu planning systems can create menus by capturing user voice input, but they lack the functionality for real-time menu suggestions and in-store navigation in physical stores, making it difficult to streamline the user's shopping experience.There is a need for a means to provide shopping support linked to real-time menu suggestions in physical stores.

[1385] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1386] In this invention, the server includes a means for receiving menu suggestions in real time using a smartphone or smart glasses when a user is shopping in a physical store, a means for providing location information of each product in the store and performing navigation, and a means for using a generative AI model to generate menu suggestions, thereby enabling users to shop efficiently in the physical store.

[1387] "User" refers to an individual or organization that uses this system.

[1388] "Voice capture means" refers to a device such as a microphone that captures the user's spoken voice in digital form.

[1389] "Speech-to-text conversion means" refers to software or hardware that analyzes acquired voice data and converts it into text information.

[1390] The "profile data acquisition means" refers to a function or device for acquiring data such as personal information, preferences, and allergy information about a user.

[1391] "Menu plan generation means" refers to software or algorithms for suggesting dishes based on the acquired text data and profile data.

[1392] "Feedback receiving means" refers to a means for receiving additional requests or correction instructions provided by a user.

[1393] "Retuning tools" refers to algorithms or software that modify and improve existing menu suggestions based on user feedback.

[1394] "Final menu determination method" refers to the software or algorithm used to determine the final menu after receiving feedback.

[1395] "Shopping list generation means" refers to a function for generating a list of necessary ingredients based on a confirmed menu.

[1396] "Net supermarket sending means" refers to a function for sending the generated shopping list to an online supermarket.

[1397] "Real-time menu suggestion means" refers to a function that allows users to receive real-time menu suggestions via their smartphone or smart glasses while shopping in a physical store.

[1398] "In-store navigation means" refers to a system that provides location information of products in a physical store and guides users based on an automatically generated shopping list.

[1399] "Generative AI model" refers to a program that uses artificial intelligence to generate optimal menus based on a user's profile data and voice data.

[1400] A "prompt sentence" refers to an input sentence used to instruct a generative AI model on appropriate processing.

[1401] The system of the present invention supports users in planning meals and purchasing ingredients while efficiently shopping in a physical store. The system includes a user, a terminal (such as a smartphone or smart glasses), and multiple processes operated through a server.

[1402] System Overview

[1403] The system starts by having the user use their smartphone or smart glasses in a physical store to voice their menu request. For example, the user might say, "I'm busy tomorrow, so please make a simple dinner." The device receives this voice and converts it into text data using a speech recognition API.

[1404] Hardware and software used

[1405] Audio acquisition method: Microphone built into smartphone or smart glasses

[1406] Speech-to-text conversion method: Google Cloud Speech-to-Text API

[1407] Generative AI model: OpenAI API (e.g. GPT-3, ChatGPT)

[1408] Profile data acquisition method: User information database

[1409] Menu planning method: Menu generation algorithm using generative AI model

[1410] Feedback reception method: Interface via smartphone or smart glasses

[1411] Retuning method: Retraining the algorithm based on user feedback

[1412] Shopping list generator: Menu-based list generator software

[1413] In-store navigation: Using AR features on smartphones and smart glasses

[1414] Process Details

[1415] Acquiring and converting voice input

[1416] The user speaks into a smartphone or smart glasses, and the speech is picked up by a microphone on the device and converted into text data using the Google Cloud Speech-to-Text API.

[1417] Obtaining user data and generating menu plans

[1418] The text request is sent to the server, which then references the user's profile data, which includes the user's preferences, allergy information, health status, etc. The server uses this data to generate an optimal menu plan using a generative AI model (such as GPT-3 or ChatGPT).

[1419] As an example, the following prompt sentence will be used:

[1420] User profile: Family: 4 people, Fish lover, Chicken allergy

[1421] Request: I'm busy tomorrow, so I'd like a quick dinner please.

[1422] Please suggest a proposal.

[1423] Menu suggestions and feedback

[1424] The generated menu plan is sent back to the device and presented to the user through the interface of a smartphone or smart glasses, and the user can provide feedback on the proposal via voice or text with any additional requests or corrections.

[1425] Retuning and finalizing the menu

[1426] The server receives feedback from the user and again uses the generative AI model to refine the menu plan, and this process is repeated until the user is satisfied, at which point the final menu is finalized.

[1427] Shopping list generation and in-store navigation

[1428] Based on the final menu, a list of necessary ingredients is automatically generated. The generated shopping list is linked to the location information of products in the store via a smartphone or smart glasses, providing navigation information to the user.

