System

Abstract

To provide a system for responding to a user's questions while taking into account personal data and emotions.SOLUTION: A system includes means for: storing personal data including at least one of a user's preference, health data and an action log; using an emotion engine to specify the user's emotional state; receiving the user's question; generating a prompt sentence for instructing generation of an initial answer based on the user's question and the user's personal data; using the generated prompt sentence and a generative AI model to generate the initial answer; generating the prompt sentence for instructing generation of an interdepartmental advice based on the generated initial answer and the user's emotional state; using the generated prompt sentence and the generative AI model to generate the interdepartmental advice for the user; and presenting the generated interdepartmental advice to the user.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional AI personal assistants have been unable to fully utilize the user's personal data when generating answers to users' questions, making it difficult to provide optimal advice tailored to each individual user. [Means for solving the problem]

[0005] The system of the present invention includes an intermediate AI that stores personal data (preferences, health data, behavioral logs, etc.) in response to user questions, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. This makes it possible to make maximum use of the user's personal data and provide optimal advice to each individual user. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 11 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] 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.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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 wide area network (WAN) and / or a local area network (LAN).

[0018] 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.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. 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.

[0020] 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 (e.g., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0023] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

[0024] 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.

[0025] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores a reception output program 60. The reception output program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. The intermediate AI stores the user's personal data and uses it to generate answers to the user's questions. The generative AI adds the personal data provided by the intermediate AI and the user's personal information to generate cross-sectional advice tailored to the individual. As a specific example, if a user asks, "Please let me know when a DVD of the boxing match I just watched is released," the intermediate AI will take into account the user's viewing history and preferences and send a notification when a new boxing match DVD is released. In addition, in response to the question, "What UVER EATS (registered trademark) menu would suit my physical condition today?", the intermediate AI will suggest an appropriate menu in consideration of the user's health data and food preferences.

[0029] "Example 2"

[0030] The system of the present invention includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. The intermediate AI stores the user's personal data and uses it to generate answers to the user's questions. The generative AI adds the personal data provided by the intermediate AI and the user's personal information to generate cross-sectional advice tailored to the individual. As a specific example, if a user asks, "Please let me know when a DVD of the boxing match I just watched is released," the intermediate AI will take into account the user's viewing history and preferences and send a notification when a new boxing match DVD is released. In addition, in response to the question, "What UVER EATS menu would suit my physical condition today?", the intermediate AI will suggest an appropriate menu, taking into account the user's health data and food preferences.

[0031] The process flow of each embodiment will be described below.

[0032] "Example 1"

[0033] Step 1: The user asks the system a question, for example, "Tell me when the DVD of the boxing match I just watched is released."

[0034] Step 2: The intermediate AI receives the user's question and generates an answer taking into account the user's personal data (viewing history, preferences, etc.). In this case, set it to send notifications when new boxing match DVDs are released.

[0035] Step 3: The generative AI adds the personal data provided by the intermediate AI and the user’s personal information to generate personalized cross-sectional advice, in this case notifying the user when a new boxing match DVD is released.

[0036] "Example 2"

[0037] Step 1: The user asks the system a question. For example, "What UVER EATS menu would suit my mood today?"

[0038] Step 2: The intermediate AI receives the user's question and generates an answer based on the user's personal data (health data, food preferences, etc.). In this case, it suggests UVER EATS menu items tailored to the user's physical condition and food preferences.

[0039] Step 3: The generative AI adds the personal data provided by the intermediate AI and the user’s personal information to generate personalized cross-sectional advice. In this case, it would suggest UVER EATS menu items to the user based on their physical condition and food preferences.

[0040] Example 1

[0041] Next, a description will be given of Example 1 of the embodiment 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".

[0042] Conventional systems have had difficulty effectively utilizing users' personal data to provide individually optimized advice. In addition, they lacked the means to generate quick and accurate answers to users' questions. This resulted in low user satisfaction and limited the value of the system.

[0043] 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.

[0044] In this invention, the server includes a means for starting an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for receiving the user's question through the intermediate AI and generating an initial answer using the stored personal data, a means for making an inquiry to the generative AI by adding the personal data provided by the intermediate AI and the user's personal information, and a means for providing the user with cross-sectional advice tailored to the individual obtained from the generative AI. This makes it possible to effectively utilize the user's personal data and quickly and accurately provide individually optimized advice.

[0045] "User" refers to an individual who utilizes the System to enter questions and receive answers and advice.

[0046] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[0047] "Middle AI" refers to artificial intelligence that stores a user's personal data and generates initial answers to the user's questions.

[0048] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[0049] "Cross-sectional advice" refers to advice that is individually optimized based on the user's personal data and information.

[0050] "Server" refers to a computer system that receives users' questions, uses intermediate AI and generative AI to generate answers and advice, and provides them to the users.

[0051] "Terminal" refers to the device (smartphone, PC, etc.) that a user uses to enter questions and receive answers or advice.

[0052] "Question" refers to an inquiry that a user inputs into the system.

[0053] "Answer" refers to a system-generated response to a user's question.

[0054] "Inquiry" refers to the sending of data by the server to the generative AI and receiving a response.

[0055] This invention is a system that uses personal data to provide individually optimized advice in response to user questions. The system is mainly composed of three elements: a server, a terminal, and a user.

[0056] server

[0057] The server receives the user's questions and generates individually optimized advice using an intermediate AI that stores and uses personal data, and a generative AI. Specifically, the following hardware and software are used:

[0058] Hardware: High-performance server machine

[0059] Software: Intermediate AI, Generative AI, Database Management System

[0060] The server first launches the intermediate AI and saves the user's personal data (preferences, health data, behavioral logs, etc.). Next, it receives the user's question and generates an initial answer using the personal data saved through the intermediate AI. It then queries the generative AI by adding the personal data provided by the intermediate AI and the user's personal information. Finally, it provides the user with cross-sectional advice tailored to the individual obtained from the generative AI.

[0061] Terminal

[0062] The terminal is a device where the user inputs questions and receives answers and advice from the server. Specifically, the terminal uses the following hardware and software:

[0063] Hardware: Smartphones, PCs, tablets, etc.

[0064] Software: User Interface Application

[0065] The terminal provides an interface for accepting questions from the user, transmits the received questions to the server, and displays answers and advice returned from the server to the user.

[0066] user

[0067] The user inputs a question through the terminal and receives answers and advice provided by the server. The user decides on an action based on the information provided.

[0068] Examples

[0069] Example 1: Boxing match DVD notification

[0070] User Question: "Please let me know when the boxing match I just watched comes out on DVD."

[0071] Process flow:

[0072] 1. The user enters a question via the terminal.

[0073] 2. The device sends a query to the server.

[0074] 3. The server receives the question and uses the intermediary AI to check the user's viewing history and preferences.

[0075] 4. The server adds personal data to the generative AI and queries it.

[0076] 5. Generative AI will generate notifications when new boxing match DVDs are released.

[0077] 6. The server sends a notification to the user's device.

[0078] 7. The user receives a notification on their device.

[0079] Example 2: UBER EATS menu suggestions

[0080] User question: "What UBER EATS menu would suit my mood today?"

[0081] Process flow:

[0082] 1. The user enters a question via the terminal.

[0083] 2. The device sends a query to the server.

[0084] 3. The server receives the question and uses an intermediate AI to verify the user's health data and dietary preferences.

[0085] 4. The server adds personal data to the generative AI and queries it.

[0086] 5. Generative AI suggests appropriate menus.

[0087] 6. The server sends the proposed menu to the user's device.

[0088] 7. The user reviews the suggested menu on their device.

[0089] Examples of prompt statements

[0090] Prompt: "How can I notify users when new boxing match DVDs are released, taking into account their viewing history and preferences?"

[0091] Prompt: "How can you suggest UBER EATS menu options based on a user's health profile and food preferences?"

[0092] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0093] Step 1:

[0094] The server receives the user's question sent from the terminal.

[0095] Input: User question (e.g. "Please let me know when the DVD of that boxing match I just watched is released")

[0096] Specific operation: The server receives an HTTP request and analyzes the question.

[0097] Output: Analysis result of question

[0098] Step 2:

[0099] The server uses the intermediary AI to obtain the user's personal data (preferences, health data, behavioral logs, etc.).

[0100] Input: Analysis result of question content

[0101] What happens: The server executes a database query to retrieve the user's personal data.

[0102] Output: User's personal data

[0103] Step 3:

[0104] The server generates an initial answer based on the personal data acquired using the intermediate AI.

[0105] Input: User's personal data

[0106] Specific operation: The server calls the API of the intermediate AI and sends data to generate an initial answer.

[0107] Output: Initial answer

[0108] Step 4:

[0109] The server queries the generative AI, adding the personal data provided by the intermediate AI and the user's personal information.

[0110] Input: Initial answers, user's personal information

[0111] Specific operation: The server calls the generative AI's API and sends the necessary data.

[0112] Output: Response from the generative AI

[0113] Step 5:

[0114] The server provides users with cross-sectional advice tailored to each individual, obtained from generative AI.

[0115] Input: Response from generative AI

[0116] Specific operation: The server receives and analyzes the response from the generative AI.

[0117] Output: Personalized cross-sectional advice

[0118] Step 6:

[0119] The server sends the advice obtained from the generative AI to the user's device.

[0120] Input: Personalized cross-sectional advice

[0121] Specific behavior: The server generates an HTTP response and sends it to the user's device.

[0122] Output: Advice sent to the user's terminal

[0123] Step 7:

[0124] The terminal displays the answers and advice returned from the server to the user.

[0125] Input: HTTP response from the server

[0126] Specific operation: The terminal receives and analyzes the HTTP response.

[0127] Output: The answer or advice that is displayed to the user

[0128] Step 8:

[0129] Users can review the answers and advice provided through their device and decide on their course of action.

[0130] Input: Answer or advice displayed on the terminal

[0131] Specific action: The user looks at the device screen and reads the information displayed.

[0132] Output: User action decision (e.g., order a suggested menu, receive a notification, etc.)

[0133] (Application example 1)

[0134] Next, a description will be given of Application Example 1 of Form 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."

[0135] Conventional food delivery services have had problems with the difficulty of proposing individual menus based on the user's health condition and preferences, and it takes time for users to choose meals that suit their physical condition and preferences. In addition, there was a lack of a system that effectively utilizes the user's personal data to provide individually optimized advice.

[0136] 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.

[0137] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to an individual, a means for proposing an optimal food delivery menu based on the user's health data, preferences, and past order history, and a means including an application installed on a smartphone or smart glasses. This makes it possible to propose individual menus based on the user's health condition and preferences.

[0138] A "user question" is any inquiry or request made by a user to the system.

[0139] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[0140] "Middle AI" refers to artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[0141] "Generative AI" refers to artificial intelligence that adds personal data provided by the intermediate AI with the user's personal information to generate cross-sectional advice tailored to the individual.

[0142] "Cross-sectional advice" refers to providing individually optimized advice based on the user's personal data.

[0143] "Health Data" refers to information relating to a user's health, such as blood pressure, weight, and allergy information.

[0144] "Preferences" refer to personal preferences such as the dishes a user likes or ingredients they want to avoid.

[0145] "Past Order History" means a record of orders previously placed by a User.

[0146] "Food Delivery Menu" means the food options available for delivery to a User.

[0147] A "smartphone" is a type of mobile phone, a device that can connect to the Internet and use applications.

[0148] "Smart glasses" are glasses-type devices that are wearable devices that can display information and connect to the Internet.

[0149] An "application" is a software program designed to accomplish a particular function.

[0150] The system for implementing this invention includes an intermediate AI that stores and analyzes personal data in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing personalized cross-sectional advice, a means for proposing optimal food delivery menus based on the user's health data, preferences, and past order history, and an application installed on a smartphone or smart glasses.

[0151] Hardware and Software Used

[0152] Hardware:

[0153] Smartphone

[0154] Smart Glasses

[0155] software:

[0156] Python

[0157] The Requests library (for sending HTTP requests)

[0158] JSON library (data parsing and storage)

[0159] Data processing and calculation

[0160] 1. Data collection:

[0161] The server collects the user's health data (e.g., blood pressure, weight, allergy information), preferences (e.g., favorite dishes, ingredients to avoid), and past ordering history, and stores them in JSON format.

[0162] 2. Data Analysis:

[0163] The intermediate AI analyzes the collected personal data and generates data based on the user's health condition and preferences.

[0164] 3. Querying the generative AI:

[0165] The intermediate AI sends the analysis results to the generative AI and receives personalized menu suggestions.

[0166] Examples

[0167] When a user asks, "What menu would suit my physical condition today?", the server takes into account the user's health data (e.g., blood pressure 120 / 80, weight 70kg, allergy: peanuts) and preferences (e.g., favorite foods: sushi, pasta; least favorite foods: spicy food) and queries the generative AI. The generative AI then suggests a specific menu, such as, "Since your blood pressure is normal today, I recommend low-salt sushi."

[0168] Examples of prompt statements

[0169] User health data: Blood pressure 120 / 80, Weight 70kg, Allergies: peanuts

[0170] User preferences: Favorite foods: sushi, pasta; Dislikes: spicy food

[0171] User Question: What's the best meal for me today?

[0172] In this way, it is possible to provide individual menu suggestions based on the user's health condition and preferences.

[0173] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0174] Step 1:

[0175] The user inputs a question using a device (smartphone or smart glasses).

[0176] Input: User question (e.g. "What menu would suit my physical condition today?")

[0177] Output: User question data

[0178] Step 2:

[0179] The terminal transmits the user's question data to the server.

[0180] Input: User question data

[0181] Output: The question data sent to the server.

[0182] Step 3:

[0183] The server uses the intermediary AI to collect the user's personal data (health data, preferences, past order history).

[0184] Input: User ID and authentication information

[0185] Output: User's personal data (JSON format)

[0186] Step 4:

[0187] The intermediate AI analyzes the collected personal data and generates data based on the user's health condition and preferences.

[0188] Input: User's personal data

[0189] Output: Analysis result data (e.g. health condition, preferences, foods to avoid, etc.)

[0190] Step 5:

[0191] The server sends the analysis results data to the generative AI and receives personalized menu suggestions.

[0192] Input: Analysis result data

[0193] Output: Menu suggestion data from generative AI

[0194] Step 6:

[0195] The generative AI generates prompt text based on the analysis results data and suggests the optimal menu.

[0196] Input: Analysis result data

[0197] Output: Menu suggestions (e.g. "Your blood pressure is normal today, so I recommend low-sodium sushi.")

[0198] Step 7:

[0199] The server sends the menu suggestion data received from the generative AI to the terminal.

[0200] Input: Menu suggestion data

[0201] Output: Menu suggestion data sent to the device

[0202] Step 8:

[0203] The terminal displays menu suggestions to the user.

[0204] Input: Menu suggestion data

[0205] Output: The menu suggestions displayed to the user

[0206] In this way, it is possible to provide individual menu suggestions based on the user's health condition and preferences.

[0207] Example 2

[0208] Next, a second embodiment of the second embodiment will be described. 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."

[0209] With conventional systems, it was difficult to provide individually optimized advice in response to user questions. In particular, there is a demand for effective use of users' personal data to provide specific advice in real time based on the user's preferences and health condition, but there has been a lack of technology to achieve this.

[0210] 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.

[0211] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for implementing cross-sectional advice tailored to an individual, a terminal that receives the user's question and transmits it to the server, a server that transmits the initial answer generated by the intermediate AI and the user's personal information to the generative AI, a server that transmits the advice generated by the generative AI to the user's terminal, and a means for the terminal to display the advice to the user. This makes it possible to effectively utilize the user's personal data and provide individually optimized advice in real time.

[0212] "User" refers to an individual who utilizes the System to enter questions and receive advice.

[0213] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[0214] "Middle AI" refers to artificial intelligence that stores a user's personal data and generates initial answers to the user's questions.

[0215] "Generative AI" refers to artificial intelligence that generates individually optimized advice based on personal data provided by the intermediate AI and the user's personal information.

[0216] "Terminal" refers to the device (such as a smartphone or PC) that a user uses to enter questions and receive advice from the server.

[0217] "Server" refers to a computer system that passes the user's question to the intermediate AI, transmits data to the generative AI, and transmits the generated advice to the terminal.

[0218] "Cross-sectional advice" refers to advice that is individually optimized based on the user's personal data and information.

[0219] "Initial answer" refers to the first answer to a question that the intermediate AI generates based on the user's personal data.

[0220] This invention is a system that uses personal data to provide individually optimized advice in response to user questions. The system is mainly composed of an intermediate AI, a generative AI, a server, and a terminal.

[0221] The intermediate AI stores the user's personal data (preferences, health data, behavioral logs, etc.) and generates initial answers to the user's questions. The intermediate AI runs in a server and has access to the user's personal data.

[0222] The generative AI generates individually optimized cross-sectional advice based on the personal data provided by the intermediate AI and the user's personal information. The generative AI also runs on the server and processes data to provide the user with optimized advice.

[0223] The server passes the user's question to the intermediate AI, transmits the initial answer generated by the intermediate AI and the user's personal information to the generative AI, and also transmits the advice generated by the generative AI to the user's device.

[0224] The terminal is a device that the user uses to input questions and receive advice from the server. Terminals include smartphones and PCs.

[0225] As a specific example of how it works, consider the case where a user asks, "Let me know when the DVD of the boxing match I just watched is released." The user enters the question into their smartphone, and the device sends the question to the server. The server passes the question to the intermediate AI, which references the user's viewing history to generate an initial answer. The server then sends the initial answer and the user's personal information to the generative AI, which then generates advice to send a notification when a new boxing match DVD is released. Finally, the server sends the generated advice to the device, which displays the notification to the user.

[0226] As another concrete example, consider the case where a user asks, "What UVER EATS menu would suit my physical condition today?" The user enters the question into their PC, and the device sends the question to the server. The server passes the question to the intermediate AI, which generates an initial answer by referencing the user's health data and food preferences. The server then sends the initial answer and the user's personal information to the generative AI, which then generates advice suggesting a menu that suits today's physical condition. Finally, the server sends the generated advice to the terminal, which displays the menu to the user.

[0227] Examples of prompts include:

[0228] "How can I notify users when new boxing match DVDs are released based on their viewing history and preferences?"

[0229] "Please tell me how to suggest UVER EATS menu items that suit today's physical condition based on the user's health data and food preferences."

[0230] In this way, the system can effectively utilize the user's personal data and provide individually optimized advice in real time.

[0231] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0232] Step 1:

[0233] The user enters a question.

[0234] A user uses a terminal (such as a smartphone or PC) to input a question into the system. For example, the user might input, "Please let me know when the DVD of the boxing match I just watched is released." The input data is the user's question.

[0235] Step 2:

[0236] The terminal sends a question to the server.

[0237] The terminal transmits the question entered by the user to the server. This communication is usually carried out over the Internet. The input data is the user's question and the output data is the question sent to the server.

[0238] Step 3:

[0239] The server passes the question to the intermediate AI.

[0240] The server passes the received question to the intermediate AI, which runs in the server and has access to the user's personal data. The input data is the question sent to the server, and the output data is the question passed to the intermediate AI.

[0241] Step 4:

[0242] The intermediate AI refers to the user's personal data and generates an initial answer.

[0243] The intermediate AI refers to the user's personal data, such as viewing history, preferences, and health data, to generate an initial answer to the question. For example, if a user asks about a boxing match DVD, the intermediate AI checks the viewing history and extracts relevant information. The input data are the user's personal data and the question, and the output data is the initial answer.

[0244] Step 5:

[0245] The server sends the intermediate AI's initial answer and the user's personal information to the generative AI.

[0246] The server sends the initial answer generated by the intermediate AI and the user's personal information to the generative AI. The generative AI generates optimized advice based on this information. The input data are the initial answer and the user's personal information, and the output data is the data sent to the generative AI.

[0247] Step 6:

[0248] Generative AI generates optimized cross-cutting advice.

[0249] The generative AI combines the data provided by the intermediate AI with the user's personal information to generate individually optimized cross-sectional advice. For example, it suggests a menu that suits the user's physical condition for the day based on their health data and food preferences. The input data are the initial answer and the user's personal information, and the output data is optimized advice.

[0250] Step 7:

[0251] The server transmits the generated advice to the terminal.

[0252] The server receives the advice generated by the generative AI and sends it to the user's device. The input data is the optimized advice, and the output data is the advice sent to the device.

[0253] Step 8:

[0254] The terminal displays the advice to the user.

[0255] The terminal displays the advice received from the server to the user, allowing the user to obtain individually optimized information. Input data is the advice sent to the terminal, and output data is the advice displayed to the user.

[0256] (Application example 2)

[0257] Next, a description will be given of Application Example 2 of Form 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."

[0258] Conventional food delivery services have a problem in that it is difficult to provide users with the most suitable menu because they do not fully consider the user's health condition, preferences, or past order history. In addition, there are problems in that it is difficult to increase user satisfaction because there are not enough menu suggestions that match the user's physical condition and mood, or notifications of new menus or campaign information.

[0259] 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.

[0260] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for implementing cross-sectional advice tailored to the individual, a means for proposing an optimal food delivery menu based on the user's health data, preferences, and past order history, a means for proposing a menu that matches the user's physical condition and mood, and a means for notifying the user of new menus and campaign information. This makes it possible to propose an optimal menu that takes into account the user's health condition, preferences, and past order history, and by proposing a menu that matches the user's physical condition and mood and notifying the user of new menus and campaign information, it is possible to increase user satisfaction.

[0261] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[0262] An "intermediate AI" is an artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[0263] "Generative AI" is artificial intelligence that adds personal data provided by the intermediate AI with the user's personal information to generate cross-sectional advice tailored to the individual.

[0264] "Cross-sectional advice" means providing suggestions and advice that are best suited to each individual based on the user's personal data.

[0265] "Health Data" refers to information relating to a user's health, such as their calorie restrictions or allergy information.

[0266] "Preferences" is information about a user's likes and preferences.

