System

Abstract

To provide a system.SOLUTION: A system includes: means for connecting map data, store data, and personal data on a user with a generative AI and providing the most enjoyable answer through a dialogue with the user; means for proposing contents which the user can enjoy most at a designated point or a current location; feeling recognition means for recognizing feeling of the user and generating personal data on the basis of the feeling; means for proposing an optimum product and service on the basis of feeling data on the user and the personal data; and means for providing navigation in a store. Means for displaying to the user provides the store data that the user can enjoy most on the basis of a prompt sentence.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional information provision systems have difficulty in fully utilizing users' personal data to provide the content that users will enjoy the most. In addition, content suggestions that take into account the user's current location, destination, time of day, weather, and other information are not provided. [Means for solving the problem]

[0005] The present invention connects map data, store data, and the user's personal data with a generative AI, and through dialogue with the user, provides the most enjoyable answer. It also suggests content that the user will enjoy most at a specified location or current location. Furthermore, the generative AI generates personal data based on the user's past behavioral data and preference data, and the content suggestion means suggests content taking into account information such as the user's current location, destination, time of day, and weather. [Brief description of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a process flow of the data processing system in the first embodiment of the first form example. [Figure 12]FIG. 11 is a sequence diagram showing a process flow of the data processing system in the application example 1 of the first embodiment. [Figure 13] FIG. 11 is a sequence diagram showing a process flow of the data processing system in the second embodiment of the second embodiment. [Figure 14] FIG. 13 is a sequence diagram showing a process flow of the data processing system in application example 2 of embodiment 2. [Figure 15] FIG. 13 is a sequence diagram showing a process flow of a data processing system in a third embodiment of the present invention. [Figure 16] FIG. 13 is a sequence diagram showing a process flow of the data processing system in the third application example of the third embodiment. [Figure 17] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 13 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when combined with an emotion engine. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

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

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] [First embodiment]

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

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (e.g., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

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

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

[0023] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0027] "Example 1"

[0028] One embodiment of the present invention is a system that connects map data, store data, and user personal data with a generative AI. This system provides the most enjoyable answer through dialogue with the user. Specifically, when a user asks, "Tell me about a recommended restaurant nearby," the generative AI provides an answer such as, "It seems you like Italian food. Here are some nearby Italian restaurants with good reviews," based on the user's past behavioral data and preference data.

[0029] "Example 2"

[0030] Another embodiment of the present invention is a system that suggests content that a user would enjoy most at a specified location or current location. This system suggests content taking into consideration information such as the user's current location, destination, time of day, and weather. Specifically, when a user asks "I'd like to go to lunch now" based on their current location, the system will make a suggestion such as "There's an Italian restaurant you might like, a five-minute walk from your current location" taking into consideration restaurant information around the current location, the user's preferences, and the current time of day.

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

[0032] "Example 1"

[0033] Step 1: The user asks the system a question. For example, "Tell me about some recommended restaurants nearby."

[0034] Step 2: The generative AI generates personal data based on the user's past behavioral data and preference data.

[0035] Step 3: Based on the personal data generated by the generative AI, the AI ​​provides the answer that the user will enjoy the most. Specifically, it provides an answer such as, "It seems that you like Italian food. Here are some good Italian restaurants nearby."

[0036] "Example 2"

[0037] Step 1: The user asks the system a question. For example, the user might say, "I want to go to lunch now" based on their current location.

[0038] Step 2: The system takes into account restaurant information around the current location, the user's preferences, and the current time of day.

[0039] Step 3: Based on the information taken into account, the system suggests the content the user will enjoy the most. For example, it might suggest, "There's an Italian restaurant you might like within a five-minute walk from your current location."

[0040] Example 1

[0041] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0042] Conventional systems did not adequately collect and analyze data to provide users with the content they would most enjoy, and did not fully utilize user preferences or past behavioral data. As a result, they were unable to make optimal suggestions to users, resulting in a decline in satisfaction. In addition, it was difficult to make suggestions that took into account dynamic information such as the user's current location, time of day, and weather.

[0043] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0044] In this invention, the server includes a means for connecting map data, store data, and user's personal data to the generative AI model and providing the most enjoyable answer through dialogue with the user, a means for suggesting the most enjoyable content for the user at a specified location or current location, a means for receiving the user's inquiry, collecting necessary data, and sending a prompt to the generative AI model, and a means for receiving the answer from the generative AI model and displaying it to the user. This enables optimal suggestions based on the user's preferences and past behavioral data, and further enables suggestions that take into account dynamic information such as the current location, time of day, and weather.

[0045] "Map data" refers to data that includes geographical information, such as location information, topography, roads, and buildings.

[0046] "Store data" refers to data that includes information about each store, such as the store's name, location, business hours, ratings, and menu.

[0047] "User's personal data" refers to data that contains personal information about the user, including past behavioral data, preference data, evaluation data, etc.

[0048] A "generative AI model" is a model that uses artificial intelligence technology to generate and analyze data, and uses natural language processing and machine learning to generate optimal responses to user inquiries.

[0049] A "prompt sentence" is a sentence input to a generative AI model that contains the user's inquiry and related data.

[0050] The term "means for providing an answer that is most enjoyable to a user through dialogue" refers to a method or technology for providing an answer that is most enjoyable to a user through dialogue with the user.

[0051] "Means for suggesting content that a user would enjoy most at a specified location or current location" refers to methods and technologies for suggesting content that a user would enjoy most based on a location specified by the user or the current location.

[0052] "Means of collecting necessary data and sending prompt sentences to the generative AI model" refers to methods and technologies for collecting necessary data based on a user's inquiry and sending prompt sentences to the generative AI model based on that data.

[0053] "Means of receiving an answer from a generative AI model and displaying it to a user" refers to methods or technologies for displaying the answer received from a generative AI model to a user.

[0054] This invention is a system that connects map data, store data, and user personal data with a generative AI model and provides the most enjoyable answer through dialogue with the user. A specific embodiment of this system will be described below.

[0055] Hardware and Software Used

[0056] Server: Database server (MySQL (registered trademark)), generative AI model (e.g., GPT-4 (registered trademark))

[0057] Device: Smartphone (iOS, ANDROID (registered trademark))

[0058] Software: Map application (Google (registered trademark) Maps API), store database (Yelp API)

[0059] Program Processing

[0060] Server Processing

[0061] The server collects map data, store data, and the user's personal data, and connects this data to the generative AI model. When the server receives an inquiry from a user, it generates and sends a prompt sentence to the generative AI model. For example, if a user asks, "Tell me about some recommended restaurants nearby," the server refers to the user's past behavioral data and preference data, and sends a prompt sentence to the generative AI model to generate an answer such as, "It seems you like Italian food. Here are some nearby Italian restaurants with good reviews."

[0062] Terminal handling

[0063] The terminal receives an inquiry from a user and transmits it to the server. The terminal receives the generated answer from the server and displays it to the user. For example, when a user uses a smartphone to input "Tell me about some recommended restaurants nearby," the terminal transmits this inquiry to the server, receives the answer from the server, and displays it on the screen.

[0064] User Actions

[0065] A user uses a terminal to make inquiries to the system. For example, if the user inputs "Tell me about some recommended restaurants nearby," the system will suggest the best restaurant based on the user's past behavioral data and preference data. The user can check the information on the suggested restaurant and visit it.

[0066] Examples

[0067] Examples of prompt statements

[0068] Example prompt text for when a user types "What are some good restaurants near me?":

[0069] User past behavior data:

[0070] Previously visited restaurants: Italian, French

[0071] Rated restaurants: Italian (5 stars), French (4 stars)

[0072] User Preference Data:

[0073] Favorite food: Italian

[0074] Disliked food: Chinese food

[0075] Prompt statement:

[0076] "Users seem to like Italian food. Can you recommend a good Italian restaurant nearby?"

[0077] By sending this prompt text to a generative AI model, the system can provide the user with the best restaurant suggestions.

[0078] Data processing and calculation

[0079] The server retrieves the user's past behavioral data and preference data from a MySQL database, and generates a prompt sentence based on this data. A generative AI model (e.g., GPT-4) analyzes this prompt sentence and generates an optimal answer. The generated answer is sent to the terminal via the server and displayed to the user.

[0080] In this way, it becomes possible to provide optimal suggestions based on the user's preferences and past behavioral data, and also to take into account dynamic information such as current location, time of day, and weather.

[0081] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0082] Step 1:

[0083] A user inputs a query into a terminal.

[0084] A user uses a device such as a smartphone or tablet to input a question to the system. For example, the user might type, "Tell me about some recommended restaurants nearby." The input data is then sent to the device's application.

[0085] Step 2:

[0086] The terminal sends a query to the server.

[0087] The terminal sends the query entered by the user to the server. At this time, the terminal transmits data to the server via the Internet. The input data includes the user ID and current location information. The output of the terminal is the query data that is sent to the server.

[0088] Step 3:

[0089] The server receives the query and collects the required data.

[0090] The server analyzes the inquiry received from the terminal and retrieves the user's past behavioral data and preference data from a database. For example, it retrieves the user's past restaurant visit history and rating data from a MySQL database. The input is the inquiry data from the terminal, and the output is the collected user data.

[0091] Step 4:

[0092] The server sends a prompt to the generative AI model.

[0093] The server generates a prompt sentence based on the collected data and sends it to the generative AI model. The prompt sentence includes the user's preferences and past behavioral data. For example, a prompt sentence may be generated that reads, "The user seems to like Italian food. Can you tell me about a nearby Italian restaurant with a good reputation?" The input is the collected user data, and the output is the generated prompt sentence.

[0094] Step 5:

[0095] The generative AI model generates an answer and returns it to the server.

[0096] The generative AI model generates the best answer for the user based on the prompt it receives. For example, it generates an answer such as, "It seems you like Italian food. Here are some good Italian restaurants nearby." The input is the prompt, and the output is the generated answer.

[0097] Step 6:

[0098] The server sends the generated answer to the terminal.

[0099] The server sends the answer received from the generative AI model to the terminal. At this time, the server converts the answer into an appropriate format and sends it. For example, it converts it into JSON format and sends it. The input is the generated answer, and the output is the data sent to the terminal.

[0100] Step 7:

[0101] The terminal displays the answer to the user.

[0102] The terminal displays the answer received from the server to the user. The user can check the suggested restaurant information on the terminal screen. The input is the answer data from the server, and the output is the information displayed to the user.

[0103] (Application example 1)

[0104] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0105] Conventional store recommendation systems do not fully consider the user's preferences or past behavior data, and can only make general recommendations, making it difficult for users to find stores that they will truly enjoy. In addition, real-time recommendations based on the user's current location are not made, resulting in low user convenience.

[0106] 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.

[0107] In this invention, the server includes a means for connecting map data, store data, and user's personal data to generative AI and providing the most enjoyable answer through dialogue with the user, a means for proposing content that the user will enjoy most at a specified location or current location, and a means for searching for stores within a certain radius from the user's current location and recommending the most suitable store based on the user's preferences. This enables personalized store recommendations that take into account the user's preferences and past behavioral data, making it possible to find the most suitable store in real time.

[0108] "Map Data" means data that includes geographic location information and provides detailed information about specific points or regions.

[0109] "Store data" is data that includes information about each store, providing details such as the store's name, location, opening hours, and ratings.

[0110] "User's personal data" refers to data that contains information about the individual user, including preferences, past behavioral history, current location information, etc.

[0111] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide optimal answers through dialogue with users.

[0112] "Means for providing the most enjoyable answers through dialogue" are means that have the function of providing information and suggestions that users will find most enjoyable through communication with the users.

[0113] "Means for suggesting content that the user will enjoy most at a specified location or current location" refers to means having a function for suggesting content that the user will enjoy based on a location specified by the user or current location information.

[0114] The "means for searching for stores within a certain radius from the user's current location" refers to a means having a function for searching for stores within a certain distance based on the user's current location.

[0115] A "means for recommending the most suitable store based on the user's preferences" is a means having a function for selecting and recommending the most suitable store to a user based on the user's preference data.

[0116] The system for implementing this invention connects map data, store data, and user's personal data with generative AI, and provides the most enjoyable answer through dialogue with the user. A specific embodiment of this system is described below.

[0117] System Configuration

[0118] The system includes a server, a user's smartphone, and a generative AI model. The server manages map data, store data, and the user's personal data, and processes the data in cooperation with the generative AI model. The user's smartphone functions as a dialogue interface, receives input from the user, and communicates with the server.

[0119] Program Processing

[0120] The server processes the data in the following steps:

[0121] 1. Data Acquisition: The server acquires the user's current location information, past behavior data, and preference data, including GPS data, past search history, and rating data.

[0122] 2. Store search: The server uses map services such as Google Maps API to search for stores within a certain radius of the user's current location.

[0123] 3. Data analysis: The server uses a generative AI model to analyze the acquired data and select the most suitable store based on the user's preferences.

[0124] 4. Generation of recommendation results: The server uses a generative AI model to generate a list of stores that are most suitable for the user and sends it to the smartphone.

[0125] Hardware and software used

[0126] Hardware: Smartphones, servers

[0127] Software: Python, Google Maps API, generative AI model

[0128] Examples

[0129] When a user asks their smartphone, "Can you recommend a nearby cafe?" the system operates as follows.

[0130] 1. The server obtains that the user's current location is "Shibuya-ku, Tokyo."

[0131] 2. The server learns that the user's preferences are "cafe" and "Italian."

[0132] 3. The server uses the Google Maps API to search for cafes in Shibuya Ward.

[0133] 4. The server uses a generative AI model to filter the search results to cafes that match the user's preferences.

[0134] 5. The server generates a list of the best cafes and sends it to the smartphone.

[0135] Examples of prompt statements

[0136] "Please tell me about some recommended cafes nearby. I like cafes and Italian food. I'm currently located in Shibuya Ward, Tokyo."

[0137] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0138] Step 1:

[0139] The server acquires the user's current location information.

[0140] Input: GPS data from the user's smartphone

[0141] Output: User's current location (e.g. Shibuya-ku, Tokyo)

[0142] Specific operation: The server receives the GPS data sent from the smartphone and determines the user's current location.

[0143] Step 2:

[0144] The server acquires the user's past behavioral data and preference data.

[0145] Input: User's past search history, rating data, preference data

[0146] Output: User's past behavioral data and preference data (e.g., likes cafes and Italian food)

[0147] Specific operations: The server retrieves the user's past behavioral history and preference data from the database and prepares it for analysis.

[0148] Step 3:

[0149] The server uses the Google Maps API to search for stores within a certain radius of the user's current location.

[0150] Input: User's current location, search radius (e.g. 1500 meters)

[0151] Output: A list of stores as search results

[0152] Specific operation: The server sends a request to the Google Maps API specifying the user's current location and search radius, and receives the returned list of stores.

[0153] Step 4:

[0154] The server uses a generative AI model to analyze the acquired data and select the most suitable store based on the user's preferences.

[0155] Input: Store list, user preference data

[0156] Output: A list of optimal stores that match the user's preferences

[0157] Specific operation: The server inputs the store list and user preference data into the generative AI model, and the AI ​​model filters the most suitable stores based on the preferences.

[0158] Step 5:

[0159] The server uses a generative AI model to generate a list of stores that are best suited for the user and sends it to the user's smartphone.

[0160] Input: Optimal store list

[0161] Output: A list of recommended stores displayed on the user's smartphone

[0162] Specific operation: The server sends the optimal store list generated by the generative AI model to the user's smartphone and displays it on the smartphone screen.

[0163] Example 2

[0164] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0165] Conventional content suggestion systems have difficulty in suggesting optimal content by comprehensively considering various information such as the user's current location, preferences, time of day, and weather. As a result, it has been difficult to provide content that users can truly enjoy, and improving user satisfaction has been an issue.

[0166] 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.

[0167] In this invention, the server includes a means for acquiring user's current location information, a means for acquiring weather information, a means for acquiring time zone information, a means for acquiring user's preference data, a means for analyzing the acquired data, a means for generating content using a generative AI model, a means for transmitting the generated content to the user's terminal, and a means for displaying the content on the user's terminal. This makes it possible to propose content that the user will enjoy most by comprehensively considering the user's various information.

[0168] The "means for acquiring the user's current location information" is a function for acquiring location information from the user's terminal.

[0169] "Means for acquiring weather information" is a function for acquiring current weather data from an external weather information service.

[0170] The "means for acquiring time zone information" is a function for acquiring current time zone data from a system clock or an external time information providing service.

[0171] "Means for acquiring user preference data" refers to a function for acquiring a user's past behavior history and preference data from a database.

[0172] The "means for analyzing acquired data" refers to a data analysis function for identifying the most suitable content for a user based on various collected data.

[0173] A "means for generating content using a generative AI model" is a function for using a generative AI model to generate content to be suggested to users.

[0174] The "means for transmitting the generated content to the user's terminal" is a function for transmitting the generated content to the user's terminal via a network.

[0175] The "means for displaying content on a user's terminal" is a function for displaying received content on the screen of the user's terminal.

[0176] This invention is a system that proposes optimal content by comprehensively considering information such as the user's current location, preferences, time of day, weather, etc. This system is realized using a server, a terminal, and a generative AI model.

[0177] First, the server uses the GPS function of the terminal to obtain the user's current location information. Specifically, the terminal uses the built-in GPS module to obtain the user's current location and transmits the information to the server.

[0178] The server then uses an external weather information service (e.g., OpenWeatherMap API) to obtain weather information. The server retrieves current weather data based on the user's current location.

[0179] Additionally, the server uses the system clock to obtain time zone information. The server obtains the current time zone data and combines it with other data for analysis.

[0180] The server uses a database (e.g., MySQL) to obtain user preference data. The server obtains the user's past behavior history and preference data from the database and uses this for analysis.

[0181] The server performs data analysis based on the various data acquired. This analysis uses a machine learning model (e.g., TENSORFLOW (registered trademark)). The server identifies the most suitable content for the user based on the collected data.

[0182] The server then uses a generative AI model (e.g., GPT-3 (registered trademark)) to generate content to suggest to the user, taking into account the user's current location, preferences, time of day, weather, etc.

[0183] The generated content is sent from the server to the user's device. The device displays the received content to the user. For example, the application screen may display, "There's an Italian restaurant you might like, just a five-minute walk from your current location."

[0184] Examples:

[0185] When a user inputs "I want to go to lunch now" into the terminal, the following specific actions are taken:

[0186] 1. The user opens the application on their smartphone and types, "I want to go to lunch now."

[0187] 2. The device uses its built-in GPS function to obtain its current location, "Shibuya Ward, Tokyo."

[0188] 3. The server uses the OpenWeatherMap API to obtain weather information such as "sunny, temperature 25 degrees."

[0189] 4. The server obtains the time zone information for "12:30 PM" from the system clock.

[0190] 5. The server retrieves the user's preference data, such as "I like Italian food," from the database.

[0191] 6. The server uses TensorFlow to identify "Italian restaurants within a 5-minute walk from the current location."

[0192] 7. The server uses GPT-3 to generate suggestions such as, "There's an Italian restaurant you might like within a five-minute walk from your current location."

[0193] 8. The server sends the generated proposal to the user's device.