[1429] This allows users to efficiently purchase the ingredients they need in-store, greatly improving their shopping experience.

[1430] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1431] Step 1:

[1432] The user provides voice input. The user's request is recorded using a microphone on a smartphone or smart glasses, providing voice data as input.

[1433] Step 2:

[1434] The device converts the voice data into text using the Google Cloud Speech-to-Text API. Real-time voice analysis is performed to obtain the converted text data.

[1435] Step 3:

[1436] The device sends the textual request to the server, which then transfers the text data to the server via the Internet and retrieves the text data as input. This data is then used to generate the menu.

[1437] Step 4:

[1438] The server retrieves the user's profile data, such as preferences, allergies, and health status, from a profile database or user information database. User-specific data is collected through database query processing.

[1439] Step 5:

[1440] The server generates a menu plan using a generative AI model. For example, it uses GPT-3 or ChatGPT to generate a menu plan based on the acquired text data and profile data. The prompt sentence is input into the generative AI model, and the optimal menu plan is output.

[1441] Examples of prompts include:

[1442] User profile: Family: 4 people, Fish lover, Chicken allergy

[1443] Request: I'm busy tomorrow, so I'd like a quick dinner please.

[1444] Please suggest a proposal.

[1445] Step 6:

[1446] The server returns the generated menu plan to the terminal, which then transfers the plan to the terminal via the Internet. Specifically, the data is preprocessed and transmitted.

[1447] Step 7:

[1448] The device presents the menu plan to the user. The generated menu plan is displayed to the user through the interface of a smartphone or smart glasses. The output may include text data and image data.

[1449] Step 8:

[1450] The user provides feedback, either by voice or text, regarding further requests or corrections to the proposal. This feedback is then sent back to the server via the device. The input is voice or text data.

[1451] Step 9:

[1452] The server retunes the menu plan based on the feedback. It then uses the generative AI model again to generate a new menu plan that reflects the feedback. The adjusted menu plan is output by reapplying the model.

[1453] Step 10:

[1454] The device then presents the retuned menu plan to the user again, and the new proposal is displayed on the smartphone or smart glasses, where a final confirmation is made.

[1455] Step 11:

[1456] The server generates a shopping list based on the final menu. The server lists the ingredients and their quantities needed based on the finalized menu plan. The server retrieves ingredient information from the database and builds the shopping list.

[1457] Step 12:

[1458] Send the shopping list to the online supermarket. The generated shopping list is sent using the online supermarket's API, and ingredients are automatically ordered.

[1459] Step 13:

[1460] Users can receive real-time menu suggestions in-store via their smartphones or smart glasses, enabling efficient in-store purchasing.

[1461] Step 14:

[1462] Provide in-store navigation. Using the AR function of smartphones or smart glasses, the system provides users with location information for the ingredients they need, helping them navigate smoothly within the store. This step involves acquiring location data and providing navigation.

[1463] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1464] The system of the present invention assists users in efficiently planning daily meals and shopping, and also recognizes the user's emotions and suggests meals based on those emotions. The system includes a series of processes operated by the user, a terminal, and a server. Specific examples are described below.

[1465] System Overview

[1466] The system begins when a user uses a device such as a smartphone or smart speaker to voice their menu request. The request is converted into text data using voice recognition technology and sent to a server. The server then references the user's profile data to generate an optimal menu plan and presents it to the user. It then uses an emotion engine to analyze the user's emotions and optimize the suggestions. The user can then re-tune the menu plan based on their feedback, and a shopping list is automatically generated based on the final menu, and ingredients are ordered via an online supermarket.

[1467] Program processing

[1468] Acquiring voice input

[1469] The user speaks into a smartphone or smart speaker, saying, "Please help me think about what to have for dinner next week. I don't like chicken, but I like fish." The device receives this speech and begins analyzing it using a speech recognition API.

[1470] Audio data conversion

[1471] The device sends the voice data to the speech recognition API, which converts it into text data. The speech recognition API analyzes the voice signal and generates corresponding text.

[1472] emotion recognition

[1473] Once the voice and text data are generated, the device uses an emotion engine to recognize the user's emotions. This process analyzes voice characteristics such as tone, speed, and strength to determine whether the user is relaxed or stressed.

[1474] Retrieving User Data

[1475] The server then consults the user's profile database to obtain information about likes, dislikes, allergies, family structure, and health conditions, and is then ready to generate a menu plan that takes into account the user's individual requirements.

[1476] Menu generation

[1477] The server uses an AI model to generate menu suggestions based on the text, profile, and emotion data it acquires. For example, it might suggest "grilled salmon," "tuna and daikon salad," or "potage soup." These menu suggestions are tailored to match the user's past data.