[0267] "Behavior log" is information about a user's past behavior and history.

[0268] "Order History" means a record of orders a User has placed in the past.

[0269] "Food Delivery Menu" means the meal options offered to Users.

[0270] "Health" is information about the user's current health condition and mood.

[0271] "Campaign Information" is information about new menu items or special promotions.

[0272] The system for carrying out this invention includes an intermediate AI that saves personal data in response to a user's question, adds the user's personal information, and queries the generative AI. It also includes a means for implementing cross-sectional advice tailored to individuals. Specifically, it includes a means for proposing optimal food delivery menus based on the user's health data, preferences, and past order history, a means for proposing menus tailored to the user's physical condition and mood, and a means for notifying the user of new menus and campaign information.

[0273] System Configuration

[0274] Hardware

[0275] Server: A server for running intermediate AI and generative AI.

[0276] Device: The smartphone used by the user.

[0277] software

[0278] Intermediate AI: AI that stores the user's personal data and generates answers to questions.

[0279] Generative AI: An AI that generates cross-sectional advice tailored to the individual based on the personal data provided by the intermediate AI and the user's personal information.

[0280] Food delivery application: An application that suggests optimal menu items based on the user's health data, preferences, and past ordering history, suggests menu items that match the user's physical condition and mood, and notifies the user of new menu items and campaign information.

[0281] Data processing and calculation

[0282] 1. Acquisition of user data:

[0283] The server transmits the user's health data, preferences, and past order history to the intermediary AI to obtain personal data.

[0284] 2. Use of Personal Data:

[0285] The server sends the acquired personal data and additional information about the user to the generative AI, which then generates the optimal food delivery menu.

[0286] 3. Display the results:

[0287] The terminal displays the generated menu to the user, suggests menu items tailored to the user's physical condition and mood, and notifies the user of new menu items and campaign information.

[0288] Examples

[0289] When a user asks, "What food delivery menu would suit my physical condition today?" the system operates as follows. First, the server sends the user's health data (e.g. calorie restrictions and allergy information) to the intermediate AI and obtains personal data. Next, the server sends this personal data and additional information about the user to the generative AI, which generates the optimal menu. Finally, the device displays the generated menu to the user, suggests menu items tailored to their physical condition and mood, and notifies them of new menu items and campaign information.

[0290] Examples of prompt statements

[0291] Consider the user's health data and preferences and suggest the best food delivery menu for today's physical condition. The user's health data is as follows: Calorie limit 2000kcal, Allergy: Nuts. Preferences are Japanese food, and spiciness level is medium spicy.

[0292] In this way, a system can be realized that suggests optimal food delivery menus to users.

[0293] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0294] Step 1:

[0295] A user launches a food delivery application and enters a question.

[0296] Input: User question (e.g. "What food delivery menu would suit my physical condition today?")

[0297] Output: User question data

[0298] Specific actions: The user opens the application on their smartphone and enters a question via text.

[0299] Step 2:

[0300] The terminal transmits the user's health data, preferences, and past order history to the intermediary AI.

[0301] Input: User's health data, preferences, past order history

[0302] Output: Personal data sent to the intermediate AI

[0303] Specific operation: The terminal transmits the user's health data (e.g., calorie limit 2000 kcal, allergies: nuts), preferences (e.g., Japanese food, medium spiciness level), and past order history to the intermediate AI.

[0304] Step 3:

[0305] The server obtains personal data from the intermediate AI and sends it to the generative AI.

[0306] Input: Personal data obtained from the intermediate AI

[0307] Output: Personal data sent to the generative AI

[0308] Specific operation: The server obtains the user's personal data from the intermediate AI and sends it to the generative AI.

[0309] Step 4:

[0310] Generative AI will generate the optimal food delivery menu based on personal data and additional information from the user.

[0311] Input: Personal data, additional information of the user

[0312] Output: Optimal food delivery menu

[0313] Specific operation: The generative AI analyzes personal data and additional information from the user to generate a menu that is optimal for the user's health condition and preferences.

[0314] Step 5:

[0315] The server transmits the generated menu to the terminal.

[0316] Input: Generated food delivery menu

[0317] Output: Menu sent to the terminal

[0318] Specific operation: The server sends the optimal menu received from the generative AI to the terminal.

[0319] Step 6:

[0320] The device displays the most suitable food delivery menu for the user.

[0321] Input: Menu sent from the server

[0322] Output: The menu displayed to the user.

[0323] Specific operation: The device displays the received menu to the user, suggests menu items based on the user's physical condition and mood, and notifies the user of new menu items and campaign information.

[0324] In this way, a system is realized that suggests optimal food delivery menus to users.

[0325] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0326] "Example 1"

[0327] As one embodiment of the present invention, there is a system that combines an emotion engine that recognizes the user's emotions. This system includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, if the user says, "I'm very tired today," the emotion engine recognizes that the user is tired. Then, the intermediate AI takes that information into consideration and generates advice such as, for example, "play music that has a relaxing effect." The generative AI combines that advice with the user's personal information to provide cross-sectional advice tailored to the individual.

[0328] "Example 2"

[0329] As one embodiment of the present invention, there is a system that combines an emotion engine that recognizes the user's emotions. This system includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, if the user says, "I'm very tired today," the emotion engine recognizes that the user is tired. Then, the intermediate AI takes that information into consideration and generates advice such as, for example, "play music that has a relaxing effect." The generative AI combines that advice with the user's personal information to provide cross-sectional advice tailored to the individual.

[0330] The process flow of each embodiment will be described below.

[0331] "Example 1"

[0332] Step 1: A user says, "I'm very tired today."

[0333] Step 2: The emotion engine recognizes from the user's comments that they are tired.

[0334] Step 3: The intermediate AI receives information from the emotion engine and generates advice that takes into account the user's condition. Specifically, it generates advice such as "play music that has a relaxing effect."

[0335] Step 4: The generative AI combines the advice from the intermediate AI with the user's personal information to provide tailored cross-sectional advice.

[0336] "Example 2"

[0337] Step 1: A user says, "I'm very tired today."

[0338] Step 2: The emotion engine recognizes from the user's comments that they are tired.

[0339] Step 3: The intermediate AI receives information from the emotion engine and generates advice that takes into account the user's condition. Specifically, it generates advice such as "play music that has a relaxing effect."

[0340] Step 4: The generative AI combines the advice from the intermediate AI with the user's personal information to provide tailored cross-sectional advice.

[0341] Example 1

[0342] Next, a description will be given of Example 1 of the embodiment 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".

[0343] Conventional systems have had the problem that it is difficult to generate answers to user questions that take into account individual personal data, and can only provide general answers. It is also difficult to provide advice that takes into account dynamic information such as the user's emotions and health status. This means that the system is unable to provide optimal advice to users, and the user experience does not improve.

[0344] 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.

[0345] In this invention, the server includes a means for operating an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for implementing cross-sectional advice tailored to the individual, a means for analyzing the user's question and generating an answer using the personal data, a means for generating and sending a prompt to the generative AI, and a means for displaying the answer from the generative AI to the user. This makes it possible to generate an individual answer that takes into account the user's personal data and provide optimal advice that takes into account dynamic information such as the user's emotions and health condition.

[0346] "User" means an individual who utilizes the System to enter questions and receive answers.

[0347] "Question" refers to information or a request that a user inputs into the system.

[0348] "Personal data" refers to information relating to an individual, such as user preferences, health data, and behavioral logs.

[0349] "Middle AI" refers to artificial intelligence that stores users' personal data and uses it to generate answers to their questions.

[0350] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[0351] "Cross-sectional advice" refers to advice that is individually optimized by taking into account the user's personal data and the content of their questions.

[0352] A "prompt" refers to a set of instructions sent to a generative AI, containing the user's personal data and a question.

[0353] "Server" refers to the central computer that runs the entire system and processes user queries.

[0354] "Terminal" refers to the device used by a User to enter questions and receive answers.

[0355] "Analysis" refers to the process of understanding the user's question and grasping their intent.

[0356] "Answer" refers to information or advice that the system generates in response to a user's question.

[0357] This invention is a system that generates individually optimized answers to user questions using personal data. The system is composed of three main elements: a server, a terminal, and a user.

[0358] server

[0359] The server operates an intermediate AI that stores personal data (preferences, health data, behavioral logs, etc.) in response to user questions. The intermediate AI retrieves the user's personal data from a database and uses it to generate answers to questions. The server also has the means to generate and send prompts to the generative AI. The generative AI generates cross-sectional advice tailored to the individual based on the personal data provided by the intermediate AI and the user's personal information.

[0360] Specifically, the server uses the following hardware and software:

[0361] Hardware: High-performance server machine, database server

[0362] Software: Natural language processing libraries, machine learning models, database management systems

[0363] Terminal

[0364] The terminal receives a question from the user and sends the question to the server. After receiving the answer from the server, the terminal displays the answer to the user. The terminal is a device such as a smartphone or PC, and is operated through a dedicated application.

[0365] user

[0366] Users input questions through their devices and receive answers to those questions. For example, if a user asks, "Please let me know when the DVD of the boxing match I just watched is released," the intermediate AI will take into account the user's viewing history and preferences and send a notification when a new boxing match DVD is released. In addition, in response to the question, "What UVER EATS menu would suit my physical condition today?" the intermediate AI will suggest an appropriate menu item, taking into account the user's health data and food preferences.

[0367] Examples

[0368] Example 1: Boxing match DVD announcement

[0369] User Question: "Please let me know when the boxing match I just watched comes out on DVD."

[0370] Intermediate AI processing: Considers user viewing history and preferences

[0371] Generative AI Processing: Sending a notification when a new boxing match DVD is released

[0372] Example 2: UVER EATS menu suggestions

[0373] User question: "Which UVER EATS menu item would suit my mood today?"

[0374] Intermediate AI processing: Considers user health data and dietary preferences

[0375] Generative AI processing: Providing appropriate menu suggestions

[0376] Example 3: Advice using emotion engine

[0377] User says: "I'm so tired today."

[0378] Emotion engine processing: Recognizing that the user is tired

[0379] Intermediate AI processing: Generates advice such as "Play music that has a relaxing effect."

[0380] Generative AI processing: Combining advice with the user's personal information to provide personalized, cross-disciplinary advice

[0381] Examples of prompt statements

[0382] Prompt 1: "If a user asks, 'Can you tell me when the boxing fight I just watched is released on DVD?' Please explain how this can be handled."

[0383] Prompt 2: "If a user asks, 'What UVER EATS menu item would suit my mood today?' Please explain how to handle this."

[0384] Prompt 3: "If a user says, 'I'm very tired today,' explain how that would be processed using the emotion engine."

[0385] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0386] Step 1:

[0387] The user inputs a question through the terminal. For example, he / she types, "Please let me know when the DVD of the boxing match I just watched is released," and presses the send button.

[0388] Input: User question text

[0389] Output: The question text is sent from the terminal to the server.

[0390] Step 2:

[0391] The terminal transmits the user's question to the server, including the question content and identification information such as the user's ID.

[0392] Input: User question text, User ID

[0393] Output: The question text and the user ID are sent to the server.

[0394] Step 3:

[0395] The server uses the intermediate AI to retrieve the user's personal data (preferences, health data, behavioral logs, etc.) from the database. For example, it retrieves the data by executing an SQL query.

[0396] Input: User ID

[0397] Output: User's personal data

[0398] Step 4:

[0399] The server uses an intermediate AI to analyze the user's question, for example using a natural language processing library to understand the intent of the question.

[0400] Input: User question text

[0401] Output: The question intent (e.g., asking for information about boxing match DVDs)

[0402] Step 5:

[0403] The server takes into account the user's preferences and past behavior based on the acquired personal data, for example filtering out boxing matches the user has watched in the past and information about favorite fighters.

[0404] Input: User's personal data, question intent

[0405] Output: Filtered personal data

[0406] Step 6:

[0407] The server generates a prompt for the generative AI, including the user's personal data and the question, and sends it through the API, for example, by using an HTTP request.

[0408] Input: Filtered personal data, question intent

[0409] Output: The prompt sent to the generative AI

[0410] Step 7:

[0411] Generative AI uses the prompts it receives to generate tailored answers for users, for example using a machine learning model to generate an answer like "send me a notification when a new boxing match DVD is released."

[0412] Input: prompt

[0413] Output: A personalized answer

[0414] Step 8:

[0415] The server sends the answer received from the generative AI to the terminal. For example, it sends the answer using an HTTP response.

[0416] Input: Answer from generative AI

[0417] Output: Answer sent to the terminal

[0418] Step 9:

[0419] The device displays the response received from the server to the user, for example using the notification feature of the application to display a message such as "We will notify you when new boxing match DVDs are released."

[0420] Input: Response from the server

[0421] Output: The answer that is displayed to the user

[0422] (Application example 1)

[0423] Next, a description will be given of Application Example 1 of Form 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."

[0424] Conventional food delivery systems have the problem that it is difficult to make personalized suggestions that take into account the user's health data, preferences, behavioral logs, and emotional state. In addition, it is not possible to recognize the user's emotional state in real time and suggest the optimal menu based on that. This makes it difficult to increase user satisfaction.

[0425] 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.

[0426] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to the individual, a means including an emotion engine that recognizes the user's emotions, and a means for generating optimal suggestions taking into account the user's health data, preferences, behavior log, and emotional state. This makes it possible to propose a personalized food delivery menu that comprehensively takes into account the user's health data, preferences, behavior log, and emotional state.

[0427] A "user question" is any inquiry or request made by a user to the system.

[0428] "Personal data" is a general term for information about individuals, such as user preferences, health data, and behavioral logs.

[0429] An "intermediate AI" is an artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[0430] "Generative AI" is artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[0431] "Cross-sectional advice" refers to individually optimized advice provided by taking into consideration a user's personal data and information in a comprehensive manner.

[0432] An "emotion engine" is an engine that recognizes the user's emotions and provides that information to the system.

[0433] "Health Data" refers to information relating to a user's health condition, such as allergy information and calorie intake goals.

[0434] "Preferences" are information about what a user likes and dislikes.

[0435] "Behavior log" is data regarding a user's behavioral history.

[0436] "Emotional state" is information that indicates the user's current emotions or mood.

[0437] A "Suggestion" is a specific piece of advice or recommendation that the system provides to the User.

[0438] The system for implementing this invention includes an intermediate AI and a generative AI to store personal data in response to user questions and provide personalized advice, and also combines an emotion engine that recognizes the user's emotions to generate optimal suggestions that take into account the user's health data, preferences, behavioral logs, and emotional state.

[0439] Hardware and Software Used

[0440] Hardware: Smartphones, smart glasses

[0441] software:

[0442] emotion_recognition: A library of emotion engines

[0443] food_recommendation: A food recommendation AI library

[0444] json: Used to save and load user data

[0445] Data processing and calculation

[0446] Loading user data

[0447] The server reads the user's health data and preferences from a JSON file, which brings the user's personal data into the system.

[0448] emotion recognition

[0449] The device uses the emotion engine to analyze the user's utterances and recognize their emotional state. For example, if the user says, "I'm very tired today," the emotion engine will recognize the emotion "tired."

[0450] Generate personalized suggestions

[0451] The server uses food recommendation AI to suggest optimal menus based on user data and emotional state, making it possible to propose personalized food delivery menus that comprehensively take into account the user's health data, preferences, behavioral log, and emotional state.

[0452] Examples

[0453] If a user says, "I'm very tired today," the emotion engine will recognize the emotion, and the food recommendation AI will suggest a menu that includes ingredients that have a relaxing effect.

[0454] Examples of prompt statements

[0455] User says: "I'm so tired today"

[0456] Emotion engine output: "Tired"

[0457] Food recommendation AI output: "Relaxing herbal tea and snack set"

[0458] In this way, a system can be created that suggests optimal food delivery menus based on the user's emotional state and personal data.

[0459] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0460] Step 1:

[0461] The server reads the user's health data and preferences from a JSON file. As input, it receives a JSON file containing the user's personal data and as output, it produces a data structure containing the user's health data and preferences, which are used in subsequent processing steps.

[0462] Step 2:

[0463] The device uses the emotion engine to analyze the user's utterances and recognize the emotional state. It receives the user's utterance text as input and generates the recognized emotional state as output. Specifically, if the user utters "I'm very tired today," the emotion engine recognizes the emotion "tired."

[0464] Step 3:

[0465] The server uses food recommendation AI to suggest optimal menus based on user data and emotional state. It receives the user's health data, preferences, behavioral logs, and recognized emotional state as input, and generates a personalized food delivery menu as output. Specifically, if the emotion engine recognizes the emotion "tired," the food recommendation AI will suggest a menu that includes ingredients that have a relaxation effect.

[0466] Step 4:

[0467] The terminal presents the personalized food delivery menu received from the server to the user. As an input, it receives the menu suggestions from the server, and as an output, it presents the menu to the user visually or audibly, for example by displaying the menu on the display of a smartphone or smart glasses, or by reading the menu through a voice assistant.

[0468] Step 5:

[0469] The user makes a selection from the presented menu and confirms the order. The system receives the user's selection as input and generates the order information for the selected menu as output. Specifically, the user taps the smartphone screen to select a menu item and presses the order button to confirm the order.

[0470] Step 6:

[0471] The server sends the user's order information to the food delivery service. It receives the user's order information as input and sends an order request to the food delivery service as output. Specifically, it sends the order information to the food delivery service through an API, and the order is processed.

[0472] In this way, the optimal food delivery menu is suggested based on the user's emotional state and personal data, and the entire process up to ordering is completed.

[0473] Example 2

[0474] Next, a second embodiment of the second embodiment will be described. 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."

[0475] Conventional systems had difficulty in providing appropriate answers to user questions that took into account individual personal data. They also lacked the ability to recognize users' emotions and provide advice based on them, so they were unable to fully meet users' needs. Furthermore, it was difficult to integrate multiple data sources to generate cross-sectional advice, making it impossible to provide consistent advice to users.

[0476] 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.

[0477] In this invention, the server includes a means for starting an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for receiving the user's question through the intermediate AI and generating an initial answer using the stored personal data, a means for making an inquiry to the generative AI by adding the personal data provided by the intermediate AI and the user's personal information, a means for providing the user with cross-sectional advice tailored to the individual obtained from the generative AI, and a means including an emotion engine that recognizes the user's emotions. This makes it possible to provide consistent cross-sectional advice that takes into account the user's personal data and emotions.

[0478] A "user question" is a query or request entered into the system by a user.

[0479] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[0480] "Middle AI" refers to artificial intelligence that stores a user's personal data and generates initial answers to the user's questions.

[0481] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[0482] "Federrated advice" refers to consistent advice to users that is generated by integrating multiple data sources.

[0483] An "emotion engine" is an engine that recognizes the user's emotions and provides information based on them.

[0484] A "server" is a computer system that receives users' questions, works with intermediate AI and generative AI to generate answers, and provides them to the users.

[0485] A "terminal" is a device through which a user enters questions and receives answers from the server.

[0486] An "initial answer" is the first answer to a user's question that the intermediate AI generates based on the user's personal data.

[0487] An "inquiry" is the act of the server sending data to the generative AI and requesting a response.

[0488] The present invention is a system for providing individually optimized advice to a user in response to a question by utilizing personal data. A specific embodiment of this system will be described below.

[0489] Hardware and Software Configuration

[0490] server

[0491] The server plays a central role in connecting the intermediate AI, which receives user questions and stores and processes personal data, with the generative AI. The server has the following functions:

[0492] An interface for receiving user questions

[0493] A database that runs the intermediate AI and acquires and stores personal data

[0494] An API client that queries generative AI

[0495] Response generation function that provides advice to users

[0496] Terminal

[0497] A terminal is a device through which a user enters questions and receives answers from the server. A terminal has the following functions:

[0498] An interface for entering user questions

[0499] Communication function to send questions to the server

[0500] Screen showing the response from the server

[0501] User

[0502] The user enters a question into the system through a terminal and receives advice provided by the server. The user performs the following actions:

[0503] Enter your question into the input form on your device

[0504] Click the Send button to send the question to the server.

[0505] Check the advice displayed on the device screen

[0506] Data processing and calculation

[0507] Intermediate AI

[0508] The intermediate AI stores the user's personal data (preferences, health data, behavioral logs, etc.) and generates initial answers to the user's questions. Specifically, it performs the following processes:

[0509] Retrieving user personal data from a database

[0510] Analyzes the question and generates an initial answer based on relevant personal data

[0511] Generative AI

[0512] The generative AI generates cross-sectional advice tailored to the individual based on the personal data provided by the intermediate AI and the user's personal information. Specifically, it performs the following processes:

[0513] Receive data provided by the intermediate AI

[0514] Add user's personal information for analysis

[0515] Generates personalized, optimized advice

[0516] Emotion Engine

[0517] The emotion engine recognizes the user's emotions and provides information based on them. Specifically, it does the following:

[0518] Analyze user comments and input

[0519] Recognize emotional states and generate appropriate advice

[0520] Examples

[0521] Example 1: Boxing match DVD notification

[0522] A user asks, "Please let me know when the DVD of the boxing match I just watched is released." The server uses the intermediate AI to consider the user's viewing history and preferences, and sends a notification when a new boxing match DVD is released.

[0523] Example 2: UBER EATS menu suggestions

[0524] The user asks, "What UBER EATS menu would suit my physical condition today?" The server uses the intermediate AI to consider the user's health data and food preferences and suggests an appropriate menu.

[0525] Example 3: Advice using emotion engine

[0526] A user says, "I'm very tired today." The emotion engine recognizes that the user is tired. The server uses the intermediate AI to generate advice such as "play music that has a relaxing effect," and works with the generative AI to provide cross-sectional advice tailored to the individual.

[0527] Examples of prompt statements

[0528] 1. "How can I notify users when new boxing match DVDs are released, taking into account their viewing history?"

[0529] 2. "How can UBER EATS suggest a menu that fits my health status based on my health data and food preferences?"

[0530] 3. "How can I generate a recommendation to play relaxing music if I detect that the user is tired?"