[0194] 9. The device will display the message on the application screen: "There's an Italian restaurant you might like, just a five-minute walk from your current location."

[0195] In this way, it is possible to comprehensively consider the user's various information and propose content that the user will enjoy most.

[0196] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0197] Step 1:

[0198] The user types a prompt into the terminal.

[0199] Specific actions: The user opens an application on their smartphone and types, "I want to go to lunch now."

[0200] Input: User prompt

[0201] Output: The prompt is sent to the terminal.

[0202] Step 2:

[0203] The device obtains location information.

[0204] Specific operation: The device uses the built-in GPS function to obtain the user's current location information. For example, the device obtains the current location information of "Shibuya-ku, Tokyo."

[0205] Input: GPS data

[0206] Output: Current location is saved on the device

[0207] Step 3:

[0208] The device sends its current location information to the server.

[0209] Specific operation: The device sends the obtained location information to the server.

[0210] Input: Current location

[0211] Output: Current location is sent to the server

[0212] Step 4:

[0213] The server obtains weather information.

[0214] Specific operation: The server obtains weather information for the user's current location using an external weather information service (e.g., OpenWeatherMap API). For example, it obtains weather information such as "sunny, temperature 25 degrees."

[0215] Input: Current location

[0216] Output: Weather information is stored on the server.

[0217] Step 5:

[0218] The server obtains the time zone information.

[0219] Specific behavior: The server uses the system clock to obtain the current time zone information, for example, "12:30 PM."

[0220] Input: System clock data

[0221] Output: Time zone information is saved on the server

[0222] Step 6:

[0223] The server obtains the user's preference data.

[0224] Specific operation: The server retrieves the user's past behavior history and preference data from a database (e.g., MySQL). For example, it retrieves preference data such as "I like Italian food."

[0225] Input: User ID

[0226] Output: Preference data is stored on the server

[0227] Step 7:

[0228] The server analyzes the obtained data.

[0229] Specific operation: The server uses a machine learning model (e.g. TensorFlow) to identify the most suitable content for the user based on the collected current location information, weather information, time zone information, and preference data. For example, it identifies "Italian restaurants within a 5-minute walk from the current location."

[0230] Input: Current location, weather, time zone, preference data

[0231] Output: The analysis results are stored on the server.

[0232] Step 8:

[0233] The server generates content using a generative AI model.

[0234] Specific operation: The server uses a generative AI model (e.g. GPT-3) to generate content to suggest to the user. For example, it generates suggestions such as "There is an Italian restaurant you might like within a five-minute walk from your current location."

[0235] Input: Analysis results

[0236] Output: The generated content is saved on the server.

[0237] Step 9:

[0238] The server transmits the generated content to the terminal.

[0239] Specific operation: The server sends the generated proposal to the user's device.

[0240] Input: Generated content

[0241] Output: Content is sent to the device

[0242] Step 10:

[0243] The terminal displays the content to the user.

[0244] Specific operation: The device displays the received content to the user. For example, the application screen displays "There is an Italian restaurant you might like, just a five-minute walk from your current location."

[0245] Input: Generated content

[0246] Output: The content is displayed on the device screen.

[0247] (Application example 2)

[0248] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0249] Conventional content recommendation systems do not take into consideration the user's current location, time of day, weather, and preferences, making it difficult to provide users with the content they would most enjoy. In addition, there is a lack of interactive recommendation systems using smartphones or smart glasses, which makes it difficult for users to find the best restaurants and delivery options in real time.

[0250] 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.

[0251] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for suggesting the most enjoyable content for the user at a specified location or current location, a means for suggesting optimal restaurants and delivery options taking into consideration the user's current location, time zone, weather, and preferences, and a means for making suggestions based on the content of a question posed by the user using a smartphone or smart glasses. This enables the user to find the optimal restaurants and delivery options in real time.

[0252] "Map Data" means data that includes geographic location information and is used to identify a User's current location and destination.

[0253] "Store data" refers to data that includes information about stores such as restaurants and retail outlets, providing details such as store location, opening hours, and menu.

[0254] "User's personal data" refers to data that includes personal information such as the user's preferences and past behavioral history, and is used to make optimal suggestions to the user.

[0255] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide optimal answers through dialogue with users.

[0256] "Means for providing the most enjoyable answers through dialogue" refers to methods and technologies for providing content or information that users will find most enjoyable through dialogue with the user.

[0257] "Means for suggesting content that a user will enjoy most at a specified location or current location" refers to a method or technology for suggesting content that a user will enjoy based on a location specified by the user or the current location.

[0258] "Means for suggesting optimal restaurants and delivery options taking into consideration a user's current location, time of day, weather, and preferences" refers to methods and technologies for suggesting optimal restaurants and delivery options based on information such as a user's current location, time of day, weather, and preferences.

[0259] "Means for making suggestions based on questions posed by a user using a smartphone or smart glasses" refers to a method or technology for making optimal suggestions based on questions posed by a user using a smartphone or smart glasses.

[0260] A system for implementing this invention connects map data, store data, and user personal data with generative AI, and provides the most enjoyable answer through dialogue with the user. The system includes a means for suggesting the most enjoyable content for the user at a specified location or current location. The system also includes a means for suggesting optimal restaurants and delivery options taking into account the user's current location, time of day, weather, and preferences, and a means for making suggestions based on the content of a question posed by the user using a smartphone or smart glasses.

[0261] The server first obtains the user's personal data, including the user's preferences and past behavioral history. Next, the server obtains information such as the user's current location, time of day, and weather. Based on this information, the server uses generative AI to suggest optimal restaurants and delivery options.

[0262] Specifically, the server performs the following process. First, it obtains the user's current location and compares map data with store data. Next, it creates a list of the user's favorite restaurants based on the user's preference data. It also takes into account the current time of day and weather to make optimal suggestions. For example, if it is raining, it will give priority to delivery options.

[0263] The system makes suggestions based on questions posed by users using a smartphone or smart glasses. If a user says, "I'd like to go to lunch now," the system will suggest the most suitable restaurant, taking into account the user's current location, preferences, and time of day. For example, the system might suggest, "There's an Italian restaurant you might like, just a five-minute walk from your current location. They also offer delivery."

[0264] The hardware used is a smartphone or smart glasses, and the software uses Python. API requests are used to obtain data, and the standard Python library is used to process and calculate the data.

[0265] As a concrete example, the prompt sentence to be input to the generative AI model is shown below.

[0266] User ID: user123

[0267] Current location: Tokyo Station

[0268] Preferences: Italian, Price range: Medium, Allergies: Nuts

[0269] Weather: Sunny

[0270] If you enter this prompt, you will get the following suggestions:

[0271] There is an Italian restaurant you might like within a 5-minute walk from your current location, and they also offer delivery.

[0272] This way, users can find the best dining and delivery options in real time.

[0273] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0274] Step 1:

[0275] The server obtains the user's personal data. Specifically, it obtains data such as the user's preferences and past behavioral history from a database. The input is the user ID, and the output is the user's personal data. This data is used for subsequent suggestions.

[0276] Step 2:

[0277] The server obtains the user's current location. Specifically, it obtains the current location information using the GPS function of the smartphone or smart glasses. The input is the location information from the user's device, and the output is the coordinate data of the current location. This data is used to match map data with store data.

[0278] Step 3:

[0279] The server obtains weather information for the current location. Specifically, it obtains weather data for the current location using a weather API. The input is the coordinate data of the current location, and the output is weather information. This data influences the content of the suggestions.

[0280] Step 4:

[0281] The server compares the map data with the store data. Specifically, it obtains information about restaurants in the vicinity of the current location. The input is the coordinate data of the current location and the store data, and the output is a list of restaurants in the vicinity. This list is compared with the user's preference data.

[0282] Step 5:

[0283] The server filters the restaurant list based on the user's preference data. Specifically, it narrows down the list by considering the user's preferred cuisine, price range, and allergy information. The input is the user's preference data and a list of nearby restaurants, and the output is the filtered restaurant list.

[0284] Step 6:

[0285] The server selects the best restaurant considering the current time slot. Specifically, it makes suggestions considering business hours and congestion status. The input is the filtered restaurant list and the current time slot information, and the output is the best restaurant suggestions.

[0286] Step 7:

[0287] The server makes suggestions to the user through a smartphone or smart glasses. Specifically, the server makes suggestions based on the user's question, such as "I want to go to lunch now." The input is the user's question and a suggestion for the most suitable restaurant, and the output is a suggested message to the user.

[0288] This way, users can find the best dining and delivery options in real time.

[0289] Furthermore, an emotion engine that estimates the emotion of the user may be combined. That is, the identification processing unit 290 may estimate the emotion of the user using the emotion identification model 59, and perform identification processing using the emotion of the user.

[0290] "Example 1"

[0291] In one embodiment of the present invention, the generative AI includes an emotion engine. The emotion engine recognizes the emotion of the user and generates personal data based on the emotion. Specifically, when the user says, "Today is fun!", the emotion engine recognizes that the user is in a happy mood and provides the information to the generative AI as personal data. Based on the personal data, the generative AI provides an answer that enhances the user's happy mood. For example, it provides an answer such as, "That's great! There's a newly opened amusement park nearby. Why don't you have even more fun there?"

[0292] "Example 2"

[0293] In another embodiment of the present invention, the emotion engine recognizes the emotion of the user and suggests content based on the emotion. Specifically, if the user says, "I feel depressed today," the emotion engine recognizes that the user is depressed and provides this information to the content suggestion means. Based on this information, the content suggestion means suggests content that will lighten the user's mood. For example, it makes a suggestion such as, "Are you okay? There's a relaxing cafe nearby. Why don't you take a break there?"

[0294] The process flow of each embodiment will be described below.

[0295] "Example 1"

[0296] Step 1: The user says, "Today is fun!"

[0297] Step 2: The emotion engine recognizes from the user's comments that they are in a happy mood.

[0298] Step 3: The emotion engine provides the information to the generative AI as personal data. Step 4: The generative AI uses the personal data to provide an answer that will make the user feel even more happy.

[0299] "Example 2"

[0300] Step 1: The person says, "I'm feeling down today."

[0301] Step 2: The emotion engine recognizes that something is depressed from the user's statement.

[0302] Step 3: The emotion engine provides the information to the content suggestion engine.

[0303] Step 4: The content suggestion tool uses the information to suggest content that will lighten the user's mood.

[0304] Example 1

[0305] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal".

[0306] Conventional information provision systems have had the problem of being difficult to propose optimal content that fully takes into account the user's preferences and emotions. In particular, it was difficult to increase user satisfaction because it was not possible to generate personal data based on the user's past behavioral data and preference data, or to analyze the user's emotions and provide answers based on them.

[0307] 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.

[0308] In this invention, the server includes a means for connecting map data, store data, and user's personal data to the generative AI and providing the most enjoyable answer through dialogue with the user, a means for proposing content that the user will enjoy most at a specified location or current location, a means for analyzing the user's question and collecting necessary data, a means for integrating the collected data and sending it to the generative AI, a means for the generative AI to generate an answer, a means for the emotion engine to analyze the user's emotions and adjust the answer based on the emotion data, and a means for providing the answer to the user. This makes it possible to propose optimal content that takes the user's preferences and emotions into consideration.

[0309] "Map Data" is data that includes geographic location information and points on a map.

[0310] "Store data" refers to data including information such as the store's name, location, ratings, and business hours.

[0311] "User's personal data" refers to data related to an individual, such as a user's past behavioral history, preferences, and emotions.

[0312] "Generative AI" is an artificial intelligence system that generates optimal answers based on user input.

[0313] "Means for providing the most enjoyable answers through dialogue" refers to methods and technologies for providing information and suggestions that users will find most enjoyable through dialogue with the user.

[0314] "Means for suggesting content that a user will enjoy most at a specified location or current location" refers to a method or technology for suggesting content that a user will enjoy based on a location specified by the user or the current location.

[0315] The "means for analyzing a user's question and collecting necessary data" refers to a method or technique for analyzing a user's question and collecting data necessary to answer the question.

[0316] "Means of integrating collected data and transmitting it to the generative AI" refers to the methods or technologies for integrating collected data and transmitting that data to the generative AI.

[0317] "Means by which a generative AI generates an answer" refers to the methods or technologies that a generative AI uses to generate optimal answers based on collected data.

[0318] "Means for the emotion engine to analyze the user's emotions and adjust the response based on the emotion data" refers to methods or technologies for the emotion engine to analyze the user's emotions and adjust the response generated by the generative AI based on the emotion data.

[0319] "Means of providing an answer to a user" refers to the methods or technologies for providing the answer generated by the generative AI to the user.

[0320] This invention is a system that connects map data, store data, and user's personal data with generative AI and provides the most enjoyable answer through dialogue with the user. This system includes a server, a terminal, generative AI, and an emotion engine.

[0321] The server integrates map data, store data, and user personal data and connects to generative AI. Specifically, the server obtains map data using Google Maps API and store data using Yelp API. It also collects users' past behavioral history and preference data and integrates them as personal data.

[0322] The terminal receives questions from the user in the form of voice or text. For example, if the user asks, "Can you recommend a restaurant nearby?", the terminal converts the question into text using voice recognition technology and sends it to the server.

[0323] The server uses natural language processing (NLP) techniques to analyze the received question and understand the intent of the question. It then collects and integrates the necessary data. For example, it can integrate information that the user likes Italian restaurants with a list of nearby Italian restaurants.

[0324] Generative AI generates optimal answers based on the integrated data, such as "It seems you like Italian food. Here are some good Italian restaurants nearby."

[0325] The emotion engine analyzes the user's emotions. For example, if the user says, "Today is fun!", the emotion engine recognizes that the user is in a happy mood. The server provides the emotion data obtained from the emotion engine to the generative AI.

[0326] Generative AI adjusts responses based on emotion data. For example, if a user is in a good mood, it generates responses that will enhance that mood. For example, it generates a response like, "That's great! There's a new amusement park nearby. Why don't you go there and have even more fun?"

[0327] The device provides the user with the answer generated by the generative AI, for example by voice or text.

[0328] As an example, consider the following prompt:

[0329] "Tell me about some recommended restaurants nearby."

[0330] "Today is fun!"

[0331] "Tell me about the new amusement park."

[0332] In this way, the server, terminal, generative AI, and emotion engine work together to create a system that provides users with the most appropriate information.

[0333] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0334] Step 1:

[0335] Receive user questions

[0336] The device receives questions from the user via voice or text. For example, the user might ask, "Can you recommend some nearby restaurants?"

[0337] Input: The user's voice or text question.

[0338] Output: The text data of the question.

[0339] Specific operation: The device uses voice recognition technology to convert the voice into text and sends the text data to the server.

[0340] Step 2:

[0341] Parse the question

[0342] The server analyzes the received question using natural language processing (NLP) technology to understand the intent of the question and extract the necessary information.

[0343] Input: The text data of the question.

[0344] Output: The question intent and the information required.

[0345] What happens: The server uses NLP algorithms to analyze the keywords and context of the question and understand that you're looking for a restaurant.

[0346] Step 3:

[0347] Collect the necessary data

[0348] The server collects map data, store data, and users' personal data.

[0349] Input: The question you want to ask and the information you need.

[0350] Output: Map data, store data, user personal data.

[0351] Specific operation: The server retrieves map data using the Google Maps API, retrieves store data using the Yelp API, and retrieves the user's past behavior history and preference data from the database.

[0352] Step 4:

[0353] Integrate data and send it to generative AI

[0354] The server consolidates the collected data and sends it to the generative AI.

[0355] Input: map data, store data, user personal data.

[0356] Output: The consolidated data.

[0357] What it does: The server combines the user's preference for Italian restaurants with a list of nearby Italian restaurants and sends it to the generative AI.

[0358] Step 5:

[0359] Generative AI generates answers

[0360] Generative AI generates optimal answers based on the integrated data.

[0361] Input: Consolidated data.

[0362] Output: The best answer.

[0363] How it works: The generative AI analyzes the integrated data and generates answers such as, "It seems you like Italian food. Here are some good Italian restaurants nearby."

[0364] Step 6:

[0365] Emotion engine analyzes user emotions

[0366] The server uses an emotion engine to analyze the user's emotions.

[0367] Input: User statements and behavior data.

[0368] Output: User emotion data.

[0369] Specific operation: When a user says, "Today is fun!", the server uses the emotion engine to recognize that the user is in a happy mood.

[0370] Step 7:

[0371] Tailor your answers based on sentiment data

[0372] The generative AI tailors its answers based on emotional data.

[0373] Input: User emotion data.

[0374] Output: The adjusted answer.

[0375] Specific behavior: If the user is in a good mood, the generative AI generates answers that will make the user feel even better. For example, it generates answers like, "That's great! There's a new amusement park nearby. Why don't you go there and have even more fun?"

[0376] Step 8:

[0377] Providing answers for users

[0378] The terminal provides the user with the answers generated by the generative AI.

[0379] Input: The adjusted answer.

[0380] Output: The answer provided to the user.

[0381] Specific operation: The device provides the user with the answer generated by the generative AI via voice or text.

[0382] (Application example 1)

[0383] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0384] Conventional systems were unable to fully utilize users' emotions and personal data, making it difficult to provide users with optimal content and services. In addition, in-store navigation and real-time promotion information were insufficient, making it impossible to improve users' shopping experience. This could lead to a decrease in user satisfaction and a negative impact on store sales.

[0385] 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.

[0386] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for proposing content that the user will enjoy most at a specified location or current location, an emotion recognition means for recognizing the user's emotion and generating personal data based on the emotion, a means for proposing optimal products and services based on the user's emotion data and personal data, and a means for providing in-store navigation. This makes it possible to provide optimal content and services by utilizing the user's emotion and personal data, and to provide in-store navigation and real-time promotional information.

[0387] "Map Data" means data containing geographical information and used to indicate specific locations and routes.

[0388] "Store Data" means data that includes information about a particular store, such as details about the store's location, opening hours, and the products and services it offers.

[0389] "User's personal data" refers to data that contains personal information about the user, such as past behavioral history, preferences, and emotional state.

[0390] "Generative AI" is an artificial intelligence system that generates optimal answers and suggestions based on user input and data.

[0391] The term "means for providing an answer that is most enjoyable through dialogue" refers to a method or technology for providing an answer that is most enjoyable to a user through dialogue with the user.

[0392] "Means for suggesting content that a user would enjoy most at a specified location or current location" refers to methods and technologies for suggesting content that a user would enjoy most based on a location specified by the user or the current location.

[0393] "Emotion recognition means" refers to a method or technology for recognizing a user's emotion and generating data based on that emotion.

[0394] "Means for suggesting optimal products and services" refers to methods and technologies for suggesting optimal products and services to users based on their emotional data and personal data.

[0395] "Means for providing in-store navigation" refers to methods and technologies for providing a route for a user to reach a destination within a store.

[0396] As an embodiment of the present invention, a smart shopping assistant system will be described as an example. This system connects map data, store data, and user's personal data with generative AI, and provides the most enjoyable answer through dialogue with the user.

[0397] Hardware and Software Used

[0398] Smartphone: A device on which users install and use applications.

[0399] EmotionEngine: An engine for recognizing the user's emotions.