[1478] User Review and Feedback

[1479] The terminal displays the generated menu plan on the user's device. The user can review the proposed content on the screen or via voice prompts and make additional requests or corrections. For example, the user can input a voice command such as, "I'd like a dish that's easier to make."

[1480] Retuning

[1481] The server receives the user's feedback and again uses the AI ​​model to fine-tune the menu plan. Based on the feedback, a new menu plan is generated and presented to the user again. This process is repeated until the user is satisfied.

[1482] Finalize the menu and generate a shopping list

[1483] The server automatically generates a shopping list based on the final menu. For example, it creates a specific shopping list such as "300g of salmon, 1 daikon radish, and 500ml of milk."

[1484] Collaboration with online supermarkets

[1485] The device sends the generated shopping list to the online supermarket's API, which seamlessly orders ingredients. The user can check the order details and make any necessary changes. Once the order is confirmed, the ingredients are delivered directly to the user's home from the online supermarket.

[1486] Specific examples

[1487] For example, a user might say to a smart speaker, "I'm very tired today, so please make me a simple and delicious dinner." At this time, the device converts the voice into text data, and at the same time, the emotion engine recognizes the user's level of fatigue. The server takes into account the user's profile data and level of fatigue and suggests a menu item such as "simple grilled salmon with salad." When the user responds with "That'll do," the server automatically generates a shopping list and sends it to the online supermarket.

[1488] In this way, this system proposes optimal menus based on the user's emotional state and provides easy support for shopping.

[1489] The processing flow will be explained below.

[1490] Step 1:

[1491] The user speaks to a smartphone or smart speaker, for example, saying, "Please help me come up with a menu for dinner next week. I don't like chicken, but I like fish."

[1492] Step 2:

[1493] The terminal receives the voice data. Specifically, the microphone in the device captures the user's voice and inputs it into the voice recognition application.

[1494] Step 3:

[1495] The device sends the voice data to the speech recognition API, which converts it into text data. The speech recognition API analyzes the voice signal and generates corresponding text.

[1496] Step 4:

[1497] The terminal transmits the generated text data to the server, where the text data is encoded and sent to the server via a communication network.

[1498] Step 5:

[1499] The device sends voice and text data to the emotion engine, which recognizes the user's emotions. The emotion engine analyzes features such as tone, speed, and strength of the voice to determine whether the user is relaxed or stressed.

[1500] Step 6:

[1501] The server accesses the user's profile database and retrieves data such as likes and dislikes, allergies, health status, and family composition based on the user's identification information.

[1502] Step 7:

[1503] The server uses an AI model to generate multiple menu suggestions based on the text data, profile data, and emotional data it acquires. For example, it generates "grilled salmon," "tuna and daikon salad," and "potage soup." These menu suggestions are adjusted according to the user's past data and current emotional state.

[1504] Step 8:

[1505] The server generates a menu plan and sends it to the device, where it is formatted in a user-friendly format and displayed on a smartphone or smart speaker.

[1506] Step 9:

[1507] The device presents the user with a menu plan. The user can then review the proposal on screen or by voice, and provide additional requests or corrections. For example, they can input a command such as, "I'd like a dish that's easier to make."

[1508] Step 10:

[1509] The terminal receives the user's feedback, converts it into text data, and sends it to the server.

[1510] Step 11:

[1511] The server receives the feedback and uses the AI ​​model to re-tune the menu plan, regenerating a new plan that reflects the user's new preferences. This cycle is repeated until the user is satisfied.

[1512] Step 12:

[1513] The server determines the final menu plan and generates a list of ingredients based on it. For example, it creates a list tailored to the user's needs, such as "300g of salmon, 1 daikon radish, 500ml of milk" or even "recipes with short cooking times."

[1514] Step 13:

[1515] The server sends the shopping list to the terminal, which displays it to the user.

[1516] Step 14:

[1517] The device sends the shopping list to the online supermarket's API, and each item on the list is checked against the online supermarket's database to confirm availability and price.

[1518] Step 15:

[1519] The user checks the order details and makes any necessary corrections. After checking, the order is confirmed via the terminal.

[1520] Step 16:

[1521] The server sends the confirmed order details to the online supermarket, completing the order process. The online supermarket confirms the order and prepares for delivery.

[1522] These steps allow users to easily receive appropriate menu suggestions and seamlessly complete the entire process, right up to the point of shopping. Taking the user's emotional state into consideration also enables suggestions that better fit their needs, improving the user experience.