[0531] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0532] Step 1:

[0533] The server receives the user's question sent from the terminal.

[0534] Specific operation: The server receives the HTTP request and analyzes the question.

[0535] Input: User question (e.g. "Let me know when the DVD of that boxing match I just watched is released")

[0536] Output: Parsed question content

[0537] Step 2:

[0538] The server launches the intermediary AI and obtains the user's personal data.

[0539] Specific operation: The server executes database queries to obtain user preferences, health data, behavioral logs, etc.

[0540] Input: User ID and authentication information

[0541] Output: User's personal data

[0542] Step 3:

[0543] The server generates an initial answer based on the personal data acquired using the intermediate AI.

[0544] Specific operation: The server runs the intermediate AI's algorithm and generates an initial answer.

[0545] Input: User's personal data, analyzed questions

[0546] Output: Initial answer

[0547] Step 4:

[0548] The server queries the generative AI, adding the personal data provided by the intermediate AI and the user's personal information.

[0549] Specific operation: The server calls the API of the generative AI and sends the necessary data.

[0550] Input: Initial answers, user's personal data, additional personal information

[0551] Output: Answer from generative AI

[0552] Step 5:

[0553] The server receives personalized cross-sectional advice obtained from the generative AI.

[0554] Specific operation: The server receives and analyzes the response from the generative AI.

[0555] Input: Answer from generative AI

[0556] Output: Parsed federated advice

[0557] Step 6:

[0558] The server transmits the generated federated advice to the terminal.

[0559] Specific operation: The server generates an HTTP response and sends it to the terminal.

[0560] Input: Parsed federated advice

[0561] Output: Advice sent to terminal

[0562] Step 7:

[0563] The terminal displays the cross-sectional advice received from the server to the user.

[0564] Specific operation: The terminal receives the HTTP response and displays the advice content on the screen.

[0565] Input: Advice from the server

[0566] Output: Advice displayed to the user

[0567] Step 8:

[0568] The user checks the advice displayed on the terminal screen.

[0569] Specific Actions: The user looks at the device screen and confirms the advice provided.

[0570] Input: Advice displayed on terminal

[0571] Output: User understanding and action

[0572] (Application example 2)

[0573] Next, a description will be given of Application Example 2 of Form 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."

[0574] Conventional food delivery systems have difficulty making individual suggestions that take into account the user's health data, preferences, and behavioral logs, and they do not suggest optimal menus based on the user's emotions. As a result, it is not possible to suggest optimal meals based on the user's health condition and emotions, making it difficult to increase user satisfaction.

[0575] 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.

[0576] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to the individual, a means including an emotion engine that recognizes the user's emotions, and a means for generating optimal suggestions based on the user's emotions. This makes it possible to propose optimal food delivery menus based on the user's emotions, taking into account the user's health data, preferences, and behavior log.

[0577] A "user question" is a query or request entered into the system by a user.

[0578] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[0579] "Middle AI" refers to artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[0580] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[0581] "Cross-sectional advice" means providing optimal suggestions and advice to individuals based on their personal data and information.

[0582] An "emotion engine" is an engine that recognizes a user's emotions and provides information based on those emotions.

[0583] "Optimal suggestions" refers to providing the most appropriate suggestions and advice to the user based on the user's personal data and emotions.

[0584] A system for implementing this invention includes an intermediate AI that stores and manages personal data in response to user questions, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to individuals, an emotion engine that recognizes the user's emotions, and a means for generating optimal suggestions based on the user's emotions.

[0585] System Programming

[0586] The program of this system uses the following hardware and software:

[0587] Hardware: Smartphone

[0588] software:

[0589] EmotionEngine: An engine for recognizing user emotions

[0590] Food Recommendation Engine: An engine that suggests food delivery menus based on users' personal data and emotions

[0591] Processing flow

[0592] The server first loads user data from a JSON file. Then it uses the EmotionEngine to recognize emotions from the user's input. Based on the recognized emotions, it uses the FoodRecommendationEngine to suggest the best menu based on the user's personal data and emotions. The suggested menu is returned to the user.

[0593] Examples

[0594] For example, if a user types "I'm very tired today," the emotion engine will recognize the word "tired." The Food Recommendation Engine will then suggest foods and drinks that have a relaxing effect. In this way, the system can suggest the best food delivery menu based on the user's emotions and personal data.

[0595] Examples of prompt statements

[0596] User input: "I'm very tired today"

[0597] Emotion Engine Output: "Tired"

[0598] Food recommendation engine output: "Suggest relaxing herbal teas and light snacks."

[0599] This system takes into account a user's health data, preferences, behavioral logs, and even emotions, making it possible to suggest optimal food delivery menus.

[0600] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0601] Step 1:

[0602] The server loads user data from a JSON file. Specifically, it opens the corresponding JSON file based on the user's ID and reads the user's personal data, such as preferences, health data, and behavior logs, into memory. The input is the user ID, and the output is the user's personal data.

[0603] Step 2:

[0604] The server uses the EmotionEngine to recognize emotions from user input. Specifically, the user passes the text entered by the user to the EmotionEngine, which then analyzes the text to identify emotions. The input is the user's text input, and the output is the recognized emotion.

[0605] Step 3:

[0606] The server uses the FoodRecommendationEngine to suggest optimal menus based on the recognized emotions and the user's personal data. Specifically, the emotions and personal data are passed to the FoodRecommendationEngine, which then analyzes them to generate optimal food delivery menus. The inputs are the recognized emotions and the user's personal data, and the output is the suggested menu.

[0607] Step 4:

[0608] The server returns the proposed menu to the user, i.e., it sends the generated menu to the user's terminal for the user to review. The input is the proposed menu, and the output is the menu displayed on the user's terminal.

[0609] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0610] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[0611] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0612] In the above embodiment, an example has been given in which the specific process is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0613] [Second embodiment]

[0614] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0615] 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.

[0616] 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 wide area network (WAN) and / or a local area network (LAN).

[0617] 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.

[0618] 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 the voice according to instructions from the processor 46.

[0619] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0620] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0621] Fig. 4 shows an example of 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.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0626] "Example 1"

[0627] The system of the present invention includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. The intermediate AI stores the user's personal data and uses it to generate answers to the user's questions. The generative AI adds the personal data provided by the intermediate AI and the user's personal information to generate cross-sectional advice tailored to the individual. As a specific example, if a user asks, "Please let me know when a DVD of the boxing match I just watched is released," the intermediate AI will take into account the user's viewing history and preferences and send a notification when a new boxing match DVD is released. In addition, in response to the question, "What UVER EATS menu would suit my physical condition today?", the intermediate AI will suggest an appropriate menu, taking into account the user's health data and food preferences.

[0628] "Example 2"

[0629] The system of the present invention includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. The intermediate AI stores the user's personal data and uses it to generate answers to the user's questions. The generative AI adds the personal data provided by the intermediate AI and the user's personal information to generate cross-sectional advice tailored to the individual. As a specific example, if a user asks, "Please let me know when a DVD of the boxing match I just watched is released," the intermediate AI will take into account the user's viewing history and preferences and send a notification when a new boxing match DVD is released. In addition, in response to the question, "What UVER EATS menu would suit my physical condition today?", the intermediate AI will suggest an appropriate menu, taking into account the user's health data and food preferences.

[0630] The process flow of each embodiment will be described below.

[0631] "Example 1"

[0632] Step 1: The user asks the system a question, for example, "Tell me when the DVD of the boxing match I just watched is released."

[0633] Step 2: The intermediate AI receives the user's question and generates an answer taking into account the user's personal data (viewing history, preferences, etc.). In this case, set it to send notifications when new boxing match DVDs are released.

[0634] Step 3: The generative AI adds the personal data provided by the intermediate AI and the user’s personal information to generate personalized cross-sectional advice, in this case notifying the user when a new boxing match DVD is released.

[0635] "Example 2"

[0636] Step 1: The user asks the system a question. For example, "What UVER EATS menu would suit my mood today?"

[0637] Step 2: The intermediate AI receives the user's question and generates an answer based on the user's personal data (health data, food preferences, etc.). In this case, it suggests UVER EATS menu items tailored to the user's physical condition and food preferences.

[0638] Step 3: The generative AI adds the personal data provided by the intermediate AI and the user’s personal information to generate personalized cross-sectional advice. In this case, it would suggest UVER EATS menu items to the user based on their physical condition and food preferences.

[0639] Example 1

[0640] Next, a description will be given of Example 1 of Form 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".

[0641] Conventional systems have had difficulty effectively utilizing users' personal data to provide individually optimized advice. In addition, they lacked the means to generate quick and accurate answers to users' questions. This resulted in low user satisfaction and limited the value of the system.

[0642] 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.

[0643] In this invention, the server includes a means for starting an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for receiving the user's question through the intermediate AI and generating an initial answer using the stored personal data, a means for making an inquiry to the generative AI by adding the personal data provided by the intermediate AI and the user's personal information, and a means for providing the user with cross-sectional advice tailored to the individual obtained from the generative AI. This makes it possible to effectively utilize the user's personal data and quickly and accurately provide individually optimized advice.

[0644] "User" refers to an individual who utilizes the System to enter questions and receive answers and advice.

[0645] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[0646] "Middle AI" refers to artificial intelligence that stores a user's personal data and generates initial answers to the user's questions.

[0647] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[0648] "Cross-sectional advice" refers to advice that is individually optimized based on the user's personal data and information.

[0649] "Server" refers to a computer system that receives users' questions, uses intermediate AI and generative AI to generate answers and advice, and provides them to the users.

[0650] "Terminal" refers to the device (smartphone, PC, etc.) that a user uses to enter questions and receive answers or advice.

[0651] "Question" refers to an inquiry that a user inputs into the system.

[0652] "Answer" refers to a system-generated response to a user's question.

[0653] "Inquiry" refers to the sending of data by the server to the generative AI and receiving a response.

[0654] This invention is a system that uses personal data to provide individually optimized advice in response to user questions. The system is mainly composed of three elements: a server, a terminal, and a user.

[0655] server

[0656] The server receives the user's questions and generates individually optimized advice using an intermediate AI that stores and uses personal data, and a generative AI. Specifically, the following hardware and software are used:

[0657] Hardware: High-performance server machine

[0658] Software: Intermediate AI, Generative AI, Database Management System

[0659] The server first launches the intermediate AI and saves the user's personal data (preferences, health data, behavioral logs, etc.). Next, it receives the user's question and generates an initial answer using the personal data saved through the intermediate AI. It then queries the generative AI by adding the personal data provided by the intermediate AI and the user's personal information. Finally, it provides the user with cross-sectional advice tailored to the individual obtained from the generative AI.

[0660] Terminal

[0661] The terminal is a device where the user inputs questions and receives answers and advice from the server. Specifically, the terminal uses the following hardware and software:

[0662] Hardware: Smartphones, PCs, tablets, etc.

[0663] Software: User Interface Application

[0664] The terminal provides an interface for accepting questions from the user, transmits the received questions to the server, and displays answers and advice returned from the server to the user.

[0665] user

[0666] The user inputs a question through the terminal and receives answers and advice provided by the server. The user decides on an action based on the information provided.

[0667] Examples

[0668] Example 1: Boxing match DVD notification

[0669] User Question: "Please let me know when the boxing match I just watched comes out on DVD."

[0670] Process flow:

[0671] 1. The user enters a question via the terminal.

[0672] 2. The device sends a query to the server.

[0673] 3. The server receives the question and uses the intermediary AI to check the user's viewing history and preferences.

[0674] 4. The server adds personal data to the generative AI and queries it.

[0675] 5. Generative AI will generate notifications when new boxing match DVDs are released.

[0676] 6. The server sends a notification to the user's device.

[0677] 7. The user receives a notification on their device.

[0678] Example 2: UBER EATS menu suggestions

[0679] User question: "What UBER EATS menu would suit my mood today?"

[0680] Process flow:

[0681] 1. The user enters a question via the terminal.

[0682] 2. The device sends a query to the server.

[0683] 3. The server receives the question and uses an intermediate AI to verify the user's health data and dietary preferences.

[0684] 4. The server adds personal data to the generative AI and queries it.

[0685] 5. Generative AI suggests appropriate menus.

[0686] 6. The server sends the proposed menu to the user's device.

[0687] 7. The user reviews the suggested menu on their device.

[0688] Examples of prompt statements

[0689] Prompt: "How can I notify users when new boxing match DVDs are released, taking into account their viewing history and preferences?"

[0690] Prompt: "How can you suggest UBER EATS menu options based on a user's health profile and food preferences?"

[0691] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0692] Step 1:

[0693] The server receives the user's question sent from the terminal.

[0694] Input: User question (e.g. "Please let me know when the DVD of that boxing match I just watched is released")

[0695] Specific operation: The server receives an HTTP request and analyzes the question.

[0696] Output: Analysis result of question

[0697] Step 2:

[0698] The server uses the intermediary AI to obtain the user's personal data (preferences, health data, behavioral logs, etc.).

[0699] Input: Analysis result of question content

[0700] What happens: The server executes a database query to retrieve the user's personal data.

[0701] Output: User's personal data

[0702] Step 3:

[0703] The server generates an initial answer based on the personal data acquired using the intermediate AI.

[0704] Input: User's personal data

[0705] Specific operation: The server calls the API of the intermediate AI and sends data to generate an initial answer.

[0706] Output: Initial answer

[0707] Step 4:

[0708] The server queries the generative AI, adding the personal data provided by the intermediate AI and the user's personal information.

[0709] Input: Initial answers, user's personal information

[0710] Specific operation: The server calls the generative AI's API and sends the necessary data.

[0711] Output: Response from the generative AI

[0712] Step 5:

[0713] The server provides users with cross-sectional advice tailored to each individual, obtained from generative AI.

[0714] Input: Response from generative AI

[0715] Specific operation: The server receives and analyzes the response from the generative AI.

[0716] Output: Personalized cross-sectional advice

[0717] Step 6:

[0718] The server sends the advice obtained from the generative AI to the user's device.

[0719] Input: Personalized cross-sectional advice

[0720] Specific behavior: The server generates an HTTP response and sends it to the user's device.

[0721] Output: Advice sent to the user's terminal

[0722] Step 7:

[0723] The terminal displays the answers and advice returned from the server to the user.

[0724] Input: HTTP response from the server

[0725] Specific operation: The terminal receives and analyzes the HTTP response.

[0726] Output: The answer or advice that is displayed to the user

[0727] Step 8:

[0728] Users can review the answers and advice provided through their device and decide on their course of action.

[0729] Input: Answer or advice displayed on the terminal

[0730] Specific action: The user looks at the device screen and reads the information displayed.

[0731] Output: User action decision (e.g., order a suggested menu, receive a notification, etc.)

[0732] (Application example 1)

[0733] Next, a description will be given of Application Example 1 of Form 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".

[0734] Conventional food delivery services have had problems with the difficulty of proposing individual menus based on the user's health condition and preferences, and it takes time for users to choose meals that suit their physical condition and preferences. In addition, there was a lack of a system that effectively utilizes the user's personal data to provide individually optimized advice.

[0735] 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.

[0736] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to an individual, a means for proposing an optimal food delivery menu based on the user's health data, preferences, and past order history, and a means including an application installed on a smartphone or smart glasses. This makes it possible to propose individual menus based on the user's health condition and preferences.

[0737] A "user question" is any inquiry or request made by a user to the system.

[0738] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[0739] "Middle AI" refers to artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[0740] "Generative AI" refers to artificial intelligence that adds personal data provided by the intermediate AI with the user's personal information to generate cross-sectional advice tailored to the individual.

[0741] "Cross-sectional advice" refers to providing individually optimized advice based on the user's personal data.

[0742] "Health Data" refers to information relating to a user's health, such as blood pressure, weight, and allergy information.

[0743] "Preferences" refer to personal preferences such as the dishes a user likes or ingredients they want to avoid.

[0744] "Past Order History" means a record of orders previously placed by a User.

[0745] "Food Delivery Menu" means the food options available for delivery to a User.

[0746] A "smartphone" is a type of mobile phone, a device that can connect to the Internet and use applications.

[0747] "Smart glasses" are glasses-type devices that are wearable devices that can display information and connect to the Internet.

[0748] An "application" is a software program designed to accomplish a particular function.

[0749] The system for implementing this invention includes an intermediate AI that stores and analyzes personal data in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing personalized cross-sectional advice, a means for proposing optimal food delivery menus based on the user's health data, preferences, and past order history, and an application installed on a smartphone or smart glasses.

[0750] Hardware and Software Used

[0751] Hardware:

[0752] Smartphone

[0753] Smart Glasses

[0754] software:

[0755] Python

[0756] The Requests library (for sending HTTP requests)

[0757] JSON library (data parsing and storage)

[0758] Data processing and calculation

[0759] 1. Data collection:

[0760] The server collects the user's health data (e.g., blood pressure, weight, allergy information), preferences (e.g., favorite dishes, ingredients to avoid), and past ordering history, and stores them in JSON format.

[0761] 2. Data Analysis:

[0762] The intermediate AI analyzes the collected personal data and generates data based on the user's health condition and preferences.

[0763] 3. Querying the generative AI:

[0764] The intermediate AI sends the analysis results to the generative AI and receives personalized menu suggestions.

[0765] Examples

[0766] When a user asks, "What menu would suit my physical condition today?", the server takes into account the user's health data (e.g., blood pressure 120 / 80, weight 70kg, allergy: peanuts) and preferences (e.g., favorite foods: sushi, pasta; least favorite foods: spicy food) and queries the generative AI. The generative AI then suggests a specific menu, such as, "Since your blood pressure is normal today, I recommend low-salt sushi."

[0767] Examples of prompt statements

[0768] User health data: Blood pressure 120 / 80, Weight 70kg, Allergies: peanuts

[0769] User preferences: Favorite foods: sushi, pasta; Dislikes: spicy food

[0770] User Question: What's the best meal for me today?

[0771] In this way, it is possible to provide individual menu suggestions based on the user's health condition and preferences.

[0772] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0773] Step 1:

[0774] The user inputs a question using a device (smartphone or smart glasses).

[0775] Input: User question (e.g. "What menu would suit my physical condition today?")

[0776] Output: User question data

[0777] Step 2:

[0778] The terminal transmits the user's question data to the server.

[0779] Input: User question data

[0780] Output: The question data sent to the server.

[0781] Step 3:

[0782] The server uses the intermediary AI to collect the user's personal data (health data, preferences, past order history).

[0783] Input: User ID and authentication information

[0784] Output: User's personal data (JSON format)

[0785] Step 4:

[0786] The intermediate AI analyzes the collected personal data and generates data based on the user's health condition and preferences.

[0787] Input: User's personal data

[0788] Output: Analysis result data (e.g. health condition, preferences, foods to avoid, etc.)

[0789] Step 5:

[0790] The server sends the analysis results data to the generative AI and receives personalized menu suggestions.

[0791] Input: Analysis result data

[0792] Output: Menu suggestion data from generative AI

[0793] Step 6:

[0794] The generative AI generates prompt text based on the analysis results data and suggests the optimal menu.

[0795] Input: Analysis result data

[0796] Output: Menu suggestions (e.g. "Your blood pressure is normal today, so I recommend low-sodium sushi.")

[0797] Step 7:

[0798] The server sends the menu suggestion data received from the generative AI to the terminal.

[0799] Input: Menu suggestion data

[0800] Output: Menu suggestion data sent to the device

[0801] Step 8:

[0802] The terminal displays menu suggestions to the user.

[0803] Input: Menu suggestion data

[0804] Output: The menu suggestions displayed to the user

[0805] In this way, it is possible to provide individual menu suggestions based on the user's health condition and preferences.

[0806] Example 2

[0807] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0808] With conventional systems, it was difficult to provide individually optimized advice in response to user questions. In particular, there is a demand for effective use of users' personal data to provide specific advice in real time based on the user's preferences and health condition, but there has been a lack of technology to achieve this.

[0809] 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.

[0810] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for implementing cross-sectional advice tailored to an individual, a terminal that receives the user's question and transmits it to the server, a server that transmits the initial answer generated by the intermediate AI and the user's personal information to the generative AI, a server that transmits the advice generated by the generative AI to the user's terminal, and a means for the terminal to display the advice to the user. This makes it possible to effectively utilize the user's personal data and provide individually optimized advice in real time.

[0811] "User" refers to an individual who utilizes the System to enter questions and receive advice.

[0812] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[0813] "Middle AI" refers to artificial intelligence that stores a user's personal data and generates initial answers to the user's questions.

[0814] "Generative AI" refers to artificial intelligence that generates individually optimized advice based on personal data provided by the intermediate AI and the user's personal information.

[0815] "Terminal" refers to the device (such as a smartphone or PC) that a user uses to enter questions and receive advice from the server.

[0816] "Server" refers to a computer system that passes the user's question to the intermediate AI, transmits data to the generative AI, and transmits the generated advice to the terminal.

[0817] "Cross-sectional advice" refers to advice that is individually optimized based on the user's personal data and information.

[0818] "Initial answer" refers to the first answer to a question that the intermediate AI generates based on the user's personal data.

[0819] This invention is a system that uses personal data to provide individually optimized advice in response to user questions. The system is mainly composed of an intermediate AI, a generative AI, a server, and a terminal.

[0820] The intermediate AI stores the user's personal data (preferences, health data, behavioral logs, etc.) and generates initial answers to the user's questions. The intermediate AI runs in a server and has access to the user's personal data.

[0821] The generative AI generates individually optimized cross-sectional advice based on the personal data provided by the intermediate AI and the user's personal information. The generative AI also runs on the server and processes data to provide the user with optimized advice.

[0822] The server passes the user's question to the intermediate AI, transmits the initial answer generated by the intermediate AI and the user's personal information to the generative AI, and also transmits the advice generated by the generative AI to the user's device.

[0823] The terminal is a device that the user uses to input questions and receive advice from the server. Terminals include smartphones and PCs.