[0400] Recommendation Engine: An engine that suggests optimal products and services based on users' personal data.

[0401] NavigationSystem: A system that provides in-store navigation.

[0402] Data processing and calculation

[0403] 1. Emotion recognition: The EmotionEngine analyzes text entered by the user into their smartphone (e.g., "Today is fun!") and generates emotion data. The EmotionEngine uses natural language processing technology to extract emotions from the text.

[0404] 2. Recommendation Generation: Based on emotional and personal data, the Recommendation Engine will suggest optimal products and services. The Recommendation Engine uses machine learning algorithms to analyze user preferences and past behavioral data to make optimal suggestions.

[0405] 3. Navigation: Based on the current location and destination, the Navigation System provides the optimal route. The Navigation System uses map data to calculate the route within the store and guides the customer.

[0406] Examples

[0407] When a user types "Today is fun!" into their smartphone, the system behaves as follows:

[0408] 1. The EmotionEngine recognizes the emotion of "fun" and generates emotional data.

[0409] 2. Based on emotional data and personal data, the Recommendation Engine will suggest products and services that will enhance the user's enjoyment. For example, it will make a suggestion such as, "We have a new game corner. Why not spend your time there even more enjoyably?"

[0410] 3. The Navigation System calculates the optimal route from the user's current location to the game corner and guides them there.

[0411] Examples of prompt statements

[0412] "Today is fun!"

[0413] "Tell me some recommended products near me"

[0414] "Tell me the recommended route within this store."

[0415] In this way, the smart shopping assistant system can utilize users' emotions and personal data to provide an optimal shopping experience.

[0416] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0417] Step 1:

[0418] A user inputs text into a smartphone, for example, "Today is fun!" The input text is sent to the system.

[0419] Step 2:

[0420] The server receives the entered text and sends it to EmotionEngine. EmotionEngine uses natural language processing technology to extract emotions from the text. Specifically, it generates emotion data for "fun" from the text "Today is fun!"

[0421] Step 3:

[0422] The server receives emotion data from the Emotion Engine. It then sends the emotion data and the user's personal data to the Recommendation Engine. The Recommendation Engine uses machine learning algorithms to analyze the emotion data and personal data and recommend optimal products and services. For example, it generates a suggestion such as, "We have a new game corner. Why not spend some more time there and have some fun?"

[0423] Step 4:

[0424] The server receives the recommendation data from the RecommendationEngine. It then sends the current location and the suggested destination (e.g., game corner) to the NavigationSystem. The NavigationSystem uses the map data to calculate the optimal route within the store.

[0425] Step 5:

[0426] The server receives the route data from the Navigation System. It then combines the proposed data with the route data and sends it to the user. The user's smartphone displays the in-store navigation along with the message, "We have a new game corner. Why not have even more fun there? Here is the route."

[0427] Step 6:

[0428] The user checks the navigation on the smartphone and follows the suggested route to the destination. The user can then enjoy the suggested products and services.

[0429] Example 2

[0430] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0431] Conventional content suggestion systems have difficulty in suggesting optimal content by comprehensively considering the user's current location, preferences, and emotions. In addition, they are unable to reflect dynamic elements such as weather information and time of day in real time, which means that they are unable to make optimal suggestions for users. Furthermore, they lack the functionality to recognize the user's emotions and suggest content based on them, which makes it difficult to increase user satisfaction.

[0432] 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.

[0433] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for suggesting the most enjoyable content for the user at a specified location or current location, a location information acquisition means for acquiring the user's current location, a weather information acquisition means for acquiring weather information for the current location, and an emotion recognition means for recognizing the user's emotion and suggesting content based on the emotion. This makes it possible to comprehensively consider various factors such as the user's current location, preferences, emotion, weather information, and time of day, and suggest optimal content in real time.

[0434] "Map data" refers to data that includes geographical information, and provides information on locations, routes, topography, and the like.

[0435] "Store data" refers to data that includes information about a specific store, such as the store's location, opening hours, menu, and ratings.

[0436] "User personal data" means data that contains information about an individual User, such as preferences, past behavioral history, and emotional state.

[0437] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data, and suggests optimal content based on users' personal data.

[0438] "Location information acquisition means" refers to technology or devices for identifying a user's current location, such as using GPS or Wi-Fi location information.

[0439] A "weather information acquisition means" is a technology or device for acquiring weather information for a current location, and utilizes a weather information API, etc.

[0440] An "emotion recognition means" is a technology or device for recognizing a user's emotions, and utilizes voice recognition or natural language processing technology.

[0441] "Content suggestion means" refers to technologies or devices for suggesting optimal content based on user information, and utilizes generative AI.

[0442] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0443] This invention is a system that suggests the most enjoyable content to users, taking into consideration information such as their current location, destination, time of day, weather, etc. It also has a function to recognize the user's emotions and suggest content based on those emotions.

[0444] Hardware and Software Used

[0445] Hardware: Servers, terminals (smartphones, tablets, etc.)

[0446] Software: Generative AI models, emotion engines, location services, weather APIs

[0447] System Configuration

[0448] 1. A means to connect map data, store data, and user personal data with generative AI

[0449] The server connects map data, store data, and users' personal data to the generative AI, and generates optimal content based on this data.

[0450] 2. A way to suggest content that users will enjoy most at a given location or their current location

[0451] The server takes into account information such as the user's current location, destination, time of day, and weather to suggest the content that the user will enjoy most.

[0452] 3. Location information acquisition means for acquiring the user's current location

[0453] The device uses its built-in GPS module to obtain the user's current location and transmits that information to the server.

[0454] 4. Weather information acquisition method to acquire weather information for the current location

[0455] The server obtains weather information for the current location using a weather information API (e.g., OpenWeatherMap API).

[0456] 5. Emotion recognition means to recognize a user's emotions and suggest content based on those emotions

[0457] The device uses voice recognition technology to convert the user's speech into text, and the emotion engine analyzes the text. The emotion engine uses natural language processing technology to recognize the user's emotions.

[0458] Examples and prompts

[0459] Example 1: When a user enters "I want to go to lunch now"

[0460] Prompt: "If a user says, 'I want to go to lunch now,' based on their current location, the system will take into account nearby restaurant information, the user's preferences, and the current time of day and suggest, 'There's an Italian restaurant you might like, just a five-minute walk from your current location.'"

[0461] Example 2: When a user says, "I'm feeling down today"

[0462] Prompt: "If the user says, 'I'm feeling down today,' the emotion engine will recognize that the user is feeling down and provide that information to the content suggestion tool. The content suggestion tool will then use that information to suggest, 'Are you okay? There's a nice, relaxing cafe nearby. Why don't you take a break there?'"

[0463] In this way, the system can suggest the most suitable content based on the user's location and emotions.

[0464] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0465] Step 1:

[0466] User Input

[0467] How it works: A user uses a device to enter information such as current location, destination, time of day, and weather.

[0468] Input: A user opens an app on their smartphone and speaks or texts, "I want to go to lunch now."

[0469] Data processing: The device converts the voice into text and sends the text data to the server.

[0470] Output: The text data sent to the server.

[0471] Step 2:

[0472] Obtaining location information

[0473] How it works: The device uses location services to obtain the user's current location.

[0474] Input: Your device uses the built-in GPS module to determine your location.

[0475] Data processing: The latitude and longitude information acquired by the device is sent to the server.

[0476] Output: The latitude and longitude of your current location sent to the server.

[0477] Step 3:

[0478] Obtaining weather information

[0479] How it works: The server uses the weather information API to obtain weather information for the current location.

[0480] Input: The server makes a request to the weather API with the current latitude and longitude data.

[0481] Data processing: Analyze the weather data obtained from the weather information API and extract information such as temperature, probability of precipitation, and wind speed.

[0482] Output: Parsed weather data.

[0483] Step 4:

[0484] Content Suggestions

[0485] How it works: The server uses a generative AI model to generate content that takes into account the user's preferences and the current time of day.

[0486] Input: The server uses the user's past behavior and preference data, current latitude and longitude, weather data, and time zone information.

[0487] Data processing: Generative AI models analyze this data and generate optimal content.

[0488] Output: A suggestion such as "There's an Italian restaurant you might like just a 5-minute walk from your current location."

[0489] Step 5:

[0490] Emotion recognition

[0491] How it works: If a user says, "I'm feeling down today," the device uses an emotion engine to recognize that emotion.

[0492] Input: User's voice input.

[0493] Data processing: The device uses voice recognition technology to convert the user's speech into text, and the emotion engine analyzes the text.

[0494] Output: Emotional information "I feel depressed".

[0495] Step 6:

[0496] Sentiment-based content suggestions

[0497] How it works: The server uses information from the emotion engine to suggest content that will lighten the user's mood.

[0498] Input: Emotional information "feeling depressed".

[0499] Data processing: The server searches for relaxing places and activities based on emotional information and generates optimal suggestions.

[0500] Output: A suggestion such as "Are you okay? There's a nice, relaxing cafe nearby. How about we take a break there?"

[0501] (Application example 2)

[0502] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0503] Conventional content suggestion systems can suggest content based on a user's current location and preferences, but they have the problem of being unable to make suggestions that take into account dynamic factors such as the user's emotions and the weather. In addition, there is a lack of personalized suggestions that match the user's mood, making it difficult to increase user satisfaction. Furthermore, in the field of food delivery, there is a demand for optimal restaurant and cafe suggestions that take into account the user's current emotions and weather.

[0504] 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.

[0505] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for suggesting content that the user will enjoy most at a specified location or current location, a means for recognizing the user's emotions and suggesting content based on the emotions, a means for suggesting the most suitable restaurant or cafe in consideration of the user's current location, destination, time zone, weather, and other information, and a means for suggesting a personalized menu based on the user's preferences and past order history. This makes it possible to suggest optimal content taking into account dynamic factors such as the user's emotions and weather.

[0506] "Map Data" means data that includes geographic location information and is used to identify a User's current location and destination.

[0507] "Store data" refers to data including information about stores such as restaurants and cafes, and provides information such as the store's location, type, and opening hours.

[0508] "User personal data" refers to data that includes information about an individual, such as a user's preferences, past behavioral history, or emotional state.

[0509] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide optimal answers through dialogue with users.

[0510] "Means for providing the most enjoyable answers through dialogue" refers to means that have the function of providing the user with content or information that the user will enjoy most through communication with the user.

[0511] The "means for suggesting content that the user will enjoy most at a specified location or current location" refers to a means having a function for suggesting content that the user will enjoy based on the user's current location or a specified location.

[0512] "Means for recognizing a user's emotions and suggesting content based on those emotions" refers to means having a function for detecting the emotional state of a user and suggesting optimal content according to those emotions.

[0513] "A means for suggesting the most suitable restaurant or cafe by taking into consideration information such as the user's current location, destination, time of day, and weather" refers to a means that has the function of suggesting the most suitable restaurant or cafe based on information such as the user's current location, destination, time of day, and weather.

[0514] "Means for proposing personalized menu items based on a user's preferences and past order history" refers to means having a function for proposing individually optimized menu items taking into account a user's preferences and past order history.

[0515] The system for implementing this invention is composed of three main components: a server, a terminal, and a user. The server has a means for connecting map data, store data, and the user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user. It also includes a means for proposing content that the user will enjoy most at a specified location or current location, a means for recognizing the user's emotions and proposing content based on those emotions, a means for proposing the most suitable restaurant or cafe considering the user's current location, destination, time of day, weather, and other information, and a means for proposing a personalized menu based on the user's preferences and past order history.

[0516] Hardware and Software Used

[0517] Hardware: Smartphones, servers

[0518] Software: Python, Weather API, Restaurant Information API, Generative AI model

[0519] Data processing and calculation

[0520] The server first uses map data to obtain the user's current location. It then uses the Weather API to obtain weather information for the current location. This makes it possible to make suggestions that take into account the weather conditions when the user goes out. It also uses the restaurant information API to obtain information on restaurants and cafes near the user's current location.

[0521] The generative AI model generates personal data based on the user's past behavioral and preference data, and suggests optimal content through dialogue with the user. The emotion engine recognizes the user's emotions and suggests content based on those emotions. For example, if a user says, "I'm feeling down today," the emotion engine will use that information to suggest a cafe that will lighten the user's mood.

[0522] Examples

[0523] For example, if a user uses a smartphone to type, "Please tell me recommended restaurants near my current location," the server will identify the current location based on map data and obtain weather information using the Weather API. After that, it will obtain restaurant information near the current location using the restaurant information API, and the generative AI model will suggest the most suitable restaurant based on the user's preferences and past ordering history.

[0524] Also, if a user types, "I'm feeling down today," the emotion engine will recognize that information and suggest cafes that will lighten the user's mood.

[0525] Examples of prompt statements

[0526] "What restaurants do you recommend near my current location?"

[0527] "I'm feeling down today, please suggest some place that will make me feel better."

[0528] In this way, a system can be created that makes optimal food delivery suggestions based on a user's current location and emotions.

[0529] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0530] Step 1:

[0531] The user inputs a prompt into the terminal. For example, "Please tell me some recommended restaurants near my current location." This input becomes the starting point for the system's processing.

[0532] Step 2:

[0533] The terminal obtains the user's current location information. Using the smartphone's GPS function, it obtains the latitude and longitude information of the current location and sends it to the server. The input is the user's current location information, and the output is the latitude and longitude information sent to the server.

[0534] Step 3:

[0535] The server uses the Weather API to get weather information for the current location. The server sends latitude and longitude information to the Weather API to get current weather data. The input is latitude and longitude information, and the output is weather information.

[0536] Step 4:

[0537] The server uses the restaurant information API to obtain restaurant information around the current location. The server sends latitude and longitude information to the restaurant information API to obtain nearby restaurant data. The input is latitude and longitude information, and the output is restaurant information.

[0538] Step 5:

[0539] The server uses a generative AI model to make personalized restaurant suggestions based on the user's preferences and past ordering history. The server inputs the user's personal data and restaurant information into the generative AI model and selects the best restaurant. The input is the user's personal data and restaurant information, and the output is the best restaurant suggestion.

[0540] Step 6:

[0541] The server uses the emotion engine to recognize the user's emotion and suggest content based on the emotion. For example, if a user inputs "I'm feeling down today," the emotion engine uses that information to suggest a cafe that will lighten the mood. The input is the user's emotion information, and the output is content suggestions based on the emotion.

[0542] Step 7:

[0543] The server sends the final proposal to the device. The server integrates the output of the generative AI model and the emotion engine, and sends the best restaurant or cafe proposal to the user to the device. The input is the output of the generative AI model and the emotion engine, and the output is the proposal information sent to the user.

[0544] Step 8:

[0545] The terminal displays the suggested information received from the server to the user. The terminal displays the suggested restaurant or cafe information on the screen for the user to confirm. The input is the suggested information from the server, and the output is the information displayed to the user.

[0546] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0547] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0548] Another example of generative AI is Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0549] In the above embodiment, an example was given in which the specific process was performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0550] [Second embodiment]

[0551] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0552] 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.

[0553] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0554] 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.

[0555] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0556] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0557] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0558] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0559] 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.

[0560] 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.

[0561] 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.

[0562] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0563] "Example 1"

[0564] One embodiment of the present invention is a system that connects map data, store data, and user personal data with a generative AI. This system provides the most enjoyable answer through dialogue with the user. Specifically, when a user asks, "Tell me about a recommended restaurant nearby," the generative AI provides an answer such as, "It seems you like Italian food. Here are some nearby Italian restaurants with good reviews," based on the user's past behavioral data and preference data.

[0565] "Example 2"

[0566] Another embodiment of the present invention is a system that suggests content that a user would enjoy most at a specified location or current location. This system suggests content taking into consideration information such as the user's current location, destination, time of day, and weather. Specifically, when a user asks "I'd like to go to lunch now" based on their current location, the system will make a suggestion such as "There's an Italian restaurant you might like, a five-minute walk from your current location" taking into consideration restaurant information around the current location, the user's preferences, and the current time of day.

[0567] The process flow of each embodiment will be described below.

[0568] "Example 1"

[0569] Step 1: The user asks the system a question. For example, "Tell me about some recommended restaurants nearby."

[0570] Step 2: The generative AI generates personal data based on the user's past behavioral data and preference data.

[0571] Step 3: Based on the personal data generated by the generative AI, the AI ​​provides the answer that the user will enjoy the most. Specifically, it provides an answer such as, "It seems that you like Italian food. Here are some good Italian restaurants nearby."

[0572] "Example 2"

[0573] Step 1: The user asks the system a question. For example, the user might say, "I want to go to lunch now" based on their current location.

[0574] Step 2: The system takes into account restaurant information around the current location, the user's preferences, and the current time of day.

[0575] Step 3: Based on the information taken into account, the system suggests the content the user will enjoy the most. For example, it may suggest, "There is an Italian restaurant you might like within a five-minute walk from your current location."

[0576] Example 1

[0577] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0578] Conventional systems did not adequately collect and analyze data to provide users with the content they would most enjoy, and did not fully utilize user preferences or past behavioral data. As a result, they were unable to make optimal suggestions to users, resulting in a decline in satisfaction. In addition, it was difficult to make suggestions that took into account dynamic information such as the user's current location, time of day, and weather.

[0579] 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.

[0580] In this invention, the server includes a means for connecting map data, store data, and user's personal data to the generative AI model and providing the most enjoyable answer through dialogue with the user, a means for suggesting the most enjoyable content for the user at a specified location or current location, a means for receiving the user's inquiry, collecting necessary data, and sending a prompt to the generative AI model, and a means for receiving the answer from the generative AI model and displaying it to the user. This enables optimal suggestions based on the user's preferences and past behavioral data, and further enables suggestions that take into account dynamic information such as the current location, time of day, and weather.

[0581] "Map data" refers to data that includes geographical information, such as location information, topography, roads, and buildings.

[0582] "Store data" refers to data that includes information about each store, such as the store's name, location, business hours, ratings, and menu.

[0583] "User's personal data" refers to data that contains personal information about the user, including past behavioral data, preference data, evaluation data, etc.

[0584] A "generative AI model" is a model that uses artificial intelligence technology to generate and analyze data, and uses natural language processing and machine learning to generate optimal responses to user inquiries.

[0585] A "prompt sentence" is a sentence input to a generative AI model that contains the user's inquiry and related data.

[0586] The term "means for providing an answer that is most enjoyable to a user through dialogue" refers to a method or technology for providing an answer that is most enjoyable to a user through dialogue with the user.

[0587] "Means for suggesting content that a user would enjoy most at a specified location or current location" refers to methods and technologies for suggesting content that a user would enjoy most based on a location specified by the user or the current location.

[0588] "Means of collecting necessary data and sending prompt sentences to the generative AI model" refers to methods and technologies for collecting necessary data based on a user's inquiry and sending prompt sentences to the generative AI model based on that data.

[0589] "Means of receiving an answer from a generative AI model and displaying it to a user" refers to methods or technologies for displaying the answer received from a generative AI model to a user.

[0590] This invention is a system that connects map data, store data, and user personal data with a generative AI model and provides the most enjoyable answer through dialogue with the user. A specific embodiment of this system will be described below.

