System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving input information of a user; means for authenticating the user based on the acquired authentication information; means for acquiring location information of the user; means for acquiring hobby information and profile information of the user; means for acquiring weather information and time information; means for generating an idea based on the acquired information; means for providing the generated idea to the user; and means for acquiring feedback from the user and updating a database.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Many people find it difficult to continually generate new ideas in their daily lives and work. This is especially true in professions like design, writing, art, marketing, engineering, and entrepreneurship, where new ideas are constantly required, but it can be difficult to gain a fresh perspective when the same routines are repeated over and over again. The objective of this invention is to provide personalized ideas based on the user's information and current situation, helping them gain fresh perspectives in their daily lives and work. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means. The system includes a means for receiving user input information, a means for authenticating the user based on the acquired authentication information, a means for acquiring the user's location information, a means for acquiring the user's hobby information and profile information, a means for acquiring weather information and time information, a means for generating ideas based on the acquired information, a means for providing the generated ideas to the user, and a means for acquiring feedback from the user and updating the database. This allows the user to constantly obtain new ideas suited to the situation, and further allows the system to continue evolving based on the feedback. Furthermore, by including a means for generating ideas based on location information and weather information acquired using an external API, the system provides ideas in more real time and with higher accuracy.

[0006] The "means for receiving user input information" is a function for capturing information input by a user through a device or application and transmitting the information to a server.

[0007] The "means for authenticating a user based on the acquired authentication information" is a function for checking the user's login information and verifying whether the information is valid.

[0008] "Means for obtaining user location information" refers to a function for obtaining current geographical location information using GPS or IP address.

[0009] The "means for acquiring user hobby information and profile information" is a function for collecting data such as the user's registered hobbies, interests, and past usage history.

[0010] "Means for obtaining weather information and time information" refers to a function for obtaining current weather data and time information using an external API, etc.

[0011] "Means for generating ideas based on acquired information" refers to a function that generates new ideas using a specific algorithm or generation AI based on the user's location information, hobby information, weather information, and time information.

[0012] The "means for providing the generated ideas to the user" is a function for transmitting the generated ideas to the user's device and displaying them so that the user can see them.

[0013] "Means for obtaining feedback from users and updating the database" refers to a function that provides an interface for users to input their opinions and evaluations of the ideas provided, collects that data, and reflects it in the next idea generation.

[0014] "Means for generating ideas based on location information and weather information obtained using external APIs" refers to a function that obtains location information and weather information from external API services and generates new ideas based on that information. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] 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.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention is a system that performs authentication based on user input information, analyzes the user's current situation, generates personalized ideas, and provides them to the user. Furthermore, it is characterized by incorporating feedback from the user into the next idea submission. This system is configured based on communication between the user's device (terminal) and a server.

[0037] Program processing

[0038] User Authentication

[0039] When a user starts the application and enters their email address and password on the login screen, the user's device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the user's device. The user's device receives this session ID and transitions to the main screen.

[0040] User Data Acquisition

[0041] After a user logs in, the server retrieves information such as past usage history, hobbies, and profile information from the database based on the user's session ID. It also uses an external API to retrieve current location and weather information, allowing for a detailed understanding of the user's current situation.

[0042] Data analysis and idea generation

[0043] The server analyzes the acquired user information, location information, weather information, and time of day, and sends this data to the generation AI to generate personalized ideas. For example, the generation AI generates a "homemade bread recipe to enjoy on a rainy day" based on the conditions "10 AM, rainy, at home" and the hobby information "cooking."

[0044] Idea submission

[0045] The generated ideas are sent from the server to the user's device. The user's device displays the received ideas on the application, allowing the user to view them. Specifically, the idea displays the necessary ingredients, steps, and related tips for a "homemade bread recipe."

[0046] Feedback function

[0047] After trying out the provided ideas, the user enters their opinions and ratings in the feedback form within the application. The user's device sends this feedback to the server, which then stores the user's feedback in a database and uses it as a reference when generating ideas next time.

[0048] Example flow

[0049] For example, let's say a user uses an application at 10:00 AM at home in Shibuya, Tokyo, and their hobby is cooking. The user launches the application and logs in, and the server verifies the user's authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information, and determines that the user is currently at home and that it is raining. Based on this information, the server sends a request to the generation AI to generate a "homemade bread recipe to enjoy on a rainy day."

[0050] The server sends the generated ideas to the user's device, which displays the recipe on the app. After the user has tried the recipe, they enter their opinion in the feedback form within the app, such as "I'd like to know the dessert recipe next time," and the server receives this and stores it in a database.

[0051] This system allows users to constantly obtain new ideas that are relevant to the situation, and the system continues to evolve based on feedback.

[0052] The processing flow will be explained below.

[0053] Program processing steps

[0054] User Authentication

[0055] Step 1:

[0056] The user launches the application and enters their email address and password on the login screen.

[0057] Step 2:

[0058] The user terminal transmits the entered authentication information to the server.

[0059] Step 3:

[0060] The server checks the received authentication information against the registered information in the database, and generates a session ID if authentication is successful.

[0061] Step 4:

[0062] The server sends the generated session ID to the user terminal.

[0063] Step 5:

[0064] The user terminal receives the session ID and transitions to the main screen.

[0065] User Data Acquisition

[0066] Step 6:

[0067] The server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID.

[0068] Step 7:

[0069] The server uses an external API to obtain the user's current location information.

[0070] Step 8:

[0071] The server uses an external API based on the user's location information to obtain current weather information.

[0072] Step 9:

[0073] The server obtains the current time information.

[0074] Data analysis and idea generation

[0075] Step 10:

[0076] The server analyzes the acquired location information, weather information, time information, user profile information, and hobby information.

[0077] Step 11:

[0078] Based on the analysis results, the server sends data to the generative AI and requests it to generate ideas.

[0079] Step 12:

[0080] The generative AI generates personalized ideas based on the data it receives and sends the results back to the server.

[0081] Idea submission

[0082] Step 13:

[0083] The server transmits the generated ideas to the user terminal.

[0084] Step 14:

[0085] The user terminal displays the received ideas on the application.

[0086] Feedback function

[0087] Step 15:

[0088] Users try out the ideas provided and provide feedback.

[0089] Step 16:

[0090] The user terminal transmits feedback information to the server.

[0091] Step 17:

[0092] The server stores the received feedback information in a database and reflects it the next time ideas are generated.

[0093] Example flow

[0094] For example, if a user uses an application at 10:00 AM at home in Shibuya Ward, Tokyo, and his hobby is cooking, the following processing steps are performed.

[0095] User Authentication

[0096] Step 1:

[0097] The user launches the application and enters their email address and password on the login screen.

[0098] Step 2:

[0099] The user terminal transmits this authentication information to the server.

[0100] Step 3:

[0101] The server compares the information with the registration information in the database and generates a session ID if authentication is successful.

[0102] Step 4:

[0103] The server sends the generated session ID to the user terminal.

[0104] Step 5:

[0105] The user terminal receives the session ID and transitions to the main screen.

[0106] User Data Acquisition

[0107] Step 6:

[0108] The server retrieves the user's profile information (e.g., hobby is cooking) from the database based on the session ID.

[0109] Step 7:

[0110] The server uses an external API to obtain the user's location information (for example, Shibuya-ku, Tokyo).

[0111] Step 8:

[0112] The server uses an external API to obtain current weather information (e.g., rain) based on the location information.

[0113] Step 9:

[0114] The server gets the current time (e.g. 10:00 AM).

[0115] Data analysis and idea generation

[0116] Step 10:

[0117] The server analyzes the acquired location information, weather information, time information, and user hobby information.

[0118] Step 11:

[0119] Based on the analysis results, the server sends data to the generative AI and requests personalized idea generation.

[0120] Step 12:

[0121] The generative AI generates ideas such as "homemade bread recipes to enjoy on rainy days" and sends them back to the server.

[0122] Idea submission

[0123] Step 13:

[0124] The server transmits the generated ideas to the user terminal.

[0125] Step 14:

[0126] The user terminal displays the details of the "homemade bread recipe" on the application.

[0127] Feedback function

[0128] Step 15:

[0129] The user tries out the provided ideas and enters their opinion in the feedback form, saying, "Next time, I'd like to know the dessert recipe too."

[0130] Step 16:

[0131] The user terminal transmits feedback information to the server.

[0132] Step 17:

[0133] The server stores the received feedback information in a database and reflects this feedback the next time ideas are generated.

[0134] The process proceeds in these steps.

[0135] Example 1

[0136] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0137] Conventional user assistance systems have difficulty making appropriate suggestions based on the user's environment and conditions, and are unable to effectively utilize user feedback to improve their next suggestions. As a result, they are prone to providing suggestions that do not meet the user's needs, resulting in a poor user experience.

[0138] 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.

[0139] In this invention, the server includes means for receiving user input information, means for authenticating the user based on the acquired authentication information, means for acquiring user location information, means for acquiring user hobby information and profile information, means for acquiring weather information and time information, means for analyzing the acquired information to generate prompt sentences and sending the data to a generative AI model to generate personalized ideas, means for providing the generated ideas to the user, and means for acquiring user feedback and updating the database, thereby making it possible to generate appropriate suggestions according to the user's environment and conditions and to improve subsequent suggestions by reflecting the user's feedback.

[0140] "User Input Information" means data that a user enters into the system, including email addresses, passwords, and other authentication information.

[0141] "Authentication information" refers to data required to verify a user's identity, such as an email address and password.

[0142] "Location information" is data that indicates a user's current geographic location, and is provided in the form of GPS coordinates or the like.

[0143] "Hobby information" refers to information about activities or themes that interest a user.

[0144] "Profile information" is data such as the user's personal information and past activity history, and is information that indicates the user's characteristics.

[0145] "Weather information" is data that indicates the weather conditions at the user's current location.

[0146] "Time information" is data that indicates a specific time or the current time.

[0147] A "generative AI model" is an artificial intelligence model that generates personalized ideas for users based on multiple input data.

[0148] A "prompt sentence" is a sentence to be input into a generative AI model, and is generated based on the user's situation and conditions.

[0149] "Personalized ideas" are individual suggestions or advice provided to a specific user based on the user's individual information and requirements.

[0150] "Feedback" refers to reactions such as opinions and evaluations made by users in response to ideas provided.

[0151] A "database" is a system for storing and managing large amounts of data.

[0152] An "external API" is an interface for communicating with external systems and services to obtain data.

[0153] This invention is a system that performs authentication based on user input information, analyzes the user's current situation, generates personalized ideas, and provides them to the user. Furthermore, it is characterized by incorporating feedback from the user into the next idea submission. This system is configured based on communication between the user's device (terminal) and a server.

[0154] User Authentication

[0155] When a user starts the application and enters their email address and password on the login screen, the device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the device. The device receives this session ID and transitions to the main screen.

[0156] User Data Acquisition

[0157] After the user logs in, the server retrieves information such as past usage history, hobbies, and profile information from the database based on the user's session ID. The server then uses external APIs (e.g., Google Maps API and OpenWeatherMap API) to retrieve current location and weather information, providing a detailed understanding of the user's current situation.

[0158] Data analysis and idea generation

[0159] The server analyzes the acquired user information, location information, weather information, and time of day, generates a prompt text, and sends it to the generative AI model. An example of a generative AI model is GPT-4. The generative AI generates personalized ideas based on the received prompt text. As a specific example, based on the conditions "10 AM, rainy, at home" and the hobby information "cooking," it generates a "homemade bread recipe to enjoy on a rainy day."

[0160] ※Example prompt:

[0161] 1. "Generate ideas that are suitable for the user when they are at home in Shibuya, Tokyo at 10 AM and their hobby is cooking."

[0162] 2. "User is currently looking for fun activities to do on a rainy day. Can you suggest a homemade bread recipe?"

[0163] Idea submission

[0164] The generated ideas are sent from the server to the device, which then displays them on the application so that the user can view them. Specifically, the idea displays the necessary ingredients, steps, and related tips for a "homemade bread recipe."

[0165] Feedback function

[0166] After trying out the provided ideas, the user enters their opinions and ratings in a feedback form within the application. The device then sends this feedback to the server. The server stores the user's feedback in a database and uses it as a reference when generating ideas next time. For example, based on the user's comment, "I'd like to know the dessert recipe next time," the server can refine the next prompt and provide more personalized suggestions.

[0167] This system allows users to constantly obtain new ideas that are relevant to the situation, and the system can continue to evolve based on feedback.

[0168] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0169] Step 1:

[0170] Receive user input information

[0171] The user launches the application and enters their email address and password on the login screen.

[0172] Input: Email address, password

[0173] How it works: The device obtains these credentials and sends them to the server.

[0174] Output: Authentication information sent to the server

[0175] Step 2:

[0176] Credentials validation

[0177] The server checks the authentication information it receives against the registration information in its database.

[0178] Input: Authentication information (email address, password)

[0179] How it works: The server checks the database and generates a session ID if authentication is successful. The database used is, for example, MySQL or PostgreSQL.

[0180] Output: Generated session ID

[0181] Step 3:

[0182] Sending a Session ID

[0183] The server sends the generated session ID to the terminal.

[0184] Input: Session ID

[0185] Operation: The session ID is sent to the terminal, which receives it and transitions to the main screen.

[0186] Output: Transition to main screen

[0187] Step 4:

[0188] Retrieving User Data

[0189] After the user logs in, the server retrieves information such as past usage history, hobbies, and profile from the database based on the user's session ID.

[0190] Input: Session ID

[0191] How it works: User information is retrieved from the database. The server uses an external API (e.g., Google Maps API or OpenWeatherMap API) to retrieve current location and weather information.

[0192] Output: User information, location information, weather information

[0193] Step 5:

[0194] Data analysis and prompt generation

[0195] The server analyzes the data based on the information it has acquired and generates a prompt.

[0196] Input: User information, location information, weather information, time information

[0197] How it works: The server generates a prompt to send data to a generative AI model (e.g., GPT-4).

[0198] Output: Generated prompt statement

[0199] Step 6:

[0200] Idea generation

[0201] The server sends prompts to the generative AI model to generate personalized ideas.

[0202] Input: prompt statement

[0203] How it works: A generative AI model generates ideas based on a prompt.

[0204] Output: Generated ideas

[0205] Step 7:

[0206] Providing ideas

[0207] The server transmits the generated ideas to the terminal.

[0208] Input: Generated ideas

[0209] Operation: The device displays the received ideas on the application so that the user can view them.

[0210] Output: Ideas displayed in the application

[0211] Step 8:

[0212] Getting and processing feedback

[0213] After users try out the ideas provided, they enter their opinions and ratings in a feedback form within the application.

[0214] Input: User feedback

[0215] How it works: The device sends this feedback to the server, which stores it in a database.

[0216] Output: Saved feedback

[0217] Step 9:

[0218] Reflect feedback in next idea generation

[0219] The server will use the saved feedback to refine the next prompt.

[0220] Input: Saved Feedback

[0221] Behavior: The server analyzes the feedback and uses it in the next prompt.

[0222] Output: Improved prompt

[0223] (Application example 1)

[0224] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0225] Food delivery applications that users use daily are unable to appropriately suggest the best dishes for each user's individual situation. This makes it difficult for users to choose dishes that suit their mood or situation at the time, and the suggestions provided are often off-target. Furthermore, systems for incorporating feedback on the provided dishes into future suggestions are inadequate. The objective of this invention is to solve these problems, provide users with highly satisfying suggestions, and improve the user experience.

[0226] 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.

[0227] In this invention, the server includes means for receiving user input information, means for authenticating the user based on the acquired authentication information, means for acquiring the user's location information, means for acquiring the user's hobby information and profile information, means for acquiring weather information and time information, means for generating ideas based on the acquired information, means for making recipe suggestions based on the generated ideas, means for providing the generated ideas to the user, and means for acquiring feedback from the user and updating the database. This makes it possible to make optimal recipe suggestions based on the user's current situation. Furthermore, by continuously learning from user feedback, the system can improve the accuracy of future suggestions, thereby continuously improving the user experience.

[0228] "User input information" refers to all information provided by a user through a device.

[0229] "Authentication information" is data used to verify the identity of a user, and includes, for example, an email address and a password.

[0230] "Location information" is data that indicates the user's current location and is obtained using technology such as GPS.

[0231] "Hobby information" refers to information about a user's favorite activities and areas of interest.

[0232] "Profile information" is a user's basic personal information, including name, age, gender, etc.

[0233] "Weather information" is data that indicates the weather conditions in the user's location and is updated in real time.

[0234] "Time information" refers to data relating to the current time and date.

[0235] "Idea generation" refers to the automatic creation of content and actions to be suggested to users based on acquired information.

[0236] "Dish suggestions" refers to recommending the best dishes based on the user's current situation and preferences.

[0237] "Feedback" refers to the evaluation or opinion a user gives of a provided idea or suggestion.

[0238] "Database update" refers to adding or modifying the obtained feedback information to the database.

[0239] An "external API" refers to an interface for using the functions of other services or applications.

[0240] "Generative AI" refers to an artificial intelligence model that automatically generates new information or suggestions based on given data.

[0241] The following describes an embodiment of the invention. This invention is a system that makes personalized recipe suggestions based on a user's location information, time information, weather information, hobby information, and profile information. This system communicates between the user's terminal and a server to provide the user with optimal ideas. Furthermore, the accuracy of the suggestions is improved by incorporating feedback from the user.

[0242] The server first receives the user's input information. The user enters their email address and password for authentication and receives a session ID. If authentication is successful, the user can access the system. At this time, the server retrieves the user's past usage history, hobby information, and profile information from the database. It also uses an external API to retrieve current location and weather information.

[0243] The server then performs data analysis based on this information and sends prompts to the generative AI model to generate personalized food suggestions for the user. For example, the generative AI model receives a prompt such as, "The user lives in Shibuya Ward, Tokyo, and the current weather is rainy. The user likes Italian food, so please suggest dishes that fit these conditions." and generates appropriate suggestions.

[0244] The generated suggestions are sent from the server to the user's device, where the user can view them on the app. When the user selects a suggested dish and actually places an order, the app sends that feedback to the server, which stores this feedback in a database and uses it as a reference the next time ideas are generated.

[0245] This system allows users to receive optimal dish suggestions tailored to their current situation, and the accuracy of these suggestions is continuously improved. Users can also provide feedback to receive better suggestions tailored to their preferences in the future.

[0246] The hardware used is the user's smartphone or tablet, while the software includes the Python Requests library, external APIs, and generative AI models. The combination of these technologies enables a complete process from data collection and analysis to generating recommendations and incorporating feedback.

[0247] As a concrete example, consider a case where a user is using an application at 10:00 AM at home in Shibuya Ward, Tokyo, and their hobby is Italian food. The user launches the application and logs in, and the server verifies the user's authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information and determines that the user is currently at home and that it is raining. Based on this information, the server sends a request to the generation AI, sending a prompt saying, "The user lives in Shibuya Ward, Tokyo, and the current weather is rainy. The user likes Italian food, so please suggest dishes that fit these conditions." If the generation AI suggests "carbonara as a recommended Italian dish for a rainy day," the server sends this to the user's device, and the user can view the suggestion on the app. If the user actually orders carbonara and provides feedback afterwards, the server stores this feedback and can use it for future idea generation.

[0248] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0249] Step 1:

[0250] The user launches the smartphone application and enters their email address and password on the login screen. The input information (email address and password) is sent to the server as the user's authentication information. The server compares this authentication information with the registered information in the database. If authentication is successful, the server generates a session ID and sends it to the user's terminal. The user's terminal receives this session ID and transitions to the main screen. In this processing step, a session ID is generated based on the user authentication and returned to the user's terminal.

[0251] Step 2:

[0252] After the user logs in, the server retrieves the user's past usage history, hobby information, and profile information from the database based on the user's session ID. In addition, the server calls the location information API and weather information API based on a request from the user's device to obtain current location information and weather information using external APIs. The retrieved information is aggregated on the server as the user's current situation. In this processing step, the user's past data and current situation data are retrieved.

[0253] Step 3:

[0254] The server analyzes the acquired user information, location information, weather information, and time information, and generates a prompt for the generative AI model based on that information. For example, it creates a prompt such as, "The user lives in Shibuya Ward, Tokyo, and the current weather is rainy. The user likes Italian food, so please suggest dishes that fit these conditions." The generated prompt is sent to the generative AI model, which generates personalized food suggestions. In this processing step, the server analyzes data and requests the generative AI model to suggest the best dishes for the user based on the prompt.