[0824] As a specific example of how it works, consider the case where a user asks, "Let me know when the DVD of the boxing match I just watched is released." The user enters the question into their smartphone, and the device sends the question to the server. The server passes the question to the intermediate AI, which references the user's viewing history to generate an initial answer. The server then sends the initial answer and the user's personal information to the generative AI, which then generates advice to send a notification when a new boxing match DVD is released. Finally, the server sends the generated advice to the device, which displays the notification to the user.

[0825] As another concrete example, consider the case where a user asks, "What UVER EATS menu would suit my physical condition today?" The user enters the question into their PC, and the device sends the question to the server. The server passes the question to the intermediate AI, which generates an initial answer by referencing the user's health data and food preferences. The server then sends the initial answer and the user's personal information to the generative AI, which then generates advice suggesting a menu that suits today's physical condition. Finally, the server sends the generated advice to the terminal, which displays the menu to the user.

[0826] Examples of prompts include:

[0827] "How can I notify users when new boxing match DVDs are released based on their viewing history and preferences?"

[0828] "Please tell me how to suggest UVER EATS menu items that suit today's physical condition based on the user's health data and food preferences."

[0829] In this way, the system can effectively utilize the user's personal data and provide individually optimized advice in real time.

[0830] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0831] Step 1:

[0832] The user enters a question.

[0833] A user uses a terminal (such as a smartphone or PC) to input a question into the system. For example, the user might input, "Please let me know when the DVD of the boxing match I just watched is released." The input data is the user's question.

[0834] Step 2:

[0835] The terminal sends a question to the server.

[0836] The terminal transmits the question entered by the user to the server. This communication is usually carried out over the Internet. The input data is the user's question and the output data is the question sent to the server.

[0837] Step 3:

[0838] The server passes the question to the intermediate AI.

[0839] The server passes the received question to the intermediate AI, which runs in the server and has access to the user's personal data. The input data is the question sent to the server, and the output data is the question passed to the intermediate AI.

[0840] Step 4:

[0841] The intermediate AI refers to the user's personal data and generates an initial answer.

[0842] The intermediate AI refers to the user's personal data, such as viewing history, preferences, and health data, to generate an initial answer to the question. For example, if a user asks about a boxing match DVD, the intermediate AI checks the viewing history and extracts relevant information. The input data are the user's personal data and the question, and the output data is the initial answer.

[0843] Step 5:

[0844] The server sends the intermediate AI's initial answer and the user's personal information to the generative AI.

[0845] The server sends the initial answer generated by the intermediate AI and the user's personal information to the generative AI. The generative AI generates optimized advice based on this information. The input data are the initial answer and the user's personal information, and the output data is the data sent to the generative AI.

[0846] Step 6:

[0847] Generative AI generates optimized cross-cutting advice.

[0848] The generative AI combines the data provided by the intermediate AI with the user's personal information to generate individually optimized cross-sectional advice. For example, it suggests a menu that suits the user's physical condition for the day based on their health data and food preferences. The input data are the initial answer and the user's personal information, and the output data is optimized advice.

[0849] Step 7:

[0850] The server transmits the generated advice to the terminal.

[0851] The server receives the advice generated by the generative AI and sends it to the user's device. The input data is the optimized advice, and the output data is the advice sent to the device.

[0852] Step 8:

[0853] The terminal displays the advice to the user.

[0854] The terminal displays the advice received from the server to the user, allowing the user to obtain individually optimized information. Input data is the advice sent to the terminal, and output data is the advice displayed to the user.

[0855] (Application example 2)

[0856] Next, a description will be given of Application Example 2 of Form 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".

[0857] Conventional food delivery services have a problem in that it is difficult to provide users with the most suitable menu because they do not fully consider the user's health condition, preferences, or past order history. In addition, there are problems in that it is difficult to increase user satisfaction because there are not enough menu suggestions that match the user's physical condition and mood, or notifications of new menus or campaign information.

[0858] 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.

[0859] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for implementing cross-sectional advice tailored to the individual, a means for proposing an optimal food delivery menu based on the user's health data, preferences, and past order history, a means for proposing a menu that matches the user's physical condition and mood, and a means for notifying the user of new menus and campaign information. This makes it possible to propose an optimal menu that takes into account the user's health condition, preferences, and past order history, and by proposing a menu that matches the user's physical condition and mood and notifying the user of new menus and campaign information, it is possible to increase user satisfaction.

[0860] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[0861] An "intermediate AI" is an artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[0862] "Generative AI" is artificial intelligence that adds personal data provided by the intermediate AI with the user's personal information to generate cross-sectional advice tailored to the individual.

[0863] "Cross-sectional advice" means providing suggestions and advice that are best suited to each individual based on the user's personal data.

[0864] "Health Data" refers to information relating to a user's health, such as their calorie restrictions or allergy information.

[0865] "Preferences" is information about a user's likes and preferences.

[0866] "Behavior log" is information about a user's past behavior and history.

[0867] "Order History" means a record of orders a User has placed in the past.

[0868] "Food Delivery Menu" means the meal options offered to Users.

[0869] "Health" is information about the user's current health condition and mood.

[0870] "Campaign Information" is information about new menu items or special promotions.

[0871] The system for carrying out this invention includes an intermediate AI that saves personal data in response to a user's question, adds the user's personal information, and queries the generative AI. It also includes a means for implementing cross-sectional advice tailored to individuals. Specifically, it includes a means for proposing optimal food delivery menus based on the user's health data, preferences, and past order history, a means for proposing menus tailored to the user's physical condition and mood, and a means for notifying the user of new menus and campaign information.

[0872] System Configuration

[0873] Hardware

[0874] Server: A server for running intermediate AI and generative AI.

[0875] Device: The smartphone used by the user.

[0876] software

[0877] Intermediate AI: AI that stores the user's personal data and generates answers to questions.

[0878] Generative AI: An AI that generates cross-sectional advice tailored to the individual based on the personal data provided by the intermediate AI and the user's personal information.

[0879] Food delivery application: An application that suggests optimal menu items based on the user's health data, preferences, and past ordering history, suggests menu items that match the user's physical condition and mood, and notifies the user of new menu items and campaign information.

[0880] Data processing and calculation

[0881] 1. Acquisition of user data:

[0882] The server transmits the user's health data, preferences, and past order history to the intermediary AI to obtain personal data.

[0883] 2. Use of Personal Data:

[0884] The server sends the acquired personal data and additional information about the user to the generative AI, which then generates the optimal food delivery menu.

[0885] 3. Display the results:

[0886] The terminal displays the generated menu to the user, suggests menu items tailored to the user's physical condition and mood, and notifies the user of new menu items and campaign information.

[0887] Examples

[0888] When a user asks, "What food delivery menu would suit my physical condition today?" the system operates as follows. First, the server sends the user's health data (e.g. calorie restrictions and allergy information) to the intermediate AI and obtains personal data. Next, the server sends this personal data and additional information about the user to the generative AI, which generates the optimal menu. Finally, the device displays the generated menu to the user, suggests menu items tailored to their physical condition and mood, and notifies them of new menu items and campaign information.

[0889] Examples of prompt statements

[0890] Consider the user's health data and preferences and suggest the best food delivery menu for today's physical condition. The user's health data is as follows: Calorie limit 2000kcal, Allergy: Nuts. Preferences are Japanese food, and spiciness level is medium spicy.

[0891] In this way, a system can be realized that suggests optimal food delivery menus to users.

[0892] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0893] Step 1:

[0894] A user launches a food delivery application and enters a question.

[0895] Input: User question (e.g. "What food delivery menu would suit my physical condition today?")

[0896] Output: User question data

[0897] Specific actions: The user opens the application on their smartphone and enters a question via text.

[0898] Step 2:

[0899] The terminal transmits the user's health data, preferences, and past order history to the intermediary AI.

[0900] Input: User's health data, preferences, past order history

[0901] Output: Personal data sent to the intermediate AI

[0902] Specific operation: The terminal transmits the user's health data (e.g., calorie limit 2000 kcal, allergies: nuts), preferences (e.g., Japanese food, medium spiciness level), and past order history to the intermediate AI.

[0903] Step 3:

[0904] The server obtains personal data from the intermediate AI and sends it to the generative AI.

[0905] Input: Personal data obtained from the intermediate AI

[0906] Output: Personal data sent to the generative AI

[0907] Specific operation: The server obtains the user's personal data from the intermediate AI and sends it to the generative AI.

[0908] Step 4:

[0909] Generative AI will generate the optimal food delivery menu based on personal data and additional information from the user.

[0910] Input: Personal data, additional information of the user

[0911] Output: Optimal food delivery menu

[0912] Specific operation: The generative AI analyzes personal data and additional information from the user to generate a menu that is optimal for the user's health condition and preferences.

[0913] Step 5:

[0914] The server transmits the generated menu to the terminal.

[0915] Input: Generated food delivery menu

[0916] Output: Menu sent to the terminal

[0917] Specific operation: The server sends the optimal menu received from the generative AI to the terminal.

[0918] Step 6:

[0919] The device displays the most suitable food delivery menu for the user.

[0920] Input: Menu sent from the server

[0921] Output: The menu displayed to the user.

[0922] Specific operation: The device displays the received menu to the user, suggests menu items based on the user's physical condition and mood, and notifies the user of new menu items and campaign information.

[0923] In this way, a system is realized that suggests optimal food delivery menus to users.

[0924] In addition, 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.

[0925] "Example 1"

[0926] As one embodiment of the present invention, there is a system that combines an emotion engine that recognizes the user's emotions. This system includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, if the user says, "I'm very tired today," the emotion engine recognizes that the user is tired. Then, the intermediate AI takes that information into consideration and generates advice such as, for example, "play music that has a relaxing effect." The generative AI combines that advice with the user's personal information to provide cross-sectional advice tailored to the individual.

[0927] "Example 2"

[0928] As one embodiment of the present invention, there is a system that combines an emotion engine that recognizes the user's emotions. This system includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, if the user says, "I'm very tired today," the emotion engine recognizes that the user is tired. Then, the intermediate AI takes that information into consideration and generates advice such as, for example, "play music that has a relaxing effect." The generative AI combines that advice with the user's personal information to provide cross-sectional advice tailored to the individual.

[0929] The process flow of each embodiment will be described below.

[0930] "Example 1"

[0931] Step 1: A user says, "I'm very tired today."

[0932] Step 2: The emotion engine recognizes from the user's comments that they are tired.

[0933] Step 3: The intermediate AI receives information from the emotion engine and generates advice that takes into account the user's condition. Specifically, it generates advice such as "play music that has a relaxing effect."

[0934] Step 4: The generative AI combines the advice from the intermediate AI with the user's personal information to provide tailored cross-sectional advice.

[0935] Example 1

[0936] Next, a description will be given of Example 1 of Form 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".

[0937] Conventional systems have had the problem that it is difficult to generate answers to user questions that take into account individual personal data, and can only provide general answers. It is also difficult to provide advice that takes into account dynamic information such as the user's emotions and health status. This means that the system is unable to provide optimal advice to users, and the user experience does not improve.

[0938] 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.

[0939] In this invention, the server includes a means for operating an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for implementing cross-sectional advice tailored to the individual, a means for analyzing the user's question and generating an answer using the personal data, a means for generating and sending a prompt to the generative AI, and a means for displaying the answer from the generative AI to the user. This makes it possible to generate an individual answer that takes into account the user's personal data and provide optimal advice that takes into account dynamic information such as the user's emotions and health condition.

[0940] "User" means an individual who utilizes the System to enter questions and receive answers.

[0941] "Question" refers to information or a request that a user inputs into the system.

[0942] "Personal data" refers to information relating to an individual, such as user preferences, health data, and behavioral logs.

[0943] "Middle AI" refers to artificial intelligence that stores users' personal data and uses it to generate answers to their questions.

[0944] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[0945] "Cross-sectional advice" refers to advice that is individually optimized by taking into account the user's personal data and the content of their questions.

[0946] A "prompt" refers to a set of instructions sent to a generative AI, containing the user's personal data and a question.

[0947] "Server" refers to the central computer that runs the entire system and processes user queries.

[0948] "Terminal" refers to the device used by a User to enter questions and receive answers.

[0949] "Analysis" refers to the process of understanding the user's question and grasping their intent.

[0950] "Answer" refers to information or advice that the system generates in response to a user's question.

[0951] This invention is a system that generates individually optimized answers to user questions using personal data. The system is composed of three main elements: a server, a terminal, and a user.

[0952] server

[0953] The server operates an intermediate AI that stores personal data (preferences, health data, behavioral logs, etc.) in response to user questions. The intermediate AI retrieves the user's personal data from a database and uses it to generate answers to questions. The server also has the means to generate and send prompts to the generative AI. The generative AI generates cross-sectional advice tailored to the individual based on the personal data provided by the intermediate AI and the user's personal information.

[0954] Specifically, the server uses the following hardware and software:

[0955] Hardware: High-performance server machine, database server

[0956] Software: Natural language processing libraries, machine learning models, database management systems

[0957] Terminal

[0958] The terminal receives a question from the user and sends the question to the server. After receiving the answer from the server, the terminal displays the answer to the user. The terminal is a device such as a smartphone or PC, and is operated through a dedicated application.

[0959] user

[0960] Users input questions through their devices and receive answers to those questions. For example, if a user asks, "Please let me know when the DVD of the boxing match I just watched is released," the intermediate AI will take into account the user's viewing history and preferences and send a notification when a new boxing match DVD is released. In addition, in response to the question, "What UVER EATS menu would suit my physical condition today?" the intermediate AI will suggest an appropriate menu item, taking into account the user's health data and food preferences.

[0961] Examples

[0962] Example 1: Boxing match DVD announcement

[0963] User Question: "Please let me know when the boxing match I just watched comes out on DVD."

[0964] Intermediate AI processing: Considers user viewing history and preferences

[0965] Generative AI Processing: Sending a notification when a new boxing match DVD is released

[0966] Example 2: UVER EATS menu suggestions

[0967] User question: "Which UVER EATS menu item would suit my mood today?"

[0968] Intermediate AI processing: Considers user health data and dietary preferences

[0969] Generative AI processing: Providing appropriate menu suggestions

[0970] Example 3: Advice using emotion engine

[0971] User says: "I'm so tired today."

[0972] Emotion engine processing: Recognizing that the user is tired

[0973] Intermediate AI processing: Generates advice such as "Play music that has a relaxing effect."

[0974] Generative AI processing: Combining advice with the user's personal information to provide personalized, cross-disciplinary advice

[0975] Examples of prompt statements

[0976] Prompt 1: "If a user asks, 'Can you tell me when the boxing fight I just watched is released on DVD?' Please explain how this can be handled."

[0977] Prompt 2: "If a user asks, 'What UVER EATS menu item would suit my mood today?' Please explain how to handle this."

[0978] Prompt 3: "If a user says, 'I'm very tired today,' explain how that would be processed using the emotion engine."

[0979] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0980] Step 1:

[0981] The user inputs a question through the terminal. For example, he / she types, "Please let me know when the DVD of the boxing match I just watched is released," and presses the send button.

[0982] Input: User question text

[0983] Output: The question text is sent from the terminal to the server.

[0984] Step 2:

[0985] The terminal transmits the user's question to the server, including the question content and identification information such as the user's ID.

[0986] Input: User question text, User ID

[0987] Output: The question text and the user ID are sent to the server.

[0988] Step 3:

[0989] The server uses the intermediate AI to retrieve the user's personal data (preferences, health data, behavioral logs, etc.) from the database. For example, it retrieves the data by executing an SQL query.

[0990] Input: User ID

[0991] Output: User's personal data

[0992] Step 4:

[0993] The server uses an intermediate AI to analyze the user's question, for example using a natural language processing library to understand the intent of the question.

[0994] Input: User question text

[0995] Output: The question intent (e.g., asking for information about boxing match DVDs)

[0996] Step 5:

[0997] The server takes into account the user's preferences and past behavior based on the acquired personal data, for example filtering out boxing matches the user has watched in the past and information about favorite fighters.

[0998] Input: User's personal data, question intent

[0999] Output: Filtered personal data

[1000] Step 6:

[1001] The server generates a prompt for the generative AI, including the user's personal data and the question, and sends it through the API, for example, by using an HTTP request.

[1002] Input: Filtered personal data, question intent

[1003] Output: The prompt sent to the generative AI

[1004] Step 7:

[1005] Generative AI uses the prompts it receives to generate tailored answers for users, for example using a machine learning model to generate an answer like "send me a notification when a new boxing match DVD is released."

[1006] Input: prompt

[1007] Output: A personalized answer

[1008] Step 8:

[1009] The server sends the answer received from the generative AI to the terminal. For example, it sends the answer using an HTTP response.

[1010] Input: Answer from generative AI

[1011] Output: Answer sent to the terminal

[1012] Step 9:

[1013] The device displays the response received from the server to the user, for example using the notification feature of the application to display a message such as "We will notify you when new boxing match DVDs are released."

[1014] Input: Response from the server

[1015] Output: The answer that is displayed to the user

[1016] (Application example 1)

[1017] Next, a description will be given of Application Example 1 of Form 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".

[1018] Conventional food delivery systems have the problem that it is difficult to make personalized suggestions that take into account the user's health data, preferences, behavioral logs, and emotional state. In addition, it is not possible to recognize the user's emotional state in real time and suggest the optimal menu based on that. This makes it difficult to increase user satisfaction.

[1019] 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.

[1020] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to the individual, a means including an emotion engine that recognizes the user's emotions, and a means for generating optimal suggestions taking into account the user's health data, preferences, behavior log, and emotional state. This makes it possible to propose a personalized food delivery menu that comprehensively takes into account the user's health data, preferences, behavior log, and emotional state.

[1021] A "user question" is any inquiry or request made by a user to the system.

[1022] "Personal data" is a general term for information about individuals, such as user preferences, health data, and behavioral logs.

[1023] An "intermediate AI" is an artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[1024] "Generative AI" is artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[1025] "Cross-sectional advice" refers to individually optimized advice provided by taking into consideration a user's personal data and information in a comprehensive manner.

[1026] An "emotion engine" is an engine that recognizes the user's emotions and provides that information to the system.

[1027] "Health Data" refers to information relating to a user's health condition, such as allergy information and calorie intake goals.

[1028] "Preferences" are information about what a user likes and dislikes.

[1029] "Behavior log" is data regarding a user's behavioral history.

[1030] "Emotional state" is information that indicates the user's current emotions or mood.

[1031] A "Suggestion" is a specific piece of advice or recommendation that the system provides to the User.

[1032] The system for implementing this invention includes an intermediate AI and a generative AI to store personal data in response to user questions and provide personalized advice, and also combines an emotion engine that recognizes the user's emotions to generate optimal suggestions that take into account the user's health data, preferences, behavioral logs, and emotional state.

[1033] Hardware and Software Used

[1034] Hardware: Smartphones, smart glasses

[1035] software:

[1036] emotion_recognition: A library of emotion engines

[1037] food_recommendation: A food recommendation AI library

[1038] json: Used to save and load user data

[1039] Data processing and calculation

[1040] Loading user data

[1041] The server reads the user's health data and preferences from a JSON file, which brings the user's personal data into the system.

[1042] emotion recognition

[1043] The device uses the emotion engine to analyze the user's utterances and recognize their emotional state. For example, if the user says, "I'm very tired today," the emotion engine will recognize the emotion "tired."

[1044] Generate personalized suggestions

[1045] The server uses food recommendation AI to suggest optimal menus based on user data and emotional state, making it possible to propose personalized food delivery menus that comprehensively take into account the user's health data, preferences, behavioral log, and emotional state.

[1046] Examples

[1047] If a user says, "I'm very tired today," the emotion engine will recognize the emotion, and the food recommendation AI will suggest a menu that includes ingredients that have a relaxing effect.

[1048] Examples of prompt statements

[1049] User says: "I'm so tired today"

[1050] Emotion engine output: "Tired"

[1051] Food recommendation AI output: "Relaxing herbal tea and snack set"

[1052] In this way, a system can be created that suggests optimal food delivery menus based on the user's emotional state and personal data.

[1053] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1054] Step 1:

[1055] The server reads the user's health data and preferences from a JSON file. As input, it receives a JSON file containing the user's personal data and as output, it produces a data structure containing the user's health data and preferences, which are used in subsequent processing steps.

[1056] Step 2:

[1057] The device uses the emotion engine to analyze the user's utterances and recognize the emotional state. It receives the user's utterance text as input and generates the recognized emotional state as output. Specifically, if the user utters "I'm very tired today," the emotion engine recognizes the emotion "tired."

[1058] Step 3:

[1059] The server uses food recommendation AI to suggest optimal menus based on user data and emotional state. It receives the user's health data, preferences, behavioral logs, and recognized emotional state as input, and generates a personalized food delivery menu as output. Specifically, if the emotion engine recognizes the emotion "tired," the food recommendation AI will suggest a menu that includes ingredients that have a relaxation effect.

[1060] Step 4:

[1061] The terminal presents the personalized food delivery menu received from the server to the user. As an input, it receives the menu suggestions from the server, and as an output, it presents the menu to the user visually or audibly, for example by displaying the menu on the display of a smartphone or smart glasses, or by reading the menu through a voice assistant.

[1062] Step 5:

[1063] The user makes a selection from the presented menu and confirms the order. The system receives the user's selection as input and generates the order information for the selected menu as output. Specifically, the user taps the smartphone screen to select a menu item and presses the order button to confirm the order.

[1064] Step 6:

[1065] The server sends the user's order information to the food delivery service. It receives the user's order information as input and sends an order request to the food delivery service as output. Specifically, it sends the order information to the food delivery service through an API, and the order is processed.

[1066] In this way, the optimal food delivery menu is suggested based on the user's emotional state and personal data, and the entire process up to ordering is completed.

[1067] Example 2

[1068] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[1069] Conventional systems had difficulty in providing appropriate answers to user questions that took into account individual personal data. They also lacked the ability to recognize users' emotions and provide advice based on them, so they were unable to fully meet users' needs. Furthermore, it was difficult to integrate multiple data sources to generate cross-sectional advice, making it impossible to provide consistent advice to users.