[0591] Hardware and Software Used

[0592] Server: Database server (MySQL), generative AI model (e.g., GPT-4)

[0593] Device: Smartphone (iOS, Android)

[0594] Software: Map app (Google Maps API), store database (Yelp API)

[0595] Program Processing

[0596] Server Processing

[0597] The server collects map data, store data, and the user's personal data, and connects this data to the generative AI model. When the server receives an inquiry from a user, it generates and sends a prompt sentence to the generative AI model. For example, if a user asks, "Tell me about some recommended restaurants nearby," the server refers to the user's past behavioral data and preference data, and sends a prompt sentence to the generative AI model to generate an answer such as, "It seems you like Italian food. Here are some nearby Italian restaurants with good reviews."

[0598] Terminal handling

[0599] The terminal receives an inquiry from a user and transmits it to the server. The terminal receives the generated answer from the server and displays it to the user. For example, when a user uses a smartphone to input "Tell me about some recommended restaurants nearby," the terminal transmits this inquiry to the server, receives the answer from the server, and displays it on the screen.

[0600] User Actions

[0601] A user uses a terminal to make inquiries to the system. For example, if the user inputs "Tell me about some recommended restaurants nearby," the system will suggest the best restaurant based on the user's past behavioral data and preference data. The user can check the information on the suggested restaurant and visit it.

[0602] Examples

[0603] Examples of prompt statements

[0604] Example prompt text for when a user types "What are some good restaurants near me?":

[0605] User past behavior data:

[0606] Previously visited restaurants: Italian, French

[0607] Rated restaurants: Italian (5 stars), French (4 stars)

[0608] User Preference Data:

[0609] Favorite food: Italian

[0610] Disliked food: Chinese food

[0611] Prompt statement:

[0612] "Users seem to like Italian food. Can you recommend a good Italian restaurant nearby?"

[0613] By sending this prompt text to a generative AI model, the system can provide the user with the best restaurant suggestions.

[0614] Data processing and calculation

[0615] The server retrieves the user's past behavioral data and preference data from a MySQL database, and generates a prompt sentence based on this data. A generative AI model (e.g., GPT-4) analyzes this prompt sentence and generates an optimal answer. The generated answer is sent to the terminal via the server and displayed to the user.

[0616] In this way, it becomes possible to provide optimal suggestions based on the user's preferences and past behavioral data, and also to take into account dynamic information such as current location, time of day, and weather.

[0617] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0618] Step 1:

[0619] A user inputs a query into a terminal.

[0620] A user uses a device such as a smartphone or tablet to input a question to the system. For example, the user might type, "Tell me about some recommended restaurants nearby." The input data is then sent to the device's application.

[0621] Step 2:

[0622] The terminal sends a query to the server.

[0623] The terminal sends the query entered by the user to the server. At this time, the terminal transmits data to the server via the Internet. The input data includes the user ID and current location information. The output of the terminal is the query data that is sent to the server.

[0624] Step 3:

[0625] The server receives the query and collects the required data.

[0626] The server analyzes the inquiry received from the terminal and retrieves the user's past behavioral data and preference data from a database. For example, it retrieves the user's past restaurant visit history and rating data from a MySQL database. The input is the inquiry data from the terminal, and the output is the collected user data.

[0627] Step 4:

[0628] The server sends a prompt to the generative AI model.

[0629] The server generates a prompt sentence based on the collected data and sends it to the generative AI model. The prompt sentence includes the user's preferences and past behavioral data. For example, a prompt sentence may be generated that reads, "The user seems to like Italian food. Can you tell me about a nearby Italian restaurant with a good reputation?" The input is the collected user data, and the output is the generated prompt sentence.

[0630] Step 5:

[0631] The generative AI model generates an answer and returns it to the server.

[0632] The generative AI model generates the best answer for the user based on the prompt it receives. For example, it generates an answer such as, "It seems you like Italian food. Here are some good Italian restaurants nearby." The input is the prompt, and the output is the generated answer.

[0633] Step 6:

[0634] The server sends the generated answer to the terminal.

[0635] The server sends the answer received from the generative AI model to the terminal. At this time, the server converts the answer into an appropriate format and sends it. For example, it converts it into JSON format and sends it. The input is the generated answer, and the output is the data sent to the terminal.

[0636] Step 7:

[0637] The terminal displays the answer to the user.

[0638] The terminal displays the answer received from the server to the user. The user can check the suggested restaurant information on the terminal screen. The input is the answer data from the server, and the output is the information displayed to the user.

[0639] (Application example 1)

[0640] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0641] Conventional store recommendation systems do not fully consider the user's preferences or past behavior data, and can only make general recommendations, making it difficult for users to find stores that they will truly enjoy. In addition, real-time recommendations based on the user's current location are not made, resulting in low user convenience.

[0642] 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.

[0643] In this invention, the server includes a means for connecting map data, store data, and user's personal data to generative AI and providing the most enjoyable answer through dialogue with the user, a means for proposing content that the user will enjoy most at a specified location or current location, and a means for searching for stores within a certain radius from the user's current location and recommending the most suitable store based on the user's preferences. This enables personalized store recommendations that take into account the user's preferences and past behavioral data, making it possible to find the most suitable store in real time.

[0644] "Map Data" means data that includes geographic location information and provides detailed information about specific points or regions.

[0645] "Store data" is data that includes information about each store, providing details such as the store's name, location, opening hours, and ratings.

[0646] "User's personal data" refers to data that contains information about the individual user, including preferences, past behavioral history, current location information, etc.

[0647] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide optimal answers through dialogue with users.

[0648] "Means for providing the most enjoyable answers through dialogue" are means that have the function of providing information and suggestions that users will find most enjoyable through communication with the users.

[0649] "Means for suggesting content that the user will enjoy most at a specified location or current location" refers to means having a function for suggesting content that the user will enjoy based on a location specified by the user or current location information.

[0650] The "means for searching for stores within a certain radius from the user's current location" refers to a means having a function for searching for stores within a certain distance based on the user's current location.

[0651] A "means for recommending the most suitable store based on the user's preferences" is a means having a function for selecting and recommending the most suitable store to a user based on the user's preference data.

[0652] The system for implementing this invention connects map data, store data, and user's personal data with generative AI, and provides the most enjoyable answer through dialogue with the user. A specific embodiment of this system is described below.

[0653] System Configuration

[0654] The system includes a server, a user's smartphone, and a generative AI model. The server manages map data, store data, and the user's personal data, and processes the data in cooperation with the generative AI model. The user's smartphone functions as a dialogue interface, receives input from the user, and communicates with the server.

[0655] Program Processing

[0656] The server processes the data in the following steps:

[0657] 1. Data Acquisition: The server acquires the user's current location information, past behavior data, and preference data, including GPS data, past search history, and rating data.

[0658] 2. Store search: The server uses map services such as Google Maps API to search for stores within a certain radius of the user's current location.

[0659] 3. Data analysis: The server uses a generative AI model to analyze the acquired data and select the most suitable store based on the user's preferences.

[0660] 4. Generation of recommendation results: The server uses a generative AI model to generate a list of stores that are most suitable for the user and sends it to the smartphone.

[0661] Hardware and software used

[0662] Hardware: Smartphones, servers

[0663] Software: Python, Google Maps API, generative AI model

[0664] Examples

[0665] When a user asks their smartphone, "Can you recommend a nearby cafe?" the system operates as follows.

[0666] 1. The server obtains that the user's current location is "Shibuya-ku, Tokyo."

[0667] 2. The server learns that the user's preferences are "cafe" and "Italian."

[0668] 3. The server uses the Google Maps API to search for cafes in Shibuya Ward.

[0669] 4. The server uses a generative AI model to filter the search results to cafes that match the user's preferences.

[0670] 5. The server generates a list of the best cafes and sends it to the smartphone.

[0671] Examples of prompt statements

[0672] "Please tell me about some recommended cafes nearby. I like cafes and Italian food. I'm currently located in Shibuya Ward, Tokyo."

[0673] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0674] Step 1:

[0675] The server acquires the user's current location information.

[0676] Input: GPS data from the user's smartphone

[0677] Output: User's current location (e.g. Shibuya-ku, Tokyo)

[0678] Specific operation: The server receives the GPS data sent from the smartphone and determines the user's current location.

[0679] Step 2:

[0680] The server acquires the user's past behavioral data and preference data.

[0681] Input: User's past search history, rating data, preference data

[0682] Output: User's past behavioral data and preference data (e.g., likes cafes and Italian food)

[0683] Specific operations: The server retrieves the user's past behavioral history and preference data from the database and prepares it for analysis.

[0684] Step 3:

[0685] The server uses the Google Maps API to search for stores within a certain radius of the user's current location.

[0686] Input: User's current location, search radius (e.g. 1500 meters)

[0687] Output: A list of stores as search results

[0688] Specific operation: The server sends a request to the Google Maps API specifying the user's current location and search radius, and receives the returned list of stores.

[0689] Step 4:

[0690] The server uses a generative AI model to analyze the acquired data and select the most suitable store based on the user's preferences.

[0691] Input: Store list, user preference data

[0692] Output: A list of optimal stores that match the user's preferences

[0693] Specific operation: The server inputs the store list and user preference data into the generative AI model, and the AI ​​model filters the most suitable stores based on the preferences.

[0694] Step 5:

[0695] The server uses a generative AI model to generate a list of stores that are best suited for the user and sends it to the user's smartphone.

[0696] Input: Optimal store list

[0697] Output: A list of recommended stores displayed on the user's smartphone

[0698] Specific operation: The server sends the optimal store list generated by the generative AI model to the user's smartphone and displays it on the smartphone screen.

[0699] Example 2

[0700] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0701] Conventional content suggestion systems have difficulty in suggesting optimal content by comprehensively considering various information such as the user's current location, preferences, time of day, and weather. As a result, it has been difficult to provide content that users can truly enjoy, and improving user satisfaction has been an issue.

[0702] 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.

[0703] In this invention, the server includes a means for acquiring user's current location information, a means for acquiring weather information, a means for acquiring time zone information, a means for acquiring user's preference data, a means for analyzing the acquired data, a means for generating content using a generative AI model, a means for transmitting the generated content to the user's terminal, and a means for displaying the content on the user's terminal. This makes it possible to propose content that the user will enjoy most by comprehensively considering the user's various information.

[0704] The "means for acquiring the user's current location information" is a function for acquiring location information from the user's terminal.

[0705] "Means for acquiring weather information" is a function for acquiring current weather data from an external weather information service.

[0706] The "means for acquiring time zone information" is a function for acquiring current time zone data from a system clock or an external time information providing service.

[0707] "Means for acquiring user preference data" refers to a function for acquiring a user's past behavior history and preference data from a database.

[0708] The "means for analyzing acquired data" refers to a data analysis function for identifying the most suitable content for a user based on various collected data.

[0709] A "means for generating content using a generative AI model" is a function for using a generative AI model to generate content to be suggested to users.

[0710] The "means for transmitting the generated content to the user's terminal" is a function for transmitting the generated content to the user's terminal via a network.

[0711] The "means for displaying content on a user's terminal" is a function for displaying received content on the screen of the user's terminal.

[0712] This invention is a system that proposes optimal content by comprehensively considering information such as the user's current location, preferences, time of day, weather, etc. This system is realized using a server, a terminal, and a generative AI model.

[0713] First, the server uses the GPS function of the terminal to obtain the user's current location information. Specifically, the terminal uses the built-in GPS module to obtain the user's current location and transmits the information to the server.

[0714] The server then uses an external weather information service (e.g., OpenWeatherMap API) to obtain weather information. The server retrieves current weather data based on the user's current location.

[0715] Additionally, the server uses the system clock to obtain time zone information. The server obtains the current time zone data and combines it with other data for analysis.

[0716] The server uses a database (e.g., MySQL) to obtain user preference data. The server obtains the user's past behavior history and preference data from the database and uses it for analysis.

[0717] The server performs data analysis based on the various data acquired. This analysis uses machine learning models (e.g. TensorFlow). The server identifies the most suitable content for the user based on the collected data.

[0718] The server then uses a generative AI model (e.g., GPT-3) to generate content to suggest to the user, taking into account the user's current location, preferences, time of day, weather, etc.

[0719] The generated content is sent from the server to the user's device. The device displays the received content to the user. For example, the application screen may display, "There's an Italian restaurant you might like, just a five-minute walk from your current location."

[0720] Examples:

[0721] When a user inputs "I want to go to lunch now" into the terminal, the following specific actions are taken:

[0722] 1. The user opens the application on their smartphone and types, "I want to go to lunch now."

[0723] 2. The device uses its built-in GPS function to obtain its current location, "Shibuya Ward, Tokyo."

[0724] 3. The server uses the OpenWeatherMap API to obtain weather information such as "sunny, temperature 25 degrees."

[0725] 4. The server obtains the time zone information for "12:30 PM" from the system clock.

[0726] 5. The server retrieves the user's preference data, such as "I like Italian food," from the database.

[0727] 6. The server uses TensorFlow to identify "Italian restaurants within a 5-minute walk from the current location."

[0728] 7. The server uses GPT-3 to generate suggestions such as, "There's an Italian restaurant you might like within a five-minute walk from your current location."

[0729] 8. The server sends the generated proposal to the user's device.

[0730] 9. The device will display the message on the application screen: "There's an Italian restaurant you might like, just a five-minute walk from your current location."

[0731] In this way, it is possible to comprehensively consider the user's various information and propose content that the user will enjoy most.

[0732] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0733] Step 1:

[0734] The user types a prompt into the terminal.

[0735] Specific actions: The user opens an application on their smartphone and types, "I want to go to lunch now."

[0736] Input: User prompt

[0737] Output: The prompt is sent to the terminal.

[0738] Step 2:

[0739] The device obtains location information.

[0740] Specific operation: The device uses the built-in GPS function to obtain the user's current location information. For example, the device obtains the current location information of "Shibuya-ku, Tokyo."

[0741] Input: GPS data

[0742] Output: Current location is saved on the device

[0743] Step 3:

[0744] The device sends its current location information to the server.

[0745] Specific operation: The device sends the obtained location information to the server.

[0746] Input: Current location

[0747] Output: Current location is sent to the server

[0748] Step 4:

[0749] The server obtains weather information.

[0750] Specific operation: The server obtains weather information for the user's current location using an external weather information service (e.g., OpenWeatherMap API). For example, it obtains weather information such as "sunny, temperature 25 degrees."

[0751] Input: Current location

[0752] Output: Weather information is stored on the server.

[0753] Step 5:

[0754] The server obtains the time zone information.

[0755] Specific behavior: The server uses the system clock to obtain the current time zone information, for example, "12:30 PM."

[0756] Input: System clock data

[0757] Output: Time zone information is saved on the server

[0758] Step 6:

[0759] The server obtains the user's preference data.

[0760] Specific operation: The server retrieves the user's past behavior history and preference data from a database (e.g., MySQL). For example, it retrieves preference data such as "I like Italian food."

[0761] Input: User ID

[0762] Output: Preference data is stored on the server

[0763] Step 7:

[0764] The server analyzes the obtained data.

[0765] Specific operation: The server uses a machine learning model (e.g. TensorFlow) to identify the most suitable content for the user based on the collected current location information, weather information, time zone information, and preference data. For example, it identifies "Italian restaurants within a 5-minute walk from the current location."

[0766] Input: Current location, weather, time zone, preference data

[0767] Output: The analysis results are stored on the server.

[0768] Step 8:

[0769] The server generates content using a generative AI model.

[0770] Specific operation: The server uses a generative AI model (e.g. GPT-3) to generate content to suggest to the user. For example, it generates suggestions such as "There is an Italian restaurant you might like within a five-minute walk from your current location."

[0771] Input: Analysis results

[0772] Output: The generated content is saved on the server.

[0773] Step 9:

[0774] The server transmits the generated content to the terminal.

[0775] Specific operation: The server sends the generated proposal to the user's device.

[0776] Input: Generated content

[0777] Output: Content is sent to the device

[0778] Step 10:

[0779] The terminal displays the content to the user.

[0780] Specific operation: The device displays the received content to the user. For example, the application screen displays "There is an Italian restaurant you might like, just a five-minute walk from your current location."

[0781] Input: Generated content

[0782] Output: The content is displayed on the device screen.

[0783] (Application example 2)

[0784] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[0785] Conventional content recommendation systems do not take into consideration the user's current location, time of day, weather, and preferences, making it difficult to provide users with the content they would most enjoy. In addition, there is a lack of interactive recommendation systems using smartphones or smart glasses, which makes it difficult for users to find the best restaurants and delivery options in real time.

[0786] 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.

[0787] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for suggesting the most enjoyable content for the user at a specified location or current location, a means for suggesting optimal restaurants and delivery options taking into consideration the user's current location, time zone, weather, and preferences, and a means for making suggestions based on the content of a question posed by the user using a smartphone or smart glasses. This enables the user to find the optimal restaurants and delivery options in real time.

[0788] "Map Data" means data that includes geographic location information and is used to identify a User's current location and destination.

[0789] "Store data" refers to data that includes information about stores such as restaurants and retail outlets, providing details such as store location, opening hours, and menu.

[0790] "User's personal data" refers to data that includes personal information such as the user's preferences and past behavioral history, and is used to make optimal suggestions to the user.

[0791] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide optimal answers through dialogue with users.

[0792] "Means for providing the most enjoyable answers through dialogue" refers to methods and technologies for providing content or information that users will find most enjoyable through dialogue with the user.

[0793] "Means for suggesting content that a user will enjoy most at a specified location or current location" refers to a method or technology for suggesting content that a user will enjoy based on a location specified by the user or the current location.

[0794] "Means for suggesting optimal restaurants and delivery options taking into consideration a user's current location, time of day, weather, and preferences" refers to methods and technologies for suggesting optimal restaurants and delivery options based on information such as a user's current location, time of day, weather, and preferences.

[0795] "Means for making suggestions based on questions posed by a user using a smartphone or smart glasses" refers to a method or technology for making optimal suggestions based on questions posed by a user using a smartphone or smart glasses.

[0796] A system for implementing this invention connects map data, store data, and user personal data with generative AI, and provides the most enjoyable answer through dialogue with the user. The system includes a means for suggesting the most enjoyable content for the user at a specified location or current location. The system also includes a means for suggesting optimal restaurants and delivery options taking into account the user's current location, time of day, weather, and preferences, and a means for making suggestions based on the content of a question posed by the user using a smartphone or smart glasses.

[0797] The server first obtains the user's personal data, including the user's preferences and past behavioral history. Next, the server obtains information such as the user's current location, time of day, and weather. Based on this information, the server uses generative AI to suggest optimal restaurants and delivery options.

[0798] Specifically, the server performs the following process. First, it obtains the user's current location and compares map data with store data. Next, it creates a list of the user's favorite restaurants based on the user's preference data. It also takes into account the current time of day and weather to make optimal suggestions. For example, if it is raining, it will give priority to delivery options.

[0799] The system makes suggestions based on questions posed by users using a smartphone or smart glasses. If a user says, "I'd like to go to lunch now," the system will suggest the most suitable restaurant, taking into account the user's current location, preferences, and time of day. For example, the system might suggest, "There's an Italian restaurant you might like, just a five-minute walk from your current location. They also offer delivery."