[0255] Step 4:

[0256] The generated recipe suggestions are sent from the server to the user terminal, and the user terminal displays the received suggestions on the application. The user can check the suggested dishes through the application and select a favorite dish. In this processing step, data transmission and display processing are performed to provide the generated ideas to the user.

[0257] Step 5:

[0258] After a user orders a dish, they provide feedback through a feedback form within the application. The user device sends this feedback information to the server. The server stores the received feedback in a database and uses this information as a reference the next time it generates ideas to improve the accuracy of suggestions. In this processing step, feedback from users is collected and the database is updated.

[0259] Through these five steps, users can receive the best recipe suggestions based on their current situation, and the accuracy of the suggestions will continue to improve. Furthermore, by providing feedback, users can receive better suggestions that suit their preferences in the future.

[0260] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0261] The present invention is a system that performs authentication based on user input information, analyzes the user's current situation and emotions, generates personalized ideas, and provides them to the user. It also features a feature that feedback and emotional data from the user are reflected in the next idea submission. This system is configured based on communication between the user's device (terminal) and a server. Furthermore, by using an emotion engine, idea generation takes the user's emotions into consideration.

[0262] Program processing

[0263] User Authentication

[0264] When a user starts the application and enters their email address and password on the login screen, the user's device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the user's device. The user's device receives this session ID and transitions to the main screen.

[0265] User Data Acquisition

[0266] After a user logs in, the server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID. It also uses an external API to obtain current location and weather information. It then uses an emotion engine to recognize the user's current emotional state based on their input information and behavioral data.

[0267] Data analysis and idea generation

[0268] The server analyzes the acquired user information, location information, weather information, time of day, and emotional data, and sends this data to a generation AI to generate personalized ideas. For example, the generation AI generates a "homemade bread recipe to enjoy on a rainy day" based on the conditions "10 AM, rainy, at home," the hobby information "cooking," and the emotional state "relaxed."

[0269] Idea submission

[0270] The generated ideas are sent from the server to the user's device. The user's device displays the received ideas on the application, allowing the user to view them. Specifically, the idea displays the necessary ingredients, steps, and related tips for a "homemade bread recipe."

[0271] Feedback function

[0272] After trying out the provided ideas, the user enters their opinions and ratings in a feedback form within the application. The user's device then sends this feedback to the server. The server receives the user's feedback along with their emotional data and stores it in a database. The next time ideas are generated, the system will refer to this feedback and emotional data to provide more personalized ideas.

[0273] Example flow

[0274] For example, let's say a user uses an application at 10:00 AM at home in Shibuya, Tokyo, and their hobby is cooking. The user launches the application and logs in, and the server verifies the user's authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information, and determines that the user is currently at home and that it is raining. Furthermore, the emotion engine recognizes that the user is in a relaxed state. Based on this information, the server sends a request to the generation AI to generate a "homemade bread recipe to enjoy on a rainy day."

[0275] The server sends the generated ideas to the user's device, which displays the recipe on the app. After the user has tried the recipe, they enter their opinion in a feedback form, such as "I'd like to know the dessert recipe next time," and the server receives and stores this in a database, along with emotional data.

[0276] This system allows users to constantly obtain new ideas that are relevant to the situation, and the system continues to evolve based on feedback and emotional data.

[0277] The processing flow will be explained below.

[0278] Program processing steps

[0279] User Authentication

[0280] Step 1:

[0281] The user launches the application and enters their email address and password on the login screen.

[0282] Step 2:

[0283] The user terminal transmits the entered authentication information to the server.

[0284] Step 3:

[0285] The server checks the received authentication information against the registered information in the database, and generates a session ID if authentication is successful.

[0286] Step 4:

[0287] The server sends the generated session ID to the user terminal.

[0288] Step 5:

[0289] The user terminal receives the session ID and transitions to the main screen.

[0290] User Data Acquisition

[0291] Step 6:

[0292] The server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID.

[0293] Step 7:

[0294] The server uses an external API to obtain the user's current location information.

[0295] Step 8:

[0296] The server uses an external API based on the user's location information to obtain current weather information.

[0297] Step 9:

[0298] The server obtains the current time information.

[0299] Step 10:

[0300] The server uses an emotion engine to analyze the user's input information and behavioral data to recognize their current emotional state.

[0301] Data analysis and idea generation

[0302] Step 11:

[0303] The server analyzes the acquired location information, weather information, time information, user profile information, hobby information, and emotion data.

[0304] Step 12:

[0305] Based on the analysis results, the server sends data to the generative AI and requests it to generate ideas.

[0306] Step 13:

[0307] The generative AI generates personalized ideas based on the data it receives and sends the results back to the server.

[0308] Idea submission

[0309] Step 14:

[0310] The server transmits the generated ideas to the user terminal.

[0311] Step 15:

[0312] The user terminal displays the received ideas on the application.

[0313] Feedback function

[0314] Step 16:

[0315] Users try out the ideas provided and provide feedback.

[0316] Step 17:

[0317] The user terminal transmits feedback information to the server.

[0318] Step 18:

[0319] The server stores the received feedback information and emotion data in a database.

[0320] Step 19:

[0321] The server will reflect the saved feedback information and emotional data the next time it generates ideas.

[0322] Example flow

[0323] For example, if a user uses an application at home in Shibuya Ward, Tokyo at 10:00 AM, his hobby is cooking, and he is feeling relaxed, the following processing is performed.

[0324] User Authentication

[0325] Step 1:

[0326] The user launches the application and enters their email address and password on the login screen.

[0327] Step 2:

[0328] The user terminal transmits this authentication information to the server.

[0329] Step 3:

[0330] The server compares the information with the registration information in the database and generates a session ID if authentication is successful.

[0331] Step 4:

[0332] The server sends the generated session ID to the user terminal.

[0333] Step 5:

[0334] The user terminal receives the session ID and transitions to the main screen.

[0335] User Data Acquisition

[0336] Step 6:

[0337] The server retrieves the user's profile information (e.g., hobby is cooking) from the database based on the session ID.

[0338] Step 7:

[0339] The server uses an external API to obtain the user's location information (for example, Shibuya-ku, Tokyo).

[0340] Step 8:

[0341] The server uses an external API to obtain current weather information (e.g., rain) based on the location information.

[0342] Step 9:

[0343] The server gets the current time (e.g. 10:00 AM).

[0344] Step 10:

[0345] The server uses an emotion engine to recognize the emotional state of "relaxed" from the user's input information and behavioral data.

[0346] Data analysis and idea generation

[0347] Step 11:

[0348] The server analyzes the acquired location information, weather information, time information, user's hobby information, and emotion data.

[0349] Step 12:

[0350] Based on the analysis results, the server sends data to the generation AI, requesting it to generate a "homemade bread recipe that can be enjoyed on a rainy day."

[0351] Step 13:

[0352] The generation AI generates a "homemade bread recipe" based on the conditions and sends it back to the server.

[0353] Idea submission

[0354] Step 14:

[0355] The server transmits the generated recipe to the user terminal.

[0356] Step 15:

[0357] The user terminal displays the details of the recipe on the application.

[0358] Feedback function

[0359] Step 16:

[0360] A user tries a recipe and fills in a feedback form saying, "Next time, I'd like to know the dessert recipe too."

[0361] Step 17:

[0362] The user terminal transmits feedback information to the server.

[0363] Step 18:

[0364] The server stores the feedback information and current emotion data in a database.

[0365] Step 19:

[0366] The server will reflect the saved feedback information and emotional data the next time it generates ideas, generating more personalized ideas.

[0367] The process proceeds in these steps, providing the user with the best ideas.

[0368] Example 2

[0369] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0370] Conventional personalized idea generation systems provide ideas based solely on the user's profile information and hobby information, and therefore do not take into account the user's current emotional state, location information, weather, and other circumstances, which can result in the system failing to provide suggestions that fully satisfy the user. Additionally, there is a lack of a mechanism for effectively utilizing user feedback and reflecting it in the next idea generation.

[0371] 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.

[0372] In this invention, the server includes a means for receiving user input information, a means for authenticating the user based on the acquired authentication information, and a means for acquiring the user's location information and weather information. This makes it possible to utilize rich user data to provide personalized ideas that are more tailored to the situation. Furthermore, by including an analysis means using an emotion engine and an idea generation means using a generative AI model, it becomes possible to propose ideas with high accuracy that take the user's emotional state into consideration. Furthermore, by including a means for improving the next idea generation based on user feedback, the quality of the user experience can be continuously improved.

[0373] "User input information" refers to all data provided by the user through the application, including, for example, email addresses, passwords, hobbies, and feedback.

[0374] "Authentication information" refers to information such as an email address and password used to verify a user's identity.

[0375] "Location information" is data that indicates the user's current location and is obtained through GPS information or location services.

[0376] "Weather information" is data indicating the current weather conditions in the area where the user is located, and is obtained through an external API.

[0377] "Profile information" is data for identifying a user, such as the user's basic personal information, hobbies, and past usage history.

[0378] An "emotion engine" is a system that analyzes the user's input information and behavioral data to recognize their emotional state at that time.

[0379] A "generative AI model" is an artificial intelligence model that generates personalized ideas based on input data, and includes, for example, GPT-3 and ChatGPT.

[0380] "Personalized ideas" are content such as suggestions, recipes, and advice that are customized to a user's individual situation and emotional state.

[0381] "Feedback" refers to reaction data such as opinions and evaluations provided by users in response to ideas provided.

[0382] A "session ID" is a unique identifier generated by the server after successful authentication and used to identify a user's session.

[0383] The present invention is a system that performs authentication based on user input information, analyzes the user's current situation and emotions, generates personalized ideas, and provides them to the user. It also features a feature that feedback and emotional data from the user are reflected in the next idea submission. This system is configured based on communication between the user's device (terminal) and a server. Furthermore, by using an emotion engine, idea generation takes the user's emotions into consideration.

[0384] The server includes a means for receiving user input information, a means for authenticating the user based on the acquired authentication information, a means for acquiring the user's location information and weather information, a means for acquiring the user's hobby information and profile information, a means for analyzing emotional data using an emotion engine, and a means for generating personalized ideas using a generative AI model, etc. This makes it possible to utilize rich user data to provide ideas that are more suited to the situation.

[0385] The terminal acquires a session ID through communication with the server, and uses this to authenticate the user and acquire subsequent data. It also has a means for providing the user with personalized ideas generated by the server based on the acquired data. Furthermore, the terminal transmits feedback and emotional data from the user to the server, which reflects this in the next idea generation.

[0386] As a concrete example, consider a case where a user is using an application at 10:00 AM at home in Shibuya, Tokyo, and their hobby is cooking. The user launches the application, enters their email address and password on the login screen, and clicks the "Login" button. The device sends this information to the server, which collates it with the registered information in the database. If authentication is successful, the server generates a session ID and sends it to the device. The device receives the session ID and transitions to the main screen.

[0387] When the user then accesses the main screen, the device sends a request to the server based on the user's session ID. The server retrieves the user's profile information, hobbies, and past usage history from the database, and obtains current location and weather information from an external API. Furthermore, the emotion engine recognizes that the user is in a "relaxed" state.

[0388] The server compiles this information and sends it to a generative AI model. The generative AI model uses, for example, GPT-3 or ChatGPT to generate a "homemade bread recipe to enjoy on a rainy day" based on the conditions "10 AM, rainy, at home, cooking, relaxing." The server sends the generated ideas to the user's device, which displays the recipe as an application UI. The user can then actually make homemade bread while looking at the recipe.

[0389] After trying the provided recipes, the user enters their opinion in the feedback form, such as "Next time, I'd like to know the dessert recipe as well." The device then sends this feedback information and the session ID to the server. The server then stores the feedback and emotion data in a database and reflects it in the next idea generation. This allows the user to always obtain new ideas that are appropriate for the situation, and the system continues to evolve based on the feedback and emotion data.

[0390] An example of a prompt sentence is, "If the user is at home in Shibuya-ku, Tokyo at 10:00 AM and his hobby is cooking, please generate a recipe to provide the user with personalized ideas suitable for a relaxed state."

[0391] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0392] Step 1: User authentication

[0393] Input: The user launches the application and enters their email address and password on the login screen.

[0394] Specific action: The user clicks the "Login" button.

[0395] Data processing and calculation: The device sends an HTTP POST request including the entered authentication information to the server.

[0396] Output: The server checks the authentication information against the database, and if successful, generates a session ID and sends it to the terminal. The user terminal receives the session ID and transitions to the main screen.

[0397] Step 2: Obtain user data

[0398] Input: The user accesses the main screen and the terminal sends the session ID to the server.

[0399] Specific operation: The server executes a database query based on the session ID sent by the device.

[0400] Data processing and calculation: The server acquires the user's profile information, hobbies, past usage history, location information, and weather information. Furthermore, the emotion engine analyzes the user's input information and behavioral data to recognize their emotional state.

[0401] Output: The acquired user data, location information, weather information, and emotional state are integrated.

[0402] Step 3: Data analysis and idea generation

[0403] Input: The server sends the integrated data (user information, location information, weather information, and emotion data) acquired in step 2 to the generative AI model.

[0404] Specific operation: The server sends a request to the generative AI model.

[0405] Data processing and calculation: Generative AI models (such as GPT-3 and ChatGPT) generate personalized ideas based on the data they receive. For example, they generate a "homemade bread recipe for a rainy day" based on the conditions "10 AM, rainy, at home, cooking, relaxing."

[0406] Output: The generated ideas are sent to the server.

[0407] Step 4: Submit your idea

[0408] Input: The generated ideas are sent from the server to the user terminal.

[0409] Specific operation: The device analyzes the received ideas and prepares them for display in the application UI.

[0410] Data processing and calculation: The terminal formats the idea content (recipe, steps, ingredients, etc.) into an appropriate format.

[0411] Output: A personalized idea displayed on the user's device, which the user can view and act on.

[0412] Step 5: Get feedback

[0413] Input: Users try out the ideas provided and enter their opinions and ratings in the feedback form.

[0414] Specific Action: The user fills out the feedback form and clicks the "Submit" button.

[0415] Data processing and calculation: The device sends a request including the feedback data and the session ID to the server. The server stores the feedback data and emotion data in a database.

[0416] Output: The feedback data and sentiment data are stored in a database and used for the next idea generation.

[0417] This series of steps allows users to get personalized ideas tailored to their individual situations, and the system continues to evolve through feedback.

[0418] (Application example 2)

[0419] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0420] Conventional systems have difficulty providing personalized ideas that take into account the user's current situation and emotional state. Furthermore, they lack the means to effectively utilize the user's feedback and emotional data on the provided ideas. Therefore, there is a need for a system that can make the shopping experience in brick-and-mortar stores more efficient and comfortable.

[0421] 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.

[0422] In this invention, the server includes means for receiving user input information, means for authenticating the user based on the acquired authentication information, means for acquiring user location information, means for acquiring user hobby information and profile information, means for acquiring weather information and time information, means for generating ideas based on the acquired information and acquired emotional data, means for providing the generated ideas to the user, means for analyzing the user's emotional data, and means for acquiring feedback from the user and updating the database. This makes it possible to provide personalized ideas that take into account the user's current situation and emotional state, and to improve the next idea provision based on the feedback.

[0423] The "means for receiving user input information" is a means for obtaining information input by a user.

[0424] The "means for authenticating a user based on the acquired authentication information" refers to a means for verifying the authentication information provided by the user and authenticating the user.

[0425] The "means for acquiring user location information" is a means for acquiring information about the user's current location.

[0426] The "means for acquiring user hobby information and profile information" refers to a means for acquiring information about the user's interests and personal information.

[0427] The "means for acquiring weather information and time information" is a means for acquiring information about the current weather conditions and time.

[0428] The "means for generating ideas based on acquired information and acquired emotional data" refers to a means for generating personalized ideas based on various acquired information and user emotional data.

[0429] The "means for providing the generated idea to the user" refers to a means for presenting the generated idea to the user.

[0430] The "means for analyzing user emotional data" refers to a means for analyzing data relating to the user's emotional state.

[0431] The "means for obtaining feedback from users and updating the database" refers to a means for receiving feedback provided by users and reflecting that information in the database.

[0432] This system authenticates users based on their input information, analyzes their current situation and emotions, and generates personalized ideas to provide to them. The system is based on communication between the user's device (terminal) and a server. Details of its implementation are explained below.

[0433] Hardware and software used

[0434] User devices: Mobile devices such as smartphones and tablets

[0435] Server: Cloud Server

[0436] Emotion Engine: Software for analyzing user emotions

[0437] Generative AI Model: An artificial intelligence model for generating personalized ideas

[0438] System Configuration and Operation

[0439] 1. User Authentication

[0440] When a user starts the application and enters their email address and password on the login screen, the user's device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the user's device. The user's device receives this session ID and transitions to the main screen.

[0441] 2. User Data Acquisition

[0442] After a user logs in, the server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID. It also uses an external API to obtain current location and weather information. It then uses an emotion engine to recognize the user's current emotional state based on their input information and behavioral data.

[0443] 3. Data analysis and idea generation

[0444] The server analyzes the acquired user information, location information, weather information, time of day, and emotional data, and sends this data to a generation AI to generate personalized ideas. For example, based on the conditions "10 AM, rainy, at home," the hobby information "cooking," and the emotional state "relaxed," it generates a "homemade bread recipe to enjoy on a rainy day."

[0445] 4. Idea submission

[0446] The generated ideas are sent from the server to the user's device. The user's device displays the received ideas on the application, allowing the user to view the ideas. For example, for a "homemade bread recipe," the application displays the necessary ingredients, steps, and related tips.

[0447] 5. Feedback function

[0448] After trying out the provided ideas, the user enters their opinions and ratings in a feedback form within the application. The user's device then sends this feedback to the server. The server receives the user's feedback along with their emotional data and stores it in a database. The next time ideas are generated, the system will refer to this feedback and emotional data to provide more personalized ideas.

[0449] Specific examples

[0450] For example, consider a case where a user uses an application at home at 10:00 AM and their hobby is cooking. The user launches the application and logs in, and the server verifies the authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information, and determines that the user is currently at home and that it is raining. The emotion engine then confirms that the user is relaxed. Based on this information, the server sends a request to the generation AI to generate a "homemade bread recipe to enjoy on a rainy day."

[0451] The server sends the generated ideas to the user's device, which displays the recipe on the app. After the user has tried the recipe, they enter their opinion in a feedback form, such as "I'd like to know the dessert recipe next time," and the server receives and stores this in a database, along with emotional data.

[0452] Example prompt sentence:

[0453] "10am, raining, at home, cooking, relaxing"

[0454] This system allows users to constantly get new ideas that are relevant to the situation, and the system continues to evolve based on feedback and emotional data.

[0455] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0456] Step 1:

[0457] The user starts the application and enters their email address and password on the login screen. The device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, generates a session ID and sends it to the user's device. The device receives this session ID and transitions to the main screen.

[0458] Input: Email address, password

[0459] Data processing: The server checks the registered information in the database

[0460] Output: Session ID

[0461] Step 2:

[0462] After a user logs in, the server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID. The server then obtains the user's current location and weather information using an external API. Furthermore, the server uses an emotion engine to recognize the user's current emotional state based on the user's input information and behavioral data.

[0463] Input: Session ID, location and weather information from external API, input information for emotion recognition

[0464] Data processing: Retrieving information from the database and analyzing emotional states

[0465] Output: Profile information, hobbies, past usage history, location information, weather information, emotional data

[0466] Step 3:

[0467] The server analyzes the acquired user information, location information, weather information, time of day, and emotional data, and sends this data to a generative AI model to generate personalized ideas. For example, based on the conditions "10 AM, rainy, at home," the hobby information "cooking," and the emotional state "relaxed," it generates a "homemade bread recipe to enjoy on a rainy day."

[0468] Input: Profile information, hobby information, past usage history, location information, weather information, emotional data

[0469] Data processing: Generative AI models generate personalized ideas

[0470] Output: Personalized ideas (e.g. homemade bread recipes)

[0471] Step 4:

[0472] The generated ideas are sent from the server to the user's device, where they are displayed on the application, allowing the user to view them. For example, a "homemade bread recipe" might be displayed, showing the necessary ingredients, steps, and related tips.

[0473] Input: Personalized Ideas

[0474] Data processing: Display ideas on the application

[0475] Output: Displayed idea content (e.g. homemade bread recipe, ingredients, steps)

[0476] Step 5:

[0477] After trying out the provided ideas, users enter their opinions and ratings in the feedback form within the application. The device then sends this feedback to the server. The server receives the user's feedback along with their emotional data and stores it in a database. The next time ideas are generated, the system will refer to this feedback and emotional data to provide more personalized ideas.