[1070] 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.

[1071] In this invention, the server includes a means for starting an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for receiving the user's question through the intermediate AI and generating an initial answer using the stored personal data, a means for making an inquiry to the generative AI by adding the personal data provided by the intermediate AI and the user's personal information, a means for providing the user with cross-sectional advice tailored to the individual obtained from the generative AI, and a means including an emotion engine that recognizes the user's emotions. This makes it possible to provide consistent cross-sectional advice that takes into account the user's personal data and emotions.

[1072] A "user question" is a query or request entered into the system by a user.

[1073] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[1074] "Middle AI" refers to artificial intelligence that stores a user's personal data and generates initial answers to the user's questions.

[1075] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[1076] "Federrated advice" refers to consistent advice to users that is generated by integrating multiple data sources.

[1077] An "emotion engine" is an engine that recognizes the user's emotions and provides information based on them.

[1078] A "server" is a computer system that receives users' questions, works with intermediate AI and generative AI to generate answers, and provides them to the users.

[1079] A "terminal" is a device through which a user enters questions and receives answers from the server.

[1080] An "initial answer" is the first answer to a user's question that the intermediate AI generates based on the user's personal data.

[1081] An "inquiry" is the act of the server sending data to the generative AI and requesting a response.

[1082] The present invention is a system for providing individually optimized advice to a user in response to a question by utilizing personal data. A specific embodiment of this system will be described below.

[1083] Hardware and Software Configuration

[1084] server

[1085] The server plays a central role in connecting the intermediate AI, which receives user questions and stores and processes personal data, with the generative AI. The server has the following functions:

[1086] An interface for receiving user questions

[1087] A database that runs the intermediate AI and acquires and stores personal data

[1088] An API client that queries generative AI

[1089] Response generation function that provides advice to users

[1090] Terminal

[1091] A terminal is a device through which a user enters questions and receives answers from the server. A terminal has the following functions:

[1092] An interface for entering user questions

[1093] Communication function to send questions to the server

[1094] Screen showing the response from the server

[1095] User

[1096] The user enters a question into the system through a terminal and receives advice provided by the server. The user performs the following actions:

[1097] Enter your question into the input form on your device

[1098] Click the Send button to send the question to the server.

[1099] Check the advice displayed on the device screen

[1100] Data processing and calculation

[1101] Intermediate AI

[1102] The intermediate AI stores the user's personal data (preferences, health data, behavioral logs, etc.) and generates initial answers to the user's questions. Specifically, it performs the following processes:

[1103] Retrieving user personal data from a database

[1104] Analyzes the question and generates an initial answer based on relevant personal data

[1105] Generative AI

[1106] The generative AI generates cross-sectional advice tailored to the individual based on the personal data provided by the intermediate AI and the user's personal information. Specifically, it performs the following processes:

[1107] Receive data provided by the intermediate AI

[1108] Add user's personal information for analysis

[1109] Generates personalized, optimized advice

[1110] Emotion Engine

[1111] The emotion engine recognizes the user's emotions and provides information based on them. Specifically, it does the following:

[1112] Analyze user comments and input

[1113] Recognize emotional states and generate appropriate advice

[1114] Examples

[1115] Example 1: Boxing match DVD notification

[1116] A user asks, "Please let me know when the DVD of the boxing match I just watched is released." The server uses the intermediate AI to consider the user's viewing history and preferences, and sends a notification when a new boxing match DVD is released.

[1117] Example 2: UBER EATS menu suggestions

[1118] The user asks, "What UBER EATS menu would suit my physical condition today?" The server uses the intermediate AI to consider the user's health data and food preferences and suggests an appropriate menu.

[1119] Example 3: Advice using emotion engine

[1120] A user says, "I'm very tired today." The emotion engine recognizes that the user is tired. The server uses the intermediate AI to generate advice such as "play music that has a relaxing effect," and works with the generative AI to provide cross-sectional advice tailored to the individual.

[1121] Examples of prompt statements

[1122] 1. "How can I notify users when new boxing match DVDs are released, taking into account their viewing history?"

[1123] 2. "How can UBER EATS suggest a menu that fits my health status based on my health data and food preferences?"

[1124] 3. "How can I generate a recommendation to play relaxing music if I detect that the user is tired?"

[1125] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1126] Step 1:

[1127] The server receives the user's question sent from the terminal.

[1128] Specific operation: The server receives the HTTP request and analyzes the question.

[1129] Input: User question (e.g. "Let me know when the DVD of that boxing match I just watched is released")

[1130] Output: Parsed question content

[1131] Step 2:

[1132] The server launches the intermediary AI and obtains the user's personal data.

[1133] Specific operation: The server executes database queries to obtain user preferences, health data, behavioral logs, etc.

[1134] Input: User ID and authentication information

[1135] Output: User's personal data

[1136] Step 3:

[1137] The server generates an initial answer based on the personal data acquired using the intermediate AI.

[1138] Specific operation: The server runs the intermediate AI's algorithm and generates an initial answer.

[1139] Input: User's personal data, analyzed questions

[1140] Output: Initial answer

[1141] Step 4:

[1142] The server queries the generative AI, adding the personal data provided by the intermediate AI and the user's personal information.

[1143] Specific operation: The server calls the API of the generative AI and sends the necessary data.

[1144] Input: Initial answers, user's personal data, additional personal information

[1145] Output: Answer from generative AI

[1146] Step 5:

[1147] The server receives personalized cross-sectional advice obtained from the generative AI.

[1148] Specific operation: The server receives and analyzes the response from the generative AI.

[1149] Input: Answer from generative AI

[1150] Output: Parsed federated advice

[1151] Step 6:

[1152] The server transmits the generated federated advice to the terminal.

[1153] Specific operation: The server generates an HTTP response and sends it to the terminal.

[1154] Input: Parsed federated advice

[1155] Output: Advice sent to terminal

[1156] Step 7:

[1157] The terminal displays the cross-sectional advice received from the server to the user.

[1158] Specific operation: The terminal receives the HTTP response and displays the advice content on the screen.

[1159] Input: Advice from the server

[1160] Output: Advice displayed to the user

[1161] Step 8:

[1162] The user checks the advice displayed on the terminal screen.

[1163] Specific Actions: The user looks at the device screen and confirms the advice provided.

[1164] Input: Advice displayed on terminal

[1165] Output: User understanding and action

[1166] (Application example 2)

[1167] Next, a description will be given of Application Example 2 of Form 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".

[1168] Conventional food delivery systems have difficulty making individual suggestions that take into account the user's health data, preferences, and behavioral logs, and they do not suggest optimal menus based on the user's emotions. As a result, it is not possible to suggest optimal meals based on the user's health condition and emotions, making it difficult to increase user satisfaction.

[1169] 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.

[1170] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to the individual, a means including an emotion engine that recognizes the user's emotions, and a means for generating optimal suggestions based on the user's emotions. This makes it possible to propose optimal food delivery menus based on the user's emotions, taking into account the user's health data, preferences, and behavior log.

[1171] A "user question" is a query or request entered into the system by a user.

[1172] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[1173] "Middle AI" refers to artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[1174] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[1175] "Cross-sectional advice" means providing optimal suggestions and advice to individuals based on their personal data and information.

[1176] An "emotion engine" is an engine that recognizes a user's emotions and provides information based on those emotions.

[1177] "Optimal suggestions" refers to providing the most appropriate suggestions and advice to the user based on the user's personal data and emotions.

[1178] A system for implementing this invention includes an intermediate AI that stores and manages personal data in response to user questions, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to individuals, an emotion engine that recognizes the user's emotions, and a means for generating optimal suggestions based on the user's emotions.

[1179] System Programming

[1180] The program of this system uses the following hardware and software:

[1181] Hardware: Smartphone

[1182] software:

[1183] EmotionEngine: An engine for recognizing user emotions

[1184] Food Recommendation Engine: An engine that suggests food delivery menus based on users' personal data and emotions

[1185] Processing flow

[1186] The server first loads user data from a JSON file. Then it uses the EmotionEngine to recognize emotions from the user's input. Based on the recognized emotions, it uses the FoodRecommendationEngine to suggest the best menu based on the user's personal data and emotions. The suggested menu is returned to the user.

[1187] Examples

[1188] For example, if a user types "I'm very tired today," the emotion engine will recognize the word "tired." The Food Recommendation Engine will then suggest foods and drinks that have a relaxing effect. In this way, the system can suggest the best food delivery menu based on the user's emotions and personal data.

[1189] Examples of prompt statements

[1190] User input: "I'm very tired today"

[1191] Emotion Engine Output: "Tired"

[1192] Food recommendation engine output: "Suggest relaxing herbal teas and light snacks."

[1193] This system takes into account a user's health data, preferences, behavioral logs, and even emotions, making it possible to suggest optimal food delivery menus.

[1194] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1195] Step 1:

[1196] The server loads user data from a JSON file. Specifically, it opens the corresponding JSON file based on the user's ID and reads the user's personal data, such as preferences, health data, and behavior logs, into memory. The input is the user ID, and the output is the user's personal data.

[1197] Step 2:

[1198] The server uses the EmotionEngine to recognize emotions from user input. Specifically, the user passes the text entered by the user to the EmotionEngine, which then analyzes the text to identify emotions. The input is the user's text input, and the output is the recognized emotion.

[1199] Step 3:

[1200] The server uses the FoodRecommendationEngine to suggest optimal menus based on the recognized emotions and the user's personal data. Specifically, the emotions and personal data are passed to the FoodRecommendationEngine, which then analyzes them to generate optimal food delivery menus. The inputs are the recognized emotions and the user's personal data, and the output is the suggested menu.

[1201] Step 4:

[1202] The server returns the proposed menu to the user, i.e., it sends the generated menu to the user's terminal for the user to review. The input is the proposed menu, and the output is the menu displayed on the user's terminal.

[1203] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[1204] 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> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to 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.

[1205] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1206] 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 smart glasses 214.

[1207] [Third embodiment]

[1208] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1209] 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.

[1210] 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 wide area network (WAN) and / or a local area network (LAN).

[1211] 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.

[1212] 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 the voice according to instructions from the processor 46.

[1213] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[1214] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1215] Fig. 6 shows an example of 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.

[1216] 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.

[1217] 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.

[1218] In the headset type terminal 314, 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.

[1219] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1220] "Example 1"

[1221] The system of the present invention includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. The intermediate AI stores the user's personal data and uses it to generate answers to the user's questions. The generative AI adds the personal data provided by the intermediate AI and the user's personal information to generate cross-sectional advice tailored to the individual. As a specific example, if a user asks, "Please let me know when a DVD of the boxing match I just watched is released," the intermediate AI will take into account the user's viewing history and preferences and send a notification when a new boxing match DVD is released. In addition, in response to the question, "What UVER EATS menu would suit my physical condition today?", the intermediate AI will suggest an appropriate menu, taking into account the user's health data and food preferences.

[1222] "Example 2"

[1223] The system of the present invention includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. The intermediate AI stores the user's personal data and uses it to generate answers to the user's questions. The generative AI adds the personal data provided by the intermediate AI and the user's personal information to generate cross-sectional advice tailored to the individual. As a specific example, if a user asks, "Please let me know when a DVD of the boxing match I just watched is released," the intermediate AI will take into account the user's viewing history and preferences and send a notification when a new boxing match DVD is released. In addition, in response to the question, "What UVER EATS menu would suit my physical condition today?", the intermediate AI will suggest an appropriate menu, taking into account the user's health data and food preferences.

[1224] The process flow of each embodiment will be described below.

[1225] "Example 1"

[1226] Step 1: The user asks the system a question, for example, "Tell me when the DVD of the boxing match I just watched is released."

[1227] Step 2: The intermediate AI receives the user's question and generates an answer taking into account the user's personal data (viewing history, preferences, etc.). In this case, set it to send notifications when new boxing match DVDs are released.

[1228] Step 3: The generative AI adds the personal data provided by the intermediate AI and the user’s personal information to generate personalized cross-sectional advice, in this case notifying the user when a new boxing match DVD is released.

[1229] "Example 2"

[1230] Step 1: The user asks the system a question. For example, "What UVER EATS menu would suit my mood today?"

[1231] Step 2: The intermediate AI receives the user's question and generates an answer based on the user's personal data (health data, food preferences, etc.). In this case, it suggests UVER EATS menu items tailored to the user's physical condition and food preferences.

[1232] Step 3: The generative AI adds the personal data provided by the intermediate AI and the user’s personal information to generate personalized cross-sectional advice. In this case, it would suggest UVER EATS menu items to the user based on their physical condition and food preferences.

[1233] Example 1

[1234] Next, a description will be given of Example 1 of the embodiment 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".

[1235] Conventional systems have had difficulty effectively utilizing users' personal data to provide individually optimized advice. In addition, they lacked the means to generate quick and accurate answers to users' questions. This resulted in low user satisfaction and limited the value of the system.

[1236] 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.

[1237] In this invention, the server includes a means for starting an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for receiving the user's question through the intermediate AI and generating an initial answer using the stored personal data, a means for making an inquiry to the generative AI by adding the personal data provided by the intermediate AI and the user's personal information, and a means for providing the user with cross-sectional advice tailored to the individual obtained from the generative AI. This makes it possible to effectively utilize the user's personal data and quickly and accurately provide individually optimized advice.

[1238] "User" refers to an individual who utilizes the System to enter questions and receive answers and advice.

[1239] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[1240] "Middle AI" refers to artificial intelligence that stores a user's personal data and generates initial answers to the user's questions.

[1241] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[1242] "Cross-sectional advice" refers to advice that is individually optimized based on the user's personal data and information.

[1243] "Server" refers to a computer system that receives users' questions, uses intermediate AI and generative AI to generate answers and advice, and provides them to the users.

[1244] "Terminal" refers to the device (smartphone, PC, etc.) that a user uses to enter questions and receive answers or advice.

[1245] "Question" refers to an inquiry that a user inputs into the system.

[1246] "Answer" refers to a system-generated response to a user's question.

[1247] "Inquiry" refers to the sending of data by the server to the generative AI and receiving a response.

[1248] This invention is a system that uses personal data to provide individually optimized advice in response to user questions. The system is mainly composed of three elements: a server, a terminal, and a user.

[1249] server

[1250] The server receives the user's questions and generates individually optimized advice using an intermediate AI that stores and uses personal data, and a generative AI. Specifically, the following hardware and software are used:

[1251] Hardware: High-performance server machine

[1252] Software: Intermediate AI, Generative AI, Database Management System

[1253] The server first launches the intermediate AI and saves the user's personal data (preferences, health data, behavioral logs, etc.). Next, it receives the user's question and generates an initial answer using the personal data saved through the intermediate AI. It then queries the generative AI by adding the personal data provided by the intermediate AI and the user's personal information. Finally, it provides the user with cross-sectional advice tailored to the individual obtained from the generative AI.

[1254] Terminal

[1255] The terminal is a device where the user inputs questions and receives answers and advice from the server. Specifically, the terminal uses the following hardware and software:

[1256] Hardware: Smartphones, PCs, tablets, etc.

[1257] Software: User Interface Application

[1258] The terminal provides an interface for accepting questions from the user, transmits the received questions to the server, and displays answers and advice returned from the server to the user.

[1259] user

[1260] The user inputs a question through the terminal and receives answers and advice provided by the server. The user decides on an action based on the information provided.

[1261] Examples

[1262] Example 1: Boxing match DVD notification

[1263] User Question: "Please let me know when the boxing match I just watched comes out on DVD."

[1264] Process flow:

[1265] 1. The user enters a question via the terminal.

[1266] 2. The device sends a query to the server.

[1267] 3. The server receives the question and uses the intermediary AI to check the user's viewing history and preferences.

[1268] 4. The server adds personal data to the generative AI and queries it.

[1269] 5. Generative AI will generate notifications when new boxing match DVDs are released.

[1270] 6. The server sends a notification to the user's device.

[1271] 7. The user receives a notification on their device.

[1272] Example 2: UBER EATS menu suggestions

[1273] User question: "What UBER EATS menu would suit my mood today?"

[1274] Process flow:

[1275] 1. The user enters a question via the terminal.

[1276] 2. The device sends a query to the server.

[1277] 3. The server receives the question and uses an intermediate AI to verify the user's health data and dietary preferences.

[1278] 4. The server adds personal data to the generative AI and queries it.

[1279] 5. Generative AI suggests appropriate menus.

[1280] 6. The server sends the proposed menu to the user's device.

[1281] 7. The user reviews the suggested menu on their device.

[1282] Examples of prompt statements

[1283] Prompt: "How can I notify users when new boxing match DVDs are released, taking into account their viewing history and preferences?"

[1284] Prompt: "How can you suggest UBER EATS menu options based on a user's health profile and food preferences?"

[1285] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1286] Step 1:

[1287] The server receives the user's question sent from the terminal.

[1288] Input: User question (e.g. "Please let me know when the DVD of that boxing match I just watched is released")

[1289] Specific operation: The server receives an HTTP request and analyzes the question.

[1290] Output: Analysis result of question

[1291] Step 2:

[1292] The server uses the intermediary AI to obtain the user's personal data (preferences, health data, behavioral logs, etc.).

[1293] Input: Analysis result of question content

[1294] What happens: The server executes a database query to retrieve the user's personal data.

[1295] Output: User's personal data

[1296] Step 3:

[1297] The server generates an initial answer based on the personal data acquired using the intermediate AI.

[1298] Input: User's personal data

[1299] Specific operation: The server calls the API of the intermediate AI and sends data to generate an initial answer.

[1300] Output: Initial answer

[1301] Step 4:

[1302] The server queries the generative AI, adding the personal data provided by the intermediate AI and the user's personal information.

[1303] Input: Initial answers, user's personal information

[1304] Specific operation: The server calls the generative AI's API and sends the necessary data.

[1305] Output: Response from the generative AI

[1306] Step 5:

[1307] The server provides users with cross-sectional advice tailored to each individual, obtained from generative AI.

[1308] Input: Response from generative AI

[1309] Specific operation: The server receives and analyzes the response from the generative AI.

[1310] Output: Personalized cross-sectional advice

[1311] Step 6:

[1312] The server sends the advice obtained from the generative AI to the user's device.

[1313] Input: Personalized cross-sectional advice

[1314] Specific behavior: The server generates an HTTP response and sends it to the user's device.

[1315] Output: Advice sent to the user's terminal

[1316] Step 7:

[1317] The terminal displays the answers and advice returned from the server to the user.

[1318] Input: HTTP response from the server

[1319] Specific operation: The terminal receives and analyzes the HTTP response.

[1320] Output: The answer or advice that is displayed to the user

[1321] Step 8:

[1322] Users can review the answers and advice provided through their device and decide on their course of action.

[1323] Input: Answer or advice displayed on the terminal

[1324] Specific action: The user looks at the device screen and reads the information displayed.

[1325] Output: User action decision (e.g., order a suggested menu, receive a notification, etc.)

[1326] (Application example 1)

[1327] Next, a description will be given of Application Example 1 of Form 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".

[1328] Conventional food delivery services have had problems with the difficulty of proposing individual menus based on the user's health condition and preferences, and it takes time for users to choose meals that suit their physical condition and preferences. In addition, there was a lack of a system that effectively utilizes the user's personal data to provide individually optimized advice.

[1329] 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.

[1330] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to an individual, a means for proposing an optimal food delivery menu based on the user's health data, preferences, and past order history, and a means including an application installed on a smartphone or smart glasses. This makes it possible to propose individual menus based on the user's health condition and preferences.

[1331] A "user question" is any inquiry or request made by a user to the system.

[1332] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[1333] "Middle AI" refers to artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[1334] "Generative AI" refers to artificial intelligence that adds personal data provided by the intermediate AI with the user's personal information to generate cross-sectional advice tailored to the individual.

[1335] "Cross-sectional advice" refers to providing individually optimized advice based on the user's personal data.

[1336] "Health Data" refers to information relating to a user's health, such as blood pressure, weight, and allergy information.

[1337] "Preferences" refer to personal preferences such as the dishes a user likes or ingredients they want to avoid.

[1338] "Past Order History" means a record of orders previously placed by a User.

[1339] "Food Delivery Menu" means the food options available for delivery to a User.

[1340] A "smartphone" is a type of mobile phone, a device that can connect to the Internet and use applications.

[1341] "Smart glasses" are glasses-type devices that are wearable devices that can display information and connect to the Internet.

[1342] An "application" is a software program designed to accomplish a particular function.

[1343] The system for implementing this invention includes an intermediate AI that stores and analyzes personal data in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing personalized cross-sectional advice, a means for proposing optimal food delivery menus based on the user's health data, preferences, and past order history, and an application installed on a smartphone or smart glasses.

[1344] Hardware and Software Used

[1345] Hardware:

[1346] Smartphone

[1347] Smart Glasses

[1348] software:

[1349] Python

[1350] The Requests library (for sending HTTP requests)

[1351] JSON library (data parsing and storage)

[1352] Data processing and calculation

[1353] 1. Data collection:

[1354] The server collects the user's health data (e.g., blood pressure, weight, allergy information), preferences (e.g., favorite dishes, ingredients to avoid), and past ordering history, and stores them in JSON format.

[1355] 2. Data Analysis:

[1356] The intermediate AI analyzes the collected personal data and generates data based on the user's health condition and preferences.

[1357] 3. Querying the generative AI:

[1358] The intermediate AI sends the analysis results to the generative AI and receives personalized menu suggestions.

[1359] Examples

[1360] When a user asks, "What menu would suit my physical condition today?", the server takes into account the user's health data (e.g., blood pressure 120 / 80, weight 70kg, allergy: peanuts) and preferences (e.g., favorite foods: sushi, pasta; least favorite foods: spicy food) and queries the generative AI. The generative AI then suggests a specific menu, such as, "Since your blood pressure is normal today, I recommend low-salt sushi."