[1523] Example 2

[1524] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1525] While conventional menu planning systems can take into account user preferences and allergy information, they have difficulty reflecting the user's emotional state or temporary requests in real time. Furthermore, they lack the ability to automatically generate shopping lists and integrate with online sales systems, leaving room for improvement in user convenience. Therefore, there was a need for a system that could recognize the user's emotional state, suggest optimal menus based on that, automatically generate shopping lists based on those menus, and order ingredients in cooperation with online sales systems, thereby reducing the burden on users.

[1526] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1527] In this invention, the server includes means for acquiring a user's voice, means for converting the voice into text data, means for analyzing the text data and voice data to recognize the user's emotions, means for acquiring user profile data, means for generating a menu plan based on the text data, the profile data, and the emotion data, means for presenting the menu plan to the user and receiving feedback, means for re-tuning the menu plan based on the feedback, means for finalizing the menu, means for generating a shopping list based on the menu, and means for transmitting the shopping list to an online sales system and ordering ingredients. This makes it possible to propose an optimal menu taking into account the user's emotional state, automatically generate a shopping list based on the menu, and seamlessly link with the online sales system.

[1528] A "user" is a person who uses the system to create menu plans and order ingredients.

[1529] "Means for acquiring audio" refers to a function for capturing a user's voice using a device such as a smartphone or smart speaker.

[1530] "Means for converting speech to text data" refers to the process of using speech recognition technology to analyze speech signals and convert them into corresponding text information.

[1531] "Means for recognizing emotions" refers to a function for analyzing voice data and text data to determine the user's emotional state.

[1532] "User profile data" refers to data including the user's individual preferences, allergy information, family structure, health status, and the like.

[1533] "Means for generating menu suggestions" refers to the process of using an AI model to suggest optimal menu suggestions based on the acquired text data, profile data, and emotional data.

[1534] "Means for presenting menu plans and receiving feedback" refers to a function for displaying the generated menu plans to the user and receiving opinions and requests from the user.

[1535] "Means for retuning menu suggestions" refers to the process of readjusting suggested menu suggestions based on user feedback.

[1536] The "means for finalizing the menu" refers to the process of finalizing the menu plan after the user has made adjustments until they are satisfied.

[1537] "Means for generating a shopping list" refers to a function that automatically generates a list of necessary ingredients based on the finalized menu.

[1538] An "online sales system" is a system for ordering and purchasing ingredients and products online.

[1539] "Means for sending to the online sales system and ordering ingredients" refers to the function of sending the automatically generated shopping list to the online sales system and ordering the necessary ingredients.

[1540] The system according to the present invention assists users in efficiently planning daily meals and shopping, and also recognizes the user's emotions and suggests meals based on those emotions. The system includes a series of processes operated by the user, a terminal, and a server. Specific embodiments for implementing the present invention will be described in detail below.

[1541] System Configuration

[1542] This system is constructed mainly using the following hardware and software.

[1543] Hardware

[1544] 1. User device: A device that allows voice input, such as a smartphone or smart speaker.

[1545] 2. Server: A server for processing data and running AI models.

[1546] software

[1547] 1. Speech recognition API: Convert the user's voice into text data using Google Cloud Speech-to-Text, Amazon Transcribe, etc.

[1548] 2. Emotion recognition engine: Analyzes user emotions using IBM Watson Tone Analyzer, Microsoft Azure Emotion API, etc.

[1549] 3. Generative AI model: Menu plans are generated using OpenAI GPT series, Google BERT, etc.

[1550] 4. Online sales system API: Send a shopping list and order ingredients using the APIs of Rakuten Ichiba and Amazon Fresh.

[1551] System Operation

[1552] Acquiring voice input

[1553] The user speaks to the device, saying, "Please help me decide what to have for dinner next week. I don't like chicken, but I like fish." The device receives this speech, sends it to the speech recognition API, and begins analyzing it.

[1554] Audio data conversion

[1555] The device sends the audio data to a speech recognition API, such as Google Cloud Speech-to-Text, which converts it into text. The speech recognition API then analyzes the audio signal and generates corresponding text.

[1556] emotion recognition

[1557] Once the voice and text data is generated, the device uses an emotion engine such as IBM Watson Tone Analyzer to recognize the user's emotions. This process analyzes features such as tone, speed, and strength of the voice to determine whether the user is relaxed or stressed.

[1558] Retrieving User Data

[1559] The server then consults the user's profile database to obtain information about likes, dislikes, allergies, family structure, and health conditions, and is then ready to generate a menu plan that takes into account the user's individual requirements.