[0800] The hardware used is a smartphone or smart glasses, and the software uses Python. API requests are used to obtain data, and the standard Python library is used to process and calculate the data.

[0801] As a concrete example, the prompt sentence to be input to the generative AI model is shown below.

[0802] User ID: user123

[0803] Current location: Tokyo Station

[0804] Preferences: Italian, Price range: Medium, Allergies: Nuts

[0805] Weather: Sunny

[0806] If you enter this prompt, you will get the following suggestions:

[0807] There is an Italian restaurant you might like within a 5-minute walk from your current location, and they also offer delivery.

[0808] This way, users can find the best dining and delivery options in real time.

[0809] The flow of the specific process in the application example 2 will be described with reference to FIG.

[0810] Step 1:

[0811] The server obtains the user's personal data. Specifically, it obtains data such as the user's preferences and past behavioral history from a database. The input is the user ID, and the output is the user's personal data. This data is used for subsequent suggestions.

[0812] Step 2:

[0813] The server obtains the user's current location. Specifically, it obtains the current location information using the GPS function of the smartphone or smart glasses. The input is the location information from the user's device, and the output is the coordinate data of the current location. This data is used to match map data with store data.

[0814] Step 3:

[0815] The server obtains weather information for the current location. Specifically, it obtains weather data for the current location using a weather API. The input is the coordinate data of the current location, and the output is weather information. This data influences the content of the suggestions.

[0816] Step 4:

[0817] The server compares the map data with the store data. Specifically, it obtains information about restaurants in the vicinity of the current location. The input is the coordinate data of the current location and the store data, and the output is a list of restaurants in the vicinity. This list is compared with the user's preference data.

[0818] Step 5:

[0819] The server filters the restaurant list based on the user's preference data. Specifically, it narrows down the list by considering the user's preferred cuisine, price range, and allergy information. The input is the user's preference data and a list of nearby restaurants, and the output is the filtered restaurant list.

[0820] Step 6:

[0821] The server selects the best restaurant considering the current time slot. Specifically, it makes suggestions considering business hours and congestion status. The input is the filtered restaurant list and the current time slot information, and the output is the best restaurant suggestions.

[0822] Step 7:

[0823] The server makes suggestions to the user through a smartphone or smart glasses. Specifically, the server makes suggestions based on the user's question, such as "I want to go to lunch now." The input is the user's question and a suggestion for the most suitable restaurant, and the output is a suggested message to the user.

[0824] This way, users can find the best dining and delivery options in real time.

[0825] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0826] "Example 1"

[0827] In one embodiment of the present invention, the generative AI includes an emotion engine. The emotion engine recognizes the emotion of the user and generates personal data based on the emotion. Specifically, when the user says, "Today is fun!", the emotion engine recognizes that the user is in a happy mood and provides the information to the generative AI as personal data. Based on the personal data, the generative AI provides an answer that enhances the user's happy mood. For example, it provides an answer such as, "That's great! There's a newly opened amusement park nearby. Why don't you have even more fun there?"

[0828] "Example 2"

[0829] In another embodiment of the present invention, the emotion engine recognizes the emotion of the user and suggests content based on the emotion. Specifically, if the user says, "I feel depressed today," the emotion engine recognizes that the user is depressed and provides the information to the content suggestion means. The content suggestion means suggests content that will lighten the user's mood based on the information. For example, "Are you okay? There's a nice, relaxing cafe nearby.

[0830] Why not take a break here?'

[0831] The process flow of each embodiment will be described below.

[0832] "Example 1"

[0833] Step 1: The user says, "Today is fun!"

[0834] Step 2: The emotion engine recognizes from the user's comments that they are in a happy mood.

[0835] Step 3: The emotion engine provides the information to the generative AI as personal data. Step 4: The generative AI uses the personal data to provide an answer that will make the user feel even more happy.

[0836] "Example 2"

[0837] Step 1: The person says, "I'm feeling down today."

[0838] Step 2: The emotion engine recognizes that something is depressed from the user's statement.

[0839] Step 3: The emotion engine provides the information to the content suggestion engine.

[0840] Step 4: The content suggestion tool uses the information to suggest content that will lighten the user's mood.

[0841] Example 1

[0842] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0843] Conventional information provision systems have had the problem of being difficult to propose optimal content that fully takes into account the user's preferences and emotions. In particular, it was difficult to increase user satisfaction because it was not possible to generate personal data based on the user's past behavioral data and preference data, or to analyze the user's emotions and provide answers based on them.

[0844] 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.

[0845] In this invention, the server includes a means for connecting map data, store data, and user's personal data to the generative AI and providing the most enjoyable answer through dialogue with the user, a means for proposing content that the user will enjoy most at a specified location or current location, a means for analyzing the user's question and collecting necessary data, a means for integrating the collected data and sending it to the generative AI, a means for the generative AI to generate an answer, a means for the emotion engine to analyze the user's emotions and adjust the answer based on the emotion data, and a means for providing the answer to the user. This makes it possible to propose optimal content that takes the user's preferences and emotions into consideration.

[0846] "Map Data" is data that includes geographic location information and points on a map.

[0847] "Store data" refers to data including information such as the store's name, location, ratings, and business hours.

[0848] "User's personal data" refers to data related to an individual, such as a user's past behavioral history, preferences, and emotions.

[0849] "Generative AI" is an artificial intelligence system that generates optimal answers based on user input.

[0850] "Means for providing the most enjoyable answers through dialogue" refers to methods and technologies for providing information and suggestions that users will find most enjoyable through dialogue with the user.

[0851] "Means for suggesting content that a user will enjoy most at a specified location or current location" refers to a method or technology for suggesting content that a user will enjoy based on a location specified by the user or the current location.

[0852] The "means for analyzing a user's question and collecting necessary data" refers to a method or technique for analyzing a user's question and collecting data necessary to answer the question.

[0853] "Means of integrating collected data and transmitting it to the generative AI" refers to the methods or technologies for integrating collected data and transmitting that data to the generative AI.

[0854] "Means by which a generative AI generates an answer" refers to the methods or technologies that a generative AI uses to generate optimal answers based on collected data.

[0855] "Means for the emotion engine to analyze the user's emotions and adjust the response based on the emotion data" refers to methods or technologies for the emotion engine to analyze the user's emotions and adjust the response generated by the generative AI based on the emotion data.

[0856] "Means of providing an answer to a user" refers to the methods or technologies for providing the answer generated by the generative AI to the user.

[0857] This invention is a system that connects map data, store data, and user's personal data with generative AI and provides the most enjoyable answer through dialogue with the user. This system includes a server, a terminal, generative AI, and an emotion engine.

[0858] The server integrates map data, store data, and user personal data and connects to generative AI. Specifically, the server obtains map data using Google Maps API and store data using Yelp API. It also collects users' past behavioral history and preference data and integrates them as personal data.

[0859] The terminal receives questions from the user in the form of voice or text. For example, if the user asks, "Can you recommend a restaurant nearby?", the terminal converts the question into text using voice recognition technology and sends it to the server.

[0860] The server uses natural language processing (NLP) techniques to analyze the received question and understand the intent of the question. It then collects and integrates the necessary data. For example, it can integrate information that the user likes Italian restaurants with a list of nearby Italian restaurants.

[0861] Generative AI generates optimal answers based on the integrated data, such as "It seems you like Italian food. Here are some good Italian restaurants nearby."

[0862] The emotion engine analyzes the user's emotions. For example, if the user says, "Today is fun!", the emotion engine recognizes that the user is in a happy mood. The server provides the emotion data obtained from the emotion engine to the generative AI.

[0863] Generative AI adjusts responses based on emotion data. For example, if a user is in a good mood, it generates responses that will enhance that mood. For example, it generates a response like, "That's great! There's a new amusement park nearby. Why don't you go there and have even more fun?"

[0864] The device provides the user with the answer generated by the generative AI, for example by voice or text.

[0865] As an example, consider the following prompt:

[0866] "Tell me about some recommended restaurants nearby."

[0867] "Today is fun!"

[0868] "Tell me about the new amusement park."

[0869] In this way, the server, terminal, generative AI, and emotion engine work together to create a system that provides users with the most appropriate information.

[0870] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0871] Step 1:

[0872] Receive user questions

[0873] The device receives questions from the user via voice or text. For example, the user might ask, "Can you recommend some nearby restaurants?"

[0874] Input: The user's voice or text question.

[0875] Output: The text data of the question.

[0876] Specific operation: The device uses voice recognition technology to convert the voice into text and sends the text data to the server.

[0877] Step 2:

[0878] Parse the question

[0879] The server analyzes the received question using natural language processing (NLP) technology to understand the intent of the question and extract the necessary information.

[0880] Input: The text data of the question.

[0881] Output: The question intent and the information required.

[0882] What happens: The server uses NLP algorithms to analyze the keywords and context of the question and understand that you're looking for a restaurant.

[0883] Step 3:

[0884] Collect the necessary data

[0885] The server collects map data, store data, and users' personal data.

[0886] Input: The question you want to ask and the information you need.

[0887] Output: Map data, store data, user personal data.

[0888] Specific operation: The server retrieves map data using the Google Maps API, retrieves store data using the Yelp API, and retrieves the user's past behavior history and preference data from the database.

[0889] Step 4:

[0890] Integrate data and send it to generative AI

[0891] The server consolidates the collected data and sends it to the generative AI.

[0892] Input: map data, store data, user personal data.

[0893] Output: The consolidated data.

[0894] What it does: The server combines the user's preference for Italian restaurants with a list of nearby Italian restaurants and sends it to the generative AI.

[0895] Step 5:

[0896] Generative AI generates answers

[0897] Generative AI generates optimal answers based on the integrated data.

[0898] Input: Consolidated data.

[0899] Output: The best answer.

[0900] How it works: The generative AI analyzes the integrated data and generates answers such as, "It seems you like Italian food. Here are some good Italian restaurants nearby."

[0901] Step 6:

[0902] Emotion engine analyzes user emotions

[0903] The server uses an emotion engine to analyze the user's emotions.

[0904] Input: User statements and behavior data.

[0905] Output: User emotion data.

[0906] Specific operation: When a user says, "Today is fun!", the server uses the emotion engine to recognize that the user is in a happy mood.

[0907] Step 7:

[0908] Tailor your answers based on sentiment data

[0909] The generative AI tailors its answers based on emotional data.

[0910] Input: User emotion data.

[0911] Output: The adjusted answer.

[0912] Specific behavior: If the user is in a good mood, the generative AI generates answers that will make the user feel even better. For example, it generates answers like, "That's great! There's a new amusement park nearby. Why don't you go there and have even more fun?"

[0913] Step 8:

[0914] Providing answers for users

[0915] The terminal provides the user with the answers generated by the generative AI.

[0916] Input: The adjusted answer.

[0917] Output: The answer provided to the user.

[0918] Specific operation: The device provides the user with the answer generated by the generative AI via voice or text.

[0919] (Application example 1)

[0920] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal".

[0921] Conventional systems were unable to fully utilize users' emotions and personal data, making it difficult to provide users with optimal content and services. In addition, in-store navigation and real-time promotion information were insufficient, making it impossible to improve users' shopping experience. This could lead to a decrease in user satisfaction and a negative impact on store sales.

[0922] 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.

[0923] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for proposing content that the user will enjoy most at a specified location or current location, an emotion recognition means for recognizing the user's emotion and generating personal data based on the emotion, a means for proposing optimal products and services based on the user's emotion data and personal data, and a means for providing in-store navigation. This makes it possible to provide optimal content and services by utilizing the user's emotion and personal data, and to provide in-store navigation and real-time promotional information.

[0924] "Map Data" means data containing geographical information and used to indicate specific locations and routes.

[0925] "Store Data" means data that includes information about a particular store, such as details about the store's location, opening hours, and the products and services it offers.

[0926] "User's personal data" refers to data that contains personal information about the user, such as past behavioral history, preferences, and emotional state.

[0927] "Generative AI" is an artificial intelligence system that generates optimal answers and suggestions based on user input and data.

[0928] The term "means for providing an answer that is most enjoyable through dialogue" refers to a method or technology for providing an answer that is most enjoyable to a user through dialogue with the user.

[0929] "Means for suggesting content that a user would enjoy most at a specified location or current location" refers to methods and technologies for suggesting content that a user would enjoy most based on a location specified by the user or the current location.

[0930] "Emotion recognition means" refers to a method or technology for recognizing a user's emotion and generating data based on that emotion.

[0931] "Means for suggesting optimal products and services" refers to methods and technologies for suggesting optimal products and services to users based on their emotional data and personal data.

[0932] "Means for providing in-store navigation" refers to methods and technologies for providing a route for a user to reach a destination within a store.

[0933] As an embodiment of the present invention, a smart shopping assistant system will be described as an example. This system connects map data, store data, and user's personal data with generative AI, and provides the most enjoyable answer through dialogue with the user.

[0934] Hardware and Software Used

[0935] Smartphone: A device on which users install and use applications.

[0936] EmotionEngine: An engine for recognizing the user's emotions.

[0937] Recommendation Engine: An engine that suggests optimal products and services based on users' personal data.

[0938] NavigationSystem: A system that provides in-store navigation.

[0939] Data processing and calculation

[0940] 1. Emotion recognition: The EmotionEngine analyzes text entered by the user into their smartphone (e.g., "Today is fun!") and generates emotion data. The EmotionEngine uses natural language processing technology to extract emotions from the text.

[0941] 2. Recommendation Generation: Based on emotional and personal data, the Recommendation Engine will suggest optimal products and services. The Recommendation Engine uses machine learning algorithms to analyze user preferences and past behavioral data to make optimal suggestions.

[0942] 3. Navigation: Based on the current location and destination, the Navigation System provides the optimal route. The Navigation System uses map data to calculate the route within the store and guides the customer.

[0943] Examples

[0944] When a user types "Today is fun!" into their smartphone, the system behaves as follows:

[0945] 1. The EmotionEngine recognizes the emotion of "fun" and generates emotional data.

[0946] 2. Based on emotional data and personal data, the Recommendation Engine will suggest products and services that will enhance the user's enjoyment. For example, it will make a suggestion such as, "We have a new game corner. Why not spend your time there even more enjoyably?"

[0947] 3. The Navigation System calculates the optimal route from the user's current location to the game corner and guides them there.

[0948] Examples of prompt statements

[0949] "Today is fun!"

[0950] "Tell me some recommended products near me"

[0951] "Tell me the recommended route within this store."

[0952] In this way, the smart shopping assistant system can utilize users' emotions and personal data to provide an optimal shopping experience.

[0953] The flow of the specific process in the application example 1 will be described with reference to FIG.

[0954] Step 1:

[0955] A user inputs text into a smartphone, for example, "Today is fun!" The input text is sent to the system.

[0956] Step 2:

[0957] The server receives the entered text and sends it to EmotionEngine. EmotionEngine uses natural language processing technology to extract emotions from the text. Specifically, it generates emotion data for "fun" from the text "Today is fun!"

[0958] Step 3:

[0959] The server receives emotion data from the Emotion Engine. It then sends the emotion data and the user's personal data to the Recommendation Engine. The Recommendation Engine uses machine learning algorithms to analyze the emotion data and personal data and recommend optimal products and services. For example, it generates a suggestion such as, "We have a new game corner. Why not spend some more time there and have some fun?"

[0960] Step 4:

[0961] The server receives the recommendation data from the RecommendationEngine. It then sends the current location and the suggested destination (e.g., game corner) to the NavigationSystem. The NavigationSystem uses the map data to calculate the optimal route within the store.

[0962] Step 5:

[0963] The server receives the route data from the Navigation System. It then combines the proposed data with the route data and sends it to the user. The user's smartphone displays the in-store navigation along with the message, "We have a new game corner. Why not have even more fun there? Here is the route."

[0964] Step 6:

[0965] The user checks the navigation on the smartphone and follows the suggested route to the destination. The user can then enjoy the suggested products and services.

[0966] Example 2

[0967] Next, a description will be given of Example 2 of the embodiment 2. In the following description, the data processing device 12 is referred to as a "server" and the smart glasses 214 are referred to as a "terminal".

[0968] Conventional content suggestion systems have difficulty in suggesting optimal content by comprehensively considering the user's current location, preferences, and emotions. In addition, they are unable to reflect dynamic elements such as weather information and time of day in real time, which means that they are unable to make optimal suggestions for users. Furthermore, they lack the functionality to recognize the user's emotions and suggest content based on them, which makes it difficult to increase user satisfaction.

[0969] 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.

[0970] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for suggesting the most enjoyable content for the user at a specified location or current location, a location information acquisition means for acquiring the user's current location, a weather information acquisition means for acquiring weather information for the current location, and an emotion recognition means for recognizing the user's emotion and suggesting content based on the emotion. This makes it possible to comprehensively consider various factors such as the user's current location, preferences, emotion, weather information, and time of day, and suggest optimal content in real time.

[0971] "Map data" refers to data that includes geographical information, and provides information on locations, routes, topography, and the like.

[0972] "Store data" refers to data that includes information about a specific store, such as the store's location, opening hours, menu, and ratings.

[0973] "User personal data" means data that contains information about an individual User, such as preferences, past behavioral history, and emotional state.

[0974] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data, and suggests optimal content based on users' personal data.

[0975] "Location information acquisition means" refers to technology or devices for identifying a user's current location, such as using GPS or Wi-Fi location information.

[0976] A "weather information acquisition means" is a technology or device for acquiring weather information for a current location, and utilizes a weather information API, etc.

[0977] An "emotion recognition means" is a technology or device for recognizing a user's emotions, and utilizes voice recognition or natural language processing technology.

[0978] "Content suggestion means" refers to technologies or devices for suggesting optimal content based on user information, and utilizes generative AI.

[0979] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0980] This invention is a system that suggests the most enjoyable content to users, taking into consideration information such as their current location, destination, time of day, weather, etc. It also has a function to recognize the user's emotions and suggest content based on those emotions.

[0981] Hardware and Software Used

[0982] Hardware: Servers, terminals (smartphones, tablets, etc.)

[0983] Software: Generative AI models, emotion engines, location services, weather APIs

[0984] System Configuration

[0985] 1. A means to connect map data, store data, and user personal data with generative AI

[0986] The server connects map data, store data, and users' personal data to the generative AI, and generates optimal content based on this data.

[0987] 2. A way to suggest content that users will enjoy most at a given location or their current location

[0988] The server takes into account information such as the user's current location, destination, time of day, and weather to suggest the content that the user will enjoy most.

[0989] 3. Location information acquisition means for acquiring the user's current location

[0990] The device uses its built-in GPS module to obtain the user's current location and transmits that information to the server.

[0991] 4. Weather information acquisition method to acquire weather information for the current location

[0992] The server obtains weather information for the current location using a weather information API (e.g., OpenWeatherMap API).

[0993] 5. Emotion recognition means to recognize a user's emotions and suggest content based on those emotions

[0994] The device uses voice recognition technology to convert the user's speech into text, and the emotion engine analyzes the text. The emotion engine uses natural language processing technology to recognize the user's emotions.