[1361] Examples of prompt statements

[1362] User health data: Blood pressure 120 / 80, Weight 70kg, Allergies: peanuts

[1363] User preferences: Favorite foods: sushi, pasta; Dislikes: spicy food

[1364] User Question: What's the best meal for me today?

[1365] In this way, it is possible to provide individual menu suggestions based on the user's health condition and preferences.

[1366] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1367] Step 1:

[1368] The user inputs a question using a device (smartphone or smart glasses).

[1369] Input: User question (e.g. "What menu would suit my physical condition today?")

[1370] Output: User question data

[1371] Step 2:

[1372] The terminal transmits the user's question data to the server.

[1373] Input: User question data

[1374] Output: The question data sent to the server.

[1375] Step 3:

[1376] The server uses the intermediary AI to collect the user's personal data (health data, preferences, past order history).

[1377] Input: User ID and authentication information

[1378] Output: User's personal data (JSON format)

[1379] Step 4:

[1380] The intermediate AI analyzes the collected personal data and generates data based on the user's health condition and preferences.

[1381] Input: User's personal data

[1382] Output: Analysis result data (e.g. health condition, preferences, foods to avoid, etc.)

[1383] Step 5:

[1384] The server sends the analysis results data to the generative AI and receives personalized menu suggestions.

[1385] Input: Analysis result data

[1386] Output: Menu suggestion data from generative AI

[1387] Step 6:

[1388] The generative AI generates prompt text based on the analysis results data and suggests the optimal menu.

[1389] Input: Analysis result data

[1390] Output: Menu suggestions (e.g. "Your blood pressure is normal today, so I recommend low-sodium sushi.")

[1391] Step 7:

[1392] The server sends the menu suggestion data received from the generative AI to the terminal.

[1393] Input: Menu suggestion data

[1394] Output: Menu suggestion data sent to the device

[1395] Step 8:

[1396] The terminal displays menu suggestions to the user.

[1397] Input: Menu suggestion data

[1398] Output: The menu suggestions displayed to the user

[1399] In this way, it is possible to provide individual menu suggestions based on the user's health condition and preferences.

[1400] Example 2

[1401] Next, a second embodiment of the second form will be described. 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".

[1402] With conventional systems, it was difficult to provide individually optimized advice in response to user questions. In particular, there is a demand for effective use of users' personal data to provide specific advice in real time based on the user's preferences and health condition, but there has been a lack of technology to achieve this.

[1403] 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.

[1404] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for implementing cross-sectional advice tailored to an individual, a terminal that receives the user's question and transmits it to the server, a server that transmits the initial answer generated by the intermediate AI and the user's personal information to the generative AI, a server that transmits the advice generated by the generative AI to the user's terminal, and a means for the terminal to display the advice to the user. This makes it possible to effectively utilize the user's personal data and provide individually optimized advice in real time.

[1405] "User" refers to an individual who utilizes the System to enter questions and receive advice.

[1406] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[1407] "Middle AI" refers to artificial intelligence that stores a user's personal data and generates initial answers to the user's questions.

[1408] "Generative AI" refers to artificial intelligence that generates individually optimized advice based on personal data provided by the intermediate AI and the user's personal information.

[1409] "Terminal" refers to the device (such as a smartphone or PC) that a user uses to enter questions and receive advice from the server.

[1410] "Server" refers to a computer system that passes the user's question to the intermediate AI, transmits data to the generative AI, and transmits the generated advice to the terminal.

[1411] "Cross-sectional advice" refers to advice that is individually optimized based on the user's personal data and information.

[1412] "Initial answer" refers to the first answer to a question that the intermediate AI generates based on the user's personal data.

[1413] This invention is a system that uses personal data to provide individually optimized advice in response to user questions. The system is mainly composed of an intermediate AI, a generative AI, a server, and a terminal.

[1414] The intermediate AI stores the user's personal data (preferences, health data, behavioral logs, etc.) and generates initial answers to the user's questions. The intermediate AI runs in a server and has access to the user's personal data.

[1415] The generative AI generates individually optimized cross-sectional advice based on the personal data provided by the intermediate AI and the user's personal information. The generative AI also runs on the server and processes data to provide the user with optimized advice.

[1416] The server passes the user's question to the intermediate AI, sends the initial answer generated by the intermediate AI and the user's personal information to the generative AI, and also sends the advice generated by the generative AI to the user's device.

[1417] The terminal is a device that the user uses to input questions and receive advice from the server. Terminals include smartphones and PCs.

[1418] As a specific example of how it works, consider the case where a user asks, "Let me know when the DVD of the boxing match I just watched is released." The user enters the question into their smartphone, and the device sends the question to the server. The server passes the question to the intermediate AI, which references the user's viewing history to generate an initial answer. The server then sends the initial answer and the user's personal information to the generative AI, which then generates advice to send a notification when a new boxing match DVD is released. Finally, the server sends the generated advice to the device, which displays the notification to the user.

[1419] As another concrete example, consider the case where a user asks, "What UVER EATS menu would suit my physical condition today?" The user enters the question into their PC, and the device sends the question to the server. The server passes the question to the intermediate AI, which generates an initial answer by referencing the user's health data and food preferences. The server then sends the initial answer and the user's personal information to the generative AI, which then generates advice suggesting a menu that suits today's physical condition. Finally, the server sends the generated advice to the terminal, which displays the menu to the user.

[1420] Examples of prompts include:

[1421] "How can I notify users when new boxing match DVDs are released based on their viewing history and preferences?"

[1422] "Please tell me how to suggest UVER EATS menu items that suit today's physical condition based on the user's health data and food preferences."

[1423] In this way, the system can effectively utilize the user's personal data and provide individually optimized advice in real time.

[1424] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1425] Step 1:

[1426] The user enters a question.

[1427] A user uses a terminal (such as a smartphone or PC) to input a question into the system. For example, the user might input, "Please let me know when the DVD of the boxing match I just watched is released." The input data is the user's question.

[1428] Step 2:

[1429] The terminal sends a question to the server.

[1430] The terminal transmits the question entered by the user to the server. This communication is usually carried out over the Internet. The input data is the user's question and the output data is the question sent to the server.

[1431] Step 3:

[1432] The server passes the question to the intermediate AI.

[1433] The server passes the received question to the intermediate AI, which runs in the server and has access to the user's personal data. The input data is the question sent to the server, and the output data is the question passed to the intermediate AI.

[1434] Step 4:

[1435] The intermediate AI refers to the user's personal data and generates an initial answer.

[1436] The intermediate AI refers to the user's personal data, such as viewing history, preferences, and health data, to generate an initial answer to the question. For example, if a user asks about a boxing match DVD, the intermediate AI checks the viewing history and extracts relevant information. The input data are the user's personal data and the question, and the output data is the initial answer.

[1437] Step 5:

[1438] The server sends the intermediate AI's initial answer and the user's personal information to the generative AI.

[1439] The server sends the initial answer generated by the intermediate AI and the user's personal information to the generative AI. The generative AI generates optimized advice based on this information. The input data are the initial answer and the user's personal information, and the output data is the data sent to the generative AI.

[1440] Step 6:

[1441] Generative AI generates optimized cross-cutting advice.

[1442] The generative AI combines the data provided by the intermediate AI with the user's personal information to generate individually optimized cross-sectional advice. For example, it suggests a menu that suits the user's physical condition for the day based on their health data and food preferences. The input data are the initial answer and the user's personal information, and the output data is optimized advice.

[1443] Step 7:

[1444] The server transmits the generated advice to the terminal.

[1445] The server receives the advice generated by the generative AI and sends it to the user's device. The input data is the optimized advice, and the output data is the advice sent to the device.

[1446] Step 8:

[1447] The terminal displays the advice to the user.

[1448] The terminal displays the advice received from the server to the user, allowing the user to obtain individually optimized information. Input data is the advice sent to the terminal, and output data is the advice displayed to the user.

[1449] (Application example 2)

[1450] Next, a description will be given of Application Example 2 of Form 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".

[1451] Conventional food delivery services have a problem in that it is difficult to provide users with the most suitable menu because they do not fully consider the user's health condition, preferences, or past order history. In addition, there are problems in that it is difficult to increase user satisfaction because there are not enough menu suggestions that match the user's physical condition and mood, or notifications of new menus or campaign information.

[1452] 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.

[1453] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for implementing cross-sectional advice tailored to the individual, a means for proposing an optimal food delivery menu based on the user's health data, preferences, and past order history, a means for proposing a menu that matches the user's physical condition and mood, and a means for notifying the user of new menus and campaign information. This makes it possible to propose an optimal menu that takes into account the user's health condition, preferences, and past order history, and by proposing a menu that matches the user's physical condition and mood and notifying the user of new menus and campaign information, it is possible to increase user satisfaction.

[1454] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[1455] An "intermediate AI" is an artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[1456] "Generative AI" is artificial intelligence that adds personal data provided by the intermediate AI with the user's personal information to generate cross-sectional advice tailored to the individual.

[1457] "Cross-sectional advice" means providing suggestions and advice that are best suited to each individual based on the user's personal data.

[1458] "Health Data" refers to information relating to a user's health, such as their calorie restrictions or allergy information.

[1459] "Preferences" is information about a user's likes and preferences.

[1460] "Behavior log" is information about a user's past behavior and history.

[1461] "Order History" means a record of orders a User has placed in the past.

[1462] "Food Delivery Menu" means the meal options offered to Users.

[1463] "Health" is information about the user's current health condition and mood.

[1464] "Campaign Information" is information about new menu items or special promotions.

[1465] The system for carrying out this invention includes an intermediate AI that saves personal data in response to a user's question, adds the user's personal information, and queries the generative AI. It also includes a means for implementing cross-sectional advice tailored to individuals. Specifically, it includes a means for proposing optimal food delivery menus based on the user's health data, preferences, and past order history, a means for proposing menus tailored to the user's physical condition and mood, and a means for notifying the user of new menus and campaign information.

[1466] System Configuration

[1467] Hardware

[1468] Server: A server for running intermediate AI and generative AI.

[1469] Device: The smartphone used by the user.

[1470] software

[1471] Intermediate AI: AI that stores the user's personal data and generates answers to questions.

[1472] Generative AI: An AI that generates cross-sectional advice tailored to the individual based on the personal data provided by the intermediate AI and the user's personal information.

[1473] Food delivery application: An application that suggests optimal menu items based on the user's health data, preferences, and past ordering history, suggests menu items that match the user's physical condition and mood, and notifies the user of new menu items and campaign information.

[1474] Data processing and calculation

[1475] 1. Acquisition of user data:

[1476] The server transmits the user's health data, preferences, and past order history to the intermediary AI to obtain personal data.

[1477] 2. Use of Personal Data:

[1478] The server sends the acquired personal data and additional information about the user to the generative AI, which then generates the optimal food delivery menu.

[1479] 3. Display the results:

[1480] The terminal displays the generated menu to the user, suggests menu items tailored to the user's physical condition and mood, and notifies the user of new menu items and campaign information.

[1481] Examples

[1482] When a user asks, "What food delivery menu would suit my physical condition today?" the system operates as follows. First, the server sends the user's health data (e.g. calorie restrictions and allergy information) to the intermediate AI and obtains personal data. Next, the server sends this personal data and additional information about the user to the generative AI, which generates the optimal menu. Finally, the device displays the generated menu to the user, suggests menu items tailored to their physical condition and mood, and notifies them of new menu items and campaign information.

[1483] Examples of prompt statements

[1484] Consider the user's health data and preferences and suggest the best food delivery menu for today's physical condition. The user's health data is as follows: Calorie limit 2000kcal, Allergy: Nuts. Preferences are Japanese food, and spiciness level is medium spicy.

[1485] In this way, a system can be realized that suggests optimal food delivery menus to users.

[1486] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1487] Step 1:

[1488] A user launches a food delivery application and enters a question.

[1489] Input: User question (e.g. "What food delivery menu would suit my physical condition today?")

[1490] Output: User question data

[1491] Specific actions: The user opens the application on their smartphone and enters a question via text.

[1492] Step 2:

[1493] The terminal transmits the user's health data, preferences, and past order history to the intermediary AI.

[1494] Input: User's health data, preferences, past order history

[1495] Output: Personal data sent to the intermediate AI

[1496] Specific operation: The terminal transmits the user's health data (e.g., calorie limit 2000 kcal, allergies: nuts), preferences (e.g., Japanese food, medium spiciness level), and past order history to the intermediate AI.

[1497] Step 3:

[1498] The server obtains personal data from the intermediate AI and sends it to the generative AI.

[1499] Input: Personal data obtained from the intermediate AI

[1500] Output: Personal data sent to the generative AI

[1501] Specific operation: The server obtains the user's personal data from the intermediate AI and sends it to the generative AI.

[1502] Step 4:

[1503] Generative AI will generate the optimal food delivery menu based on personal data and additional information from the user.

[1504] Input: Personal data, additional information of the user

[1505] Output: Optimal food delivery menu

[1506] Specific operation: The generative AI analyzes personal data and additional information from the user to generate a menu that is optimal for the user's health condition and preferences.

[1507] Step 5:

[1508] The server transmits the generated menu to the terminal.

[1509] Input: Generated food delivery menu

[1510] Output: Menu sent to the terminal

[1511] Specific operation: The server sends the optimal menu received from the generative AI to the terminal.

[1512] Step 6:

[1513] The device displays the most suitable food delivery menu for the user.

[1514] Input: Menu sent from the server

[1515] Output: The menu displayed to the user.

[1516] Specific operation: The device displays the received menu to the user, suggests menu items based on the user's physical condition and mood, and notifies the user of new menu items and campaign information.

[1517] In this way, a system is realized that suggests optimal food delivery menus to users.

[1518] In addition, 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.

[1519] "Example 1"

[1520] As one embodiment of the present invention, there is a system that combines an emotion engine that recognizes the user's emotions. This system includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, if the user says, "I'm very tired today," the emotion engine recognizes that the user is tired. Then, the intermediate AI takes that information into consideration and generates advice such as, for example, "play music that has a relaxing effect." The generative AI combines that advice with the user's personal information to provide cross-sectional advice tailored to the individual.

[1521] "Example 2"

[1522] As one embodiment of the present invention, there is a system that combines an emotion engine that recognizes the user's emotions. This system includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, if the user says, "I'm very tired today," the emotion engine recognizes that the user is tired. Then, the intermediate AI takes that information into consideration and generates advice such as, for example, "play music that has a relaxing effect." The generative AI combines that advice with the user's personal information to provide cross-sectional advice tailored to the individual.

[1523] The process flow of each embodiment will be described below.

[1524] "Example 1"

[1525] Step 1: A user says, "I'm very tired today."

[1526] Step 2: The emotion engine recognizes from the user's comments that they are tired.

[1527] Step 3: The intermediate AI receives information from the emotion engine and generates advice that takes into account the user's condition. Specifically, it generates advice such as "play music that has a relaxing effect."

[1528] Step 4: The generative AI combines the advice from the intermediate AI with the user's personal information to provide tailored cross-sectional advice.

[1529] Example 1

[1530] Next, a description will be given of Example 1 of the embodiment 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".

[1531] Conventional systems have had the problem that it is difficult to generate answers to user questions that take into account individual personal data, and can only provide general answers. It is also difficult to provide advice that takes into account dynamic information such as the user's emotions and health status. This means that the system is unable to provide optimal advice to users, and the user experience does not improve.

[1532] 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.

[1533] In this invention, the server includes a means for operating an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for implementing cross-sectional advice tailored to the individual, a means for analyzing the user's question and generating an answer using the personal data, a means for generating and sending a prompt to the generative AI, and a means for displaying the answer from the generative AI to the user. This makes it possible to generate an individual answer that takes into account the user's personal data and provide optimal advice that takes into account dynamic information such as the user's emotions and health condition.

[1534] "User" means an individual who utilizes the System to enter questions and receive answers.

[1535] "Question" refers to information or a request that a user inputs into the system.

[1536] "Personal data" refers to information relating to an individual, such as user preferences, health data, and behavioral logs.

[1537] "Middle AI" refers to artificial intelligence that stores users' personal data and uses it to generate answers to their questions.

[1538] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[1539] "Cross-sectional advice" refers to advice that is individually optimized by taking into account the user's personal data and the content of their questions.

[1540] A "prompt" refers to a set of instructions sent to a generative AI, containing the user's personal data and a question.

[1541] "Server" refers to the central computer that runs the entire system and processes user queries.

[1542] "Terminal" refers to the device used by a User to enter questions and receive answers.

[1543] "Analysis" refers to the process of understanding the user's question and grasping their intent.

[1544] "Answer" refers to information or advice that the system generates in response to a user's question.

[1545] This invention is a system that generates individually optimized answers to user questions using personal data. The system is composed of three main elements: a server, a terminal, and a user.

[1546] server

[1547] The server operates an intermediate AI that stores personal data (preferences, health data, behavioral logs, etc.) in response to user questions. The intermediate AI retrieves the user's personal data from a database and uses it to generate answers to questions. The server also has the means to generate and send prompts to the generative AI. The generative AI generates cross-sectional advice tailored to the individual based on the personal data provided by the intermediate AI and the user's personal information.

[1548] Specifically, the server uses the following hardware and software:

[1549] Hardware: High-performance server machine, database server

[1550] Software: Natural language processing libraries, machine learning models, database management systems

[1551] Terminal

[1552] The terminal receives a question from the user and sends the question to the server. After receiving the answer from the server, the terminal displays the answer to the user. The terminal is a device such as a smartphone or PC, and is operated through a dedicated application.

[1553] user

[1554] Users input questions through their devices and receive answers to those questions. For example, if a user asks, "Please let me know when the DVD of the boxing match I just watched is released," the intermediate AI will take into account the user's viewing history and preferences and send a notification when a new boxing match DVD is released. In addition, in response to the question, "What UVER EATS menu would suit my physical condition today?" the intermediate AI will suggest an appropriate menu item, taking into account the user's health data and food preferences.

[1555] Examples

[1556] Example 1: Boxing match DVD announcement

[1557] User Question: "Please let me know when the boxing match I just watched comes out on DVD."

[1558] Intermediate AI processing: Considers user viewing history and preferences

[1559] Generative AI Processing: Sending a notification when a new boxing match DVD is released

[1560] Example 2: UVER EATS menu suggestions

[1561] User question: "Which UVER EATS menu item would suit my mood today?"

[1562] Intermediate AI processing: Considers user health data and dietary preferences

[1563] Generative AI processing: Providing appropriate menu suggestions

[1564] Example 3: Advice using emotion engine

[1565] User says: "I'm so tired today."

[1566] Emotion engine processing: Recognizing that the user is tired

[1567] Intermediate AI processing: Generates advice such as "Play music that has a relaxing effect."

[1568] Generative AI processing: Combining advice with the user's personal information to provide personalized, cross-disciplinary advice

[1569] Examples of prompt statements

[1570] Prompt 1: "If a user asks, 'Can you tell me when the boxing fight I just watched is released on DVD?' Please explain how this can be handled."

[1571] Prompt 2: "If a user asks, 'What UVER EATS menu item would suit my mood today?' Please explain how to handle this."

[1572] Prompt 3: "If a user says, 'I'm very tired today,' explain how that would be processed using the emotion engine."

[1573] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1574] Step 1:

[1575] The user inputs a question through the terminal. For example, he / she types, "Please let me know when the DVD of the boxing match I just watched is released," and presses the send button.

[1576] Input: User question text

[1577] Output: The question text is sent from the terminal to the server.

[1578] Step 2:

[1579] The terminal transmits the user's question to the server, including the question content and identification information such as the user's ID.

[1580] Input: User question text, User ID

[1581] Output: The question text and the user ID are sent to the server.

[1582] Step 3:

[1583] The server uses the intermediate AI to retrieve the user's personal data (preferences, health data, behavioral logs, etc.) from the database. For example, it retrieves the data by executing an SQL query.

[1584] Input: User ID

[1585] Output: User's personal data

[1586] Step 4:

[1587] The server uses an intermediate AI to analyze the user's question, for example using a natural language processing library to understand the intent of the question.

[1588] Input: User question text

[1589] Output: The question intent (e.g., asking for information about boxing match DVDs)

[1590] Step 5:

[1591] The server takes into account the user's preferences and past behavior based on the acquired personal data, for example filtering out boxing matches the user has watched in the past and information about favorite fighters.

[1592] Input: User's personal data, question intent

[1593] Output: Filtered personal data

[1594] Step 6:

[1595] The server generates a prompt for the generative AI, including the user's personal data and the question, and sends it through the API, for example, by using an HTTP request.

[1596] Input: Filtered personal data, question intent

[1597] Output: The prompt sent to the generative AI

[1598] Step 7:

[1599] Generative AI uses the prompts it receives to generate tailored answers for users, for example using a machine learning model to generate an answer like "send me a notification when a new boxing match DVD is released."

[1600] Input: prompt

[1601] Output: A personalized answer

[1602] Step 8:

[1603] The server sends the answer received from the generative AI to the terminal. For example, it sends the answer using an HTTP response.

[1604] Input: Answer from generative AI

[1605] Output: Answer sent to the terminal

[1606] Step 9:

[1607] The device displays the response received from the server to the user, for example using the notification feature of the application to display a message such as "We will notify you when new boxing match DVDs are released."

[1608] Input: Response from the server

[1609] Output: The answer that is displayed to the user

[1610] (Application example 1)

[1611] Next, a description will be given of Application Example 1 of Form 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".

[1612] Conventional food delivery systems have the problem that it is difficult to make personalized suggestions that take into account the user's health data, preferences, behavioral logs, and emotional state. In addition, it is not possible to recognize the user's emotional state in real time and suggest the optimal menu based on that. This makes it difficult to increase user satisfaction.

[1613] 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.

[1614] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to the individual, a means including an emotion engine that recognizes the user's emotions, and a means for generating optimal suggestions taking into account the user's health data, preferences, behavior log, and emotional state. This makes it possible to propose a personalized food delivery menu that comprehensively takes into account the user's health data, preferences, behavior log, and emotional state.