[1560] Menu generation

[1561] Based on the text data, profile data, and emotion data acquired by the server, the system uses generative AI models such as the OpenAI GPT series to generate menu suggestions, such as "grilled salmon," "tuna and radish salad," and "potage soup."

[1562] User Review and Feedback

[1563] The terminal displays the generated menu plan on the user's device. The user can review the proposed content on the screen or via voice prompts and make additional requests or corrections. For example, the user can input a voice command such as, "I'd like a dish that's easier to make."

[1564] Retuning

[1565] The server receives the user's feedback and uses the AI ​​model again to fine-tune the menu suggestion. A new menu suggestion, such as "Simmered fish and vegetable miso soup," is generated and presented to the user again. This process is repeated until the user is satisfied.

[1566] Finalize the menu and generate a shopping list

[1567] The server automatically generates a shopping list based on the final menu. For example, it creates a specific shopping list such as "300g of salmon, 1 daikon radish, and 500ml of milk."

[1568] Collaboration with online supermarkets

[1569] The device sends the generated shopping list to the API of online sales systems such as Rakuten Ichiba and Amazon Fresh, allowing for seamless ordering of ingredients. Users can check the order details and make any necessary changes. Once the order is confirmed, the ingredients are delivered directly to the user's home from the online supermarket.

[1570] Specific examples

[1571] For example, a user might say to a smart speaker, "I'm very tired today, so please make me a simple and delicious dinner." At this time, the device converts the voice into text data, and at the same time, the emotion engine recognizes the user's level of fatigue. The server takes into account the user's profile data and level of fatigue and suggests a menu item such as "simple grilled salmon with salad." When the user responds with "That'll do," the server automatically generates a shopping list and sends it to the online supermarket.

[1572] In this way, this system proposes optimal menus based on the user's emotional state and provides easy support for shopping.

[1573] Prompt Sentence Examples

[1574] "I'm very tired today, so please make me a simple and delicious dinner. I don't like chicken, but I do like fish."

[1575] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1576] Step 1:

[1577] A user speaks into a smartphone or smart speaker, for example, saying, "Please help me decide what to have for dinner next week. I don't like chicken, but I like fish." This speech is picked up through the device's microphone. The input is the user's voice data, and the output is a digital audio signal ready to be sent to a speech recognition API.

[1578] Step 2:

[1579] The device sends the captured voice data to a speech recognition API such as Google Cloud Speech-to-Text. The API analyzes the voice signal and generates corresponding text data. The input is the user's voice data, and the output is the analyzed text data. Specific operations include the API converting the voice signal into numerical information, identifying voice patterns, and converting them into text.

[1580] Step 3:

[1581] The device sends the generated text and voice data to an emotion recognition engine such as IBM Watson Tone Analyzer to analyze the user's emotions. The input is data containing features such as the tone, speed, and strength of the voice, and the output is the user's emotional state (relaxed, stressed, etc.). Specific operations include a process in which the emotion engine analyzes the features of the text and voice data to determine the type and intensity of the emotion.

[1582] Step 4:

[1583] The server references the user's profile database to retrieve data such as the user's preferences, allergy information, family structure, and health status. The input is the user's identification information, and the output is the user's detailed profile data. Specifically, the server executes a database query to retrieve the required data and prepares it for use in the next step.

[1584] Step 5:

[1585] Based on the text data, profile data, and emotion data acquired by the server, a generative AI model such as the OpenAI GPT series is used to generate a menu plan. The input is text data, profile data, and emotion data, and the output is the generated text of the menu plan. Specific operations include the process in which the AI ​​model takes these data as input and creates a menu plan that meets the user's requirements.

[1586] Step 6:

[1587] The terminal displays the generated menu plan on the user's device. The user can check the suggestions on the screen or through voice prompts and make additional requests or corrections. For example, the user can input feedback such as "I'd like a dish that's easier to make" through voice input. The input is the generated menu plan, and the output is the user's feedback. Specific operations include the process of the terminal displaying the menu plan using a user interface and receiving feedback from the user through voice or text input.

[1588] Step 7:

[1589] The server receives the user's feedback and uses the generative AI model again to tune the menu plan. The input is the user's feedback and the initial menu plan, and the output is a new, tuned menu plan. Specific operations include the AI ​​model taking the feedback into account to generate a new menu plan with new parameters and presenting it to the user again.

[1590] Step 8:

[1591] The server automatically generates a shopping list based on the finalized menu. The input is the text data of the finalized menu, and the output is a shopping list containing specific ingredients and quantities. Specific operations include the server calculating the ingredients and quantities of each dish included in the menu and generating the shopping list.