[0478] Input: Feedback, emotion data

[0479] Data processing: Save to database, improve next idea generation

[0480] Output: Updated database, improved idea generation algorithm

[0481] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0482] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0483] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0484] [Second embodiment]

[0485] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0486] 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.

[0487] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0488] 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.

[0489] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0490] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0491] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0492] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0493] 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.

[0494] 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.

[0495] 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.

[0496] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0497] The present invention is a system that performs authentication based on user input information, analyzes the user's current situation, generates personalized ideas, and provides them to the user. Furthermore, it is characterized by incorporating feedback from the user into the next idea submission. This system is configured based on communication between the user's device (terminal) and a server.

[0498] Program processing

[0499] User Authentication

[0500] When a user starts the application and enters their email address and password on the login screen, the user's device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the user's device. The user's device receives this session ID and transitions to the main screen.

[0501] User Data Acquisition

[0502] After a user logs in, the server retrieves information such as past usage history, hobbies, and profile information from the database based on the user's session ID. It also uses an external API to retrieve current location and weather information, allowing for a detailed understanding of the user's current situation.

[0503] Data analysis and idea generation

[0504] The server analyzes the acquired user information, location information, weather information, and time of day, and sends this data to the generation AI to generate personalized ideas. For example, the generation AI generates a "homemade bread recipe to enjoy on a rainy day" based on the conditions "10 AM, rainy, at home" and the hobby information "cooking."

[0505] Idea submission

[0506] The generated ideas are sent from the server to the user's device. The user's device displays the received ideas on the application, allowing the user to view them. Specifically, the idea displays the necessary ingredients, steps, and related tips for a "homemade bread recipe."

[0507] Feedback function

[0508] After trying out the provided ideas, the user enters their opinions and ratings in the feedback form within the application. The user's device sends this feedback to the server, which then stores the user's feedback in a database and uses it as a reference when generating ideas next time.

[0509] Example flow

[0510] For example, let's say a user uses an application at 10:00 AM at home in Shibuya, Tokyo, and their hobby is cooking. The user launches the application and logs in, and the server verifies the user's authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information, and determines that the user is currently at home and that it is raining. Based on this information, the server sends a request to the generation AI to generate a "homemade bread recipe to enjoy on a rainy day."

[0511] The server sends the generated ideas to the user's device, which displays the recipe on the app. After the user has tried the recipe, they enter their opinion in the feedback form within the app, such as "I'd like to know the dessert recipe next time," and the server receives this and stores it in a database.

[0512] This system allows users to constantly obtain new ideas that are relevant to the situation, and the system continues to evolve based on feedback.

[0513] The processing flow will be explained below.

[0514] Program processing steps

[0515] User Authentication

[0516] Step 1:

[0517] The user launches the application and enters their email address and password on the login screen.

[0518] Step 2:

[0519] The user terminal transmits the entered authentication information to the server.

[0520] Step 3:

[0521] The server checks the received authentication information against the registered information in the database, and generates a session ID if authentication is successful.

[0522] Step 4:

[0523] The server sends the generated session ID to the user terminal.

[0524] Step 5:

[0525] The user terminal receives the session ID and transitions to the main screen.

[0526] User Data Acquisition

[0527] Step 6:

[0528] The server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID.

[0529] Step 7:

[0530] The server uses an external API to obtain the user's current location information.

[0531] Step 8:

[0532] The server uses an external API based on the user's location information to obtain current weather information.

[0533] Step 9:

[0534] The server obtains the current time information.

[0535] Data analysis and idea generation

[0536] Step 10:

[0537] The server analyzes the acquired location information, weather information, time information, user profile information, and hobby information.

[0538] Step 11:

[0539] Based on the analysis results, the server sends data to the generative AI and requests it to generate ideas.

[0540] Step 12:

[0541] The generative AI generates personalized ideas based on the data it receives and sends the results back to the server.

[0542] Idea submission

[0543] Step 13:

[0544] The server transmits the generated ideas to the user terminal.

[0545] Step 14:

[0546] The user terminal displays the received ideas on the application.

[0547] Feedback function

[0548] Step 15:

[0549] Users try out the ideas provided and provide feedback.

[0550] Step 16:

[0551] The user terminal transmits feedback information to the server.

[0552] Step 17:

[0553] The server stores the received feedback information in a database and reflects it the next time ideas are generated.

[0554] Example flow

[0555] For example, if a user uses an application at 10:00 AM at home in Shibuya Ward, Tokyo, and his hobby is cooking, the following processing steps are performed.

[0556] User Authentication

[0557] Step 1:

[0558] The user launches the application and enters their email address and password on the login screen.

[0559] Step 2:

[0560] The user terminal transmits this authentication information to the server.

[0561] Step 3:

[0562] The server compares the information with the registration information in the database and generates a session ID if authentication is successful.

[0563] Step 4:

[0564] The server sends the generated session ID to the user terminal.

[0565] Step 5:

[0566] The user terminal receives the session ID and transitions to the main screen.

[0567] User Data Acquisition

[0568] Step 6:

[0569] The server retrieves the user's profile information (e.g., hobby is cooking) from the database based on the session ID.

[0570] Step 7:

[0571] The server uses an external API to obtain the user's location information (for example, Shibuya-ku, Tokyo).

[0572] Step 8:

[0573] The server uses an external API to obtain current weather information (e.g., rain) based on the location information.

[0574] Step 9:

[0575] The server gets the current time (e.g. 10:00 AM).

[0576] Data analysis and idea generation

[0577] Step 10:

[0578] The server analyzes the acquired location information, weather information, time information, and user hobby information.

[0579] Step 11:

[0580] Based on the analysis results, the server sends data to the generative AI and requests personalized idea generation.

[0581] Step 12:

[0582] The generative AI generates ideas such as "homemade bread recipes to enjoy on rainy days" and sends them back to the server.

[0583] Idea submission

[0584] Step 13:

[0585] The server transmits the generated ideas to the user terminal.

[0586] Step 14:

[0587] The user terminal displays the details of the "homemade bread recipe" on the application.

[0588] Feedback function

[0589] Step 15:

[0590] The user tries out the provided ideas and enters their opinion in the feedback form, saying, "Next time, I'd like to know the dessert recipe too."

[0591] Step 16:

[0592] The user terminal transmits feedback information to the server.

[0593] Step 17:

[0594] The server stores the received feedback information in a database and reflects this feedback the next time ideas are generated.

[0595] The process proceeds in these steps.

[0596] Example 1

[0597] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0598] Conventional user assistance systems have difficulty making appropriate suggestions based on the user's environment and conditions, and are unable to effectively utilize user feedback to improve their next suggestions. As a result, they are prone to providing suggestions that do not meet the user's needs, resulting in a poor user experience.

[0599] 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.

[0600] In this invention, the server includes means for receiving user input information, means for authenticating the user based on the acquired authentication information, means for acquiring user location information, means for acquiring user hobby information and profile information, means for acquiring weather information and time information, means for analyzing the acquired information to generate prompt sentences and sending the data to a generative AI model to generate personalized ideas, means for providing the generated ideas to the user, and means for acquiring user feedback and updating the database, thereby making it possible to generate appropriate suggestions according to the user's environment and conditions and to improve subsequent suggestions by reflecting the user's feedback.

[0601] "User Input Information" means data that a user enters into the system, including email addresses, passwords, and other authentication information.

[0602] "Authentication information" refers to data required to verify a user's identity, such as an email address and password.

[0603] "Location information" is data that indicates a user's current geographic location, and is provided in the form of GPS coordinates or the like.

[0604] "Hobby information" refers to information about activities or themes that interest a user.

[0605] "Profile information" is data such as the user's personal information and past activity history, and is information that indicates the user's characteristics.

[0606] "Weather information" is data that indicates the weather conditions at the user's current location.

[0607] "Time information" is data that indicates a specific time or the current time.

[0608] A "generative AI model" is an artificial intelligence model that generates personalized ideas for users based on multiple input data.

[0609] A "prompt sentence" is a sentence to be input into a generative AI model, and is generated based on the user's situation and conditions.

[0610] "Personalized ideas" are individual suggestions or advice provided to a specific user based on the user's individual information and requirements.

[0611] "Feedback" refers to reactions such as opinions and evaluations made by users in response to ideas provided.

[0612] A "database" is a system for storing and managing large amounts of data.

[0613] An "external API" is an interface for communicating with external systems and services to obtain data.

[0614] This invention is a system that performs authentication based on user input information, analyzes the user's current situation, generates personalized ideas, and provides them to the user. Furthermore, it is characterized by incorporating feedback from the user into the next idea submission. This system is configured based on communication between the user's device (terminal) and a server.

[0615] User Authentication

[0616] When a user starts the application and enters their email address and password on the login screen, the device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the device. The device receives this session ID and transitions to the main screen.

[0617] User Data Acquisition

[0618] After the user logs in, the server retrieves information such as past usage history, hobbies, and profile information from the database based on the user's session ID. The server then uses external APIs (e.g., Google Maps API and OpenWeatherMap API) to retrieve current location and weather information, providing a detailed understanding of the user's current situation.

[0619] Data analysis and idea generation

[0620] The server analyzes the acquired user information, location information, weather information, and time of day, generates a prompt text, and sends it to the generative AI model. An example of a generative AI model is GPT-4. The generative AI generates personalized ideas based on the received prompt text. As a specific example, based on the conditions "10 AM, rainy, at home" and the hobby information "cooking," it generates a "homemade bread recipe to enjoy on a rainy day."

[0621] ※Example prompt:

[0622] 1. "Generate ideas that are suitable for the user when they are at home in Shibuya, Tokyo at 10 AM and their hobby is cooking."

[0623] 2. "User is currently looking for fun activities to do on a rainy day. Can you suggest a homemade bread recipe?"

[0624] Idea submission

[0625] The generated ideas are sent from the server to the device, which then displays them on the application so that the user can view them. Specifically, the idea displays the necessary ingredients, steps, and related tips for a "homemade bread recipe."

[0626] Feedback function

[0627] After trying out the provided ideas, the user enters their opinions and ratings in a feedback form within the application. The device then sends this feedback to the server. The server stores the user's feedback in a database and uses it as a reference when generating ideas next time. For example, based on the user's comment, "I'd like to know the dessert recipe next time," the server can refine the next prompt and provide more personalized suggestions.

[0628] This system allows users to constantly obtain new ideas that are relevant to the situation, and the system can continue to evolve based on feedback.

[0629] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0630] Step 1:

[0631] Receive user input information

[0632] The user launches the application and enters their email address and password on the login screen.

[0633] Input: Email address, password

[0634] How it works: The device obtains these credentials and sends them to the server.

[0635] Output: Authentication information sent to the server

[0636] Step 2:

[0637] Credentials validation

[0638] The server checks the authentication information it receives against the registration information in its database.

[0639] Input: Authentication information (email address, password)

[0640] How it works: The server checks the database and generates a session ID if authentication is successful. The database used is, for example, MySQL or PostgreSQL.

[0641] Output: Generated session ID

[0642] Step 3:

[0643] Sending a Session ID

[0644] The server sends the generated session ID to the terminal.

[0645] Input: Session ID

[0646] Operation: The session ID is sent to the terminal, which receives it and transitions to the main screen.

[0647] Output: Transition to main screen

[0648] Step 4:

[0649] Retrieving User Data

[0650] After the user logs in, the server retrieves information such as past usage history, hobbies, and profile from the database based on the user's session ID.

[0651] Input: Session ID

[0652] How it works: User information is retrieved from the database. The server uses an external API (e.g., Google Maps API or OpenWeatherMap API) to retrieve current location and weather information.

[0653] Output: User information, location information, weather information

[0654] Step 5:

[0655] Data analysis and prompt generation

[0656] The server analyzes the data based on the information it has acquired and generates a prompt.

[0657] Input: User information, location information, weather information, time information

[0658] How it works: The server generates a prompt to send data to a generative AI model (e.g., GPT-4).

[0659] Output: Generated prompt statement

[0660] Step 6:

[0661] Idea generation

[0662] The server sends prompts to the generative AI model to generate personalized ideas.

[0663] Input: prompt statement

[0664] How it works: A generative AI model generates ideas based on a prompt.

[0665] Output: Generated ideas

[0666] Step 7:

[0667] Providing ideas

[0668] The server transmits the generated ideas to the terminal.

[0669] Input: Generated ideas

[0670] Operation: The device displays the received ideas on the application so that the user can view them.

[0671] Output: Ideas displayed in the application

[0672] Step 8:

[0673] Getting and processing feedback

[0674] After users try out the ideas provided, they enter their opinions and ratings in a feedback form within the application.

[0675] Input: User feedback

[0676] How it works: The device sends this feedback to the server, which stores it in a database.

[0677] Output: Saved feedback

[0678] Step 9:

[0679] Reflect feedback in next idea generation

[0680] The server will use the saved feedback to refine the next prompt.

[0681] Input: Saved Feedback

[0682] Behavior: The server analyzes the feedback and uses it in the next prompt.

[0683] Output: Improved prompt

[0684] (Application example 1)

[0685] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0686] Food delivery applications that users use daily are unable to appropriately suggest the best dishes for each user's individual situation. This makes it difficult for users to choose dishes that suit their mood or situation at the time, and the suggestions provided are often off-target. Furthermore, systems for incorporating feedback on the provided dishes into future suggestions are inadequate. The objective of this invention is to solve these problems, provide users with highly satisfying suggestions, and improve the user experience.

[0687] 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.

[0688] In this invention, the server includes means for receiving user input information, means for authenticating the user based on the acquired authentication information, means for acquiring the user's location information, means for acquiring the user's hobby information and profile information, means for acquiring weather information and time information, means for generating ideas based on the acquired information, means for making recipe suggestions based on the generated ideas, means for providing the generated ideas to the user, and means for acquiring feedback from the user and updating the database. This makes it possible to make optimal recipe suggestions based on the user's current situation. Furthermore, by continuously learning from user feedback, the system can improve the accuracy of future suggestions, thereby continuously improving the user experience.

[0689] "User input information" refers to all information provided by a user through a device.

[0690] "Authentication information" is data used to verify the identity of a user, and includes, for example, an email address and a password.

[0691] "Location information" is data that indicates the user's current location and is obtained using technology such as GPS.

[0692] "Hobby information" refers to information about a user's favorite activities and areas of interest.

[0693] "Profile information" is a user's basic personal information, including name, age, gender, etc.

[0694] "Weather information" is data that indicates the weather conditions in the user's location and is updated in real time.

[0695] "Time information" refers to data relating to the current time and date.

[0696] "Idea generation" refers to the automatic creation of content and actions to be suggested to users based on acquired information.

[0697] "Dish suggestions" refers to recommending the best dishes based on the user's current situation and preferences.

[0698] "Feedback" refers to the evaluation or opinion a user gives of a provided idea or suggestion.

[0699] "Database update" refers to adding or modifying the obtained feedback information to the database.

[0700] An "external API" refers to an interface for using the functions of other services or applications.

[0701] "Generative AI" refers to an artificial intelligence model that automatically generates new information or suggestions based on given data.

[0702] The following describes an embodiment of the invention. This invention is a system that makes personalized recipe suggestions based on a user's location information, time information, weather information, hobby information, and profile information. This system communicates between the user's terminal and a server to provide the user with optimal ideas. Furthermore, the accuracy of the suggestions is improved by incorporating feedback from the user.

[0703] The server first receives the user's input information. The user enters their email address and password for authentication and receives a session ID. If authentication is successful, the user can access the system. At this time, the server retrieves the user's past usage history, hobby information, and profile information from the database. It also uses an external API to retrieve current location and weather information.

[0704] The server then performs data analysis based on this information and sends prompts to the generative AI model to generate personalized food suggestions for the user. For example, the generative AI model receives a prompt such as, "The user lives in Shibuya Ward, Tokyo, and the current weather is rainy. The user likes Italian food, so please suggest dishes that fit these conditions." and generates appropriate suggestions.

[0705] The generated suggestions are sent from the server to the user's device, where the user can view them on the app. When the user selects a suggested dish and actually places an order, the app sends that feedback to the server, which stores this feedback in a database and uses it as a reference the next time ideas are generated.

[0706] This system allows users to receive optimal dish suggestions tailored to their current situation, and the accuracy of these suggestions is continuously improved. Users can also provide feedback to receive better suggestions tailored to their preferences in the future.

[0707] The hardware used is the user's smartphone or tablet, while the software includes the Python Requests library, external APIs, and generative AI models. The combination of these technologies enables a complete process from data collection and analysis to generating recommendations and incorporating feedback.

[0708] As a concrete example, consider a case where a user is using an application at 10:00 AM at home in Shibuya Ward, Tokyo, and their hobby is Italian food. The user launches the application and logs in, and the server verifies the user's authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information and determines that the user is currently at home and that it is raining. Based on this information, the server sends a request to the generation AI, sending a prompt saying, "The user lives in Shibuya Ward, Tokyo, and the current weather is rainy. The user likes Italian food, so please suggest dishes that fit these conditions." If the generation AI suggests "carbonara as a recommended Italian dish for a rainy day," the server sends this to the user's device, and the user can view the suggestion on the app. If the user actually orders carbonara and provides feedback afterwards, the server stores this feedback and can use it for future idea generation.

[0709] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0710] Step 1:

[0711] The user launches the smartphone application and enters their email address and password on the login screen. The input information (email address and password) is sent to the server as the user's authentication information. The server compares this authentication information with the registered information in the database. If authentication is successful, the server generates a session ID and sends it to the user's terminal. The user's terminal receives this session ID and transitions to the main screen. In this processing step, a session ID is generated based on the user authentication and returned to the user's terminal.

[0712] Step 2:

[0713] After the user logs in, the server retrieves the user's past usage history, hobby information, and profile information from the database based on the user's session ID. In addition, the server calls the location information API and weather information API based on a request from the user's device to obtain current location information and weather information using external APIs. The retrieved information is aggregated on the server as the user's current situation. In this processing step, the user's past data and current situation data are retrieved.

[0714] Step 3:

[0715] The server analyzes the acquired user information, location information, weather information, and time information, and generates a prompt for the generative AI model based on that information. For example, it creates a prompt such as, "The user lives in Shibuya Ward, Tokyo, and the current weather is rainy. The user likes Italian food, so please suggest dishes that fit these conditions." The generated prompt is sent to the generative AI model, which generates personalized food suggestions. In this processing step, the server analyzes data and requests the generative AI model to suggest the best dishes for the user based on the prompt.

[0716] Step 4:

[0717] The generated recipe suggestions are sent from the server to the user terminal, and the user terminal displays the received suggestions on the application. The user can check the suggested dishes through the application and select a favorite dish. In this processing step, data transmission and display processing are performed to provide the generated ideas to the user.

[0718] Step 5:

[0719] After a user orders a dish, they provide feedback through a feedback form within the application. The user device sends this feedback information to the server. The server stores the received feedback in a database and uses this information as a reference the next time it generates ideas to improve the accuracy of suggestions. In this processing step, feedback from users is collected and the database is updated.

[0720] Through these five steps, users can receive the best recipe suggestions based on their current situation, and the accuracy of the suggestions will continue to improve. Furthermore, by providing feedback, users can receive better suggestions that suit their preferences in the future.

[0721] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0722] The present invention is a system that performs authentication based on user input information, analyzes the user's current situation and emotions, generates personalized ideas, and provides them to the user. It also features a feature that feedback and emotional data from the user are reflected in the next idea submission. This system is configured based on communication between the user's device (terminal) and a server. Furthermore, by using an emotion engine, idea generation takes the user's emotions into consideration.

[0723] Program processing

[0724] User Authentication

[0725] When a user starts the application and enters their email address and password on the login screen, the user's device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the user's device. The user's device receives this session ID and transitions to the main screen.

[0726] User Data Acquisition

[0727] After a user logs in, the server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID. It also uses an external API to obtain current location and weather information. It then uses an emotion engine to recognize the user's current emotional state based on their input information and behavioral data.

[0728] Data analysis and idea generation

[0729] The server analyzes the acquired user information, location information, weather information, time of day, and emotional data, and sends this data to a generation AI to generate personalized ideas. For example, the generation AI generates a "homemade bread recipe to enjoy on a rainy day" based on the conditions "10 AM, rainy, at home," the hobby information "cooking," and the emotional state "relaxed."