[1615] A "user question" is any inquiry or request made by a user to the system.

[1616] "Personal data" is a general term for information about individuals, such as user preferences, health data, and behavioral logs.

[1617] An "intermediate AI" is an artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[1618] "Generative AI" is artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[1619] "Cross-sectional advice" refers to individually optimized advice provided by taking into consideration a user's personal data and information in a comprehensive manner.

[1620] An "emotion engine" is an engine that recognizes the user's emotions and provides that information to the system.

[1621] "Health Data" refers to information relating to a user's health condition, such as allergy information and calorie intake goals.

[1622] "Preferences" are information about what a user likes and dislikes.

[1623] "Behavior log" is data regarding a user's behavioral history.

[1624] "Emotional state" is information that indicates the user's current emotions or mood.

[1625] A "Suggestion" is a specific piece of advice or recommendation that the system provides to the User.

[1626] The system for implementing this invention includes an intermediate AI and a generative AI to store personal data in response to user questions and provide personalized advice, and also combines an emotion engine that recognizes the user's emotions to generate optimal suggestions that take into account the user's health data, preferences, behavioral logs, and emotional state.

[1627] Hardware and Software Used

[1628] Hardware: Smartphones, smart glasses

[1629] software:

[1630] emotion_recognition: A library of emotion engines

[1631] food_recommendation: A food recommendation AI library

[1632] json: Used to save and load user data

[1633] Data processing and calculation

[1634] Loading user data

[1635] The server reads the user's health data and preferences from a JSON file, which brings the user's personal data into the system.

[1636] emotion recognition

[1637] The device uses the emotion engine to analyze the user's utterances and recognize their emotional state. For example, if the user says, "I'm very tired today," the emotion engine will recognize the emotion "tired."

[1638] Generate personalized suggestions

[1639] The server uses food recommendation AI to suggest optimal menus based on user data and emotional state, making it possible to propose personalized food delivery menus that comprehensively take into account the user's health data, preferences, behavioral log, and emotional state.

[1640] Examples

[1641] If a user says, "I'm very tired today," the emotion engine will recognize the emotion, and the food recommendation AI will suggest a menu that includes ingredients that have a relaxing effect.

[1642] Examples of prompt statements

[1643] User says: "I'm so tired today"

[1644] Emotion engine output: "Tired"

[1645] Food recommendation AI output: "Relaxing herbal tea and snack set"

[1646] In this way, a system can be created that suggests optimal food delivery menus based on the user's emotional state and personal data.

[1647] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1648] Step 1:

[1649] The server reads the user's health data and preferences from a JSON file. As input, it receives a JSON file containing the user's personal data and as output, it produces a data structure containing the user's health data and preferences, which are used in subsequent processing steps.

[1650] Step 2:

[1651] The device uses the emotion engine to analyze the user's utterances and recognize the emotional state. It receives the user's utterance text as input and generates the recognized emotional state as output. Specifically, if the user utters "I'm very tired today," the emotion engine recognizes the emotion "tired."

[1652] Step 3:

[1653] The server uses food recommendation AI to suggest optimal menus based on user data and emotional state. It receives the user's health data, preferences, behavioral logs, and recognized emotional state as input, and generates a personalized food delivery menu as output. Specifically, if the emotion engine recognizes the emotion "tired," the food recommendation AI will suggest a menu that includes ingredients that have a relaxation effect.

[1654] Step 4:

[1655] The terminal presents the personalized food delivery menu received from the server to the user. As an input, it receives the menu suggestions from the server, and as an output, it presents the menu to the user visually or audibly, for example by displaying the menu on the display of a smartphone or smart glasses, or by reading the menu through a voice assistant.

[1656] Step 5:

[1657] The user makes a selection from the presented menu and confirms the order. The system receives the user's selection as input and generates the order information for the selected menu as output. Specifically, the user taps the smartphone screen to select a menu item and presses the order button to confirm the order.

[1658] Step 6:

[1659] The server sends the user's order information to the food delivery service. It receives the user's order information as input and sends an order request to the food delivery service as output. Specifically, it sends the order information to the food delivery service through an API, and the order is processed.

[1660] In this way, the optimal food delivery menu is suggested based on the user's emotional state and personal data, and the entire process up to ordering is completed.

[1661] Example 2

[1662] Next, a second embodiment of the second form will be described. 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".

[1663] Conventional systems had difficulty in providing appropriate answers to user questions that took into account individual personal data. They also lacked the ability to recognize users' emotions and provide advice based on them, so they were unable to fully meet users' needs. Furthermore, it was difficult to integrate multiple data sources to generate cross-sectional advice, making it impossible to provide consistent advice to users.

[1664] 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.

[1665] In this invention, the server includes a means for starting an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for receiving the user's question through the intermediate AI and generating an initial answer using the stored personal data, a means for making an inquiry to the generative AI by adding the personal data provided by the intermediate AI and the user's personal information, a means for providing the user with cross-sectional advice tailored to the individual obtained from the generative AI, and a means including an emotion engine that recognizes the user's emotions. This makes it possible to provide consistent cross-sectional advice that takes into account the user's personal data and emotions.

[1666] A "user question" is a query or request entered into the system by a user.

[1667] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[1668] "Middle AI" refers to artificial intelligence that stores a user's personal data and generates initial answers to the user's questions.

[1669] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[1670] "Federrated advice" refers to consistent advice to users that is generated by integrating multiple data sources.

[1671] An "emotion engine" is an engine that recognizes the user's emotions and provides information based on them.

[1672] A "server" is a computer system that receives users' questions, works with intermediate AI and generative AI to generate answers, and provides them to the users.

[1673] A "terminal" is a device through which a user enters questions and receives answers from the server.

[1674] An "initial answer" is the first answer to a user's question that the intermediate AI generates based on the user's personal data.

[1675] An "inquiry" is the act of the server sending data to the generative AI and requesting a response.

[1676] The present invention is a system for providing individually optimized advice to a user in response to a question by utilizing personal data. A specific embodiment of this system will be described below.

[1677] Hardware and Software Configuration

[1678] server

[1679] The server plays a central role in connecting the intermediate AI, which receives user questions and stores and processes personal data, with the generative AI. The server has the following functions:

[1680] An interface for receiving user questions

[1681] A database that runs the intermediate AI and acquires and stores personal data

[1682] An API client that queries generative AI

[1683] Response generation function that provides advice to users

[1684] Terminal

[1685] A terminal is a device through which a user enters questions and receives answers from the server. A terminal has the following functions:

[1686] An interface for entering user questions

[1687] Communication function to send questions to the server

[1688] Screen showing the response from the server

[1689] User

[1690] The user enters a question into the system through a terminal and receives advice provided by the server. The user performs the following actions:

[1691] Enter your question into the input form on your device

[1692] Click the Send button to send the question to the server.

[1693] Check the advice displayed on the device screen

[1694] Data processing and calculation

[1695] Intermediate AI

[1696] The intermediate AI stores the user's personal data (preferences, health data, behavioral logs, etc.) and generates initial answers to the user's questions. Specifically, it performs the following processes:

[1697] Retrieving user personal data from a database

[1698] Analyzes the question and generates an initial answer based on relevant personal data

[1699] Generative AI

[1700] The generative AI generates cross-sectional advice tailored to the individual based on the personal data provided by the intermediate AI and the user's personal information. Specifically, it performs the following processes:

[1701] Receive data provided by the intermediate AI

[1702] Add user's personal information for analysis

[1703] Generates personalized, optimized advice

[1704] Emotion Engine

[1705] The emotion engine recognizes the user's emotions and provides information based on them. Specifically, it does the following:

[1706] Analyze user comments and input

[1707] Recognize emotional states and generate appropriate advice

[1708] Examples

[1709] Example 1: Boxing match DVD notification

[1710] A user asks, "Please let me know when the DVD of the boxing match I just watched is released." The server uses the intermediate AI to consider the user's viewing history and preferences, and sends a notification when a new boxing match DVD is released.

[1711] Example 2: UBER EATS menu suggestions

[1712] The user asks, "What UBER EATS menu would suit my physical condition today?" The server uses the intermediate AI to consider the user's health data and food preferences and suggests an appropriate menu.

[1713] Example 3: Advice using emotion engine

[1714] A user says, "I'm very tired today." The emotion engine recognizes that the user is tired. The server uses the intermediate AI to generate advice such as "play music that has a relaxing effect," and works with the generative AI to provide cross-sectional advice tailored to the individual.

[1715] Examples of prompt statements

[1716] 1. "How can I notify users when new boxing match DVDs are released, taking into account their viewing history?"

[1717] 2. "How can UBER EATS suggest a menu that fits my health status based on my health data and food preferences?"

[1718] 3. "How can I generate a recommendation to play relaxing music if I detect that the user is tired?"

[1719] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1720] Step 1:

[1721] The server receives the user's question sent from the terminal.

[1722] Specific operation: The server receives the HTTP request and analyzes the question.

[1723] Input: User question (e.g. "Let me know when the DVD of that boxing match I just watched is released")

[1724] Output: Parsed question content

[1725] Step 2:

[1726] The server launches the intermediary AI and obtains the user's personal data.

[1727] Specific operation: The server executes database queries to obtain user preferences, health data, behavioral logs, etc.

[1728] Input: User ID and authentication information

[1729] Output: User's personal data

[1730] Step 3:

[1731] The server generates an initial answer based on the personal data acquired using the intermediate AI.

[1732] Specific operation: The server runs the intermediate AI's algorithm and generates an initial answer.

[1733] Input: User's personal data, analyzed questions

[1734] Output: Initial answer

[1735] Step 4:

[1736] The server queries the generative AI, adding the personal data provided by the intermediate AI and the user's personal information.

[1737] Specific operation: The server calls the API of the generative AI and sends the necessary data.

[1738] Input: Initial answers, user's personal data, additional personal information

[1739] Output: Answer from generative AI

[1740] Step 5:

[1741] The server receives personalized cross-sectional advice obtained from the generative AI.

[1742] Specific operation: The server receives and analyzes the response from the generative AI.

[1743] Input: Answer from generative AI

[1744] Output: Parsed federated advice

[1745] Step 6:

[1746] The server transmits the generated federated advice to the terminal.

[1747] Specific operation: The server generates an HTTP response and sends it to the terminal.

[1748] Input: Parsed federated advice

[1749] Output: Advice sent to terminal

[1750] Step 7:

[1751] The terminal displays the cross-sectional advice received from the server to the user.

[1752] Specific operation: The terminal receives the HTTP response and displays the advice content on the screen.

[1753] Input: Advice from the server

[1754] Output: Advice displayed to the user

[1755] Step 8:

[1756] The user checks the advice displayed on the terminal screen.

[1757] Specific Actions: The user looks at the device screen and confirms the advice provided.

[1758] Input: Advice displayed on terminal

[1759] Output: User understanding and action

[1760] (Application example 2)

[1761] Next, a description will be given of Application Example 2 of Form 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".

[1762] Conventional food delivery systems have difficulty making individual suggestions that take into account the user's health data, preferences, and behavioral logs, and they do not suggest optimal menus based on the user's emotions. As a result, it is not possible to suggest optimal meals based on the user's health condition and emotions, making it difficult to increase user satisfaction.

[1763] 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.

[1764] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to the individual, a means including an emotion engine that recognizes the user's emotions, and a means for generating optimal suggestions based on the user's emotions. This makes it possible to propose optimal food delivery menus based on the user's emotions, taking into account the user's health data, preferences, and behavior log.

[1765] A "user question" is a query or request entered into the system by a user.

[1766] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[1767] "Middle AI" refers to artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[1768] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[1769] "Cross-sectional advice" means providing optimal suggestions and advice to individuals based on their personal data and information.

[1770] An "emotion engine" is an engine that recognizes a user's emotions and provides information based on those emotions.

[1771] "Optimal suggestions" refers to providing the most appropriate suggestions and advice to the user based on the user's personal data and emotions.

[1772] A system for implementing this invention includes an intermediate AI that stores and manages personal data in response to user questions, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to individuals, an emotion engine that recognizes the user's emotions, and a means for generating optimal suggestions based on the user's emotions.

[1773] System Programming

[1774] The program of this system uses the following hardware and software:

[1775] Hardware: Smartphone

[1776] software:

[1777] EmotionEngine: An engine for recognizing user emotions

[1778] Food Recommendation Engine: An engine that suggests food delivery menus based on users' personal data and emotions

[1779] Processing flow

[1780] The server first loads user data from a JSON file. Then it uses the EmotionEngine to recognize emotions from the user's input. Based on the recognized emotions, it uses the FoodRecommendationEngine to suggest the best menu based on the user's personal data and emotions. The suggested menu is returned to the user.

[1781] Examples

[1782] For example, if a user types "I'm very tired today," the emotion engine will recognize the word "tired." The Food Recommendation Engine will then suggest foods and drinks that have a relaxing effect. In this way, the system can suggest the best food delivery menu based on the user's emotions and personal data.

[1783] Examples of prompt statements

[1784] User input: "I'm very tired today"

[1785] Emotion Engine Output: "Tired"

[1786] Food recommendation engine output: "Suggest relaxing herbal teas and light snacks."

[1787] This system takes into account a user's health data, preferences, behavioral logs, and even emotions, making it possible to suggest optimal food delivery menus.

[1788] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1789] Step 1:

[1790] The server loads user data from a JSON file. Specifically, it opens the corresponding JSON file based on the user's ID and reads the user's personal data, such as preferences, health data, and behavior logs, into memory. The input is the user ID, and the output is the user's personal data.

[1791] Step 2:

[1792] The server uses the EmotionEngine to recognize emotions from user input. Specifically, the user passes the text entered by the user to the EmotionEngine, which then analyzes the text to identify emotions. The input is the user's text input, and the output is the recognized emotion.

[1793] Step 3:

[1794] The server uses the FoodRecommendationEngine to suggest optimal menus based on the recognized emotions and the user's personal data. Specifically, the emotions and personal data are passed to the FoodRecommendationEngine, which then analyzes them to generate optimal food delivery menus. The inputs are the recognized emotions and the user's personal data, and the output is the suggested menu.

[1795] Step 4:

[1796] The server returns the proposed menu to the user, i.e., it sends the generated menu to the user's terminal for the user to review. The input is the proposed menu, and the output is the menu displayed on the user's terminal.

[1797] 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 voice indicating a user input for 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.

[1798] 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> ) and other generative AIs. Data generation model 58 is

[1799] , obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is 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.

[1800] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1801] 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.

[1802] [Fourth embodiment]

[1803] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1804] 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.

[1805] 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 wide area network (WAN) and / or a local area network (LAN).

[1806] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1807] 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 the voice according to instructions from the processor 46.

[1808] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[1809] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1810] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. 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.

[1811] Fig. 8 shows an example of 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.

[1812] 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.

[1813] 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.

[1814] In the robot 414, 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.

[1815] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1816] "Example 1"

[1817] The system of the present invention includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. The intermediate AI stores the user's personal data and uses it to generate answers to the user's questions. The generative AI adds the personal data provided by the intermediate AI and the user's personal information to generate cross-sectional advice tailored to the individual. As a specific example, if a user asks, "Please let me know when a DVD of the boxing match I just watched is released," the intermediate AI will take into account the user's viewing history and preferences and send a notification when a new boxing match DVD is released. In addition, in response to the question, "What UVER EATS menu would suit my physical condition today?", the intermediate AI will suggest an appropriate menu, taking into account the user's health data and food preferences.

[1818] "Example 2"

[1819] The system of the present invention includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. The intermediate AI stores the user's personal data and uses it to generate answers to the user's questions. The generative AI adds the personal data provided by the intermediate AI and the user's personal information to generate cross-sectional advice tailored to the individual. As a specific example, if a user asks, "Please let me know when a DVD of the boxing match I just watched is released," the intermediate AI will take into account the user's viewing history and preferences and send a notification when a new boxing match DVD is released. In addition, in response to the question, "What UVER EATS menu would suit my physical condition today?", the intermediate AI will suggest an appropriate menu, taking into account the user's health data and food preferences.

[1820] The process flow of each embodiment will be described below.

[1821] "Example 1"

[1822] Step 1: The user asks the system a question, for example, "Tell me when the DVD of the boxing match I just watched is released."

[1823] Step 2: The intermediate AI receives the user's question and generates an answer taking into account the user's personal data (viewing history, preferences, etc.). In this case, set it to send notifications when new boxing match DVDs are released.

[1824] Step 3: The generative AI adds the personal data provided by the intermediate AI and the user’s personal information to generate personalized cross-sectional advice, in this case notifying the user when a new boxing match DVD is released.

[1825] "Example 2"

[1826] Step 1: The user asks the system a question. For example, "What UVER EATS menu would suit my mood today?"

[1827] Step 2: The intermediate AI receives the user's question and generates an answer based on the user's personal data (health data, food preferences, etc.). In this case, it suggests UVER EATS menu items tailored to the user's physical condition and food preferences.

[1828] Step 3: The generative AI adds the personal data provided by the intermediate AI and the user’s personal information to generate personalized cross-sectional advice. In this case, it would suggest UVER EATS menu items to the user based on their physical condition and food preferences.

[1829] Example 1

[1830] Next, a description will be given of Example 1 of the embodiment 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".

[1831] Conventional systems have had difficulty effectively utilizing users' personal data to provide individually optimized advice. In addition, they lacked the means to generate quick and accurate answers to users' questions. This resulted in low user satisfaction and limited the value of the system.

[1832] 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.

[1833] In this invention, the server includes a means for starting an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for receiving the user's question through the intermediate AI and generating an initial answer using the stored personal data, a means for making an inquiry to the generative AI by adding the personal data provided by the intermediate AI and the user's personal information, and a means for providing the user with cross-sectional advice tailored to the individual obtained from the generative AI. This makes it possible to effectively utilize the user's personal data and quickly and accurately provide individually optimized advice.

[1834] "User" refers to an individual who utilizes the System to enter questions and receive answers and advice.

[1835] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[1836] "Middle AI" refers to artificial intelligence that stores a user's personal data and generates initial answers to the user's questions.

[1837] "Generative AI" refers to artificial intelligence that generates cross-sectional advice tailored to individuals based on personal data provided by the intermediate AI and the user's personal information.

[1838] "Cross-sectional advice" refers to advice that is individually optimized based on the user's personal data and information.

[1839] "Server" refers to a computer system that receives users' questions, uses intermediate AI and generative AI to generate answers and advice, and provides them to the users.

[1840] "Terminal" refers to the device (smartphone, PC, etc.) that a user uses to enter questions and receive answers or advice.

[1841] "Question" refers to an inquiry that a user inputs into the system.

[1842] "Answer" refers to a system-generated response to a user's question.

[1843] "Inquiry" refers to the sending of data by the server to the generative AI and receiving a response.

[1844] This invention is a system that uses personal data to provide individually optimized advice in response to user questions. The system is mainly composed of three elements: a server, a terminal, and a user.

[1845] server

[1846] The server receives the user's questions and generates individually optimized advice using an intermediate AI that stores and uses personal data, and a generative AI. Specifically, the following hardware and software are used:

[1847] Hardware: High-performance server machine

[1848] Software: Intermediate AI, Generative AI, Database Management System

[1849] The server first launches the intermediate AI and saves the user's personal data (preferences, health data, behavioral logs, etc.). Next, it receives the user's question and generates an initial answer using the personal data saved through the intermediate AI. It then queries the generative AI by adding the personal data provided by the intermediate AI and the user's personal information. Finally, it provides the user with cross-sectional advice tailored to the individual obtained from the generative AI.

[1850] Terminal

[1851] The terminal is a device where the user inputs questions and receives answers and advice from the server. Specifically, the terminal uses the following hardware and software:

[1852] Hardware: Smartphones, PCs, tablets, etc.

[1853] Software: User Interface Application

[1854] The terminal provides an interface for accepting questions from the user, transmits the received questions to the server, and displays answers and advice returned from the server to the user.

[1855] user

[1856] The user inputs a question through the terminal and receives answers and advice provided by the server. The user decides on an action based on the information provided.

[1857] Examples

[1858] Example 1: Boxing match DVD notification

[1859] User Question: "Please let me know when the boxing match I just watched comes out on DVD."

[1860] Process flow:

[1861] 1. The user enters a question via the terminal.

[1862] 2. The device sends a query to the server.

[1863] 3. The server receives the question and uses the intermediary AI to check the user's viewing history and preferences.

[1864] 4. The server adds personal data to the generative AI and queries it.

[1865] 5. Generative AI will generate notifications when new boxing match DVDs are released.

[1866] 6. The server sends a notification to the user's device.

[1867] 7. The user receives a notification on their device.

[1868] Example 2: UBER EATS menu suggestions

[1869] User question: "What UBER EATS menu would suit my mood today?"

[1870] Process flow:

[1871] 1. The user enters a question via the terminal.

[1872] 2. The device sends a query to the server.

[1873] 3. The server receives the question and uses an intermediate AI to verify the user's health data and dietary preferences.

[1874] 4. The server adds personal data to the generative AI and queries it.

[1875] 5. Generative AI suggests appropriate menus.

[1876] 6. The server sends the proposed menu to the user's device.

[1877] 7. The user reviews the suggested menu on their device.

[1878] Examples of prompt statements

[1879] Prompt: "How can I notify users when new boxing match DVDs are released, taking into account their viewing history and preferences?"

[1880] Prompt: "How can you suggest UBER EATS menu options based on a user's health profile and food preferences?"

[1881] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1882] Step 1:

[1883] The server receives the user's question sent from the terminal.

[1884] Input: User question (e.g. "Please let me know when the DVD of that boxing match I just watched is released")

[1885] Specific operation: The server receives an HTTP request and analyzes the question.

[1886] Output: Analysis result of question

[1887] Step 2:

[1888] The server uses the intermediary AI to obtain the user's personal data (preferences, health data, behavioral logs, etc.).

[1889] Input: Analysis result of question content

[1890] What happens: The server executes a database query to retrieve the user's personal data.