[1592] Step 9:

[1593] The device sends the generated shopping list to the API of an online retail system such as Rakuten Ichiba or Amazon Fresh, allowing for seamless ordering of ingredients. The input is the generated shopping list, and the output is an order confirmation and delivery information for the online retail system. Specific operations include the device calling the API, sending the required parameters, and providing the user with order confirmation and delivery tracking information.

[1594] (Application example 2)

[1595] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1596] In today's world, users often have little time to plan their meals due to their busy daily lives, and purchasing and cooking ingredients is a time-consuming process. Furthermore, it is important for users to eat meals that suit their health and taste preferences, but it can be difficult to find the optimal menu for that purpose. There is also a need for an efficient system that can integrate with modern food delivery services to handle everything from menu suggestions to ordering and delivery of ingredients.

[1597] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1598] In this invention, the server includes means for acquiring a user's voice, means for converting the voice into text data, means for acquiring user profile data, means for generating a menu plan based on the text data and the profile data, means for presenting the menu plan to the user and receiving feedback, means for refining the menu plan based on the feedback, means for finalizing the menu, means for generating a shopping list based on the menu, means for transmitting the shopping list to an online supermarket and ordering ingredients, and means for linking the ingredient order information with a food delivery service and delivering the ingredients to the user. This not only allows the user to easily receive optimal menu suggestions through voice input, but also allows ingredients to be purchased and delivered automatically, significantly reducing time and effort.

[1599] The "means for acquiring user's voice" is a function for collecting voice uttered by the user through the device.

[1600] The "means for converting voice into text data" is a process for analyzing acquired voice data and converting it into text information.

[1601] The "means for acquiring user profile data" is a function for acquiring personal information, preferences, allergy information, health status, etc., about the user from a database.

[1602] The "means for generating a menu plan based on text data and profile data" is a process for proposing an appropriate menu based on the text data converted from speech and the user's profile data.

[1603] The "means for presenting a menu plan to a user and receiving feedback" is a function for displaying the generated menu plan to a user and collecting opinions and requests for corrections from the user.

[1604] "Means for retuning menu plans based on feedback" refers to the process of re-adjusting the initial menu plans to take into account user comments and modifications.

[1605] The "means for determining the final menu" is the process of determining the final menu based on user feedback.

[1606] The "means for generating a shopping list based on a menu" is a function that automatically creates a list of ingredients to be purchased based on the confirmed menu contents.

[1607] The "means for sending the shopping list to the online supermarket and ordering ingredients" is the process of sending the generated shopping list to the online supermarket and ordering the necessary ingredients.

[1608] "Means of linking ingredient order information with a food delivery service and delivering ingredients to the user" refers to the process of sharing ordered ingredient information with a food delivery service and delivering the ingredients to the user's desired location.

[1609] The "Smart Food Delivery Planner" system of the present invention supports a series of processes: automatically generating a menu from a user's voice input, and purchasing and delivering ingredients based on that menu. This system is mainly composed of the following means:

[1610] Hardware and software used

[1611] Hardware: Smartphones, smart speakers

[1612] Software: AWS Lambda (voice analysis), Azure Cognitive Services (emotion engine), Google Firebase (user profile management), GPT-4 API (menu generation using AI models)

[1613] Acquiring and converting voice input

[1614] A user speaks their meal request into their smartphone or smart speaker. For example, they might say, "I'm tired today, so please make me a healthy and easy dinner." AWS Lambda receives this voice input and converts the voice data into text data.

[1615] Emotion Analysis

[1616] After the voice is converted into text data, Azure Cognitive Services is used to analyze the user's emotions, allowing it to recognize their emotional state, such as whether they are tired or stressed.

[1617] Get user profile

[1618] The server retrieves the user's profile data (food preferences, allergy information, health status, etc.) from Google Firebase and combines it with text data to prepare a menu.

[1619] Menu generation

[1620] The server inputs text data, emotion data, and user profile data into the GPT-4 API to generate optimal menu suggestions, such as "salmon salad, tofu miso soup, and avocado and tomato salad."

[1621] Prompt Sentence Examples

[1622] Suggest a menu for tonight's dinner. Consider the following:

[1623] The user is now tired.

[1624] User profile information: I like fish, I don't like chicken, I have no allergies.

[1625] Easy to cook menu.

[1626] Feedback and Retuning

[1627] The generated menu plan is presented to the user's smartphone or smart speaker. The user provides feedback such as "That's it" or "I'd prefer something simpler." Based on this feedback, the server again uses the AI ​​model to fine-tune the menu plan and present it to the user again. This process is repeated until the user is satisfied.