[0730] Idea submission

[0731] The generated ideas are sent from the server to the user's device. The user's device displays the received ideas on the application, allowing the user to view them. Specifically, the idea displays the necessary ingredients, steps, and related tips for a "homemade bread recipe."

[0732] Feedback function

[0733] After trying out the provided ideas, the user enters their opinions and ratings in a feedback form within the application. The user's device then sends this feedback to the server. The server receives the user's feedback along with their emotional data and stores it in a database. The next time ideas are generated, the system will refer to this feedback and emotional data to provide more personalized ideas.

[0734] Example flow

[0735] For example, let's say a user uses an application at 10:00 AM at home in Shibuya, Tokyo, and their hobby is cooking. The user launches the application and logs in, and the server verifies the user's authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information, and determines that the user is currently at home and that it is raining. Furthermore, the emotion engine recognizes that the user is in a relaxed state. Based on this information, the server sends a request to the generation AI to generate a "homemade bread recipe to enjoy on a rainy day."

[0736] The server sends the generated ideas to the user's device, which displays the recipe on the app. After the user has tried the recipe, they enter their opinion in a feedback form, such as "I'd like to know the dessert recipe next time," and the server receives and stores this in a database, along with emotional data.

[0737] This system allows users to constantly obtain new ideas that are relevant to the situation, and the system continues to evolve based on feedback and emotional data.

[0738] The processing flow will be explained below.

[0739] Program processing steps

[0740] User Authentication

[0741] Step 1:

[0742] The user launches the application and enters their email address and password on the login screen.

[0743] Step 2:

[0744] The user terminal transmits the entered authentication information to the server.

[0745] Step 3:

[0746] The server checks the received authentication information against the registered information in the database, and generates a session ID if authentication is successful.

[0747] Step 4:

[0748] The server sends the generated session ID to the user terminal.

[0749] Step 5:

[0750] The user terminal receives the session ID and transitions to the main screen.

[0751] User Data Acquisition

[0752] Step 6:

[0753] The server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID.

[0754] Step 7:

[0755] The server uses an external API to obtain the user's current location information.

[0756] Step 8:

[0757] The server uses an external API based on the user's location information to obtain current weather information.

[0758] Step 9:

[0759] The server obtains the current time information.

[0760] Step 10:

[0761] The server uses an emotion engine to analyze the user's input information and behavioral data to recognize their current emotional state.

[0762] Data analysis and idea generation

[0763] Step 11:

[0764] The server analyzes the acquired location information, weather information, time information, user profile information, hobby information, and emotion data.

[0765] Step 12:

[0766] Based on the analysis results, the server sends data to the generative AI and requests it to generate ideas.

[0767] Step 13:

[0768] The generative AI generates personalized ideas based on the data it receives and sends the results back to the server.

[0769] Idea submission

[0770] Step 14:

[0771] The server transmits the generated ideas to the user terminal.

[0772] Step 15:

[0773] The user terminal displays the received ideas on the application.

[0774] Feedback function

[0775] Step 16:

[0776] Users try out the ideas provided and provide feedback.

[0777] Step 17:

[0778] The user terminal transmits feedback information to the server.

[0779] Step 18:

[0780] The server stores the received feedback information and emotion data in a database.

[0781] Step 19:

[0782] The server will reflect the saved feedback information and emotional data the next time it generates ideas.

[0783] Example flow

[0784] For example, if a user uses an application at home in Shibuya Ward, Tokyo at 10:00 AM, his hobby is cooking, and he is feeling relaxed, the following processing is performed.

[0785] User Authentication

[0786] Step 1:

[0787] The user launches the application and enters their email address and password on the login screen.

[0788] Step 2:

[0789] The user terminal transmits this authentication information to the server.

[0790] Step 3:

[0791] The server compares the information with the registration information in the database and generates a session ID if authentication is successful.

[0792] Step 4:

[0793] The server sends the generated session ID to the user terminal.

[0794] Step 5:

[0795] The user terminal receives the session ID and transitions to the main screen.

[0796] User Data Acquisition

[0797] Step 6:

[0798] The server retrieves the user's profile information (e.g., hobby is cooking) from the database based on the session ID.

[0799] Step 7:

[0800] The server uses an external API to obtain the user's location information (for example, Shibuya-ku, Tokyo).

[0801] Step 8:

[0802] The server uses an external API to obtain current weather information (e.g., rain) based on the location information.

[0803] Step 9:

[0804] The server gets the current time (e.g. 10:00 AM).

[0805] Step 10:

[0806] The server uses an emotion engine to recognize the emotional state of "relaxed" from the user's input information and behavioral data.

[0807] Data analysis and idea generation

[0808] Step 11:

[0809] The server analyzes the acquired location information, weather information, time information, user's hobby information, and emotion data.

[0810] Step 12:

[0811] Based on the analysis results, the server sends data to the generation AI, requesting it to generate a "homemade bread recipe that can be enjoyed on a rainy day."

[0812] Step 13:

[0813] The generation AI generates a "homemade bread recipe" based on the conditions and sends it back to the server.

[0814] Idea submission

[0815] Step 14:

[0816] The server transmits the generated recipe to the user terminal.

[0817] Step 15:

[0818] The user terminal displays the details of the recipe on the application.

[0819] Feedback function

[0820] Step 16:

[0821] A user tries a recipe and fills in a feedback form saying, "Next time, I'd like to know the dessert recipe too."

[0822] Step 17:

[0823] The user terminal transmits feedback information to the server.

[0824] Step 18:

[0825] The server stores the feedback information and current emotion data in a database.

[0826] Step 19:

[0827] The server will reflect the saved feedback information and emotional data the next time it generates ideas, generating more personalized ideas.

[0828] The process proceeds in these steps, providing the user with the best ideas.

[0829] Example 2

[0830] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0831] Conventional personalized idea generation systems provide ideas based solely on the user's profile information and hobby information, and therefore do not take into account the user's current emotional state, location information, weather, and other circumstances, which can result in the system failing to provide suggestions that fully satisfy the user. Additionally, there is a lack of a mechanism for effectively utilizing user feedback and reflecting it in the next idea generation.

[0832] 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.

[0833] In this invention, the server includes a means for receiving user input information, a means for authenticating the user based on the acquired authentication information, and a means for acquiring the user's location information and weather information. This makes it possible to utilize rich user data to provide personalized ideas that are more tailored to the situation. Furthermore, by including an analysis means using an emotion engine and an idea generation means using a generative AI model, it becomes possible to propose ideas with high accuracy that take the user's emotional state into consideration. Furthermore, by including a means for improving the next idea generation based on user feedback, the quality of the user experience can be continuously improved.

[0834] "User input information" refers to all data provided by the user through the application, including, for example, email addresses, passwords, hobbies, and feedback.

[0835] "Authentication information" refers to information such as an email address and password used to verify a user's identity.

[0836] "Location information" is data that indicates the user's current location and is obtained through GPS information or location services.

[0837] "Weather information" is data indicating the current weather conditions in the area where the user is located, and is obtained through an external API.

[0838] "Profile information" is data for identifying a user, such as the user's basic personal information, hobbies, and past usage history.

[0839] An "emotion engine" is a system that analyzes the user's input information and behavioral data to recognize their emotional state at that time.

[0840] A "generative AI model" is an artificial intelligence model that generates personalized ideas based on input data, and includes, for example, GPT-3 and ChatGPT.

[0841] "Personalized ideas" are content such as suggestions, recipes, and advice that are customized to a user's individual situation and emotional state.

[0842] "Feedback" refers to reaction data such as opinions and evaluations provided by users in response to ideas provided.

[0843] A "session ID" is a unique identifier generated by the server after successful authentication and used to identify a user's session.

[0844] The present invention is a system that performs authentication based on user input information, analyzes the user's current situation and emotions, generates personalized ideas, and provides them to the user. It also features a feature that feedback and emotional data from the user are reflected in the next idea submission. This system is configured based on communication between the user's device (terminal) and a server. Furthermore, by using an emotion engine, idea generation takes the user's emotions into consideration.

[0845] The server includes a means for receiving user input information, a means for authenticating the user based on the acquired authentication information, a means for acquiring the user's location information and weather information, a means for acquiring the user's hobby information and profile information, a means for analyzing emotional data using an emotion engine, and a means for generating personalized ideas using a generative AI model, etc. This makes it possible to utilize rich user data to provide ideas that are more suited to the situation.

[0846] The terminal acquires a session ID through communication with the server, and uses this to authenticate the user and acquire subsequent data. It also has a means for providing the user with personalized ideas generated by the server based on the acquired data. Furthermore, the terminal transmits feedback and emotional data from the user to the server, which reflects this in the next idea generation.

[0847] As a concrete example, consider a case where a user is using an application at 10:00 AM at home in Shibuya, Tokyo, and their hobby is cooking. The user launches the application, enters their email address and password on the login screen, and clicks the "Login" button. The device sends this information to the server, which collates it with the registered information in the database. If authentication is successful, the server generates a session ID and sends it to the device. The device receives the session ID and transitions to the main screen.

[0848] When the user then accesses the main screen, the device sends a request to the server based on the user's session ID. The server retrieves the user's profile information, hobbies, and past usage history from the database, and obtains current location and weather information from an external API. Furthermore, the emotion engine recognizes that the user is in a "relaxed" state.

[0849] The server compiles this information and sends it to a generative AI model. The generative AI model uses, for example, GPT-3 or ChatGPT to generate a "homemade bread recipe to enjoy on a rainy day" based on the conditions "10 AM, rainy, at home, cooking, relaxing." The server sends the generated ideas to the user's device, which displays the recipe as an application UI. The user can then actually make homemade bread while looking at the recipe.

[0850] After trying the provided recipes, the user enters their opinion in the feedback form, such as "Next time, I'd like to know the dessert recipe as well." The device then sends this feedback information and the session ID to the server. The server then stores the feedback and emotion data in a database and reflects it in the next idea generation. This allows the user to always obtain new ideas that are appropriate for the situation, and the system continues to evolve based on the feedback and emotion data.

[0851] An example of a prompt sentence is, "If the user is at home in Shibuya-ku, Tokyo at 10:00 AM and his hobby is cooking, please generate a recipe to provide the user with personalized ideas suitable for a relaxed state."

[0852] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0853] Step 1: User authentication

[0854] Input: The user launches the application and enters their email address and password on the login screen.

[0855] Specific action: The user clicks the "Login" button.

[0856] Data processing and calculation: The device sends an HTTP POST request including the entered authentication information to the server.

[0857] Output: The server checks the authentication information against the database, and if successful, generates a session ID and sends it to the terminal. The user terminal receives the session ID and transitions to the main screen.

[0858] Step 2: Obtain user data

[0859] Input: The user accesses the main screen and the terminal sends the session ID to the server.

[0860] Specific operation: The server executes a database query based on the session ID sent by the device.

[0861] Data processing and calculation: The server acquires the user's profile information, hobbies, past usage history, location information, and weather information. Furthermore, the emotion engine analyzes the user's input information and behavioral data to recognize their emotional state.

[0862] Output: The acquired user data, location information, weather information, and emotional state are integrated.

[0863] Step 3: Data analysis and idea generation

[0864] Input: The server sends the integrated data (user information, location information, weather information, and emotion data) acquired in step 2 to the generative AI model.

[0865] Specific operation: The server sends a request to the generative AI model.

[0866] Data processing and calculation: Generative AI models (such as GPT-3 and ChatGPT) generate personalized ideas based on the data they receive. For example, they generate a "homemade bread recipe for a rainy day" based on the conditions "10 AM, rainy, at home, cooking, relaxing."

[0867] Output: The generated ideas are sent to the server.

[0868] Step 4: Submit your idea

[0869] Input: The generated ideas are sent from the server to the user terminal.

[0870] Specific operation: The device analyzes the received ideas and prepares them for display in the application UI.

[0871] Data processing and calculation: The terminal formats the idea content (recipe, steps, ingredients, etc.) into an appropriate format.

[0872] Output: A personalized idea displayed on the user's device, which the user can view and act on.

[0873] Step 5: Get feedback

[0874] Input: Users try out the ideas provided and enter their opinions and ratings in the feedback form.

[0875] Specific Action: The user fills out the feedback form and clicks the "Submit" button.

[0876] Data processing and calculation: The device sends a request including the feedback data and the session ID to the server. The server stores the feedback data and emotion data in a database.

[0877] Output: The feedback data and sentiment data are stored in a database and used for the next idea generation.

[0878] This series of steps allows users to get personalized ideas tailored to their individual situations, and the system continues to evolve through feedback.

[0879] (Application example 2)

[0880] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0881] Conventional systems have difficulty providing personalized ideas that take into account the user's current situation and emotional state. Furthermore, they lack the means to effectively utilize the user's feedback and emotional data on the provided ideas. Therefore, there is a need for a system that can make the shopping experience in brick-and-mortar stores more efficient and comfortable.

[0882] 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.

[0883] In this invention, the server includes means for receiving user input information, means for authenticating the user based on the acquired authentication information, means for acquiring user location information, means for acquiring user hobby information and profile information, means for acquiring weather information and time information, means for generating ideas based on the acquired information and acquired emotional data, means for providing the generated ideas to the user, means for analyzing the user's emotional data, and means for acquiring feedback from the user and updating the database. This makes it possible to provide personalized ideas that take into account the user's current situation and emotional state, and to improve the next idea provision based on the feedback.

[0884] The "means for receiving user input information" is a means for obtaining information input by a user.

[0885] The "means for authenticating a user based on the acquired authentication information" refers to a means for verifying the authentication information provided by the user and authenticating the user.

[0886] The "means for acquiring user location information" is a means for acquiring information about the user's current location.

[0887] The "means for acquiring user hobby information and profile information" refers to a means for acquiring information about the user's interests and personal information.

[0888] The "means for acquiring weather information and time information" is a means for acquiring information about the current weather conditions and time.

[0889] The "means for generating ideas based on acquired information and acquired emotional data" refers to a means for generating personalized ideas based on various acquired information and user emotional data.

[0890] The "means for providing the generated idea to the user" refers to a means for presenting the generated idea to the user.

[0891] The "means for analyzing user emotional data" refers to a means for analyzing data relating to the user's emotional state.

[0892] The "means for obtaining feedback from users and updating the database" refers to a means for receiving feedback provided by users and reflecting that information in the database.

[0893] This system authenticates users based on their input information, analyzes their current situation and emotions, and generates personalized ideas to provide to them. The system is based on communication between the user's device (terminal) and a server. Details of its implementation are explained below.

[0894] Hardware and software used

[0895] User devices: Mobile devices such as smartphones and tablets

[0896] Server: Cloud Server

[0897] Emotion Engine: Software for analyzing user emotions

[0898] Generative AI Model: An artificial intelligence model for generating personalized ideas

[0899] System Configuration and Operation

[0900] 1. User Authentication

[0901] When a user starts the application and enters their email address and password on the login screen, the user's device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the user's device. The user's device receives this session ID and transitions to the main screen.

[0902] 2. User Data Acquisition

[0903] After a user logs in, the server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID. It also uses an external API to obtain current location and weather information. It then uses an emotion engine to recognize the user's current emotional state based on their input information and behavioral data.

[0904] 3. Data analysis and idea generation

[0905] The server analyzes the acquired user information, location information, weather information, time of day, and emotional data, and sends this data to a generation AI to generate personalized ideas. For example, based on the conditions "10 AM, rainy, at home," the hobby information "cooking," and the emotional state "relaxed," it generates a "homemade bread recipe to enjoy on a rainy day."

[0906] 4. Idea submission

[0907] The generated ideas are sent from the server to the user's device. The user's device displays the received ideas on the application, allowing the user to view the ideas. For example, for a "homemade bread recipe," the application displays the necessary ingredients, steps, and related tips.

[0908] 5. Feedback function

[0909] After trying out the provided ideas, the user enters their opinions and ratings in a feedback form within the application. The user's device then sends this feedback to the server. The server receives the user's feedback along with their emotional data and stores it in a database. The next time ideas are generated, the system will refer to this feedback and emotional data to provide more personalized ideas.

[0910] Specific examples

[0911] For example, consider a case where a user uses an application at home at 10:00 AM and their hobby is cooking. The user launches the application and logs in, and the server verifies the authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information, and determines that the user is currently at home and that it is raining. The emotion engine then confirms that the user is relaxed. Based on this information, the server sends a request to the generation AI to generate a "homemade bread recipe to enjoy on a rainy day."

[0912] The server sends the generated ideas to the user's device, which displays the recipe on the app. After the user has tried the recipe, they enter their opinion in a feedback form, such as "I'd like to know the dessert recipe next time," and the server receives and stores this in a database, along with emotional data.

[0913] Example prompt sentence:

[0914] "10am, raining, at home, cooking, relaxing"

[0915] This system allows users to constantly get new ideas that are relevant to the situation, and the system continues to evolve based on feedback and emotional data.

[0916] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0917] Step 1:

[0918] The user starts the application and enters their email address and password on the login screen. The device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, generates a session ID and sends it to the user's device. The device receives this session ID and transitions to the main screen.

[0919] Input: Email address, password

[0920] Data processing: The server checks the registered information in the database

[0921] Output: Session ID

[0922] Step 2:

[0923] After a user logs in, the server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID. The server then obtains the user's current location and weather information using an external API. Furthermore, the server uses an emotion engine to recognize the user's current emotional state based on the user's input information and behavioral data.

[0924] Input: Session ID, location and weather information from external API, input information for emotion recognition

[0925] Data processing: Retrieving information from the database and analyzing emotional states

[0926] Output: Profile information, hobbies, past usage history, location information, weather information, emotional data

[0927] Step 3:

[0928] The server analyzes the acquired user information, location information, weather information, time of day, and emotional data, and sends this data to a generative AI model to generate personalized ideas. For example, based on the conditions "10 AM, rainy, at home," the hobby information "cooking," and the emotional state "relaxed," it generates a "homemade bread recipe to enjoy on a rainy day."

[0929] Input: Profile information, hobby information, past usage history, location information, weather information, emotional data

[0930] Data processing: Generative AI models generate personalized ideas

[0931] Output: Personalized ideas (e.g. homemade bread recipes)

[0932] Step 4:

[0933] The generated ideas are sent from the server to the user's device, where they are displayed on the application, allowing the user to view them. For example, a "homemade bread recipe" might be displayed, showing the necessary ingredients, steps, and related tips.

[0934] Input: Personalized Ideas

[0935] Data processing: Display ideas on the application

[0936] Output: Displayed idea content (e.g. homemade bread recipe, ingredients, steps)

[0937] Step 5:

[0938] After trying out the provided ideas, users enter their opinions and ratings in the feedback form within the application. The device then sends this feedback to the server. The server receives the user's feedback along with their emotional data and stores it in a database. The next time ideas are generated, the system will refer to this feedback and emotional data to provide more personalized ideas.

[0939] Input: Feedback, emotion data

[0940] Data processing: Save to database, improve next idea generation

[0941] Output: Updated database, improved idea generation algorithm

[0942] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0943] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0944] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0945] [Third embodiment]

[0946] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0947] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0948] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0949] 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.

[0950] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0951] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0952] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0953] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0954] 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.

[0955] 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.

[0956] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0957] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0958] The present invention is a system that performs authentication based on user input information, analyzes the user's current situation, generates personalized ideas, and provides them to the user. Furthermore, it is characterized by incorporating feedback from the user into the next idea submission. This system is configured based on communication between the user's device (terminal) and a server.

[0959] Program processing

[0960] User Authentication

[0961] When a user starts the application and enters their email address and password on the login screen, the user's device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the user's device. The user's device receives this session ID and transitions to the main screen.

[0962] User Data Acquisition

[0963] After a user logs in, the server retrieves information such as past usage history, hobbies, and profile information from the database based on the user's session ID. It also uses an external API to retrieve current location and weather information, allowing for a detailed understanding of the user's current situation.

[0964] Data analysis and idea generation

[0965] The server analyzes the acquired user information, location information, weather information, and time of day, and sends this data to the generation AI to generate personalized ideas. For example, the generation AI generates a "homemade bread recipe to enjoy on a rainy day" based on the conditions "10 AM, rainy, at home" and the hobby information "cooking."

[0966] Idea submission

[0967] The generated ideas are sent from the server to the user's device. The user's device displays the received ideas on the application, allowing the user to view them. Specifically, the idea displays the necessary ingredients, steps, and related tips for a "homemade bread recipe."