[0995] Examples and prompts

[0996] Example 1: When a user enters "I want to go to lunch now"

[0997] Prompt: "If a user says, 'I'd like to go to lunch now,' based on their current location, the system will take into account nearby restaurant information, the user's preferences, and the current time of day and suggest, 'There's an Italian restaurant you might like, just a five-minute walk from your current location.'"

[0998] Example 2: When a user says, "I'm feeling down today"

[0999] Prompt: "If the user says, 'I'm feeling down today,' the emotion engine will recognize that the user is feeling down and provide that information to the content suggestion tool. The content suggestion tool will then use that information to suggest, 'Are you okay? There's a nice, relaxing cafe nearby. Why don't you take a break there?'"

[1000] In this way, the system can suggest the most suitable content based on the user's location and emotions.

[1001] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1002] Step 1:

[1003] User Input

[1004] How it works: A user uses a device to enter information such as current location, destination, time of day, and weather.

[1005] Input: A user opens an app on their smartphone and speaks or texts, "I want to go to lunch now."

[1006] Data processing: The device converts the voice into text and sends the text data to the server.

[1007] Output: The text data sent to the server.

[1008] Step 2:

[1009] Obtaining location information

[1010] How it works: The device uses location services to obtain the user's current location.

[1011] Input: Your device uses the built-in GPS module to determine your location.

[1012] Data processing: The latitude and longitude information acquired by the device is sent to the server.

[1013] Output: The latitude and longitude of your current location sent to the server.

[1014] Step 3:

[1015] Obtaining weather information

[1016] How it works: The server uses the weather information API to obtain weather information for the current location.

[1017] Input: The server makes a request to the weather API with the current latitude and longitude data.

[1018] Data processing: Analyze the weather data obtained from the weather information API and extract information such as temperature, probability of precipitation, and wind speed.

[1019] Output: Parsed weather data.

[1020] Step 4:

[1021] Content Suggestions

[1022] How it works: The server uses a generative AI model to generate content that takes into account the user's preferences and the current time of day.

[1023] Input: The server uses the user's past behavior and preference data, current latitude and longitude, weather data, and time zone information.

[1024] Data processing: Generative AI models analyze this data and generate optimal content.

[1025] Output: A suggestion such as "There's an Italian restaurant you might like just a 5-minute walk from your current location."

[1026] Step 5:

[1027] Emotion recognition

[1028] How it works: If a user says, "I'm feeling down today," the device uses an emotion engine to recognize that emotion.

[1029] Input: User's voice input.

[1030] Data processing: The device uses voice recognition technology to convert the user's speech into text, and the emotion engine analyzes the text.

[1031] Output: Emotional information "I feel depressed".

[1032] Step 6:

[1033] Sentiment-based content suggestions

[1034] How it works: The server uses information from the emotion engine to suggest content that will lighten the user's mood.

[1035] Input: Emotional information "feeling depressed".

[1036] Data processing: The server searches for relaxing places and activities based on emotional information and generates optimal suggestions.

[1037] Output: A suggestion such as "Are you okay? There's a nice, relaxing cafe nearby. How about we take a break there?"

[1038] (Application example 2)

[1039] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the smart glasses 214 will be referred to as a "terminal".

[1040] Conventional content suggestion systems can suggest content based on a user's current location and preferences, but they have the problem of being unable to make suggestions that take into account dynamic factors such as the user's emotions and the weather. In addition, there is a lack of personalized suggestions that match the user's mood, making it difficult to increase user satisfaction. Furthermore, in the field of food delivery, there is a demand for optimal restaurant and cafe suggestions that take into account the user's current emotions and weather.

[1041] 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.

[1042] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for suggesting content that the user will enjoy most at a specified location or current location, a means for recognizing the user's emotions and suggesting content based on the emotions, a means for suggesting the most suitable restaurant or cafe in consideration of the user's current location, destination, time zone, weather, and other information, and a means for suggesting a personalized menu based on the user's preferences and past order history. This makes it possible to suggest optimal content taking into account dynamic factors such as the user's emotions and weather.

[1043] "Map Data" means data that includes geographic location information and is used to identify a User's current location and destination.

[1044] "Store data" refers to data including information about stores such as restaurants and cafes, and provides information such as the store's location, type, and opening hours.

[1045] "User personal data" refers to data that includes information about an individual, such as a user's preferences, past behavioral history, or emotional state.

[1046] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide optimal answers through dialogue with users.

[1047] "Means for providing the most enjoyable answers through dialogue" refers to means that have the function of providing the user with content or information that the user will enjoy most through communication with the user.

[1048] The "means for suggesting content that the user will enjoy most at a specified location or current location" refers to a means having a function for suggesting content that the user will enjoy based on the user's current location or a specified location.

[1049] "Means for recognizing a user's emotions and suggesting content based on those emotions" refers to means having a function for detecting the emotional state of a user and suggesting optimal content according to those emotions.

[1050] "A means for suggesting the most suitable restaurant or cafe by taking into consideration information such as the user's current location, destination, time of day, and weather" refers to a means that has the function of suggesting the most suitable restaurant or cafe based on information such as the user's current location, destination, time of day, and weather.

[1051] "Means for proposing personalized menu items based on a user's preferences and past order history" refers to means having a function for proposing individually optimized menu items taking into account a user's preferences and past order history.

[1052] The system for implementing this invention is composed of three main components: a server, a terminal, and a user. The server has a means for connecting map data, store data, and the user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user. It also includes a means for proposing content that the user will enjoy most at a specified location or current location, a means for recognizing the user's emotions and proposing content based on those emotions, a means for proposing the most suitable restaurant or cafe considering the user's current location, destination, time of day, weather, and other information, and a means for proposing a personalized menu based on the user's preferences and past order history.

[1053] Hardware and Software Used

[1054] Hardware: Smartphones, servers

[1055] Software: Python, Weather API, Restaurant Information API, Generative AI model

[1056] Data processing and calculation

[1057] The server first uses map data to obtain the user's current location. It then uses the Weather API to obtain weather information for the current location. This makes it possible to make suggestions that take into account the weather conditions when the user goes out. It also uses the restaurant information API to obtain information on restaurants and cafes near the user's current location.

[1058] The generative AI model generates personal data based on the user's past behavioral and preference data, and suggests optimal content through dialogue with the user. The emotion engine recognizes the user's emotions and suggests content based on those emotions. For example, if a user says, "I'm feeling down today," the emotion engine will use that information to suggest a cafe that will lighten the user's mood.

[1059] Examples

[1060] For example, if a user uses a smartphone to type, "Please tell me recommended restaurants near my current location," the server will identify the current location based on map data and obtain weather information using the Weather API. After that, it will obtain restaurant information near the current location using the restaurant information API, and the generative AI model will suggest the most suitable restaurant based on the user's preferences and past ordering history.

[1061] Also, if a user types, "I'm feeling down today," the emotion engine will recognize that information and suggest cafes that will lighten the user's mood.

[1062] Examples of prompt statements

[1063] "What restaurants do you recommend near my current location?"

[1064] "I'm feeling down today, please suggest some place that will make me feel better."

[1065] In this way, a system can be created that makes optimal food delivery suggestions based on a user's current location and emotions.

[1066] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1067] Step 1:

[1068] The user inputs a prompt into the terminal. For example, "Please tell me some recommended restaurants near my current location." This input becomes the starting point for the system's processing.

[1069] Step 2:

[1070] The terminal obtains the user's current location information. Using the smartphone's GPS function, it obtains the latitude and longitude information of the current location and sends it to the server. The input is the user's current location information, and the output is the latitude and longitude information sent to the server.

[1071] Step 3:

[1072] The server uses the Weather API to get weather information for the current location. The server sends latitude and longitude information to the Weather API to get current weather data. The input is latitude and longitude information, and the output is weather information.

[1073] Step 4:

[1074] The server uses the restaurant information API to obtain restaurant information around the current location. The server sends latitude and longitude information to the restaurant information API to obtain nearby restaurant data. The input is latitude and longitude information, and the output is restaurant information.

[1075] Step 5:

[1076] The server uses a generative AI model to make personalized restaurant suggestions based on the user's preferences and past ordering history. The server inputs the user's personal data and restaurant information into the generative AI model and selects the best restaurant. The input is the user's personal data and restaurant information, and the output is the best restaurant suggestion.

[1077] Step 6:

[1078] The server uses the emotion engine to recognize the user's emotion and suggest content based on the emotion. For example, if a user inputs "I'm feeling down today," the emotion engine uses that information to suggest a cafe that will lighten the mood. The input is the user's emotion information, and the output is content suggestions based on the emotion.

[1079] Step 7:

[1080] The server sends the final proposal to the device. The server integrates the output of the generative AI model and the emotion engine, and sends the best restaurant or cafe proposal to the user to the device. The input is the output of the generative AI model and the emotion engine, and the output is the proposal information sent to the user.

[1081] Step 8:

[1082] The terminal displays the suggested information received from the server to the user. The terminal displays the suggested restaurant or cafe information on the screen for the user to confirm. The input is the suggested information from the server, and the output is the information displayed to the user.

[1083] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1085] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1086] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1087] [Third embodiment]

[1088] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1089] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1090] 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.

[1091] The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[1092] 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.

[1093] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[1094] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1096] Fig. 6 shows an example of main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1097] 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.

[1098] 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.

[1099] In the headset type terminal 314, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1100] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1101] "Example 1"

[1102] One embodiment of the present invention is a system that connects map data, store data, and user personal data with a generative AI. This system provides the most enjoyable answer through dialogue with the user. Specifically, when a user asks, "Tell me about a recommended restaurant nearby," the generative AI provides an answer such as, "It seems you like Italian food. Here are some nearby Italian restaurants with good reviews," based on the user's past behavioral data and preference data.

[1103] "Example 2"

[1104] Another embodiment of the present invention is a system that suggests content that a user would enjoy most at a specified location or current location. This system suggests content taking into consideration information such as the user's current location, destination, time of day, and weather. Specifically, when a user asks "I'd like to go to lunch now" based on their current location, the system will make a suggestion such as "There's an Italian restaurant you might like, a five-minute walk from your current location" taking into consideration restaurant information around the current location, the user's preferences, and the current time of day.

[1105] The process flow of each embodiment will be described below.

[1106] "Example 1"

[1107] Step 1: The user asks the system a question. For example, "Tell me about some recommended restaurants nearby."

[1108] Step 2: The generative AI generates personal data based on the user's past behavioral data and preference data.

[1109] Step 3: Based on the personal data generated by the generative AI, the AI ​​provides the answer that the user will enjoy the most. Specifically, it provides an answer such as, "It seems that you like Italian food. Here are some good Italian restaurants nearby."

[1110] "Example 2"

[1111] Step 1: The user asks the system a question. For example, the user might say, "I want to go to lunch now" based on their current location.

[1112] Step 2: The system takes into account restaurant information around the current location, the user's preferences, and the current time of day.

[1113] Step 3: Based on the information taken into account, the system suggests the content the user will enjoy the most. For example, it might suggest, "There's an Italian restaurant you might like within a five-minute walk from your current location."

[1114] Example 1

[1115] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1116] Conventional systems did not adequately collect and analyze data to provide users with the content they would most enjoy, and did not fully utilize user preferences or past behavioral data. As a result, they were unable to make optimal suggestions to users, resulting in a decline in satisfaction. In addition, it was difficult to make suggestions that took into account dynamic information such as the user's current location, time of day, and weather.

[1117] 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.

[1118] In this invention, the server includes a means for connecting map data, store data, and user's personal data to the generative AI model and providing the most enjoyable answer through dialogue with the user, a means for suggesting the most enjoyable content for the user at a specified location or current location, a means for receiving the user's inquiry, collecting necessary data, and sending a prompt to the generative AI model, and a means for receiving the answer from the generative AI model and displaying it to the user. This enables optimal suggestions based on the user's preferences and past behavioral data, and further enables suggestions that take into account dynamic information such as the current location, time of day, and weather.

[1119] "Map data" refers to data that includes geographical information, such as location information, topography, roads, and buildings.

[1120] "Store data" refers to data that includes information about each store, such as the store's name, location, business hours, ratings, and menu.

[1121] "User's personal data" refers to data that contains personal information about the user, including past behavioral data, preference data, evaluation data, etc.

[1122] A "generative AI model" is a model that uses artificial intelligence technology to generate and analyze data, and uses natural language processing and machine learning to generate optimal responses to user inquiries.

[1123] A "prompt sentence" is a sentence input to a generative AI model that contains the user's inquiry and related data.

[1124] The term "means for providing an answer that is most enjoyable to a user through dialogue" refers to a method or technology for providing an answer that is most enjoyable to a user through dialogue with the user.

[1125] "Means for suggesting content that a user would enjoy most at a specified location or current location" refers to methods and technologies for suggesting content that a user would enjoy most based on a location specified by the user or the current location.

[1126] "Means of collecting necessary data and sending prompt sentences to the generative AI model" refers to methods and technologies for collecting necessary data based on a user's inquiry and sending prompt sentences to the generative AI model based on that data.

[1127] "Means of receiving an answer from a generative AI model and displaying it to a user" refers to methods or technologies for displaying the answer received from a generative AI model to a user.

[1128] This invention is a system that connects map data, store data, and user personal data with a generative AI model and provides the most enjoyable answer through dialogue with the user. A specific embodiment of this system will be described below.

[1129] Hardware and Software Used

[1130] Server: Database server (MySQL), generative AI model (e.g., GPT-4)

[1131] Device: Smartphone (iOS, Android)

[1132] Software: Map app (Google Maps API), store database (Yelp API)

[1133] Program Processing

[1134] Server Processing

[1135] The server collects map data, store data, and the user's personal data, and connects this data to the generative AI model. When the server receives an inquiry from a user, it generates and sends a prompt sentence to the generative AI model. For example, if a user asks, "Tell me about some recommended restaurants nearby," the server refers to the user's past behavioral data and preference data, and sends a prompt sentence to the generative AI model to generate an answer such as, "It seems you like Italian food. Here are some nearby Italian restaurants with good reviews."

[1136] Terminal handling

[1137] The terminal receives an inquiry from a user and transmits it to the server. The terminal receives the generated answer from the server and displays it to the user. For example, when a user uses a smartphone to input "Tell me about some recommended restaurants nearby," the terminal transmits this inquiry to the server, receives the answer from the server, and displays it on the screen.

[1138] User Actions

[1139] A user uses a terminal to make inquiries to the system. For example, if the user inputs "Tell me about some recommended restaurants nearby," the system will suggest the best restaurant based on the user's past behavioral data and preference data. The user can check the information on the suggested restaurant and visit it.

[1140] Examples

[1141] Examples of prompt statements

[1142] Example prompt text for when a user types "What are some good restaurants near me?":

[1143] User past behavior data:

[1144] Previously visited restaurants: Italian, French

[1145] Rated restaurants: Italian (5 stars), French (4 stars)

[1146] User Preference Data:

[1147] Favorite food: Italian

[1148] Disliked food: Chinese food

[1149] Prompt statement:

[1150] "Users seem to like Italian food. Can you recommend a good Italian restaurant nearby?"

[1151] By sending this prompt text to a generative AI model, the system can provide the user with the best restaurant suggestions.

[1152] Data processing and calculation

[1153] The server retrieves the user's past behavioral data and preference data from a MySQL database, and generates a prompt sentence based on this data. A generative AI model (e.g., GPT-4) analyzes this prompt sentence and generates an optimal answer. The generated answer is sent to the terminal via the server and displayed to the user.

[1154] In this way, it becomes possible to provide optimal suggestions based on the user's preferences and past behavioral data, and also to take into account dynamic information such as current location, time of day, and weather.

[1155] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1156] Step 1:

[1157] A user inputs a query into a terminal.

[1158] A user uses a device such as a smartphone or tablet to input a question to the system. For example, the user might type, "Tell me about some recommended restaurants nearby." The input data is then sent to the device's application.

[1159] Step 2:

[1160] The terminal sends a query to the server.

[1161] The terminal sends the query entered by the user to the server. At this time, the terminal transmits data to the server via the Internet. The input data includes the user ID and current location information. The output of the terminal is the query data that is sent to the server.

[1162] Step 3:

[1163] The server receives the query and collects the required data.

[1164] The server analyzes the inquiry received from the terminal and retrieves the user's past behavioral data and preference data from a database. For example, it retrieves the user's past restaurant visit history and rating data from a MySQL database. The input is the inquiry data from the terminal, and the output is the collected user data.

[1165] Step 4:

[1166] The server sends a prompt to the generative AI model.

[1167] The server generates a prompt sentence based on the collected data and sends it to the generative AI model. The prompt sentence includes the user's preferences and past behavioral data. For example, a prompt sentence may be generated that reads, "The user seems to like Italian food. Can you tell me about a nearby Italian restaurant with a good reputation?" The input is the collected user data, and the output is the generated prompt sentence.

[1168] Step 5:

[1169] The generative AI model generates an answer and returns it to the server.

[1170] The generative AI model generates the best answer for the user based on the prompt it receives. For example, it generates an answer such as, "It seems you like Italian food. Here are some good Italian restaurants nearby." The input is the prompt, and the output is the generated answer.

[1171] Step 6:

[1172] The server sends the generated answer to the terminal.

[1173] The server sends the answer received from the generative AI model to the terminal. At this time, the server converts the answer into an appropriate format and sends it. For example, it converts it into JSON format and sends it. The input is the generated answer, and the output is the data sent to the terminal.

[1174] Step 7:

[1175] The terminal displays the answer to the user.

[1176] The terminal displays the answer received from the server to the user. The user can check the suggested restaurant information on the terminal screen. The input is the answer data from the server, and the output is the information displayed to the user.

[1177] (Application example 1)

[1178] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1179] Conventional store recommendation systems do not fully consider the user's preferences or past behavior data, and can only make general recommendations, making it difficult for users to find stores that they will truly enjoy. In addition, real-time recommendations based on the user's current location are not made, resulting in low user convenience.

[1180] 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.

[1181] In this invention, the server includes a means for connecting map data, store data, and user's personal data to generative AI and providing the most enjoyable answer through dialogue with the user, a means for proposing content that the user will enjoy most at a specified location or current location, and a means for searching for stores within a certain radius from the user's current location and recommending the most suitable store based on the user's preferences. This enables personalized store recommendations that take into account the user's preferences and past behavioral data, making it possible to find the most suitable store in real time.

[1182] "Map Data" means data that includes geographic location information and provides detailed information about specific points or regions.

[1183] "Store data" is data that includes information about each store, providing details such as the store's name, location, opening hours, and ratings.

[1184] "User's personal data" refers to data that contains information about the individual user, including preferences, past behavioral history, current location information, etc.

[1185] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide optimal answers through dialogue with users.

[1186] "Means for providing the most enjoyable answers through dialogue" are means that have the function of providing information and suggestions that users will find most enjoyable through communication with the users.

[1187] "Means for suggesting content that the user will enjoy most at a specified location or current location" refers to means having a function for suggesting content that the user will enjoy based on a location specified by the user or current location information.

[1188] The "means for searching for stores within a certain radius from the user's current location" refers to a means having a function for searching for stores within a certain distance based on the user's current location.