[1891] Output: User's personal data

[1892] Step 3:

[1893] The server generates an initial answer based on the personal data acquired using the intermediate AI.

[1894] Input: User's personal data

[1895] Specific operation: The server calls the API of the intermediate AI and sends data to generate an initial answer.

[1896] Output: Initial answer

[1897] Step 4:

[1898] The server queries the generative AI, adding the personal data provided by the intermediate AI and the user's personal information.

[1899] Input: Initial answers, user's personal information

[1900] Specific operation: The server calls the generative AI's API and sends the necessary data.

[1901] Output: Response from the generative AI

[1902] Step 5:

[1903] The server provides users with cross-sectional advice tailored to each individual, obtained from generative AI.

[1904] Input: Response from generative AI

[1905] Specific operation: The server receives and analyzes the response from the generative AI.

[1906] Output: Personalized cross-sectional advice

[1907] Step 6:

[1908] The server sends the advice obtained from the generative AI to the user's device.

[1909] Input: Personalized cross-sectional advice

[1910] Specific behavior: The server generates an HTTP response and sends it to the user's device.

[1911] Output: Advice sent to the user's terminal

[1912] Step 7:

[1913] The terminal displays the answers and advice returned from the server to the user.

[1914] Input: HTTP response from the server

[1915] Specific operation: The terminal receives and analyzes the HTTP response.

[1916] Output: The answer or advice that is displayed to the user

[1917] Step 8:

[1918] Users can review the answers and advice provided through their device and decide on their course of action.

[1919] Input: Answer or advice displayed on the terminal

[1920] Specific action: The user looks at the device screen and reads the information displayed.

[1921] Output: User action decision (e.g., order a suggested menu, receive a notification, etc.)

[1922] (Application example 1)

[1923] Next, a description will be given of Application Example 1 of Form 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".

[1924] Conventional food delivery services have had problems with the difficulty of proposing individual menus based on the user's health condition and preferences, and it takes time for users to choose meals that suit their physical condition and preferences. In addition, there was a lack of a system that effectively utilizes the user's personal data to provide individually optimized advice.

[1925] 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.

[1926] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing cross-sectional advice tailored to an individual, a means for proposing an optimal food delivery menu based on the user's health data, preferences, and past order history, and a means including an application installed on a smartphone or smart glasses. This makes it possible to propose individual menus based on the user's health condition and preferences.

[1927] A "user question" is any inquiry or request made by a user to the system.

[1928] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[1929] "Middle AI" refers to artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[1930] "Generative AI" refers to artificial intelligence that adds personal data provided by the intermediate AI with the user's personal information to generate cross-sectional advice tailored to the individual.

[1931] "Cross-sectional advice" refers to providing individually optimized advice based on the user's personal data.

[1932] "Health Data" refers to information relating to a user's health, such as blood pressure, weight, and allergy information.

[1933] "Preferences" refer to personal preferences such as the dishes a user likes or ingredients they want to avoid.

[1934] "Past Order History" means a record of orders previously placed by a User.

[1935] "Food Delivery Menu" means the food options available for delivery to a User.

[1936] A "smartphone" is a type of mobile phone, a device that can connect to the Internet and use applications.

[1937] "Smart glasses" are glasses-type devices that are wearable devices that can display information and connect to the Internet.

[1938] An "application" is a software program designed to accomplish a particular function.

[1939] The system for implementing this invention includes an intermediate AI that stores and analyzes personal data in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for providing personalized cross-sectional advice, a means for proposing optimal food delivery menus based on the user's health data, preferences, and past order history, and an application installed on a smartphone or smart glasses.

[1940] Hardware and Software Used

[1941] Hardware:

[1942] Smartphone

[1943] Smart Glasses

[1944] software:

[1945] Python

[1946] The Requests library (for sending HTTP requests)

[1947] JSON library (data parsing and storage)

[1948] Data processing and calculation

[1949] 1. Data collection:

[1950] The server collects the user's health data (e.g., blood pressure, weight, allergy information), preferences (e.g., favorite dishes, ingredients to avoid), and past ordering history, and stores them in JSON format.

[1951] 2. Data Analysis:

[1952] The intermediate AI analyzes the collected personal data and generates data based on the user's health condition and preferences.

[1953] 3. Querying the generative AI:

[1954] The intermediate AI sends the analysis results to the generative AI and receives personalized menu suggestions.

[1955] Examples

[1956] When a user asks, "What menu would suit my physical condition today?", the server takes into account the user's health data (e.g., blood pressure 120 / 80, weight 70kg, allergy: peanuts) and preferences (e.g., favorite foods: sushi, pasta; least favorite foods: spicy food) and queries the generative AI. The generative AI then suggests a specific menu, such as, "Since your blood pressure is normal today, I recommend low-salt sushi."

[1957] Examples of prompt statements

[1958] User health data: Blood pressure 120 / 80, Weight 70kg, Allergies: peanuts

[1959] User preferences: Favorite foods: sushi, pasta; Dislikes: spicy food

[1960] User Question: What's the best meal for me today?

[1961] In this way, it is possible to provide individual menu suggestions based on the user's health condition and preferences.

[1962] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1963] Step 1:

[1964] The user inputs a question using a device (smartphone or smart glasses).

[1965] Input: User question (e.g. "What menu would suit my physical condition today?")

[1966] Output: User question data

[1967] Step 2:

[1968] The terminal transmits the user's question data to the server.

[1969] Input: User question data

[1970] Output: The question data sent to the server.

[1971] Step 3:

[1972] The server uses the intermediary AI to collect the user's personal data (health data, preferences, past order history).

[1973] Input: User ID and authentication information

[1974] Output: User's personal data (JSON format)

[1975] Step 4:

[1976] The intermediate AI analyzes the collected personal data and generates data based on the user's health condition and preferences.

[1977] Input: User's personal data

[1978] Output: Analysis result data (e.g. health condition, preferences, foods to avoid, etc.)

[1979] Step 5:

[1980] The server sends the analysis results data to the generative AI and receives personalized menu suggestions.

[1981] Input: Analysis result data

[1982] Output: Menu suggestion data from generative AI

[1983] Step 6:

[1984] The generative AI generates prompt text based on the analysis results data and suggests the optimal menu.

[1985] Input: Analysis result data

[1986] Output: Menu suggestions (e.g. "Your blood pressure is normal today, so I recommend low-sodium sushi.")

[1987] Step 7:

[1988] The server sends the menu suggestion data received from the generative AI to the terminal.

[1989] Input: Menu suggestion data

[1990] Output: Menu suggestion data sent to the device

[1991] Step 8:

[1992] The terminal displays menu suggestions to the user.

[1993] Input: Menu suggestion data

[1994] Output: The menu suggestions displayed to the user

[1995] In this way, it is possible to provide individual menu suggestions based on the user's health condition and preferences.

[1996] Example 2

[1997] Next, a second embodiment of the second embodiment will be described. 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".

[1998] With conventional systems, it was difficult to provide individually optimized advice in response to user questions. In particular, there is a demand for effective use of users' personal data to provide specific advice in real time based on the user's preferences and health condition, but there has been a lack of technology to achieve this.

[1999] 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.

[2000] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for implementing cross-sectional advice tailored to an individual, a terminal that receives the user's question and transmits it to the server, a server that transmits the initial answer generated by the intermediate AI and the user's personal information to the generative AI, a server that transmits the advice generated by the generative AI to the user's terminal, and a means for the terminal to display the advice to the user. This makes it possible to effectively utilize the user's personal data and provide individually optimized advice in real time.

[2001] "User" refers to an individual who utilizes the System to enter questions and receive advice.

[2002] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[2003] "Middle AI" refers to artificial intelligence that stores a user's personal data and generates initial answers to the user's questions.

[2004] "Generative AI" refers to artificial intelligence that generates individually optimized advice based on personal data provided by the intermediate AI and the user's personal information.

[2005] "Terminal" refers to the device (such as a smartphone or PC) that a user uses to enter questions and receive advice from the server.

[2006] "Server" refers to a computer system that passes the user's question to the intermediate AI, transmits data to the generative AI, and transmits the generated advice to the terminal.

[2007] "Cross-sectional advice" refers to advice that is individually optimized based on the user's personal data and information.

[2008] "Initial answer" refers to the first answer to a question that the intermediate AI generates based on the user's personal data.

[2009] This invention is a system that uses personal data to provide individually optimized advice in response to user questions. The system is mainly composed of an intermediate AI, a generative AI, a server, and a terminal.

[2010] The intermediate AI stores the user's personal data (preferences, health data, behavioral logs, etc.) and generates initial answers to the user's questions. The intermediate AI runs in a server and has access to the user's personal data.

[2011] The generative AI generates individually optimized cross-sectional advice based on the personal data provided by the intermediate AI and the user's personal information. The generative AI also runs on the server and processes data to provide the user with optimized advice.

[2012] The server passes the user's question to the intermediate AI, transmits the initial answer generated by the intermediate AI and the user's personal information to the generative AI, and also transmits the advice generated by the generative AI to the user's device.

[2013] The terminal is a device that the user uses to input questions and receive advice from the server. Terminals include smartphones and PCs.

[2014] As a specific example of how it works, consider the case where a user asks, "Let me know when the DVD of the boxing match I just watched is released." The user enters the question into their smartphone, and the device sends the question to the server. The server passes the question to the intermediate AI, which references the user's viewing history to generate an initial answer. The server then sends the initial answer and the user's personal information to the generative AI, which then generates advice to send a notification when a new boxing match DVD is released. Finally, the server sends the generated advice to the device, which displays the notification to the user.

[2015] As another concrete example, consider the case where a user asks, "What UVER EATS menu would suit my physical condition today?" The user enters the question into their PC, and the device sends the question to the server. The server passes the question to the intermediate AI, which generates an initial answer by referencing the user's health data and food preferences. The server then sends the initial answer and the user's personal information to the generative AI, which then generates advice suggesting a menu that suits today's physical condition. Finally, the server sends the generated advice to the terminal, which displays the menu to the user.

[2016] Examples of prompts include:

[2017] "How can I notify users when new boxing match DVDs are released based on their viewing history and preferences?"

[2018] "Please tell me how to suggest UVER EATS menu items that suit today's physical condition based on the user's health data and food preferences."

[2019] In this way, the system can effectively utilize the user's personal data and provide individually optimized advice in real time.

[2020] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2021] Step 1:

[2022] The user enters a question.

[2023] A user uses a terminal (such as a smartphone or PC) to input a question into the system. For example, the user might input, "Please let me know when the DVD of the boxing match I just watched is released." The input data is the user's question.

[2024] Step 2:

[2025] The terminal sends a question to the server.

[2026] The terminal transmits the question entered by the user to the server. This communication is usually carried out over the Internet. The input data is the user's question and the output data is the question sent to the server.

[2027] Step 3:

[2028] The server passes the question to the intermediate AI.

[2029] The server passes the received question to the intermediate AI, which runs in the server and has access to the user's personal data. The input data is the question sent to the server, and the output data is the question passed to the intermediate AI.

[2030] Step 4:

[2031] The intermediate AI refers to the user's personal data and generates an initial answer.

[2032] The intermediate AI refers to the user's personal data, such as viewing history, preferences, and health data, to generate an initial answer to the question. For example, if a user asks about a boxing match DVD, the intermediate AI checks the viewing history and extracts relevant information. The input data are the user's personal data and the question, and the output data is the initial answer.

[2033] Step 5:

[2034] The server sends the intermediate AI's initial answer and the user's personal information to the generative AI.

[2035] The server sends the initial answer generated by the intermediate AI and the user's personal information to the generative AI. The generative AI generates optimized advice based on this information. The input data are the initial answer and the user's personal information, and the output data is the data sent to the generative AI.

[2036] Step 6:

[2037] Generative AI generates optimized cross-cutting advice.

[2038] The generative AI combines the data provided by the intermediate AI with the user's personal information to generate individually optimized cross-sectional advice. For example, it suggests a menu that suits the user's physical condition for the day based on their health data and food preferences. The input data are the initial answer and the user's personal information, and the output data is optimized advice.

[2039] Step 7:

[2040] The server transmits the generated advice to the terminal.

[2041] The server receives the advice generated by the generative AI and sends it to the user's device. The input data is the optimized advice, and the output data is the advice sent to the device.

[2042] Step 8:

[2043] The terminal displays the advice to the user.

[2044] The terminal displays the advice received from the server to the user, allowing the user to obtain individually optimized information. Input data is the advice sent to the terminal, and output data is the advice displayed to the user.

[2045] (Application example 2)

[2046] Next, a description will be given of Application Example 2 of Form 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".

[2047] Conventional food delivery services have a problem in that it is difficult to provide users with the most suitable menu because they do not fully consider the user's health condition, preferences, or past order history. In addition, there are problems in that it is difficult to increase user satisfaction because there are not enough menu suggestions that match the user's physical condition and mood, or notifications of new menus or campaign information.

[2048] 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.

[2049] In this invention, the server includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for implementing cross-sectional advice tailored to the individual, a means for proposing an optimal food delivery menu based on the user's health data, preferences, and past order history, a means for proposing a menu that matches the user's physical condition and mood, and a means for notifying the user of new menus and campaign information. This makes it possible to propose an optimal menu that takes into account the user's health condition, preferences, and past order history, and by proposing a menu that matches the user's physical condition and mood and notifying the user of new menus and campaign information, it is possible to increase user satisfaction.

[2050] "Personal data" refers to information about an individual, such as user preferences, health data, and behavioral logs.

[2051] An "intermediate AI" is an artificial intelligence that stores a user's personal data and uses it to generate answers to the user's questions.

[2052] "Generative AI" is artificial intelligence that adds personal data provided by the intermediate AI with the user's personal information to generate cross-sectional advice tailored to the individual.

[2053] "Cross-sectional advice" means providing suggestions and advice that are best suited to each individual based on the user's personal data.

[2054] "Health Data" refers to information relating to a user's health, such as their calorie restrictions or allergy information.

[2055] "Preferences" is information about a user's likes and preferences.

[2056] "Behavior log" is information about a user's past behavior and history.

[2057] "Order History" means a record of orders a User has placed in the past.

[2058] "Food Delivery Menu" means the meal options offered to Users.

[2059] "Health" is information about the user's current health condition and mood.

[2060] "Campaign Information" is information about new menu items or special promotions.

[2061] The system for carrying out this invention includes an intermediate AI that saves personal data in response to a user's question, adds the user's personal information, and queries the generative AI. It also includes a means for implementing cross-sectional advice tailored to individuals. Specifically, it includes a means for proposing optimal food delivery menus based on the user's health data, preferences, and past order history, a means for proposing menus tailored to the user's physical condition and mood, and a means for notifying the user of new menus and campaign information.

[2062] System Configuration

[2063] Hardware

[2064] Server: A server for running intermediate AI and generative AI.

[2065] Device: The smartphone used by the user.

[2066] software

[2067] Intermediate AI: AI that stores the user's personal data and generates answers to questions.

[2068] Generative AI: An AI that generates cross-sectional advice tailored to the individual based on the personal data provided by the intermediate AI and the user's personal information.

[2069] Food delivery application: An application that suggests optimal menu items based on the user's health data, preferences, and past ordering history, suggests menu items that match the user's physical condition and mood, and notifies the user of new menu items and campaign information.

[2070] Data processing and calculation

[2071] 1. Acquisition of user data:

[2072] The server transmits the user's health data, preferences, and past order history to the intermediary AI to obtain personal data.

[2073] 2. Use of Personal Data:

[2074] The server sends the acquired personal data and additional information about the user to the generative AI, which then generates the optimal food delivery menu.

[2075] 3. Display the results:

[2076] The terminal displays the generated menu to the user, suggests menu items tailored to the user's physical condition and mood, and notifies the user of new menu items and campaign information.

[2077] Examples

[2078] When a user asks, "What food delivery menu would suit my physical condition today?" the system operates as follows. First, the server sends the user's health data (e.g. calorie restrictions and allergy information) to the intermediate AI and obtains personal data. Next, the server sends this personal data and additional information about the user to the generative AI, which generates the optimal menu. Finally, the device displays the generated menu to the user, suggests menu items tailored to their physical condition and mood, and notifies them of new menu items and campaign information.

[2079] Examples of prompt statements

[2080] Consider the user's health data and preferences and suggest the best food delivery menu for today's physical condition. The user's health data is as follows: Calorie limit 2000kcal, Allergy: Nuts. Preferences are Japanese food, and spiciness level is medium spicy.

[2081] In this way, a system can be realized that suggests optimal food delivery menus to users.

[2082] The flow of the specific process in the application example 2 will be described with reference to FIG.

[2083] Step 1:

[2084] A user launches a food delivery application and enters a question.

[2085] Input: User question (e.g. "What food delivery menu would suit my physical condition today?")

[2086] Output: User question data

[2087] Specific actions: The user opens the application on their smartphone and enters a question via text.

[2088] Step 2:

[2089] The terminal transmits the user's health data, preferences, and past order history to the intermediary AI.

[2090] Input: User's health data, preferences, past order history

[2091] Output: Personal data sent to the intermediate AI

[2092] Specific operation: The terminal transmits the user's health data (e.g., calorie limit 2000 kcal, allergies: nuts), preferences (e.g., Japanese food, medium spiciness level), and past order history to the intermediate AI.

[2093] Step 3:

[2094] The server obtains personal data from the intermediate AI and sends it to the generative AI.

[2095] Input: Personal data obtained from the intermediate AI

[2096] Output: Personal data sent to the generative AI

[2097] Specific operation: The server obtains the user's personal data from the intermediate AI and sends it to the generative AI.

[2098] Step 4:

[2099] Generative AI will generate the optimal food delivery menu based on personal data and additional information from the user.

[2100] Input: Personal data, additional information of the user

[2101] Output: Optimal food delivery menu

[2102] Specific operation: The generative AI analyzes personal data and additional information from the user to generate a menu that is optimal for the user's health condition and preferences.

[2103] Step 5:

[2104] The server transmits the generated menu to the terminal.

[2105] Input: Generated food delivery menu

[2106] Output: Menu sent to the terminal

[2107] Specific operation: The server sends the optimal menu received from the generative AI to the terminal.

[2108] Step 6:

[2109] The device displays the most suitable food delivery menu for the user.

[2110] Input: Menu sent from the server

[2111] Output: The menu displayed to the user.

[2112] Specific operation: The device displays the received menu to the user, suggests menu items based on the user's physical condition and mood, and notifies the user of new menu items and campaign information.

[2113] In this way, a system is realized that suggests optimal food delivery menus to users.

[2114] In addition, 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.

[2115] "Example 1"

[2116] As one embodiment of the present invention, there is a system that combines an emotion engine that recognizes the user's emotions. This system includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, if the user says, "I'm very tired today," the emotion engine recognizes that the user is tired. Then, the intermediate AI takes that information into consideration and generates advice such as, for example, "play music that has a relaxing effect." The generative AI combines that advice with the user's personal information to provide cross-sectional advice tailored to the individual.

[2117] "Example 2"

[2118] As one embodiment of the present invention, there is a system that combines an emotion engine that recognizes the user's emotions. This system includes an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, and a means for implementing cross-sectional advice tailored to the individual. Furthermore, this system includes an emotion engine that recognizes the user's emotions. Specifically, if the user says, "I'm very tired today," the emotion engine recognizes that the user is tired. Then, the intermediate AI takes that information into consideration and generates advice such as, for example, "play music that has a relaxing effect." The generative AI combines that advice with the user's personal information to provide cross-sectional advice tailored to the individual.

[2119] The process flow of each embodiment will be described below.

[2120] "Example 1"

[2121] Step 1: A user says, "I'm very tired today."

[2122] Step 2: The emotion engine recognizes from the user's comments that they are tired.

[2123] Step 3: The intermediate AI receives information from the emotion engine and generates advice that takes into account the user's condition. Specifically, it generates advice such as "play music that has a relaxing effect."

[2124] Step 4: The generative AI combines the advice from the intermediate AI with the user's personal information to provide tailored cross-sectional advice.

[2125] Example 1

[2126] Next, a description will be given of Example 1 of the embodiment 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".

[2127] Conventional systems have had the problem that it is difficult to generate answers to user questions that take into account individual personal data, and can only provide general answers. It is also difficult to provide advice that takes into account dynamic information such as the user's emotions and health status. This means that the system is unable to provide optimal advice to users, and the user experience does not improve.

[2128] 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.

[2129] In this invention, the server includes a means for operating an intermediate AI that stores personal data (preferences / health data / behavior log, etc.) in response to a user's question, a means for adding the user's personal information and querying the generative AI, a means for implementing cross-sectional advice tailored to the individual, a means for analyzing the user's question and generating an answer using the personal data, a means for generating and sending a prompt to the generative AI, and a means for displaying the answer from the generative AI to the user. This makes it possible to generate an individual answer that takes into account the user's personal data and provide optimal advice that takes into account dynamic information such as the user's emotions and health condition.

[2130] "User" means an individual who utilizes the System to enter questions and receive answers.

[2131] "Question" refers to information or a reques...

Claims

1. A means for storing personal data including at least one of a user's preferences, health data, and behavior log; means for determining an emotional state of a user using an emotion engine; A means for receiving user questions; and means for generating a prompt sentence for instructing generation of an initial answer based on the user's question and personal data of the user; A means for generating an initial answer using the generated prompt sentence and the generative AI model; means for generating a prompt sentence for instructing generation of cross-cutting advice based on the generated initial answer and the emotional state of the user; A means for generating cross-sectional advice for the user using the generated prompt sentence and a generative AI model; and means for presenting the generated federated advice to the user.

2. The system of claim 1 , wherein the means for generating a prompt sentence for instructing the generation of the cross-cutting advice generates a prompt sentence for instructing the generation of the cross-cutting advice based on the generated initial answer, the user's emotional state, and additional personal data.

3. The system of claim 2 , wherein the means for generating federated advice for the user provides food delivery menu suggestions tailored to the user.

Images