[0968] Feedback function

[0969] After trying out the provided ideas, the user enters their opinions and ratings in the feedback form within the application. The user's device sends this feedback to the server, which then stores the user's feedback in a database and uses it as a reference when generating ideas next time.

[0970] Example flow

[0971] For example, let's say a user uses an application at 10:00 AM at home in Shibuya, Tokyo, and their hobby is cooking. The user launches the application and logs in, and the server verifies the user's authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information, and determines that the user is currently at home and that it is raining. Based on this information, the server sends a request to the generation AI to generate a "homemade bread recipe to enjoy on a rainy day."

[0972] The server sends the generated ideas to the user's device, which displays the recipe on the app. After the user has tried the recipe, they enter their opinion in the feedback form within the app, such as "I'd like to know the dessert recipe next time," and the server receives this and stores it in a database.

[0973] This system allows users to constantly obtain new ideas that are relevant to the situation, and the system continues to evolve based on feedback.

[0974] The processing flow will be explained below.

[0975] Program processing steps

[0976] User Authentication

[0977] Step 1:

[0978] The user launches the application and enters their email address and password on the login screen.

[0979] Step 2:

[0980] The user terminal transmits the entered authentication information to the server.

[0981] Step 3:

[0982] The server checks the received authentication information against the registered information in the database, and generates a session ID if authentication is successful.

[0983] Step 4:

[0984] The server sends the generated session ID to the user terminal.

[0985] Step 5:

[0986] The user terminal receives the session ID and transitions to the main screen.

[0987] User Data Acquisition

[0988] Step 6:

[0989] The server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID.

[0990] Step 7:

[0991] The server uses an external API to obtain the user's current location information.

[0992] Step 8:

[0993] The server uses an external API based on the user's location information to obtain current weather information.

[0994] Step 9:

[0995] The server obtains the current time information.

[0996] Data analysis and idea generation

[0997] Step 10:

[0998] The server analyzes the acquired location information, weather information, time information, user profile information, and hobby information.

[0999] Step 11:

[1000] Based on the analysis results, the server sends data to the generative AI and requests it to generate ideas.

[1001] Step 12:

[1002] The generative AI generates personalized ideas based on the data it receives and sends the results back to the server.

[1003] Idea submission

[1004] Step 13:

[1005] The server transmits the generated ideas to the user terminal.

[1006] Step 14:

[1007] The user terminal displays the received ideas on the application.

[1008] Feedback function

[1009] Step 15:

[1010] Users try out the ideas provided and provide feedback.

[1011] Step 16:

[1012] The user terminal transmits feedback information to the server.

[1013] Step 17:

[1014] The server stores the received feedback information in a database and reflects it the next time ideas are generated.

[1015] Example flow

[1016] For example, if a user uses an application at 10:00 AM at home in Shibuya Ward, Tokyo, and his hobby is cooking, the following processing steps are performed.

[1017] User Authentication

[1018] Step 1:

[1019] The user launches the application and enters their email address and password on the login screen.

[1020] Step 2:

[1021] The user terminal transmits this authentication information to the server.

[1022] Step 3:

[1023] The server compares the information with the registration information in the database and generates a session ID if authentication is successful.

[1024] Step 4:

[1025] The server sends the generated session ID to the user terminal.

[1026] Step 5:

[1027] The user terminal receives the session ID and transitions to the main screen.

[1028] User Data Acquisition

[1029] Step 6:

[1030] The server retrieves the user's profile information (e.g., hobby is cooking) from the database based on the session ID.

[1031] Step 7:

[1032] The server uses an external API to obtain the user's location information (for example, Shibuya-ku, Tokyo).

[1033] Step 8:

[1034] The server uses an external API to obtain current weather information (e.g., rain) based on the location information.

[1035] Step 9:

[1036] The server gets the current time (e.g. 10:00 AM).

[1037] Data analysis and idea generation

[1038] Step 10:

[1039] The server analyzes the acquired location information, weather information, time information, and user hobby information.

[1040] Step 11:

[1041] Based on the analysis results, the server sends data to the generative AI and requests personalized idea generation.

[1042] Step 12:

[1043] The generative AI generates ideas such as "homemade bread recipes to enjoy on rainy days" and sends them back to the server.

[1044] Idea submission

[1045] Step 13:

[1046] The server transmits the generated ideas to the user terminal.

[1047] Step 14:

[1048] The user terminal displays the details of the "homemade bread recipe" on the application.

[1049] Feedback function

[1050] Step 15:

[1051] The user tries out the provided ideas and enters their opinion in the feedback form, saying, "Next time, I'd like to know the dessert recipe too."

[1052] Step 16:

[1053] The user terminal transmits feedback information to the server.

[1054] Step 17:

[1055] The server stores the received feedback information in a database and reflects this feedback the next time ideas are generated.

[1056] The process proceeds in these steps.

[1057] Example 1

[1058] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1059] Conventional user assistance systems have difficulty making appropriate suggestions based on the user's environment and conditions, and are unable to effectively utilize user feedback to improve their next suggestions. As a result, they are prone to providing suggestions that do not meet the user's needs, resulting in a poor user experience.

[1060] 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.

[1061] In this invention, the server includes means for receiving user input information, means for authenticating the user based on the acquired authentication information, means for acquiring user location information, means for acquiring user hobby information and profile information, means for acquiring weather information and time information, means for analyzing the acquired information to generate prompt sentences and sending the data to a generative AI model to generate personalized ideas, means for providing the generated ideas to the user, and means for acquiring user feedback and updating the database, thereby making it possible to generate appropriate suggestions according to the user's environment and conditions and to improve subsequent suggestions by reflecting the user's feedback.

[1062] "User Input Information" means data that a user enters into the system, including email addresses, passwords, and other authentication information.

[1063] "Authentication information" refers to data required to verify a user's identity, such as an email address and password.

[1064] "Location information" is data that indicates a user's current geographic location, and is provided in the form of GPS coordinates or the like.

[1065] "Hobby information" refers to information about activities or themes that interest a user.

[1066] "Profile information" is data such as the user's personal information and past activity history, and is information that indicates the user's characteristics.

[1067] "Weather information" is data that indicates the weather conditions at the user's current location.

[1068] "Time information" is data that indicates a specific time or the current time.

[1069] A "generative AI model" is an artificial intelligence model that generates personalized ideas for users based on multiple input data.

[1070] A "prompt sentence" is a sentence to be input into a generative AI model, and is generated based on the user's situation and conditions.

[1071] "Personalized ideas" are individual suggestions or advice provided to a specific user based on the user's individual information and requirements.

[1072] "Feedback" refers to reactions such as opinions and evaluations made by users in response to ideas provided.

[1073] A "database" is a system for storing and managing large amounts of data.

[1074] An "external API" is an interface for communicating with external systems and services to obtain data.

[1075] This invention is a system that performs authentication based on user input information, analyzes the user's current situation, generates personalized ideas, and provides them to the user. Furthermore, it is characterized by incorporating feedback from the user into the next idea submission. This system is configured based on communication between the user's device (terminal) and a server.

[1076] User Authentication

[1077] When a user starts the application and enters their email address and password on the login screen, the device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the device. The device receives this session ID and transitions to the main screen.

[1078] User Data Acquisition

[1079] After the user logs in, the server retrieves information such as past usage history, hobbies, and profile information from the database based on the user's session ID. The server then uses external APIs (e.g., Google Maps API and OpenWeatherMap API) to retrieve current location and weather information, providing a detailed understanding of the user's current situation.

[1080] Data analysis and idea generation

[1081] The server analyzes the acquired user information, location information, weather information, and time of day, generates a prompt text, and sends it to the generative AI model. An example of a generative AI model is GPT-4. The generative AI generates personalized ideas based on the received prompt text. As a specific example, based on the conditions "10 AM, rainy, at home" and the hobby information "cooking," it generates a "homemade bread recipe to enjoy on a rainy day."

[1082] ※Example prompt:

[1083] 1. "Generate ideas that are suitable for the user when they are at home in Shibuya, Tokyo at 10 AM and their hobby is cooking."

[1084] 2. "User is currently looking for fun activities to do on a rainy day. Can you suggest a homemade bread recipe?"

[1085] Idea submission

[1086] The generated ideas are sent from the server to the device, which then displays them on the application so that the user can view them. Specifically, the idea displays the necessary ingredients, steps, and related tips for a "homemade bread recipe."

[1087] Feedback function

[1088] After trying out the provided ideas, the user enters their opinions and ratings in a feedback form within the application. The device then sends this feedback to the server. The server stores the user's feedback in a database and uses it as a reference when generating ideas next time. For example, based on the user's comment, "I'd like to know the dessert recipe next time," the server can refine the next prompt and provide more personalized suggestions.

[1089] This system allows users to constantly obtain new ideas that are relevant to the situation, and the system can continue to evolve based on feedback.

[1090] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1091] Step 1:

[1092] Receive user input information

[1093] The user launches the application and enters their email address and password on the login screen.

[1094] Input: Email address, password

[1095] How it works: The device obtains these credentials and sends them to the server.

[1096] Output: Authentication information sent to the server

[1097] Step 2:

[1098] Credentials validation

[1099] The server checks the authentication information it receives against the registration information in its database.

[1100] Input: Authentication information (email address, password)

[1101] How it works: The server checks the database and generates a session ID if authentication is successful. The database used is, for example, MySQL or PostgreSQL.

[1102] Output: Generated session ID

[1103] Step 3:

[1104] Sending a Session ID

[1105] The server sends the generated session ID to the terminal.

[1106] Input: Session ID

[1107] Operation: The session ID is sent to the terminal, which receives it and transitions to the main screen.

[1108] Output: Transition to main screen

[1109] Step 4:

[1110] Retrieving User Data

[1111] After the user logs in, the server retrieves information such as past usage history, hobbies, and profile from the database based on the user's session ID.

[1112] Input: Session ID

[1113] How it works: User information is retrieved from the database. The server uses an external API (e.g., Google Maps API or OpenWeatherMap API) to retrieve current location and weather information.

[1114] Output: User information, location information, weather information

[1115] Step 5:

[1116] Data analysis and prompt generation

[1117] The server analyzes the data based on the information it has acquired and generates a prompt.

[1118] Input: User information, location information, weather information, time information

[1119] How it works: The server generates a prompt to send data to a generative AI model (e.g., GPT-4).

[1120] Output: Generated prompt statement

[1121] Step 6:

[1122] Idea generation

[1123] The server sends prompts to the generative AI model to generate personalized ideas.

[1124] Input: prompt statement

[1125] How it works: A generative AI model generates ideas based on a prompt.

[1126] Output: Generated ideas

[1127] Step 7:

[1128] Providing ideas

[1129] The server transmits the generated ideas to the terminal.

[1130] Input: Generated ideas

[1131] Operation: The device displays the received ideas on the application so that the user can view them.

[1132] Output: Ideas displayed in the application

[1133] Step 8:

[1134] Getting and processing feedback

[1135] After users try out the ideas provided, they enter their opinions and ratings in a feedback form within the application.

[1136] Input: User feedback

[1137] How it works: The device sends this feedback to the server, which stores it in a database.

[1138] Output: Saved feedback

[1139] Step 9:

[1140] Reflect feedback in next idea generation

[1141] The server will use the saved feedback to refine the next prompt.

[1142] Input: Saved Feedback

[1143] Behavior: The server analyzes the feedback and uses it in the next prompt.

[1144] Output: Improved prompt

[1145] (Application example 1)

[1146] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1147] Food delivery applications that users use daily are unable to appropriately suggest the best dishes for each user's individual situation. This makes it difficult for users to choose dishes that suit their mood or situation at the time, and the suggestions provided are often off-target. Furthermore, systems for incorporating feedback on the provided dishes into future suggestions are inadequate. The objective of this invention is to solve these problems, provide users with highly satisfying suggestions, and improve the user experience.

[1148] 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.

[1149] In this invention, the server includes means for receiving user input information, means for authenticating the user based on the acquired authentication information, means for acquiring the user's location information, means for acquiring the user's hobby information and profile information, means for acquiring weather information and time information, means for generating ideas based on the acquired information, means for making recipe suggestions based on the generated ideas, means for providing the generated ideas to the user, and means for acquiring feedback from the user and updating the database. This makes it possible to make optimal recipe suggestions based on the user's current situation. Furthermore, by continuously learning from user feedback, the system can improve the accuracy of future suggestions, thereby continuously improving the user experience.

[1150] "User input information" refers to all information provided by a user through a device.

[1151] "Authentication information" is data used to verify the identity of a user, and includes, for example, an email address and a password.

[1152] "Location information" is data that indicates the user's current location and is obtained using technology such as GPS.

[1153] "Hobby information" refers to information about a user's favorite activities and areas of interest.

[1154] "Profile information" is a user's basic personal information, including name, age, gender, etc.

[1155] "Weather information" is data that indicates the weather conditions in the user's location and is updated in real time.

[1156] "Time information" refers to data relating to the current time and date.

[1157] "Idea generation" refers to the automatic creation of content and actions to be suggested to users based on acquired information.

[1158] "Dish suggestions" refers to recommending the best dishes based on the user's current situation and preferences.

[1159] "Feedback" refers to the evaluation or opinion a user gives of a provided idea or suggestion.

[1160] "Database update" refers to adding or modifying the obtained feedback information to the database.

[1161] An "external API" refers to an interface for using the functions of other services or applications.

[1162] "Generative AI" refers to an artificial intelligence model that automatically generates new information or suggestions based on given data.

[1163] The following describes an embodiment of the invention. This invention is a system that makes personalized recipe suggestions based on a user's location information, time information, weather information, hobby information, and profile information. This system communicates between the user's terminal and a server to provide the user with optimal ideas. Furthermore, the accuracy of the suggestions is improved by incorporating feedback from the user.

[1164] The server first receives the user's input information. The user enters their email address and password for authentication and receives a session ID. If authentication is successful, the user can access the system. At this time, the server retrieves the user's past usage history, hobby information, and profile information from the database. It also uses an external API to retrieve current location and weather information.

[1165] The server then performs data analysis based on this information and sends prompts to the generative AI model to generate personalized food suggestions for the user. For example, the generative AI model receives a prompt such as, "The user lives in Shibuya Ward, Tokyo, and the current weather is rainy. The user likes Italian food, so please suggest dishes that fit these conditions." and generates appropriate suggestions.

[1166] The generated suggestions are sent from the server to the user's device, where the user can view them on the app. When the user selects a suggested dish and actually places an order, the app sends that feedback to the server, which stores this feedback in a database and uses it as a reference the next time ideas are generated.

[1167] This system allows users to receive optimal dish suggestions tailored to their current situation, and the accuracy of these suggestions is continuously improved. Users can also provide feedback to receive better suggestions tailored to their preferences in the future.

[1168] The hardware used is the user's smartphone or tablet, while the software includes the Python Requests library, external APIs, and generative AI models. The combination of these technologies enables a complete process from data collection and analysis to generating recommendations and incorporating feedback.

[1169] As a concrete example, consider a case where a user is using an application at 10:00 AM at home in Shibuya Ward, Tokyo, and their hobby is Italian food. The user launches the application and logs in, and the server verifies the user's authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information and determines that the user is currently at home and that it is raining. Based on this information, the server sends a request to the generation AI, sending a prompt saying, "The user lives in Shibuya Ward, Tokyo, and the current weather is rainy. The user likes Italian food, so please suggest dishes that fit these conditions." If the generation AI suggests "carbonara as a recommended Italian dish for a rainy day," the server sends this to the user's device, and the user can view the suggestion on the app. If the user actually orders carbonara and provides feedback afterwards, the server stores this feedback and can use it for future idea generation.

[1170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1171] Step 1:

[1172] The user launches the smartphone application and enters their email address and password on the login screen. The input information (email address and password) is sent to the server as the user's authentication information. The server compares this authentication information with the registered information in the database. If authentication is successful, the server generates a session ID and sends it to the user's terminal. The user's terminal receives this session ID and transitions to the main screen. In this processing step, a session ID is generated based on the user authentication and returned to the user's terminal.

[1173] Step 2:

[1174] After the user logs in, the server retrieves the user's past usage history, hobby information, and profile information from the database based on the user's session ID. In addition, the server calls the location information API and weather information API based on a request from the user's device to obtain current location information and weather information using external APIs. The retrieved information is aggregated on the server as the user's current situation. In this processing step, the user's past data and current situation data are retrieved.

[1175] Step 3:

[1176] The server analyzes the acquired user information, location information, weather information, and time information, and generates a prompt for the generative AI model based on that information. For example, it creates a prompt such as, "The user lives in Shibuya Ward, Tokyo, and the current weather is rainy. The user likes Italian food, so please suggest dishes that fit these conditions." The generated prompt is sent to the generative AI model, which generates personalized food suggestions. In this processing step, the server analyzes data and requests the generative AI model to suggest the best dishes for the user based on the prompt.

[1177] Step 4:

[1178] The generated recipe suggestions are sent from the server to the user terminal, and the user terminal displays the received suggestions on the application. The user can check the suggested dishes through the application and select a favorite dish. In this processing step, data transmission and display processing are performed to provide the generated ideas to the user.

[1179] Step 5:

[1180] After a user orders a dish, they provide feedback through a feedback form within the application. The user device sends this feedback information to the server. The server stores the received feedback in a database and uses this information as a reference the next time it generates ideas to improve the accuracy of suggestions. In this processing step, feedback from users is collected and the database is updated.

[1181] Through these five steps, users can receive the best recipe suggestions based on their current situation, and the accuracy of the suggestions will continue to improve. Furthermore, by providing feedback, users can receive better suggestions that suit their preferences in the future.

[1182] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1183] The present invention is a system that performs authentication based on user input information, analyzes the user's current situation and emotions, generates personalized ideas, and provides them to the user. It also features a feature that feedback and emotional data from the user are reflected in the next idea submission. This system is configured based on communication between the user's device (terminal) and a server. Furthermore, by using an emotion engine, idea generation takes the user's emotions into consideration.

[1184] Program processing

[1185] User Authentication

[1186] When a user starts the application and enters their email address and password on the login screen, the user's device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the user's device. The user's device receives this session ID and transitions to the main screen.

[1187] User Data Acquisition

[1188] After a user logs in, the server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID. It also uses an external API to obtain current location and weather information. It then uses an emotion engine to recognize the user's current emotional state based on their input information and behavioral data.

[1189] Data analysis and idea generation

[1190] The server analyzes the acquired user information, location information, weather information, time of day, and emotional data, and sends this data to a generation AI to generate personalized ideas. For example, the generation AI generates a "homemade bread recipe to enjoy on a rainy day" based on the conditions "10 AM, rainy, at home," the hobby information "cooking," and the emotional state "relaxed."

[1191] Idea submission

[1192] The generated ideas are sent from the server to the user's device. The user's device displays the received ideas on the application, allowing the user to view them. Specifically, the idea displays the necessary ingredients, steps, and related tips for a "homemade bread recipe."

[1193] Feedback function

[1194] After trying out the provided ideas, the user enters their opinions and ratings in a feedback form within the application. The user's device then sends this feedback to the server. The server receives the user's feedback along with their emotional data and stores it in a database. The next time ideas are generated, the system will refer to this feedback and emotional data to provide more personalized ideas.

[1195] Example flow

[1196] For example, let's say a user uses an application at 10:00 AM at home in Shibuya, Tokyo, and their hobby is cooking. The user launches the application and logs in, and the server verifies the user's authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information, and determines that the user is currently at home and that it is raining. Furthermore, the emotion engine recognizes that the user is in a relaxed state. Based on this information, the server sends a request to the generation AI to generate a "homemade bread recipe to enjoy on a rainy day."

[1197] The server sends the generated ideas to the user's device, which displays the recipe on the app. After the user has tried the recipe, they enter their opinion in a feedback form, such as "I'd like to know the dessert recipe next time," and the server receives and stores this in a database, along with emotional data.

[1198] This system allows users to constantly obtain new ideas that are relevant to the situation, and the system continues to evolve based on feedback and emotional data.

[1199] The processing flow will be explained below.

[1200] Program processing steps

[1201] User Authentication

[1202] Step 1:

[1203] The user launches the application and enters their email address and password on the login screen.

[1204] Step 2:

[1205] The user terminal transmits the entered authentication information to the server.

[1206] Step 3:

[1207] The server checks the received authentication information against the registered information in the database, and generates a session ID if authentication is successful.

[1208] Step 4:

[1209] The server sends the generated session ID to the user terminal.

[1210] Step 5:

[1211] The user terminal receives the session ID and transitions to the main screen.