[1189] A "means for recommending the most suitable store based on the user's preferences" is a means having a function for selecting and recommending the most suitable store to a user based on the user's preference data.

[1190] The system for implementing this invention connects map data, store data, and user's personal data with generative AI, and provides the most enjoyable answer through dialogue with the user. A specific embodiment of this system is described below.

[1191] System Configuration

[1192] The system includes a server, a user's smartphone, and a generative AI model. The server manages map data, store data, and the user's personal data, and processes the data in cooperation with the generative AI model. The user's smartphone functions as a dialogue interface, receives input from the user, and communicates with the server.

[1193] Program Processing

[1194] The server processes the data in the following steps:

[1195] 1. Data Acquisition: The server acquires the user's current location information, past behavior data, and preference data, including GPS data, past search history, and rating data.

[1196] 2. Store search: The server uses map services such as Google Maps API to search for stores within a certain radius of the user's current location.

[1197] 3. Data analysis: The server uses a generative AI model to analyze the acquired data and select the most suitable store based on the user's preferences.

[1198] 4. Generation of recommendation results: The server uses a generative AI model to generate a list of stores that are most suitable for the user and sends it to the smartphone.

[1199] Hardware and software used

[1200] Hardware: Smartphones, servers

[1201] Software: Python, Google Maps API, generative AI model

[1202] Examples

[1203] When a user asks their smartphone, "Can you recommend a nearby cafe?" the system operates as follows.

[1204] 1. The server obtains that the user's current location is "Shibuya-ku, Tokyo."

[1205] 2. The server learns that the user's preferences are "cafe" and "Italian."

[1206] 3. The server uses the Google Maps API to search for cafes in Shibuya Ward.

[1207] 4. The server uses a generative AI model to filter the search results to cafes that match the user's preferences.

[1208] 5. The server generates a list of the best cafes and sends it to the smartphone.

[1209] Examples of prompt statements

[1210] "Please tell me about some recommended cafes nearby. I like cafes and Italian food. I'm currently located in Shibuya Ward, Tokyo."

[1211] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1212] Step 1:

[1213] The server acquires the user's current location information.

[1214] Input: GPS data from the user's smartphone

[1215] Output: User's current location (e.g. Shibuya-ku, Tokyo)

[1216] Specific operation: The server receives the GPS data sent from the smartphone and determines the user's current location.

[1217] Step 2:

[1218] The server acquires the user's past behavioral data and preference data.

[1219] Input: User's past search history, rating data, preference data

[1220] Output: User's past behavioral data and preference data (e.g., likes cafes and Italian food)

[1221] Specific operations: The server retrieves the user's past behavioral history and preference data from the database and prepares it for analysis.

[1222] Step 3:

[1223] The server uses the Google Maps API to search for stores within a certain radius of the user's current location.

[1224] Input: User's current location, search radius (e.g. 1500 meters)

[1225] Output: A list of stores as search results

[1226] Specific operation: The server sends a request to the Google Maps API specifying the user's current location and search radius, and receives the returned list of stores.

[1227] Step 4:

[1228] The server uses a generative AI model to analyze the acquired data and select the most suitable store based on the user's preferences.

[1229] Input: Store list, user preference data

[1230] Output: A list of optimal stores that match the user's preferences

[1231] Specific operation: The server inputs the store list and user preference data into the generative AI model, and the AI ​​model filters the most suitable stores based on the preferences.

[1232] Step 5:

[1233] The server uses a generative AI model to generate a list of stores that are best suited for the user and sends it to the user's smartphone.

[1234] Input: Optimal store list

[1235] Output: A list of recommended stores displayed on the user's smartphone

[1236] Specific operation: The server sends the optimal store list generated by the generative AI model to the user's smartphone and displays it on the smartphone screen.

[1237] Example 2

[1238] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1239] Conventional content suggestion systems have difficulty in suggesting optimal content by comprehensively considering various information such as the user's current location, preferences, time of day, and weather. As a result, it has been difficult to provide content that users can truly enjoy, and improving user satisfaction has been an issue.

[1240] 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.

[1241] In this invention, the server includes a means for acquiring user's current location information, a means for acquiring weather information, a means for acquiring time zone information, a means for acquiring user's preference data, a means for analyzing the acquired data, a means for generating content using a generative AI model, a means for transmitting the generated content to the user's terminal, and a means for displaying the content on the user's terminal. This makes it possible to propose content that the user will enjoy most by comprehensively considering the user's various information.

[1242] The "means for acquiring the user's current location information" is a function for acquiring location information from the user's terminal.

[1243] "Means for acquiring weather information" is a function for acquiring current weather data from an external weather information service.

[1244] The "means for acquiring time zone information" is a function for acquiring current time zone data from a system clock or an external time information providing service.

[1245] "Means for acquiring user preference data" refers to a function for acquiring a user's past behavior history and preference data from a database.

[1246] The "means for analyzing acquired data" refers to a data analysis function for identifying the most suitable content for a user based on various collected data.

[1247] A "means for generating content using a generative AI model" is a function for using a generative AI model to generate content to be suggested to users.

[1248] The "means for transmitting the generated content to the user's terminal" is a function for transmitting the generated content to the user's terminal via a network.

[1249] The "means for displaying content on a user's terminal" is a function for displaying received content on the screen of the user's terminal.

[1250] This invention is a system that proposes optimal content by comprehensively considering information such as the user's current location, preferences, time of day, weather, etc. This system is realized using a server, a terminal, and a generative AI model.

[1251] First, the server uses the GPS function of the terminal to obtain the user's current location information. Specifically, the terminal uses the built-in GPS module to obtain the user's current location and transmits the information to the server.

[1252] The server then uses an external weather information service (e.g., OpenWeatherMap API) to obtain weather information. The server retrieves current weather data based on the user's current location.

[1253] Additionally, the server uses the system clock to obtain time zone information. The server obtains the current time zone data and combines it with other data for analysis.

[1254] The server uses a database (e.g., MySQL) to obtain user preference data. The server obtains the user's past behavior history and preference data from the database and uses this for analysis.

[1255] The server performs data analysis based on the various data acquired. This analysis uses machine learning models (e.g. TensorFlow). The server identifies the most suitable content for the user based on the collected data.

[1256] The server then uses a generative AI model (e.g., GPT-3) to generate content to suggest to the user, taking into account the user's current location, preferences, time of day, weather, etc.

[1257] The generated content is sent from the server to the user's device. The device displays the received content to the user. For example, the application screen may display, "There's an Italian restaurant you might like, just a five-minute walk from your current location."

[1258] Examples:

[1259] When a user inputs "I want to go to lunch now" into the terminal, the following specific actions are taken:

[1260] 1. The user opens the application on their smartphone and types, "I want to go to lunch now."

[1261] 2. The device uses its built-in GPS function to obtain its current location, "Shibuya Ward, Tokyo."

[1262] 3. The server uses the OpenWeatherMap API to obtain weather information such as "sunny, temperature 25 degrees."

[1263] 4. The server obtains the time zone information for "12:30 PM" from the system clock.

[1264] 5. The server retrieves the user's preference data, such as "I like Italian food," from the database.

[1265] 6. The server uses TensorFlow to identify "Italian restaurants within a 5-minute walk from the current location."

[1266] 7. The server uses GPT-3 to generate suggestions such as, "There's an Italian restaurant you might like within a five-minute walk from your current location."

[1267] 8. The server sends the generated proposal to the user's device.

[1268] 9. The device will display the message on the application screen: "There's an Italian restaurant you might like, just a five-minute walk from your current location."

[1269] In this way, it is possible to comprehensively consider the user's various information and propose content that the user will enjoy most.

[1270] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1271] Step 1:

[1272] The user types a prompt into the terminal.

[1273] Specific actions: The user opens an application on their smartphone and types, "I want to go to lunch now."

[1274] Input: User prompt

[1275] Output: The prompt is sent to the terminal.

[1276] Step 2:

[1277] The device obtains location information.

[1278] Specific operation: The device uses the built-in GPS function to obtain the user's current location information. For example, the device obtains the current location information of "Shibuya-ku, Tokyo."

[1279] Input: GPS data

[1280] Output: Current location is saved on the device

[1281] Step 3:

[1282] The device sends its current location information to the server.

[1283] Specific operation: The device sends the obtained location information to the server.

[1284] Input: Current location

[1285] Output: Current location is sent to the server

[1286] Step 4:

[1287] The server obtains weather information.

[1288] Specific operation: The server obtains weather information for the user's current location using an external weather information service (e.g., OpenWeatherMap API). For example, it obtains weather information such as "sunny, temperature 25 degrees."

[1289] Input: Current location

[1290] Output: Weather information is stored on the server.

[1291] Step 5:

[1292] The server obtains the time zone information.

[1293] Specific behavior: The server uses the system clock to obtain the current time zone information, for example, "12:30 PM."

[1294] Input: System clock data

[1295] Output: Time zone information is saved on the server

[1296] Step 6:

[1297] The server obtains the user's preference data.

[1298] Specific operation: The server retrieves the user's past behavior history and preference data from a database (e.g., MySQL). For example, it retrieves preference data such as "I like Italian food."

[1299] Input: User ID

[1300] Output: Preference data is stored on the server

[1301] Step 7:

[1302] The server analyzes the obtained data.

[1303] Specific operation: The server uses a machine learning model (e.g. TensorFlow) to identify the most suitable content for the user based on the collected current location information, weather information, time zone information, and preference data. For example, it identifies "Italian restaurants within a 5-minute walk from the current location."

[1304] Input: Current location, weather, time zone, preference data

[1305] Output: The analysis results are stored on the server.

[1306] Step 8:

[1307] The server generates content using a generative AI model.

[1308] Specific operation: The server uses a generative AI model (e.g. GPT-3) to generate content to suggest to the user. For example, it generates suggestions such as "There is an Italian restaurant you might like within a five-minute walk from your current location."

[1309] Input: Analysis results

[1310] Output: The generated content is saved on the server.

[1311] Step 9:

[1312] The server transmits the generated content to the terminal.

[1313] Specific operation: The server sends the generated proposal to the user's device.

[1314] Input: Generated content

[1315] Output: Content is sent to the device

[1316] Step 10:

[1317] The terminal displays the content to the user.

[1318] Specific operation: The device displays the received content to the user. For example, the application screen displays "There is an Italian restaurant you might like, just a five-minute walk from your current location."

[1319] Input: Generated content

[1320] Output: The content is displayed on the device screen.

[1321] (Application example 2)

[1322] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1323] Conventional content recommendation systems do not take into consideration the user's current location, time of day, weather, and preferences, making it difficult to provide users with the content they would most enjoy. In addition, there is a lack of interactive recommendation systems using smartphones or smart glasses, which makes it difficult for users to find the best restaurants and delivery options in real time.

[1324] 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.

[1325] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for suggesting the most enjoyable content for the user at a specified location or current location, a means for suggesting optimal restaurants and delivery options taking into consideration the user's current location, time zone, weather, and preferences, and a means for making suggestions based on the content of a question posed by the user using a smartphone or smart glasses. This enables the user to find the optimal restaurants and delivery options in real time.

[1326] "Map Data" means data that includes geographic location information and is used to identify a User's current location and destination.

[1327] "Store data" refers to data that includes information about stores such as restaurants and retail outlets, providing details such as store location, opening hours, and menu.

[1328] "User's personal data" refers to data that includes personal information such as the user's preferences and past behavioral history, and is used to make optimal suggestions to the user.

[1329] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide optimal answers through dialogue with users.

[1330] "Means for providing the most enjoyable answers through dialogue" refers to methods and technologies for providing content or information that users will find most enjoyable through dialogue with the user.

[1331] "Means for suggesting content that a user will enjoy most at a specified location or current location" refers to a method or technology for suggesting content that a user will enjoy based on a location specified by the user or the current location.

[1332] "Means for suggesting optimal restaurants and delivery options taking into consideration a user's current location, time of day, weather, and preferences" refers to methods and technologies for suggesting optimal restaurants and delivery options based on information such as a user's current location, time of day, weather, and preferences.

[1333] "Means for making suggestions based on questions posed by a user using a smartphone or smart glasses" refers to a method or technology for making optimal suggestions based on questions posed by a user using a smartphone or smart glasses.

[1334] A system for implementing this invention connects map data, store data, and user personal data with generative AI, and provides the most enjoyable answer through dialogue with the user. The system includes a means for suggesting the most enjoyable content for the user at a specified location or current location. The system also includes a means for suggesting optimal restaurants and delivery options taking into account the user's current location, time of day, weather, and preferences, and a means for making suggestions based on the content of a question posed by the user using a smartphone or smart glasses.

[1335] The server first obtains the user's personal data, including the user's preferences and past behavioral history. Next, the server obtains information such as the user's current location, time of day, and weather. Based on this information, the server uses generative AI to suggest optimal restaurants and delivery options.

[1336] Specifically, the server performs the following process. First, it obtains the user's current location and compares map data with store data. Next, it creates a list of the user's favorite restaurants based on the user's preference data. It also takes into account the current time of day and weather to make optimal suggestions. For example, if it is raining, it will give priority to delivery options.

[1337] The system makes suggestions based on questions posed by users using a smartphone or smart glasses. If a user says, "I'd like to go to lunch now," the system will suggest the most suitable restaurant, taking into account the user's current location, preferences, and time of day. For example, the system might suggest, "There's an Italian restaurant you might like, just a five-minute walk from your current location. They also offer delivery."

[1338] The hardware used is a smartphone or smart glasses, and the software uses Python. API requests are used to obtain data, and the standard Python library is used to process and calculate the data.

[1339] As a concrete example, the prompt sentence to be input to the generative AI model is shown below.

[1340] User ID: user123

[1341] Current location: Tokyo Station

[1342] Preferences: Italian, Price range: Medium, Allergies: Nuts

[1343] Weather: Sunny

[1344] If you enter this prompt, you will get the following suggestions:

[1345] There is an Italian restaurant you might like within a 5-minute walk from your current location, and they also offer delivery.

[1346] This way, users can find the best dining and delivery options in real time.

[1347] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1348] Step 1:

[1349] The server obtains the user's personal data. Specifically, it obtains data such as the user's preferences and past behavioral history from a database. The input is the user ID, and the output is the user's personal data. This data is used for subsequent suggestions.

[1350] Step 2:

[1351] The server obtains the user's current location. Specifically, it obtains the current location information using the GPS function of the smartphone or smart glasses. The input is the location information from the user's device, and the output is the coordinate data of the current location. This data is used to match map data with store data.

[1352] Step 3:

[1353] The server obtains weather information for the current location. Specifically, it obtains weather data for the current location using a weather API. The input is the coordinate data of the current location, and the output is weather information. This data influences the content of the suggestions.

[1354] Step 4:

[1355] The server compares the map data with the store data. Specifically, it obtains information about restaurants in the vicinity of the current location. The input is the coordinate data of the current location and the store data, and the output is a list of restaurants in the vicinity. This list is compared with the user's preference data.

[1356] Step 5:

[1357] The server filters the restaurant list based on the user's preference data. Specifically, it narrows down the list by considering the user's preferred cuisine, price range, and allergy information. The input is the user's preference data and a list of nearby restaurants, and the output is the filtered restaurant list.

[1358] Step 6:

[1359] The server selects the best restaurant considering the current time slot. Specifically, it makes suggestions considering business hours and congestion status. The input is the filtered restaurant list and the current time slot information, and the output is the best restaurant suggestions.

[1360] Step 7:

[1361] The server makes suggestions to the user through a smartphone or smart glasses. Specifically, the server makes suggestions based on the user's question, such as "I want to go to lunch now." The input is the user's question and a suggestion for the most suitable restaurant, and the output is a suggested message to the user.

[1362] This way, users can find the best dining and delivery options in real time.

[1363] In addition, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1364] "Example 1"

[1365] In one embodiment of the present invention, the generative AI includes an emotion engine. The emotion engine recognizes the emotion of the user and generates personal data based on the emotion. Specifically, when the user says, "Today is fun!", the emotion engine recognizes that the user is in a happy mood and provides the information to the generative AI as personal data. Based on the personal data, the generative AI provides an answer that enhances the user's happy mood. For example, it provides an answer such as, "That's great! There's a newly opened amusement park nearby. Why don't you have even more fun there?"

[1366] "Example 2"

[1367] In another embodiment of the present invention, the emotion engine recognizes the emotion of the user and suggests content based on the emotion. Specifically, if the user says, "I feel depressed today," the emotion engine recognizes that the user is depressed and provides this information to the content suggestion means. Based on this information, the content suggestion means suggests content that will lighten the user's mood. For example, it makes a suggestion such as, "Are you okay? There's a relaxing cafe nearby. Why don't you take a break there?"

[1368] The process flow of each embodiment will be described below.

[1369] "Example 1"

[1370] Step 1: The user says, "Today is fun!"

[1371] Step 2: The emotion engine recognizes from the user's comments that they are in a happy mood.

[1372] Step 3: The emotion engine provides the information to the generative AI as personal data. Step 4: The generative AI uses the personal data to provide an answer that will make the user feel even more happy.

[1373] "Example 2"

[1374] Step 1: The person says, "I'm feeling down today."

[1375] Step 2: The emotion engine recognizes that something is depressed from the user's statement.

[1376] Step 3: The emotion engine provides the information to the content suggestion engine.

[1377] Step 4: The content suggestion tool uses the information to suggest content that will lighten the user's mood.

[1378] Example 1

[1379] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1380] Conventional information provision systems have had the problem of being difficult to propose optimal content that fully takes into account the user's preferences and emotions. In particular, it was difficult to increase user satisfaction because it was not possible to generate personal data based on the user's past behavioral data and preference data, or to analyze the user's emotions and provide answers based on them.

[1381] 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.

[1382] In this invention, the server includes a means for connecting map data, store data, and user's personal data to the generative AI and providing the most enjoyable answer through dialogue with the user, a means for proposing content that the user will enjoy most at a specified location or current location, a means for analyzing the user's question and collecting necessary data, a means for integrating the collected data and sending it to the generative AI, a means for the generative AI to generate an answer, a means for the emotion engine to analyze the user's emotions and adjust the answer based on the emotion data, and a means for providing the answer to the user. This makes it possible to propose optimal content that takes the user's preferences and emotions into consideration.

[1383] "Map Data" is data that includes geographic location information and points on a map.

[1384] "Store data" refers to data including information such as the store's name, location, ratings, and business hours.

[1385] "User's personal data" refers to data related to an individual, such as a user's past behavioral history, preferences, and emotions.

[1386] "Generative AI" is an artificial intelligence system that generates optimal answers based on user input.

[1387] "Means for providing the most enjoyable answers through dialogue" refers to methods and technologies for providing information and suggestions that users will find most enjoyable through dialogue with the user.

[1388] "Means for suggesting content that a user will enjoy most at a specified location or current location" refers to a method or technology for suggesting content that a user will enjoy based on a location specified by the user or the current location.

[1389] The "means for analyzing a user's question and collecting necessary data" refers to a method or technique for analyzing a user's question and collecting data necessary to answer the question.

[1390] "Means of integrating collected data and transmitting it to the generative AI" refers to the methods or technologies for integrating collected data and transmitting that data to the generative AI.