[1628] Finalize the menu and generate a shopping list

[1629] Based on the final menu, the server automatically generates a shopping list, such as "300g of salmon, 1 daikon radish, 500ml of milk."

[1630] Collaboration with online supermarkets and food delivery services

[1631] The generated shopping list is sent to the online supermarket's API, and the necessary ingredients are automatically ordered. At the same time, this order information is linked to a food delivery service, and the ingredients are delivered to the user's home.

[1632] In this way, the present invention provides a system that proposes optimal menus based on the user's voice input and provides consistent support for the subsequent purchase and delivery of ingredients, allowing users to efficiently perform their daily cooking tasks.

[1633] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1634] Step 1: Getting User Input

[1635] The user speaks their meal request into their smartphone or smart speaker. For example, they might say, "I'm tired today, so please make me a healthy and simple dinner." This voice data is collected by the device.

[1636] Input: User's voice data

[1637] Output: Collected audio data

[1638] Step 2: Convert the audio data

[1639] The device sends the collected voice data to AWS Lambda, which converts it into text data through a speech recognition API. The device analyzes the voice signal and generates corresponding text data.

[1640] Input: Audio data

[1641] Output: Text data

[1642] Step 3: Sentiment Analysis

[1643] After the text data is generated, the device uses Azure Cognitive Services to perform sentiment analysis, which extracts data on the user's emotional state (e.g., tired, stressed) from features such as voice tone and speaking rate.

[1644] Input: Text data

[1645] Output: Emotion data

[1646] Step 4: Retrieving the user profile

[1647] The server retrieves user profile data (food preferences, allergy information, health status, etc.) from Google Firebase. This data, along with text data and emotion data, is the basis for generating menus.

[1648] Input: User identification information

[1649] Output: Profile data

[1650] Step 5: Generate a menu plan

[1651] The server inputs text data, emotion data, and profile data into the GPT-4 API to generate optimal menu suggestions.

[1652] As a specific example, we suggest a menu of "salmon salad, tofu miso soup, and avocado and tomato salad."

[1653] Input: Text data, emotion data, profile data

[1654] Output: Menu plan

[1655] Step 6: Menu presentation and feedback

[1656] The generated menu plan is presented to the user via the device, and the user provides feedback such as "That's fine" or "I'd prefer something simpler." The device collects this feedback.

[1657] Input: Menu plan

[1658] Output: User feedback

[1659] Step 7: Retune your menu

[1660] The server receives user feedback and uses the AI ​​model again to tune the menu plan, generating a new menu plan that modifies and optimizes the initial plan and presents it to the user again.

[1661] Input: User feedback

[1662] Output: Retuned menu plan

[1663] Step 8: Finalize and generate shopping list

[1664] After the user decides on a menu that satisfies them, the server automatically generates a shopping list based on that menu. For example, a specific shopping list such as "300g of salmon, 1 daikon radish, and 500ml of milk" is created.

[1665] Input: Confirmed menu plan

[1666] Output: Shopping list

[1667] Step 9: Order and deliver ingredients

[1668] The server sends the generated shopping list to the online supermarket's API, and the necessary ingredients are automatically ordered. At the same time, this order information is linked to a food delivery service, and the ingredients are delivered to the user's home.

[1669] Input: Shopping list

[1670] Output: Ingredient order and delivery information

[1671] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1672] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1673] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1674] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1675] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1676] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1677] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1678] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1679] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1680] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1681] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1682] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1683] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1684] 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.

[1685] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1686] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1687] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1688] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1689] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1690] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1691] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1692] The following is further disclosed regarding the above embodiment.

[1693] (Claim 1)

[1694] means for acquiring a user's voice;

[1695] means for converting the voice into text data;

[1696] means for obtaining user profile data;

[1697] means for generating a menu plan based on the text data and the profile data;

[1698] means for presenting said menu plan to a user and receiving feedback;

[1699] means for retuning the menu plan based on the feedback;

[1700] A means of finalizing the menu;

[1701] means for generating a shopping list based on the menu;

[1702] A means for transmitting the shopping list to an online supermarket and ordering ingredients;

[1703] A system including:

[1704] (Claim 2)

[1705] 2. The system according to claim 1, further comprising a means for referring to past data of the user and taking the data into consideration when generating a menu plan by said menu plan generating means.

[1706] (Claim 3)

[1707] 2. The system according to claim 1, further comprising means for generating the menu plan in consideration of a nutritional balance according to the user's health condition.