[1212] User Data Acquisition

[1213] Step 6:

[1214] The server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID.

[1215] Step 7:

[1216] The server uses an external API to obtain the user's current location information.

[1217] Step 8:

[1218] The server uses an external API based on the user's location information to obtain current weather information.

[1219] Step 9:

[1220] The server obtains the current time information.

[1221] Step 10:

[1222] The server uses an emotion engine to analyze the user's input information and behavioral data to recognize their current emotional state.

[1223] Data analysis and idea generation

[1224] Step 11:

[1225] The server analyzes the acquired location information, weather information, time information, user profile information, hobby information, and emotion data.

[1226] Step 12:

[1227] Based on the analysis results, the server sends data to the generative AI and requests it to generate ideas.

[1228] Step 13:

[1229] The generative AI generates personalized ideas based on the data it receives and sends the results back to the server.

[1230] Idea submission

[1231] Step 14:

[1232] The server transmits the generated ideas to the user terminal.

[1233] Step 15:

[1234] The user terminal displays the received ideas on the application.

[1235] Feedback function

[1236] Step 16:

[1237] Users try out the ideas provided and provide feedback.

[1238] Step 17:

[1239] The user terminal transmits feedback information to the server.

[1240] Step 18:

[1241] The server stores the received feedback information and emotion data in a database.

[1242] Step 19:

[1243] The server will reflect the saved feedback information and emotional data the next time it generates ideas.

[1244] Example flow

[1245] For example, if a user uses an application at home in Shibuya Ward, Tokyo at 10:00 AM, his hobby is cooking, and he is feeling relaxed, the following processing is performed.

[1246] User Authentication

[1247] Step 1:

[1248] The user launches the application and enters their email address and password on the login screen.

[1249] Step 2:

[1250] The user terminal transmits this authentication information to the server.

[1251] Step 3:

[1252] The server compares the information with the registration information in the database and generates a session ID if authentication is successful.

[1253] Step 4:

[1254] The server sends the generated session ID to the user terminal.

[1255] Step 5:

[1256] The user terminal receives the session ID and transitions to the main screen.

[1257] User Data Acquisition

[1258] Step 6:

[1259] The server retrieves the user's profile information (e.g., hobby is cooking) from the database based on the session ID.

[1260] Step 7:

[1261] The server uses an external API to obtain the user's location information (for example, Shibuya-ku, Tokyo).

[1262] Step 8:

[1263] The server uses an external API to obtain current weather information (e.g., rain) based on the location information.

[1264] Step 9:

[1265] The server gets the current time (e.g. 10:00 AM).

[1266] Step 10:

[1267] The server uses an emotion engine to recognize the emotional state of "relaxed" from the user's input information and behavioral data.

[1268] Data analysis and idea generation

[1269] Step 11:

[1270] The server analyzes the acquired location information, weather information, time information, user's hobby information, and emotion data.

[1271] Step 12:

[1272] Based on the analysis results, the server sends data to the generation AI, requesting it to generate a "homemade bread recipe that can be enjoyed on a rainy day."

[1273] Step 13:

[1274] The generation AI generates a "homemade bread recipe" based on the conditions and sends it back to the server.

[1275] Idea submission

[1276] Step 14:

[1277] The server transmits the generated recipe to the user terminal.

[1278] Step 15:

[1279] The user terminal displays the details of the recipe on the application.

[1280] Feedback function

[1281] Step 16:

[1282] A user tries a recipe and fills in a feedback form saying, "Next time, I'd like to know the dessert recipe too."

[1283] Step 17:

[1284] The user terminal transmits feedback information to the server.

[1285] Step 18:

[1286] The server stores the feedback information and current emotion data in a database.

[1287] Step 19:

[1288] The server will reflect the saved feedback information and emotional data the next time it generates ideas, generating more personalized ideas.

[1289] The process proceeds in these steps, providing the user with the best ideas.

[1290] Example 2

[1291] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1292] Conventional personalized idea generation systems provide ideas based solely on the user's profile information and hobby information, and therefore do not take into account the user's current emotional state, location information, weather, and other circumstances, which can result in the system failing to provide suggestions that fully satisfy the user. Additionally, there is a lack of a mechanism for effectively utilizing user feedback and reflecting it in the next idea generation.

[1293] 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.

[1294] In this invention, the server includes a means for receiving user input information, a means for authenticating the user based on the acquired authentication information, and a means for acquiring the user's location information and weather information. This makes it possible to utilize rich user data to provide personalized ideas that are more tailored to the situation. Furthermore, by including an analysis means using an emotion engine and an idea generation means using a generative AI model, it becomes possible to propose ideas with high accuracy that take the user's emotional state into consideration. Furthermore, by including a means for improving the next idea generation based on user feedback, the quality of the user experience can be continuously improved.

[1295] "User input information" refers to all data provided by the user through the application, including, for example, email addresses, passwords, hobbies, and feedback.

[1296] "Authentication information" refers to information such as an email address and password used to verify a user's identity.

[1297] "Location information" is data that indicates the user's current location and is obtained through GPS information or location services.

[1298] "Weather information" is data indicating the current weather conditions in the area where the user is located, and is obtained through an external API.

[1299] "Profile information" is data for identifying a user, such as the user's basic personal information, hobbies, and past usage history.

[1300] An "emotion engine" is a system that analyzes the user's input information and behavioral data to recognize their emotional state at that time.

[1301] A "generative AI model" is an artificial intelligence model that generates personalized ideas based on input data, and includes, for example, GPT-3 and ChatGPT.

[1302] "Personalized ideas" are content such as suggestions, recipes, and advice that are customized to a user's individual situation and emotional state.

[1303] "Feedback" refers to reaction data such as opinions and evaluations provided by users in response to ideas provided.

[1304] A "session ID" is a unique identifier generated by the server after successful authentication and used to identify a user's session.

[1305] The present invention is a system that performs authentication based on user input information, analyzes the user's current situation and emotions, generates personalized ideas, and provides them to the user. It also features a feature that feedback and emotional data from the user are reflected in the next idea submission. This system is configured based on communication between the user's device (terminal) and a server. Furthermore, by using an emotion engine, idea generation takes the user's emotions into consideration.

[1306] The server includes a means for receiving user input information, a means for authenticating the user based on the acquired authentication information, a means for acquiring the user's location information and weather information, a means for acquiring the user's hobby information and profile information, a means for analyzing emotional data using an emotion engine, and a means for generating personalized ideas using a generative AI model, etc. This makes it possible to utilize rich user data to provide ideas that are more suited to the situation.

[1307] The terminal acquires a session ID through communication with the server, and uses this to authenticate the user and acquire subsequent data. It also has a means for providing the user with personalized ideas generated by the server based on the acquired data. Furthermore, the terminal transmits feedback and emotional data from the user to the server, which reflects this in the next idea generation.

[1308] As a concrete example, consider a case where a user is using an application at 10:00 AM at home in Shibuya, Tokyo, and their hobby is cooking. The user launches the application, enters their email address and password on the login screen, and clicks the "Login" button. The device sends this information to the server, which collates it with the registered information in the database. If authentication is successful, the server generates a session ID and sends it to the device. The device receives the session ID and transitions to the main screen.

[1309] When the user then accesses the main screen, the device sends a request to the server based on the user's session ID. The server retrieves the user's profile information, hobbies, and past usage history from the database, and obtains current location and weather information from an external API. Furthermore, the emotion engine recognizes that the user is in a "relaxed" state.

[1310] The server compiles this information and sends it to a generative AI model. The generative AI model uses, for example, GPT-3 or ChatGPT to generate a "homemade bread recipe to enjoy on a rainy day" based on the conditions "10 AM, rainy, at home, cooking, relaxing." The server sends the generated ideas to the user's device, which displays the recipe as an application UI. The user can then actually make homemade bread while looking at the recipe.

[1311] After trying the provided recipes, the user enters their opinion in the feedback form, such as "Next time, I'd like to know the dessert recipe as well." The device then sends this feedback information and the session ID to the server. The server then stores the feedback and emotion data in a database and reflects it in the next idea generation. This allows the user to always obtain new ideas that are appropriate for the situation, and the system continues to evolve based on the feedback and emotion data.

[1312] An example of a prompt sentence is, "If the user is at home in Shibuya-ku, Tokyo at 10:00 AM and his hobby is cooking, please generate a recipe to provide the user with personalized ideas suitable for a relaxed state."

[1313] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1314] Step 1: User authentication

[1315] Input: The user launches the application and enters their email address and password on the login screen.

[1316] Specific action: The user clicks the "Login" button.

[1317] Data processing and calculation: The device sends an HTTP POST request including the entered authentication information to the server.

[1318] Output: The server checks the authentication information against the database, and if successful, generates a session ID and sends it to the terminal. The user terminal receives the session ID and transitions to the main screen.

[1319] Step 2: Obtain user data

[1320] Input: The user accesses the main screen and the terminal sends the session ID to the server.

[1321] Specific operation: The server executes a database query based on the session ID sent by the device.

[1322] Data processing and calculation: The server acquires the user's profile information, hobbies, past usage history, location information, and weather information. Furthermore, the emotion engine analyzes the user's input information and behavioral data to recognize their emotional state.

[1323] Output: The acquired user data, location information, weather information, and emotional state are integrated.

[1324] Step 3: Data analysis and idea generation

[1325] Input: The server sends the integrated data (user information, location information, weather information, and emotion data) acquired in step 2 to the generative AI model.

[1326] Specific operation: The server sends a request to the generative AI model.

[1327] Data processing and calculation: Generative AI models (such as GPT-3 and ChatGPT) generate personalized ideas based on the data they receive. For example, they generate a "homemade bread recipe for a rainy day" based on the conditions "10 AM, rainy, at home, cooking, relaxing."

[1328] Output: The generated ideas are sent to the server.

[1329] Step 4: Submit your idea

[1330] Input: The generated ideas are sent from the server to the user terminal.

[1331] Specific operation: The device analyzes the received ideas and prepares them for display in the application UI.

[1332] Data processing and calculation: The terminal formats the idea content (recipe, steps, ingredients, etc.) into an appropriate format.

[1333] Output: A personalized idea displayed on the user's device, which the user can view and act on.

[1334] Step 5: Get feedback

[1335] Input: Users try out the ideas provided and enter their opinions and ratings in the feedback form.

[1336] Specific Action: The user fills out the feedback form and clicks the "Submit" button.

[1337] Data processing and calculation: The device sends a request including the feedback data and the session ID to the server. The server stores the feedback data and emotion data in a database.

[1338] Output: The feedback data and sentiment data are stored in a database and used for the next idea generation.

[1339] This series of steps allows users to get personalized ideas tailored to their individual situations, and the system continues to evolve through feedback.

[1340] (Application example 2)

[1341] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1342] Conventional systems have difficulty providing personalized ideas that take into account the user's current situation and emotional state. Furthermore, they lack the means to effectively utilize the user's feedback and emotional data on the provided ideas. Therefore, there is a need for a system that can make the shopping experience in brick-and-mortar stores more efficient and comfortable.

[1343] 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.

[1344] In this invention, the server includes means for receiving user input information, means for authenticating the user based on the acquired authentication information, means for acquiring user location information, means for acquiring user hobby information and profile information, means for acquiring weather information and time information, means for generating ideas based on the acquired information and acquired emotional data, means for providing the generated ideas to the user, means for analyzing the user's emotional data, and means for acquiring feedback from the user and updating the database. This makes it possible to provide personalized ideas that take into account the user's current situation and emotional state, and to improve the next idea provision based on the feedback.

[1345] The "means for receiving user input information" is a means for obtaining information input by a user.

[1346] The "means for authenticating a user based on the acquired authentication information" refers to a means for verifying the authentication information provided by the user and authenticating the user.

[1347] The "means for acquiring user location information" is a means for acquiring information about the user's current location.

[1348] The "means for acquiring user hobby information and profile information" refers to a means for acquiring information about the user's interests and personal information.

[1349] The "means for acquiring weather information and time information" is a means for acquiring information about the current weather conditions and time.

[1350] The "means for generating ideas based on acquired information and acquired emotional data" refers to a means for generating personalized ideas based on various acquired information and user emotional data.

[1351] The "means for providing the generated idea to the user" refers to a means for presenting the generated idea to the user.

[1352] The "means for analyzing user emotional data" refers to a means for analyzing data relating to the user's emotional state.

[1353] The "means for obtaining feedback from users and updating the database" refers to a means for receiving feedback provided by users and reflecting that information in the database.

[1354] This system authenticates users based on their input information, analyzes their current situation and emotions, and generates personalized ideas to provide to them. The system is based on communication between the user's device (terminal) and a server. Details of its implementation are explained below.

[1355] Hardware and software used

[1356] User devices: Mobile devices such as smartphones and tablets

[1357] Server: Cloud Server

[1358] Emotion Engine: Software for analyzing user emotions

[1359] Generative AI Model: An artificial intelligence model for generating personalized ideas

[1360] System Configuration and Operation

[1361] 1. User Authentication

[1362] When a user starts the application and enters their email address and password on the login screen, the user's device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the user's device. The user's device receives this session ID and transitions to the main screen.

[1363] 2. User Data Acquisition

[1364] After a user logs in, the server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID. It also uses an external API to obtain current location and weather information. It then uses an emotion engine to recognize the user's current emotional state based on their input information and behavioral data.

[1365] 3. Data analysis and idea generation

[1366] The server analyzes the acquired user information, location information, weather information, time of day, and emotional data, and sends this data to a generation AI to generate personalized ideas. For example, based on the conditions "10 AM, rainy, at home," the hobby information "cooking," and the emotional state "relaxed," it generates a "homemade bread recipe to enjoy on a rainy day."

[1367] 4. Idea submission

[1368] The generated ideas are sent from the server to the user's device. The user's device displays the received ideas on the application, allowing the user to view the ideas. For example, for a "homemade bread recipe," the application displays the necessary ingredients, steps, and related tips.

[1369] 5. Feedback function

[1370] After trying out the provided ideas, the user enters their opinions and ratings in a feedback form within the application. The user's device then sends this feedback to the server. The server receives the user's feedback along with their emotional data and stores it in a database. The next time ideas are generated, the system will refer to this feedback and emotional data to provide more personalized ideas.

[1371] Specific examples

[1372] For example, consider a case where a user uses an application at home at 10:00 AM and their hobby is cooking. The user launches the application and logs in, and the server verifies the authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information, and determines that the user is currently at home and that it is raining. The emotion engine then confirms that the user is relaxed. Based on this information, the server sends a request to the generation AI to generate a "homemade bread recipe to enjoy on a rainy day."

[1373] The server sends the generated ideas to the user's device, which displays the recipe on the app. After the user has tried the recipe, they enter their opinion in a feedback form, such as "I'd like to know the dessert recipe next time," and the server receives and stores this in a database, along with emotional data.

[1374] Example prompt sentence:

[1375] "10am, raining, at home, cooking, relaxing"

[1376] This system allows users to constantly get new ideas that are relevant to the situation, and the system continues to evolve based on feedback and emotional data.

[1377] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1378] Step 1:

[1379] The user starts the application and enters their email address and password on the login screen. The device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, generates a session ID and sends it to the user's device. The device receives this session ID and transitions to the main screen.

[1380] Input: Email address, password

[1381] Data processing: The server checks the registered information in the database

[1382] Output: Session ID

[1383] Step 2:

[1384] After a user logs in, the server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID. The server then obtains the user's current location and weather information using an external API. Furthermore, the server uses an emotion engine to recognize the user's current emotional state based on the user's input information and behavioral data.

[1385] Input: Session ID, location and weather information from external API, input information for emotion recognition

[1386] Data processing: Retrieving information from the database and analyzing emotional states

[1387] Output: Profile information, hobbies, past usage history, location information, weather information, emotional data

[1388] Step 3:

[1389] The server analyzes the acquired user information, location information, weather information, time of day, and emotional data, and sends this data to a generative AI model to generate personalized ideas. For example, based on the conditions "10 AM, rainy, at home," the hobby information "cooking," and the emotional state "relaxed," it generates a "homemade bread recipe to enjoy on a rainy day."

[1390] Input: Profile information, hobby information, past usage history, location information, weather information, emotional data

[1391] Data processing: Generative AI models generate personalized ideas

[1392] Output: Personalized ideas (e.g. homemade bread recipes)

[1393] Step 4:

[1394] The generated ideas are sent from the server to the user's device, where they are displayed on the application, allowing the user to view them. For example, a "homemade bread recipe" might be displayed, showing the necessary ingredients, steps, and related tips.

[1395] Input: Personalized Ideas

[1396] Data processing: Display ideas on the application

[1397] Output: Displayed idea content (e.g. homemade bread recipe, ingredients, steps)

[1398] Step 5:

[1399] After trying out the provided ideas, users enter their opinions and ratings in the feedback form within the application. The device then sends this feedback to the server. The server receives the user's feedback along with their emotional data and stores it in a database. The next time ideas are generated, the system will refer to this feedback and emotional data to provide more personalized ideas.

[1400] Input: Feedback, emotion data

[1401] Data processing: Save to database, improve next idea generation

[1402] Output: Updated database, improved idea generation algorithm

[1403] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1404] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1405] 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.

[1406] [Fourth embodiment]

[1407] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1408] 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.

[1409] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1410] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1411] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1412] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1413] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1414] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1415] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1416] 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.

[1417] 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.

[1418] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1419] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1420] The present invention is a system that performs authentication based on user input information, analyzes the user's current situation, generates personalized ideas, and provides them to the user. Furthermore, it is characterized by incorporating feedback from the user into the next idea submission. This system is configured based on communication between the user's device (terminal) and a server.

[1421] Program processing

[1422] User Authentication

[1423] When a user starts the application and enters their email address and password on the login screen, the user's device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the user's device. The user's device receives this session ID and transitions to the main screen.

[1424] User Data Acquisition

[1425] After a user logs in, the server retrieves information such as past usage history, hobbies, and profile information from the database based on the user's session ID. It also uses an external API to retrieve current location and weather information, allowing for a detailed understanding of the user's current situation.

[1426] Data analysis and idea generation

[1427] The server analyzes the acquired user information, location information, weather information, and time of day, and sends this data to the generation AI to generate personalized ideas. For example, the generation AI generates a "homemade bread recipe to enjoy on a rainy day" based on the conditions "10 AM, rainy, at home" and the hobby information "cooking."

[1428] Idea submission

[1429] The generated ideas are sent from the server to the user's device. The user's device displays the received ideas on the application, allowing the user to view them. Specifically, the idea displays the necessary ingredients, steps, and related tips for a "homemade bread recipe."

[1430] Feedback function

[1431] After trying out the provided ideas, the user enters their opinions and ratings in the feedback form within the application. The user's device sends this feedback to the server, which then stores the user's feedback in a database and uses it as a reference when generating ideas next time.

[1432] Example flow

[1433] For example, let's say a user uses an application at 10:00 AM at home in Shibuya, Tokyo, and their hobby is cooking. The user launches the application and logs in, and the server verifies the user's authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information, and determines that the user is currently at home and that it is raining. Based on this information, the server sends a request to the generation AI to generate a "homemade bread recipe to enjoy on a rainy day."

[1434] The server sends the generated ideas to the user's device, which displays the recipe on the app. After the user has tried the recipe, they enter their opinion in the feedback form within the app, such as "I'd like to know the dessert recipe next time," and the server receives this and stores it in a database.

[1435] This system allows users to constantly obtain new ideas that are relevant to the situation, and the system continues to evolve based on feedback.

[1436] The processing flow will be explained below.

[1437] Program processing steps

[1438] User Authentication

[1439] Step 1:

[1440] The user launches the application and enters their email address and password on the login screen.

[1441] Step 2:

[1442] The user terminal transmits the entered authentication information to the server.

[1443] Step 3:

[1444] The server checks the received authentication information against the registered information in the database, and generates a session ID if authentication is successful.

[1445] Step 4:

[1446] The server sends the generated session ID to the user terminal.

[1447] Step 5:

[1448] The user terminal receives the session ID and transitions to the main screen.

[1449] User Data Acquisition

[1450] Step 6:

[1451] The server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID.

[1452] Step 7:

[1453] The server uses an external API to obtain the user's current location information.

[1454] Step 8:

[1455] The server uses an external API based on the user's location information to obtain current weather information.

[1456] Step 9:

[1457] The server obtains the current time information.

[1458] Data analysis and idea generation

[1459] Step 10:

[1460] The server analyzes the acquired location information, weather information, time information, user profile information, and hobby information.

[1461] Step 11:

[1462] Based on the analysis results, the server sends data to the generative AI and requests it to generate ideas.

[1463] Step 12:

[1464] The generative AI generates personalized ideas based on the data it receives and sends the results back to the server.

[1465] Idea submission

[1466] Step 13:

[1467] The server transmits the generated ideas to the user terminal.

[1468] Step 14:

[1469] The user terminal displays the received ideas on the application.

[1470] Feedback function

[1471] Step 15:

[1472] Users try out the ideas provided and provide feedback.

[1473] Step 16:

[1474] The user terminal transmits feedback information to the server.

[1475] Step 17:

[1476] The server stores the received feedback information in a database and reflects it the next time ideas are generated.

[1477] Example flow

[1478] For example, if a user uses an application at 10:00 AM at home in Shibuya Ward, Tokyo, and his hobby is cooking, the following processing steps are performed.

[1479] User Authentication

[1480] Step 1:

[1481] The user launches the application and enters their email address and password on the login screen.

[1482] Step 2:

[1483] The user terminal transmits this authentication information to the server.

[1484] Step 3:

[1485] The server compares the information with the registration information in the database and generates a session ID if authentication is successful.

[1486] Step 4:

[1487] The server sends the generated session ID to the user terminal.

[1488] Step 5:

[1489] The user terminal receives the session ID and transitions to the main screen.

[1490] User Data Acquisition

[1491] Step 6:

[1492] The server retrieves the user's profile information (e.g., hobby is cooking) from the database based on the session ID.

[1493] Step 7:

[1494] The server uses an external API to obtain the user's location information (for example, Shibuya-ku, Tokyo).

[1495] Step 8:

[1496] The server uses an external API to obtain current weather information (e.g., rain) based on the location information.

[1497] Step 9:

[1498] The server gets the current time (e.g. 10:00 AM).

[1499] Data analysis and idea generation

[1500] Step 10:

[1501] The server analyzes the acquired location information, weather information, time information, and user hobby information.

[1502] Step 11:

[1503] Based on the analysis results, the server sends data to the generative AI and requests personalized idea generation.

[1504] Step 12:

[1505] The generative AI generates ideas such as "homemade bread recipes to enjoy on rainy days" and sends them back to the server.

[1506] Idea submission

[1507] Step 13:

[1508] The server transmits the generated ideas to the user terminal.

[1509] Step 14:

[1510] The user terminal displays the details of the "homemade bread recipe" on the application.

[1511] Feedback function

[1512] Step 15:

[1513] The user tries out the provided ideas and enters their opinion in the feedback form, saying, "Next time, I'd like to know the dessert recipe too."

[1514] Step 16:

[1515] The user terminal transmits feedback information to the server.

[1516] Step 17:

[1517] The server stores the received feedback information in a database and reflects this feedback the next time ideas are generated.

[1518] The process proceeds in these steps.

[1519] Example 1

[1520] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1521] Conventional user assistance systems have difficulty making appropriate suggestions based on the user's environment and conditions, and are unable to effectively utilize user feedback to improve their next suggestions. As a result, they are prone to providing suggestions that do not meet the user's needs, resulting in a poor user experience.

[1522] 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.

[1523] In this invention, the server includes means for receiving user input information, means for authenticating the user based on the acquired authentication information, means for acquiring user location information, means for acquiring user hobby information and profile information, means for acquiring weather information and time information, means for analyzing the acquired information to generate prompt sentences and sending the data to a generative AI model to generate personalized ideas, means for providing the generated ideas to the user, and means for acquiring user feedback and updating the database, thereby making it possible to generate appropriate suggestions according to the user's environment and conditions and to improve subsequent suggestions by reflecting the user's feedback.

[1524] "User Input Information" means data that a user enters into the system, including email addresses, passwords, and other authentication information.

[1525] "Authentication information" refers to data required to verify a user's identity, such as an email address and password.

[1526] "Location information" is data that indicates a user's current geographic location, and is provided in the form of GPS coordinates or the like.

[1527] "Hobby information" refers to information about activities or themes that interest a user.

[1528] "Profile information" is data such as the user's personal information and past activity history, and is information that indicates the user's characteristics.

[1529] "Weather information" is data that indicates the weather conditions at the user's current location.

[1530] "Time information" is data that indicates a specific time or the current time.

[1531] A "generative AI model" is an artificial intelligence model that generates personalized ideas for users based on multiple input data.

[1532] A "prompt sentence" is a sentence to be input into a generative AI model, and is generated based on the user's situation and conditions.

[1533] "Personalized ideas" are individual suggestions or advice provided to a specific user based on the user's individual information and requirements.

[1534] "Feedback" refers to reactions such as opinions and evaluations made by users in response to ideas provided.

[1535] A "database" is a system for storing and managing large amounts of data.

[1536] An "external API" is an interface for communicating with external systems and services to obtain data.

[1537] This invention is a system that performs authentication based on user input information, analyzes the user's current situation, generates personalized ideas, and provides them to the user. Furthermore, it is characterized by incorporating feedback from the user into the next idea submission. This system is configured based on communication between the user's device (terminal) and a server.

[1538] User Authentication

[1539] When a user starts the application and enters their email address and password on the login screen, the device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the device. The device receives this session ID and transitions to the main screen.

[1540] User Data Acquisition

[1541] After the user logs in, the server retrieves information such as past usage history, hobbies, and profile information from the database based on the user's session ID. The server then uses external APIs (e.g., Google Maps API and OpenWeatherMap API) to retrieve current location and weather information, providing a detailed understanding of the user's current situation.

[1542] Data analysis and idea generation

[1543] The server analyzes the acquired user information, location information, weather information, and time of day, generates a prompt text, and sends it to the generative AI model. An example of a generative AI model is GPT-4. The generative AI generates personalized ideas based on the received prompt text. As a specific example, based on the conditions "10 AM, rainy, at home" and the hobby information "cooking," it generates a "homemade bread recipe to enjoy on a rainy day."

[1544] ※Example prompt:

[1545] 1. "Generate ideas that are suitable for the user when they are at home in Shibuya, Tokyo at 10 AM and their hobby is cooking."

[1546] 2. "User is currently looking for fun activities to do on a rainy day. Can you suggest a homemade bread recipe?"

[1547] Idea submission

[1548] The generated ideas are sent from the server to the device, which then displays them on the application so that the user can view them. Specifically, the idea displays the necessary ingredients, steps, and related tips for a "homemade bread recipe."

[1549] Feedback function

[1550] After trying out the provided ideas, the user enters their opinions and ratings in a feedback form within the application. The device then sends this feedback to the server. The server stores the user's feedback in a database and uses it as a reference when generating ideas next time. For example, based on the user's comment, "I'd like to know the dessert recipe next time," the server can refine the next prompt and provide more personalized suggestions.

[1551] This system allows users to constantly obtain new ideas that are relevant to the situation, and the system can continue to evolve based on feedback.

[1552] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1553] Step 1:

[1554] Receive user input information

[1555] The user launches the application and enters their email address and password on the login screen.

[1556] Input: Email address, password

[1557] How it works: The device obtains these credentials and sends them to the server.

[1558] Output: Authentication information sent to the server

[1559] Step 2:

[1560] Credentials validation

[1561] The server checks the authentication information it receives against the registration information in its database.

[1562] Input: Authentication information (email address, password)

[1563] How it works: The server checks the database and generates a session ID if authentication is successful. The database used is, for example, MySQL or PostgreSQL.

[1564] Output: Generated session ID

[1565] Step 3:

[1566] Sending a Session ID

[1567] The server sends the generated session ID to the terminal.

[1568] Input: Session ID

[1569] Operation: The session ID is sent to the terminal, which receives it and transitions to the main screen.

[1570] Output: Transition to main screen

[1571] Step 4:

[1572] Retrieving User Data

[1573] After the user logs in, the server retrieves information such as past usage history, hobbies, and profile from the database based on the user's session ID.

[1574] Input: Session ID

[1575] How it works: User information is retrieved from the database. The server uses an external API (e.g., Google Maps API or OpenWeatherMap API) to retrieve current location and weather information.

[1576] Output: User information, location information, weather information

[1577] Step 5:

[1578] Data analysis and prompt generation

[1579] The server analyzes the data based on the information it has acquired and generates a prompt.

[1580] Input: User information, location information, weather information, time information

[1581] How it works: The server generates a prompt to send data to a generative AI model (e.g., GPT-4).

[1582] Output: Generated prompt statement

[1583] Step 6:

[1584] Idea generation

[1585] The server sends prompts to the generative AI model to generate personalized ideas.

[1586] Input: prompt statement

[1587] How it works: A generative AI model generates ideas based on a prompt.

[1588] Output: Generated ideas

[1589] Step 7:

[1590] Providing ideas

[1591] The server transmits the generated ideas to the terminal.

[1592] Input: Generated ideas

[1593] Operation: The device displays the received ideas on the application so that the user can view them.

[1594] Output: Ideas displayed in the application

[1595] Step 8:

[1596] Getting and processing feedback

[1597] After users try out the ideas provided, they enter their opinions and ratings in a feedback form within the application.

[1598] Input: User feedback

[1599] How it works: The device sends this feedback to the server, which stores it in a database.

[1600] Output: Saved feedback

[1601] Step 9:

[1602] Reflect feedback in next idea generation

[1603] The server will use the saved feedback to refine the next prompt.

[1604] Input: Saved Feedback

[1605] Behavior: The server analyzes the feedback and uses it in the next prompt.

[1606] Output: Improved prompt

[1607] (Application example 1)

[1608] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1609] Food delivery applications that users use daily are unable to appropriately suggest the best dishes for each user's individual situation. This makes it difficult for users to choose dishes that suit their mood or situation at the time, and the suggestions provided are often off-target. Furthermore, systems for incorporating feedback on the provided dishes into future suggestions are inadequate. The objective of this invention is to solve these problems, provide users with highly satisfying suggestions, and improve the user experience.

[1610] 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.

[1611] In this invention, the server includes means for receiving user input information, means for authenticating the user based on the acquired authentication information, means for acquiring the user's location information, means for acquiring the user's hobby information and profile information, means for acquiring weather information and time information, means for generating ideas based on the acquired information, means for making recipe suggestions based on the generated ideas, means for providing the generated ideas to the user, and means for acquiring feedback from the user and updating the database. This makes it possible to make optimal recipe suggestions based on the user's current situation. Furthermore, by continuously learning from user feedback, the system can improve the accuracy of future suggestions, thereby continuously improving the user experience.

[1612] "User input information" refers to all information provided by a user through a device.

[1613] "Authentication information" is data used to verify the identity of a user, and includes, for example, an email address and a password.

[1614] "Location information" is data that indicates the user's current location and is obtained using technology such as GPS.

[1615] "Hobby information" refers to information about a user's favorite activities and areas of interest.

[1616] "Profile information" is a user's basic personal information, including name, age, gender, etc.

[1617] "Weather information" is data that indicates the weather conditions in the user's location and is updated in real time.

[1618] "Time information" refers to data relating to the current time and date.

[1619] "Idea generation" refers to the automatic creation of content and actions to be suggested to users based on acquired information.

[1620] "Dish suggestions" refers to recommending the best dishes based on the user's current situation and preferences.

[1621] "Feedback" refers to the evaluation or opinion a user gives of a provided idea or suggestion.

[1622] "Database update" refers to adding or modifying the obtained feedback information to the database.

[1623] An "external API" refers to an interface for using the functions of other services or applications.

[1624] "Generative AI" refers to an artificial intelligence model that automatically generates new information or suggestions based on given data.

[1625] The following describes an embodiment of the invention. This invention is a system that makes personalized recipe suggestions based on a user's location information, time information, weather information, hobby information, and profile information. This system communicates between the user's terminal and a server to provide the user with optimal ideas. Furthermore, the accuracy of the suggestions is improved by incorporating feedback from the user.

[1626] The server first receives the user's input information. The user enters their email address and password for authentication and receives a session ID. If authentication is successful, the user can access the system. At this time, the server retrieves the user's past usage history, hobby information, and profile information from the database. It also uses an external API to retrieve current location and weather information.

[1627] The server then performs data analysis based on this information and sends prompts to the generative AI model to generate personalized food suggestions for the user. For example, the generative AI model receives a prompt such as, "The user lives in Shibuya Ward, Tokyo, and the current weather is rainy. The user likes Italian food, so please suggest dishes that fit these conditions." and generates appropriate suggestions.

[1628] The generated suggestions are sent from the server to the user's device, where the user can view them on the app. When the user selects a suggested dish and actually places an order, the app sends that feedback to the server, which stores this feedback in a database and uses it as a reference the next time ideas are generated.

[1629] This system allows users to receive optimal dish suggestions tailored to their current situation, and the accuracy of these suggestions is continuously improved. Users can also provide feedback to receive better suggestions tailored to their preferences in the future.

[1630] The hardware used is the user's smartphone or tablet, while the software includes the Python Requests library, external APIs, and generative AI models. The combination of these technologies enables a complete process from data collection and analysis to generating recommendations and incorporating feedback.

[1631] As a concrete example, consider a case where a user is using an application at 10:00 AM at home in Shibuya Ward, Tokyo, and their hobby is Italian food. The user launches the application and logs in, and the server verifies the user's authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information and determines that the user is currently at home and that it is raining. Based on this information, the server sends a request to the generation AI, sending a prompt saying, "The user lives in Shibuya Ward, Tokyo, and the current weather is rainy. The user likes Italian food, so please suggest dishes that fit these conditions." If the generation AI suggests "carbonara as a recommended Italian dish for a rainy day," the server sends this to the user's device, and the user can view the suggestion on the app. If the user actually orders carbonara and provides feedback afterwards, the server stores this feedback and can use it for future idea generation.

[1632] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1633] Step 1:

[1634] The user launches the smartphone application and enters their email address and password on the login screen. The input information (email address and password) is sent to the server as the user's authentication information. The server compares this authentication information with the registered information in the database. If authentication is successful, the server generates a session ID and sends it to the user's terminal. The user's terminal receives this session ID and transitions to the main screen. In this processing step, a session ID is generated based on the user authentication and returned to the user's terminal.

[1635] Step 2:

[1636] After the user logs in, the server retrieves the user's past usage history, hobby information, and profile information from the database based on the user's session ID. In addition, the server calls the location information API and weather information API based on a request from the user's device to obtain current location information and weather information using external APIs. The retrieved information is aggregated on the server as the user's current situation. In this processing step, the user's past data and current situation data are retrieved.

[1637] Step 3:

[1638] The server analyzes the acquired user information, location information, weather information, and time information, and generates a prompt for the generative AI model based on that information. For example, it creates a prompt such as, "The user lives in Shibuya Ward, Tokyo, and the current weather is rainy. The user likes Italian food, so please suggest dishes that fit these conditions." The generated prompt is sent to the generative AI model, which generates personalized food suggestions. In this processing step, the server analyzes data and requests the generative AI model to suggest the best dishes for the user based on the prompt.

[1639] Step 4:

[1640] The generated recipe suggestions are sent from the server to the user terminal, and the user terminal displays the received suggestions on the application. The user can check the suggested dishes through the application and select a favorite dish. In this processing step, data transmission and display processing are performed to provide the generated ideas to the user.

[1641] Step 5:

[1642] After a user orders a dish, they provide feedback through a feedback form within the application. The user device sends this feedback information to the server. The server stores the received feedback in a database and uses this information as a reference the next time it generates ideas to improve the accuracy of suggestions. In this processing step, feedback from users is collected and the database is updated.

[1643] Through these five steps, users can receive the best recipe suggestions based on their current situation, and the accuracy of the suggestions will continue to improve. Furthermore, by providing feedback, users can receive better suggestions that suit their preferences in the future.

[1644] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1645] The present invention is a system that performs authentication based on user input information, analyzes the user's current situation and emotions, generates personalized ideas, and provides them to the user. It also features a feature that feedback and emotional data from the user are reflected in the next idea submission. This system is configured based on communication between the user's device (terminal) and a server. Furthermore, by using an emotion engine, idea generation takes the user's emotions into consideration.

[1646] Program processing

[1647] User Authentication

[1648] When a user starts the application and enters their email address and password on the login screen, the user's device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the user's device. The user's device receives this session ID and transitions to the main screen.

[1649] User Data Acquisition

[1650] After a user logs in, the server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID. It also uses an external API to obtain current location and weather information. It then uses an emotion engine to recognize the user's current emotional state based on their input information and behavioral data.

[1651] Data analysis and idea generation

[1652] The server analyzes the acquired user information, location information, weather information, time of day, and emotional data, and sends this data to a generation AI to generate personalized ideas. For example, the generation AI generates a "homemade bread recipe to enjoy on a rainy day" based on the conditions "10 AM, rainy, at home," the hobby information "cooking," and the emotional state "relaxed."

[1653] Idea submission

[1654] The generated ideas are sent from the server to the user's device. The user's device displays the received ideas on the application, allowing the user to view them. Specifically, the idea displays the necessary ingredients, steps, and related tips for a "homemade bread recipe."

[1655] Feedback function

[1656] After trying out the provided ideas, the user enters their opinions and ratings in a feedback form within the application. The user's device then sends this feedback to the server. The server receives the user's feedback along with their emotional data and stores it in a database. The next time ideas are generated, the system will refer to this feedback and emotional data to provide more personalized ideas.

[1657] Example flow

[1658] For example, let's say a user uses an application at 10:00 AM at home in Shibuya, Tokyo, and their hobby is cooking. The user launches the application and logs in, and the server verifies the user's authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information, and determines that the user is currently at home and that it is raining. Furthermore, the emotion engine recognizes that the user is in a relaxed state. Based on this information, the server sends a request to the generation AI to generate a "homemade bread recipe to enjoy on a rainy day."

[1659] The server sends the generated ideas to the user's device, which displays the recipe on the app. After the user has tried the recipe, they enter their opinion in a feedback form, such as "I'd like to know the dessert recipe next time," and the server receives and stores this in a database, along with emotional data.

[1660] This system allows users to constantly obtain new ideas that are relevant to the situation, and the system continues to evolve based on feedback and emotional data.

[1661] The processing flow will be explained below.

[1662] Program processing steps

[1663] User Authentication

[1664] Step 1:

[1665] The user launches the application and enters their email address and password on the login screen.

[1666] Step 2:

[1667] The user terminal transmits the entered authentication information to the server.

[1668] Step 3:

[1669] The server checks the received authentication information against the registered information in the database, and generates a session ID if authentication is successful.

[1670] Step 4:

[1671] The server sends the generated session ID to the user terminal.

[1672] Step 5:

[1673] The user terminal receives the session ID and transitions to the main screen.

[1674] User Data Acquisition

[1675] Step 6:

[1676] The server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID.

[1677] Step 7:

[1678] The server uses an external API to obtain the user's current location information.

[1679] Step 8:

[1680] The server uses an external API based on the user's location information to obtain current weather information.

[1681] Step 9:

[1682] The server obtains the current time information.

[1683] Step 10:

[1684] The server uses an emotion engine to analyze the user's input information and behavioral data to recognize their current emotional state.

[1685] Data analysis and idea generation

[1686] Step 11:

[1687] The server analyzes the acquired location information, weather information, time information, user profile information, hobby information, and emotion data.

[1688] Step 12:

[1689] Based on the analysis results, the server sends data to the generative AI and requests it to generate ideas.

[1690] Step 13:

[1691] The generative AI generates personalized ideas based on the data it receives and sends the results back to the server.

[1692] Idea submission

[1693] Step 14:

[1694] The server transmits the generated ideas to the user terminal.

[1695] Step 15:

[1696] The user terminal displays the received ideas on the application.

[1697] Feedback function

[1698] Step 16:

[1699] Users try out the ideas provided and provide feedback.

[1700] Step 17:

[1701] The user terminal transmits feedback information to the server.

[1702] Step 18:

[1703] The server stores the received feedback information and emotion data in a database.

[1704] Step 19:

[1705] The server will reflect the saved feedback information and emotional data the next time it generates ideas.

[1706] Example flow

[1707] For example, if a user uses an application at home in Shibuya Ward, Tokyo at 10:00 AM, his hobby is cooking, and he is feeling relaxed, the following processing is performed.

[1708] User Authentication

[1709] Step 1:

[1710] The user launches the application and enters their email address and password on the login screen.

[1711] Step 2:

[1712] The user terminal transmits this authentication information to the server.

[1713] Step 3:

[1714] The server compares the information with the registration information in the database and generates a session ID if authentication is successful.