[1708] "Example 1"

[1709] (Claim 1)

[1710] means for acquiring a user's voice;

[1711] means for converting the voice into text data;

[1712] means for obtaining user profile data;

[1713] means for generating a menu plan using an artificial intelligence model based on the text data and the profile data;

[1714] means for presenting said menu plan to a user and receiving feedback;

[1715] means for readjusting the menu plan based on the feedback;

[1716] A means of finalizing the menu;

[1717] means for generating a shopping list based on the menu;

[1718] means for transmitting the shopping list to an online shopping service and ordering ingredients;

[1719] A system including:

[1720] (Claim 2)

[1721] 2. The system according to claim 1, further comprising a means for referring to past data of the user and taking the data into consideration when generating a menu plan by said menu plan generating means.

[1722] (Claim 3)

[1723] 2. The system according to claim 1, further comprising means for generating the menu plan in consideration of a nutritional balance according to the user's health condition.

[1724] "Application Example 1"

[1725] (Claim 1)

[1726] means for acquiring a user's voice;

[1727] means for converting the voice into text data;

[1728] means for obtaining user profile data;

[1729] means for generating a menu plan based on the text data and the profile data;

[1730] means for presenting said menu plan to a user and receiving feedback;

[1731] means for retuning the menu plan based on the feedback;

[1732] A means of finalizing the menu;

[1733] means for generating a shopping list based on the menu;

[1734] A means for transmitting the shopping list to an online supermarket and ordering ingredients;

[1735] A means for a user to receive real-time menu suggestions using a smartphone or smart glasses while shopping in a physical store;

[1736] A means for providing location information of each product in the store and for navigation;

[1737] A system including:

[1738] (Claim 2)

[1739] 2. The system according to claim 1, further comprising a means for referring to past data of the user and taking the data into consideration when generating a menu plan by said menu plan generating means.

[1740] (Claim 3)

[1741] 2. The system according to claim 1, further comprising means for generating the menu plan in consideration of a nutritional balance according to the user's health condition.

[1742] "Example 2: Combining Emotion Engines"

[1743] (Claim 1)

[1744] means for acquiring a user's voice;

[1745] means for converting the voice into text data;

[1746] means for analyzing text data and voice data to recognize user emotions;

[1747] means for obtaining user profile data;

[1748] means for generating a menu plan based on the text data, the profile data, and the emotion data;

[1749] means for presenting said menu plan to a user and receiving feedback;

[1750] means for retuning the menu plan based on the feedback;

[1751] A means of finalizing the menu;

[1752] means for generating a shopping list based on the menu;

[1753] a means for transmitting the shopping list to an online sales system and ordering ingredients;

[1754] A system including:

[1755] (Claim 2)

[1756] 2. The system according to claim 1, further comprising a means for referring to the user's past data and feedback data and taking them into consideration when generating a menu plan by said menu plan generating means.

[1757] (Claim 3)

[1758] 2. The system according to claim 1, further comprising means for generating the menu plan taking into consideration a nutritional balance according to the health and emotional state of the user.

[1759] "Application example 2 when combining emotion engines"

[1760] (Claim 1)

[1761] means for acquiring a user's voice;

[1762] means for converting the voice into text data;

[1763] means for obtaining user profile data;

[1764] means for generating a menu plan based on the text data and the profile data;

[1765] means for presenting said menu plan to a user and receiving feedback;

[1766] means for retuning the menu plan based on the feedback;

[1767] A means of finalizing the menu;

[1768] means for generating a shopping list based on the menu;

[1769] A means for transmitting the shopping list to an online supermarket and ordering ingredients;

[1770] a means for linking the food ingredient order information with a food delivery service and delivering the food ingredients to the user;

[1771] A system including:

[1772] (Claim 2)

[1773] 2. The system according to claim 1, further comprising a means for referring to past data of the user and taking the data into consideration when generating a menu plan by said menu plan generating means.

[1774] (Claim 3)

[1775] 2. The system according to claim 1, further comprising means for generating the menu plan in consideration of a nutritional balance according to the user's health condition. [Explanation of symbols]

[1776] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for acquiring a user's voice; means for converting the voice into text data; means for obtaining user profile data; means for generating a menu plan based on the text data and the profile data; means for presenting said menu plan to a user and receiving feedback; means for retuning the menu plan based on the feedback; A means of finalizing the menu; means for generating a shopping list based on the menu; A means for transmitting the shopping list to an online supermarket and ordering ingredients; A system including:

2. 2. The system according to claim 1, further comprising a means for referring to past data of the user and taking the data into consideration when generating a menu plan by said menu plan generating means.

3. The system according to claim 1 , further comprising means for generating the menu plan in consideration of a nutritional balance according to the user's health condition.

Images