[1715] Step 4:

[1716] The server sends the generated session ID to the user terminal.

[1717] Step 5:

[1718] The user terminal receives the session ID and transitions to the main screen.

[1719] User Data Acquisition

[1720] Step 6:

[1721] The server retrieves the user's profile information (e.g., hobby is cooking) from the database based on the session ID.

[1722] Step 7:

[1723] The server uses an external API to obtain the user's location information (for example, Shibuya-ku, Tokyo).

[1724] Step 8:

[1725] The server uses an external API to obtain current weather information (e.g., rain) based on the location information.

[1726] Step 9:

[1727] The server gets the current time (e.g. 10:00 AM).

[1728] Step 10:

[1729] The server uses an emotion engine to recognize the emotional state of "relaxed" from the user's input information and behavioral data.

[1730] Data analysis and idea generation

[1731] Step 11:

[1732] The server analyzes the acquired location information, weather information, time information, user's hobby information, and emotion data.

[1733] Step 12:

[1734] Based on the analysis results, the server sends data to the generation AI, requesting it to generate a "homemade bread recipe that can be enjoyed on a rainy day."

[1735] Step 13:

[1736] The generation AI generates a "homemade bread recipe" based on the conditions and sends it back to the server.

[1737] Idea submission

[1738] Step 14:

[1739] The server transmits the generated recipe to the user terminal.

[1740] Step 15:

[1741] The user terminal displays the details of the recipe on the application.

[1742] Feedback function

[1743] Step 16:

[1744] A user tries a recipe and fills in a feedback form saying, "Next time, I'd like to know the dessert recipe too."

[1745] Step 17:

[1746] The user terminal transmits feedback information to the server.

[1747] Step 18:

[1748] The server stores the feedback information and current emotion data in a database.

[1749] Step 19:

[1750] The server will reflect the saved feedback information and emotional data the next time it generates ideas, generating more personalized ideas.

[1751] The process proceeds in these steps, providing the user with the best ideas.

[1752] Example 2

[1753] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1754] Conventional personalized idea generation systems provide ideas based solely on the user's profile information and hobby information, and therefore do not take into account the user's current emotional state, location information, weather, and other circumstances, which can result in the system failing to provide suggestions that fully satisfy the user. Additionally, there is a lack of a mechanism for effectively utilizing user feedback and reflecting it in the next idea generation.

[1755] 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.

[1756] In this invention, the server includes a means for receiving user input information, a means for authenticating the user based on the acquired authentication information, and a means for acquiring the user's location information and weather information. This makes it possible to utilize rich user data to provide personalized ideas that are more tailored to the situation. Furthermore, by including an analysis means using an emotion engine and an idea generation means using a generative AI model, it becomes possible to propose ideas with high accuracy that take the user's emotional state into consideration. Furthermore, by including a means for improving the next idea generation based on user feedback, the quality of the user experience can be continuously improved.

[1757] "User input information" refers to all data provided by the user through the application, including, for example, email addresses, passwords, hobbies, and feedback.

[1758] "Authentication information" refers to information such as an email address and password used to verify a user's identity.

[1759] "Location information" is data that indicates the user's current location and is obtained through GPS information or location services.

[1760] "Weather information" is data indicating the current weather conditions in the area where the user is located, and is obtained through an external API.

[1761] "Profile information" is data for identifying a user, such as the user's basic personal information, hobbies, and past usage history.

[1762] An "emotion engine" is a system that analyzes the user's input information and behavioral data to recognize their emotional state at that time.

[1763] A "generative AI model" is an artificial intelligence model that generates personalized ideas based on input data, and includes, for example, GPT-3 and ChatGPT.

[1764] "Personalized ideas" are content such as suggestions, recipes, and advice that are customized to a user's individual situation and emotional state.

[1765] "Feedback" refers to reaction data such as opinions and evaluations provided by users in response to ideas provided.

[1766] A "session ID" is a unique identifier generated by the server after successful authentication and used to identify a user's session.

[1767] The present invention is a system that performs authentication based on user input information, analyzes the user's current situation and emotions, generates personalized ideas, and provides them to the user. It also features a feature that feedback and emotional data from the user are reflected in the next idea submission. This system is configured based on communication between the user's device (terminal) and a server. Furthermore, by using an emotion engine, idea generation takes the user's emotions into consideration.

[1768] The server includes a means for receiving user input information, a means for authenticating the user based on the acquired authentication information, a means for acquiring the user's location information and weather information, a means for acquiring the user's hobby information and profile information, a means for analyzing emotional data using an emotion engine, and a means for generating personalized ideas using a generative AI model, etc. This makes it possible to utilize rich user data to provide ideas that are more suited to the situation.

[1769] The terminal acquires a session ID through communication with the server, and uses this to authenticate the user and acquire subsequent data. It also has a means for providing the user with personalized ideas generated by the server based on the acquired data. Furthermore, the terminal transmits feedback and emotional data from the user to the server, which reflects this in the next idea generation.

[1770] As a concrete example, consider a case where a user is using an application at 10:00 AM at home in Shibuya, Tokyo, and their hobby is cooking. The user launches the application, enters their email address and password on the login screen, and clicks the "Login" button. The device sends this information to the server, which collates it with the registered information in the database. If authentication is successful, the server generates a session ID and sends it to the device. The device receives the session ID and transitions to the main screen.

[1771] When the user then accesses the main screen, the device sends a request to the server based on the user's session ID. The server retrieves the user's profile information, hobbies, and past usage history from the database, and obtains current location and weather information from an external API. Furthermore, the emotion engine recognizes that the user is in a "relaxed" state.

[1772] The server compiles this information and sends it to a generative AI model. The generative AI model uses, for example, GPT-3 or ChatGPT to generate a "homemade bread recipe to enjoy on a rainy day" based on the conditions "10 AM, rainy, at home, cooking, relaxing." The server sends the generated ideas to the user's device, which displays the recipe as an application UI. The user can then actually make homemade bread while looking at the recipe.

[1773] After trying the provided recipes, the user enters their opinion in the feedback form, such as "Next time, I'd like to know the dessert recipe as well." The device then sends this feedback information and the session ID to the server. The server then stores the feedback and emotion data in a database and reflects it in the next idea generation. This allows the user to always obtain new ideas that are appropriate for the situation, and the system continues to evolve based on the feedback and emotion data.

[1774] An example of a prompt sentence is, "If the user is at home in Shibuya-ku, Tokyo at 10:00 AM and his hobby is cooking, please generate a recipe to provide the user with personalized ideas suitable for a relaxed state."

[1775] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1776] Step 1: User authentication

[1777] Input: The user launches the application and enters their email address and password on the login screen.

[1778] Specific action: The user clicks the "Login" button.

[1779] Data processing and calculation: The device sends an HTTP POST request including the entered authentication information to the server.

[1780] Output: The server checks the authentication information against the database, and if successful, generates a session ID and sends it to the terminal. The user terminal receives the session ID and transitions to the main screen.

[1781] Step 2: Obtain user data

[1782] Input: The user accesses the main screen and the terminal sends the session ID to the server.

[1783] Specific operation: The server executes a database query based on the session ID sent by the device.

[1784] Data processing and calculation: The server acquires the user's profile information, hobbies, past usage history, location information, and weather information. Furthermore, the emotion engine analyzes the user's input information and behavioral data to recognize their emotional state.

[1785] Output: The acquired user data, location information, weather information, and emotional state are integrated.

[1786] Step 3: Data analysis and idea generation

[1787] Input: The server sends the integrated data (user information, location information, weather information, and emotion data) acquired in step 2 to the generative AI model.

[1788] Specific operation: The server sends a request to the generative AI model.

[1789] Data processing and calculation: Generative AI models (such as GPT-3 and ChatGPT) generate personalized ideas based on the data they receive. For example, they generate a "homemade bread recipe for a rainy day" based on the conditions "10 AM, rainy, at home, cooking, relaxing."

[1790] Output: The generated ideas are sent to the server.

[1791] Step 4: Submit your idea

[1792] Input: The generated ideas are sent from the server to the user terminal.

[1793] Specific operation: The device analyzes the received ideas and prepares them for display in the application UI.

[1794] Data processing and calculation: The terminal formats the idea content (recipe, steps, ingredients, etc.) into an appropriate format.

[1795] Output: A personalized idea displayed on the user's device, which the user can view and act on.

[1796] Step 5: Get feedback

[1797] Input: Users try out the ideas provided and enter their opinions and ratings in the feedback form.

[1798] Specific Action: The user fills out the feedback form and clicks the "Submit" button.

[1799] Data processing and calculation: The device sends a request including the feedback data and the session ID to the server. The server stores the feedback data and emotion data in a database.

[1800] Output: The feedback data and sentiment data are stored in a database and used for the next idea generation.

[1801] This series of steps allows users to get personalized ideas tailored to their individual situations, and the system continues to evolve through feedback.

[1802] (Application example 2)

[1803] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1804] Conventional systems have difficulty providing personalized ideas that take into account the user's current situation and emotional state. Furthermore, they lack the means to effectively utilize the user's feedback and emotional data on the provided ideas. Therefore, there is a need for a system that can make the shopping experience in brick-and-mortar stores more efficient and comfortable.

[1805] 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.

[1806] In this invention, the server includes means for receiving user input information, means for authenticating the user based on the acquired authentication information, means for acquiring user location information, means for acquiring user hobby information and profile information, means for acquiring weather information and time information, means for generating ideas based on the acquired information and acquired emotional data, means for providing the generated ideas to the user, means for analyzing the user's emotional data, and means for acquiring feedback from the user and updating the database. This makes it possible to provide personalized ideas that take into account the user's current situation and emotional state, and to improve the next idea provision based on the feedback.

[1807] The "means for receiving user input information" is a means for obtaining information input by a user.

[1808] The "means for authenticating a user based on the acquired authentication information" refers to a means for verifying the authentication information provided by the user and authenticating the user.

[1809] The "means for acquiring user location information" is a means for acquiring information about the user's current location.

[1810] The "means for acquiring user hobby information and profile information" refers to a means for acquiring information about the user's interests and personal information.

[1811] The "means for acquiring weather information and time information" is a means for acquiring information about the current weather conditions and time.

[1812] The "means for generating ideas based on acquired information and acquired emotional data" refers to a means for generating personalized ideas based on various acquired information and user emotional data.

[1813] The "means for providing the generated idea to the user" refers to a means for presenting the generated idea to the user.

[1814] The "means for analyzing user emotional data" refers to a means for analyzing data relating to the user's emotional state.

[1815] The "means for obtaining feedback from users and updating the database" refers to a means for receiving feedback provided by users and reflecting that information in the database.

[1816] This system authenticates users based on their input information, analyzes their current situation and emotions, and generates personalized ideas to provide to them. The system is based on communication between the user's device (terminal) and a server. Details of its implementation are explained below.

[1817] Hardware and software used

[1818] User devices: Mobile devices such as smartphones and tablets

[1819] Server: Cloud Server

[1820] Emotion Engine: Software for analyzing user emotions

[1821] Generative AI Model: An artificial intelligence model for generating personalized ideas

[1822] System Configuration and Operation

[1823] 1. User Authentication

[1824] When a user starts the application and enters their email address and password on the login screen, the user's device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, it generates a session ID and sends it to the user's device. The user's device receives this session ID and transitions to the main screen.

[1825] 2. User Data Acquisition

[1826] After a user logs in, the server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID. It also uses an external API to obtain current location and weather information. It then uses an emotion engine to recognize the user's current emotional state based on their input information and behavioral data.

[1827] 3. Data analysis and idea generation

[1828] The server analyzes the acquired user information, location information, weather information, time of day, and emotional data, and sends this data to a generation AI to generate personalized ideas. For example, based on the conditions "10 AM, rainy, at home," the hobby information "cooking," and the emotional state "relaxed," it generates a "homemade bread recipe to enjoy on a rainy day."

[1829] 4. Idea submission

[1830] The generated ideas are sent from the server to the user's device. The user's device displays the received ideas on the application, allowing the user to view the ideas. For example, for a "homemade bread recipe," the application displays the necessary ingredients, steps, and related tips.

[1831] 5. Feedback function

[1832] After trying out the provided ideas, the user enters their opinions and ratings in a feedback form within the application. The user's device then sends this feedback to the server. The server receives the user's feedback along with their emotional data and stores it in a database. The next time ideas are generated, the system will refer to this feedback and emotional data to provide more personalized ideas.

[1833] Specific examples

[1834] For example, consider a case where a user uses an application at home at 10:00 AM and their hobby is cooking. The user launches the application and logs in, and the server verifies the authentication information and returns a session ID. After logging in, the server obtains the user's location and weather information, and determines that the user is currently at home and that it is raining. The emotion engine then confirms that the user is relaxed. Based on this information, the server sends a request to the generation AI to generate a "homemade bread recipe to enjoy on a rainy day."

[1835] The server sends the generated ideas to the user's device, which displays the recipe on the app. After the user has tried the recipe, they enter their opinion in a feedback form, such as "I'd like to know the dessert recipe next time," and the server receives and stores this in a database, along with emotional data.

[1836] Example prompt sentence:

[1837] "10am, raining, at home, cooking, relaxing"

[1838] This system allows users to constantly get new ideas that are relevant to the situation, and the system continues to evolve based on feedback and emotional data.

[1839] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1840] Step 1:

[1841] The user starts the application and enters their email address and password on the login screen. The device sends this authentication information to the server. The server compares it with the registered information in the database, and if authentication is successful, generates a session ID and sends it to the user's device. The device receives this session ID and transitions to the main screen.

[1842] Input: Email address, password

[1843] Data processing: The server checks the registered information in the database

[1844] Output: Session ID

[1845] Step 2:

[1846] After a user logs in, the server retrieves the user's profile information, hobbies, and past usage history from the database based on the user's session ID. The server then obtains the user's current location and weather information using an external API. Furthermore, the server uses an emotion engine to recognize the user's current emotional state based on the user's input information and behavioral data.

[1847] Input: Session ID, location and weather information from external API, input information for emotion recognition

[1848] Data processing: Retrieving information from the database and analyzing emotional states

[1849] Output: Profile information, hobbies, past usage history, location information, weather information, emotional data

[1850] Step 3:

[1851] The server analyzes the acquired user information, location information, weather information, time of day, and emotional data, and sends this data to a generative AI model to generate personalized ideas. For example, based on the conditions "10 AM, rainy, at home," the hobby information "cooking," and the emotional state "relaxed," it generates a "homemade bread recipe to enjoy on a rainy day."

[1852] Input: Profile information, hobby information, past usage history, location information, weather information, emotional data

[1853] Data processing: Generative AI models generate personalized ideas

[1854] Output: Personalized ideas (e.g. homemade bread recipes)

[1855] Step 4:

[1856] The generated ideas are sent from the server to the user's device, where they are displayed on the application, allowing the user to view them. For example, a "homemade bread recipe" might be displayed, showing the necessary ingredients, steps, and related tips.

[1857] Input: Personalized Ideas

[1858] Data processing: Display ideas on the application

[1859] Output: Displayed idea content (e.g. homemade bread recipe, ingredients, steps)

[1860] Step 5:

[1861] After trying out the provided ideas, users enter their opinions and ratings in the feedback form within the application. The device then sends this feedback to the server. The server receives the user's feedback along with their emotional data and stores it in a database. The next time ideas are generated, the system will refer to this feedback and emotional data to provide more personalized ideas.

[1862] Input: Feedback, emotion data

[1863] Data processing: Save to database, improve next idea generation

[1864] Output: Updated database, improved idea generation algorithm

[1865] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1866] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1867] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1868] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1869] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1870] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1871] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1872] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1873] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1874] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1875] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1876] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1877] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1878] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1879] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1880] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1881] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1882] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1883] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1884] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1885] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1886] The following is further disclosed regarding the above embodiment.

[1887] (Claim 1)

[1888] means for receiving user input information;

[1889] a means for authenticating a user based on the acquired authentication information;

[1890] A means for acquiring user location information;

[1891] A means for acquiring user hobby information and profile information;

[1892] means for acquiring weather information and time information;

[1893] A means of generating ideas based on the information obtained;

[1894] A means for providing the generated ideas to a user;

[1895] a means of obtaining user feedback and updating the database;

[1896] A system including:

[1897] (Claim 2)

[1898] The system according to claim 1, further comprising means for generating ideas based on location information and weather information obtained using an external API.

[1899] (Claim 3)

[1900] 2. The system of claim 1, further comprising means for improving subsequent idea generation based on feedback on generated ideas.

[1901] "Example 1"

[1902] (Claim 1)

[1903] means for receiving user input information;

[1904] a means for authenticating a user based on the acquired authentication information;

[1905] A means for acquiring user location information;

[1906] A means for acquiring user hobby information and profile information;

[1907] means for acquiring weather information and time information;

[1908] A means for analyzing the acquired information to generate prompt sentences and sending the data to a generative AI model to generate personalized ideas;

[1909] a means for providing the generated ideas to a user;

[1910] a means of obtaining user feedback and updating the database;

[1911] A system including:

[1912] (Claim 2)

[1913] The system according to claim 1, further comprising a means for generating ideas by sending prompt sentences to a generative AI model based on location information and weather information obtained using an external API.

[1914] (Claim 3)

[1915] 2. The system according to claim 1, further comprising means for improving idea generation by refining the next prompt sentence based on feedback on the generated ideas.

[1916] "Application Example 1"

[1917] (Claim 1)

[1918] means for receiving user input information;

[1919] a means for authenticating a user based on the acquired authentication information;

[1920] A means for acquiring user location information;

[1921] A means for acquiring user hobby information and profile information;

[1922] means for acquiring weather information and time information;

[1923] A means of generating ideas based on the information obtained;

[1924] A means of making cooking suggestions based on the generated ideas;

[1925] A means for providing the generated ideas to a user;

[1926] a means of obtaining user feedback and updating the database;

[1927] A system including:

[1928] (Claim 2)

[1929] The system according to claim 1, further comprising a means for generating ideas based on location information and weather information obtained using an external API, and suggesting dishes based on the ideas.

[1930] (Claim 3)

[1931] The system according to claim 1, further comprising means for improving the next generation of ideas based on feedback on cooking suggestions based on the generated ideas.

[1932] "Example 2: Combining Emotion Engines"

[1933] (Claim 1)

[1934] means for receiving user input information;

[1935] a means for authenticating a user based on the acquired authentication information;

[1936] means for acquiring user location information and weather information;

[1937] A means for acquiring user hobby information and profile information;

[1938] a means for analyzing an emotional state;

[1939] means for recognizing an emotional state of a user by an emotion engine;

[1940] a means for generating personalized ideas based on the acquired information;

[1941] A means for generating ideas using a generative AI model;

[1942] A means for providing the generated ideas to a user;

[1943] a means of obtaining user feedback and updating the database;

[1944] A system including:

[1945] (Claim 2)

[1946] The system according to claim 1, further comprising means for generating ideas based on location information and weather information obtained using an external API.

[1947] (Claim 3)

[1948] 2. The system of claim 1, further comprising means for improving subsequent idea generation based on feedback on generated ideas.

[1949] "Application example 2 when combining emotion engines"

[1950] (Claim 1)

[1951] means for receiving user input information;

[1952] a means for authenticating a user based on the acquired authentication information;

[1953] A means for acquiring user location information;

[1954] A means for acquiring user hobby information and profile information;

[1955] means for acquiring weather information and time information;

[1956] a means for generating ideas based on the acquired information and the acquired emotional data;

[1957] A means for providing the generated ideas to a user;

[1958] means for analyzing user emotion data;

[1959] a means of obtaining user feedback and updating the database;

[1960] A system including:

[1961] (Claim 2)

[1962] The system according to claim 1, further comprising means for generating ideas based on location information and weather information obtained using an external API.

[1963] (Claim 3)

[1964] 2. The system of claim 1, further comprising means for improving subsequent idea generation based on feedback and sentiment data on the generated ideas. [Explanation of symbols]

[1965] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving user input information; a means for authenticating a user based on the acquired authentication information; A means for acquiring user location information; A means for acquiring user hobby information and profile information; means for acquiring weather information and time information; A means of generating ideas based on the information obtained; A means for providing the generated ideas to a user; a means of obtaining user feedback and updating the database; A system including:

2. The system according to claim 1, further comprising means for generating ideas based on location information and weather information obtained using an external API.

3. 2. The system according to claim 1, further comprising means for improving subsequent idea generation based on feedback on the generated ideas.

Images