[1391] "Means by which a generative AI generates an answer" refers to the methods or technologies that a generative AI uses to generate optimal answers based on collected data.

[1392] "Means for the emotion engine to analyze the user's emotions and adjust the response based on the emotion data" refers to methods or technologies for the emotion engine to analyze the user's emotions and adjust the response generated by the generative AI based on the emotion data.

[1393] "Means of providing an answer to a user" refers to the methods or technologies for providing the answer generated by the generative AI to the user.

[1394] This invention is a system that connects map data, store data, and user's personal data with generative AI and provides the most enjoyable answer through dialogue with the user. This system includes a server, a terminal, generative AI, and an emotion engine.

[1395] The server integrates map data, store data, and user personal data and connects to generative AI. Specifically, the server obtains map data using Google Maps API and store data using Yelp API. It also collects users' past behavioral history and preference data and integrates them as personal data.

[1396] The terminal receives questions from the user in the form of voice or text. For example, if the user asks, "Can you recommend a restaurant nearby?", the terminal converts the question into text using voice recognition technology and sends it to the server.

[1397] The server uses natural language processing (NLP) techniques to analyze the received question and understand the intent of the question. It then collects and integrates the necessary data. For example, it can integrate information that the user likes Italian restaurants with a list of nearby Italian restaurants.

[1398] Generative AI generates optimal answers based on the integrated data, such as "It seems you like Italian food. Here are some good Italian restaurants nearby."

[1399] The emotion engine analyzes the user's emotions. For example, if the user says, "Today is fun!", the emotion engine recognizes that the user is in a happy mood. The server provides the emotion data obtained from the emotion engine to the generative AI.

[1400] Generative AI adjusts responses based on emotion data. For example, if a user is in a good mood, it generates responses that will enhance that mood. For example, it generates a response like, "That's great! There's a new amusement park nearby. Why don't you go there and have even more fun?"

[1401] The device provides the user with the answer generated by the generative AI, for example by voice or text.

[1402] As an example, consider the following prompt:

[1403] "Tell me about some recommended restaurants nearby."

[1404] "Today is fun!"

[1405] "Tell me about the new amusement park."

[1406] In this way, the server, terminal, generative AI, and emotion engine work together to create a system that provides users with the most appropriate information.

[1407] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1408] Step 1:

[1409] Receive user questions

[1410] The device receives questions from the user via voice or text. For example, the user might ask, "Can you recommend some nearby restaurants?"

[1411] Input: The user's voice or text question.

[1412] Output: The text data of the question.

[1413] Specific operation: The device uses voice recognition technology to convert the voice into text and sends the text data to the server.

[1414] Step 2:

[1415] Parse the question

[1416] The server analyzes the received question using natural language processing (NLP) technology to understand the intent of the question and extract the necessary information.

[1417] Input: The text data of the question.

[1418] Output: The question intent and the information required.

[1419] What happens: The server uses NLP algorithms to analyze the keywords and context of the question and understand that you're looking for a restaurant.

[1420] Step 3:

[1421] Collect the necessary data

[1422] The server collects map data, store data, and users' personal data.

[1423] Input: The question you want to ask and the information you need.

[1424] Output: Map data, store data, user personal data.

[1425] Specific operation: The server retrieves map data using the Google Maps API, retrieves store data using the Yelp API, and retrieves the user's past behavior history and preference data from the database.

[1426] Step 4:

[1427] Integrate data and send it to generative AI

[1428] The server consolidates the collected data and sends it to the generative AI.

[1429] Input: map data, store data, user personal data.

[1430] Output: The consolidated data.

[1431] What it does: The server combines the user's preference for Italian restaurants with a list of nearby Italian restaurants and sends it to the generative AI.

[1432] Step 5:

[1433] Generative AI generates answers

[1434] Generative AI generates optimal answers based on the integrated data.

[1435] Input: Consolidated data.

[1436] Output: The best answer.

[1437] How it works: The generative AI analyzes the integrated data and generates answers such as, "It seems you like Italian food. Here are some good Italian restaurants nearby."

[1438] Step 6:

[1439] Emotion engine analyzes user emotions

[1440] The server uses an emotion engine to analyze the user's emotions.

[1441] Input: User statements and behavior data.

[1442] Output: User emotion data.

[1443] Specific operation: When a user says, "Today is fun!", the server uses the emotion engine to recognize that the user is in a happy mood.

[1444] Step 7:

[1445] Tailor your answers based on sentiment data

[1446] The generative AI tailors its answers based on emotional data.

[1447] Input: User emotion data.

[1448] Output: The adjusted answer.

[1449] Specific behavior: If the user is in a good mood, the generative AI generates answers that will make the user feel even better. For example, it generates answers like, "That's great! There's a new amusement park nearby. Why don't you go there and have even more fun?"

[1450] Step 8:

[1451] Providing answers for users

[1452] The terminal provides the user with the answers generated by the generative AI.

[1453] Input: The adjusted answer.

[1454] Output: The answer provided to the user.

[1455] Specific operation: The device provides the user with the answer generated by the generative AI via voice or text.

[1456] (Application example 1)

[1457] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1458] Conventional systems were unable to fully utilize users' emotions and personal data, making it difficult to provide users with optimal content and services. In addition, in-store navigation and real-time promotion information were insufficient, making it impossible to improve users' shopping experience. This could lead to a decrease in user satisfaction and a negative impact on store sales.

[1459] 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.

[1460] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for proposing content that the user will enjoy most at a specified location or current location, an emotion recognition means for recognizing the user's emotion and generating personal data based on the emotion, a means for proposing optimal products and services based on the user's emotion data and personal data, and a means for providing in-store navigation. This makes it possible to provide optimal content and services by utilizing the user's emotion and personal data, and to provide in-store navigation and real-time promotional information.

[1461] "Map Data" means data containing geographical information and used to indicate specific locations and routes.

[1462] "Store Data" means data that includes information about a particular store, such as details about the store's location, opening hours, and the products and services it offers.

[1463] "User's personal data" refers to data that contains personal information about the user, such as past behavioral history, preferences, and emotional state.

[1464] "Generative AI" is an artificial intelligence system that generates optimal answers and suggestions based on user input and data.

[1465] The term "means for providing an answer that is most enjoyable through dialogue" refers to a method or technology for providing an answer that is most enjoyable to a user through dialogue with the user.

[1466] "Means for suggesting content that a user would enjoy most at a specified location or current location" refers to methods and technologies for suggesting content that a user would enjoy most based on a location specified by the user or the current location.

[1467] "Emotion recognition means" refers to a method or technology for recognizing a user's emotion and generating data based on that emotion.

[1468] "Means for suggesting optimal products and services" refers to methods and technologies for suggesting optimal products and services to users based on their emotional data and personal data.

[1469] "Means for providing in-store navigation" refers to methods and technologies for providing a route for a user to reach a destination within a store.

[1470] As an embodiment of the present invention, a smart shopping assistant system will be described as an example. This system connects map data, store data, and user's personal data with generative AI, and provides the most enjoyable answer through dialogue with the user.

[1471] Hardware and Software Used

[1472] Smartphone: A device on which users install and use applications.

[1473] EmotionEngine: An engine for recognizing the user's emotions.

[1474] Recommendation Engine: An engine that suggests optimal products and services based on users' personal data.

[1475] NavigationSystem: A system that provides in-store navigation.

[1476] Data processing and calculation

[1477] 1. Emotion Recognition: The Emotion Engine analyzes the text entered by the user into their smartphone (e.g., "Today is fun!") and generates emotion data. The Emotion Engine uses natural language processing technology to extract emotions from the text.

[1478] 2. Recommendation Generation: Based on emotional and personal data, the Recommendation Engine will suggest optimal products and services. The Recommendation Engine uses machine learning algorithms to analyze user preferences and past behavioral data to make optimal suggestions.

[1479] 3. Navigation: Based on the current location and destination, the Navigation System provides the optimal route. The Navigation System uses map data to calculate the route within the store and guides the customer.

[1480] Examples

[1481] When a user types "Today is fun!" into their smartphone, the system behaves as follows:

[1482] 1. The EmotionEngine recognizes the emotion of "fun" and generates emotional data.

[1483] 2. Based on emotional data and personal data, the Recommendation Engine will suggest products and services that will enhance the user's enjoyment. For example, it will make a suggestion such as, "We have a new game corner. Why not spend your time there even more enjoyably?"

[1484] 3. The Navigation System calculates the optimal route from the user's current location to the game corner and guides them there.

[1485] Examples of prompt statements

[1486] "Today is fun!"

[1487] "Tell me some recommended products near me"

[1488] "Tell me the recommended route within this store."

[1489] In this way, the smart shopping assistant system can utilize users' emotions and personal data to provide an optimal shopping experience.

[1490] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1491] Step 1:

[1492] A user inputs text into a smartphone, for example, "Today is fun!" The input text is sent to the system.

[1493] Step 2:

[1494] The server receives the entered text and sends it to EmotionEngine. EmotionEngine uses natural language processing technology to extract emotions from the text. Specifically, it generates emotion data for "fun" from the text "Today is fun!"

[1495] Step 3:

[1496] The server receives emotion data from the Emotion Engine. It then sends the emotion data and the user's personal data to the Recommendation Engine. The Recommendation Engine uses machine learning algorithms to analyze the emotion data and personal data and recommend optimal products and services. For example, it generates a suggestion such as, "We have a new game corner. Why not spend some more time there and have some fun?"

[1497] Step 4:

[1498] The server receives the recommendation data from the RecommendationEngine. It then sends the current location and the suggested destination (e.g., game corner) to the NavigationSystem. The NavigationSystem uses the map data to calculate the optimal route within the store.

[1499] Step 5:

[1500] The server receives the route data from the Navigation System. It then combines the proposed data with the route data and sends it to the user. The user's smartphone displays the in-store navigation along with the message, "We have a new game corner. Why not have even more fun there? Here is the route."

[1501] Step 6:

[1502] The user checks the navigation on the smartphone and follows the suggested route to the destination. The user can then enjoy the suggested products and services.

[1503] Example 2

[1504] Next, a second embodiment of the second form will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal".

[1505] Conventional content suggestion systems have difficulty in suggesting optimal content by comprehensively considering the user's current location, preferences, and emotions. In addition, they are unable to reflect dynamic elements such as weather information and time of day in real time, which means that they are unable to make optimal suggestions for users. Furthermore, they lack the functionality to recognize the user's emotions and suggest content based on them, which makes it difficult to increase user satisfaction.

[1506] 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.

[1507] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for suggesting the most enjoyable content for the user at a specified location or current location, a location information acquisition means for acquiring the user's current location, a weather information acquisition means for acquiring weather information for the current location, and an emotion recognition means for recognizing the user's emotion and suggesting content based on the emotion. This makes it possible to comprehensively consider various factors such as the user's current location, preferences, emotion, weather information, and time of day, and suggest optimal content in real time.

[1508] "Map data" refers to data that includes geographical information, and provides information on locations, routes, topography, and the like.

[1509] "Store data" refers to data that includes information about a specific store, such as the store's location, opening hours, menu, and ratings.

[1510] "User personal data" means data that contains information about an individual User, such as preferences, past behavioral history, and emotional state.

[1511] "Generative AI" is a system that uses artificial intelligence technology to generate and analyze data, and suggests optimal content based on users' personal data.

[1512] "Location information acquisition means" refers to technology or devices for identifying a user's current location, such as using GPS or Wi-Fi location information.

[1513] A "weather information acquisition means" is a technology or device for acquiring weather information for a current location, and utilizes a weather information API, etc.

[1514] An "emotion recognition means" is a technology or device for recognizing a user's emotions, and utilizes voice recognition or natural language processing technology.

[1515] "Content suggestion means" refers to technologies or devices for suggesting optimal content based on user information, and utilizes generative AI.

[1516] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[1517] This invention is a system that suggests the most enjoyable content to users, taking into consideration information such as their current location, destination, time of day, weather, etc. It also has a function to recognize the user's emotions and suggest content based on those emotions.

[1518] Hardware and Software Used

[1519] Hardware: Servers, terminals (smartphones, tablets, etc.)

[1520] Software: Generative AI models, emotion engines, location services, weather APIs

[1521] System Configuration

[1522] 1. A means to connect map data, store data, and user personal data with generative AI

[1523] The server connects map data, store data, and users' personal data to the generative AI, and generates optimal content based on this data.

[1524] 2. A way to suggest content that users will enjoy most at a given location or their current location

[1525] The server takes into account information such as the user's current location, destination, time of day, and weather to suggest the content that the user will enjoy most.

[1526] 3. Location information acquisition means for acquiring the user's current location

[1527] The device uses its built-in GPS module to obtain the user's current location and transmits that information to the server.

[1528] 4. Weather information acquisition method to acquire weather information for the current location

[1529] The server obtains weather information for the current location using a weather information API (e.g., OpenWeatherMap API).

[1530] 5. Emotion recognition means to recognize a user's emotions and suggest content based on those emotions

[1531] The device uses voice recognition technology to convert the user's speech into text, and the emotion engine analyzes the text. The emotion engine uses natural language processing technology to recognize the user's emotions.

[1532] Examples and prompts

[1533] Example 1: When a user enters "I want to go to lunch now"

[1534] Prompt: "If a user says, 'I want to go to lunch now,' based on their current location, the system will take into account nearby restaurant information, the user's preferences, and the current time of day and suggest, 'There's an Italian restaurant you might like, just a five-minute walk from your current location.'"

[1535] Example 2: When a user says, "I'm feeling down today"

[1536] Prompt: "If the user says, 'I'm feeling down today,' the emotion engine will recognize that the user is feeling down and provide that information to the content suggestion tool. The content suggestion tool will then use that information to suggest, 'Are you okay? There's a nice, relaxing cafe nearby. Why don't you take a break there?'"

[1537] In this way, the system can suggest the most suitable content based on the user's location and emotions.

[1538] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1539] Step 1:

[1540] User Input

[1541] How it works: A user uses a device to enter information such as current location, destination, time of day, and weather.

[1542] Input: A user opens an app on their smartphone and speaks or texts, "I want to go to lunch now."

[1543] Data processing: The device converts the voice into text and sends the text data to the server.

[1544] Output: The text data sent to the server.

[1545] Step 2:

[1546] Obtaining location information

[1547] How it works: The device uses location services to obtain the user's current location.

[1548] Input: Your device uses the built-in GPS module to determine your location.

[1549] Data processing: The latitude and longitude information acquired by the device is sent to the server.

[1550] Output: The latitude and longitude of your current location sent to the server.

[1551] Step 3:

[1552] Obtaining weather information

[1553] How it works: The server uses the weather information API to obtain weather information for the current location.

[1554] Input: The server makes a request to the weather API with the current latitude and longitude data.

[1555] Data processing: Analyze the weather data obtained from the weather information API and extract information such as temperature, probability of precipitation, and wind speed.

[1556] Output: Parsed weather data.

[1557] Step 4:

[1558] Content Suggestions

[1559] How it works: The server uses a generative AI model to generate content that takes into account the user's preferences and the current time of day.

[1560] Input: The server uses the user's past behavior and preference data, current latitude and longitude, weather data, and time zone information.

[1561] Data processing: Generative AI models analyze this data and generate optimal content.

[1562] Output: A suggestion such as "There's an Italian restaurant you might like just a 5-minute walk from your current location."

[1563] Step 5:

[1564] Emotion recognition

[1565] How it works: If a user says, "I'm feeling down today," the device uses an emotion engine to recognize that emotion.

[1566] Input: User's voice input.

[1567] Data processing: The device uses voice recognition technology to convert the user's speech into text, and the emotion engine analyzes the text.

[1568] Output: Emotional information "I feel depressed".

[1569] Step 6:

[1570] Sentiment-based content suggestions

[1571] How it works: The server uses information from the emotion engine to suggest content that will lighten the user's mood.

[1572] Input: Emotional information "feeling depressed".

[1573] Data processing: The server searches for relaxing places and activities based on emotional information and generates optimal suggestions.

[1574] Output: A suggestion such as "Are you okay? There's a nice, relaxing cafe nearby. How about we take a break there?"

[1575] (Application example 2)

[1576] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server", and the headset type terminal 314 will be referred to as a "terminal".

[1577] Conventional content suggestion systems can suggest content based on a user's current location and preferences, but they have the problem of being unable to make suggestions that take into account dynamic factors such as the user's emotions and the weather. In addition, there is a lack of personalized suggestions that match the user's mood, making it difficult to increase user satisfaction. Furthermore, in the field of food delivery, there is a demand for optimal restaurant and cafe suggestions that take into account the user's current emotions and weather.

[1578] 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.

[1579] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for suggesting content that the user will enjoy most at a specified location or current location, a means for recognizing the user's emotions and suggesting content based on the emotions, a means for suggesting the most suitable restaurant or cafe in consideration of the user's current location, destination, time zone, weather, and other information, and a means for suggesting a personalized menu based on the user's preferences and past order history. This makes it possible to suggest optimal content taking into account dynamic factors such as the user's emotions and weather.

[1580] "Map Data" means data that includes geographic location information and is used to identify a User's current location and destination.

[1581] "Store data" refers to data including information about stores such as restaurants and cafes, and provides information such as the store's location, type, and opening hours.

[1582] "User personal data" refers to data that includes information about an individual, such as a user's preferences, past behavioral history, or emotional state.

[1583] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide optimal answers through dialogue with users.

[1584] "Means for providing the most enjoyable answers through dialogue" refers to means that have the function of providing the user with content or information that the user will enjoy most through communication with the user.

[1585] The "means for suggesting content that the user will enjoy most at a specified location or current location" refers to a means having a function for suggesting content that the user will enjoy based on the user's current location or a specified location.

[1586] "Means for recognizing a user's emotions and suggesting content based on those emotions" refers to means having a function for detecting the emotional state of a user and suggesting optimal content according to those emotions.

[1587] "A means for suggesting the most suitable restaurant or cafe by taking into consideration information such as the user's current location, destination, time of day, and weather" refers to a means that has the function of suggesting the most suitable restaurant or cafe based on information such as the user's current location, destination, time of day, and weather.

[1588] "Means for proposing personalized menu items based on a user's preferences and past order history" refers to means having a function for proposing individually optimized menu items taking into account a user's preferences and past order history.

[1589] The system for implementing this invention is composed of three main components: a server, a terminal, and a user. The server has a means for connecting map data, store data, and the user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user. It also includes a means for proposing content that the user will enjoy most at a specified location or current location, a means for recognizing the user's emotions and proposing content based on those emotions, a means for proposing the most suitable restaurant or cafe considering the user's current location, destination, time of day, weather, and other information, and a means for proposing a personalized menu based on the user's preferences and past order history.

[1590] Hardware and Software Used

[1591] Hardware: Smartphones, servers

[1592] Software: Python, Weather API, Restaurant Information API, Generative AI model

[1593] Data processing and calculation

[1594] The server first uses map data to obtain the user's current location. It then uses the Weather API to obtain weather information for the current location. This makes it possible to make suggestions that take into account the weather conditions when the user goes out. It also uses the restaurant information API to obtain information on restaurants and cafes near the user's current location.

[1595] The generative AI model generates personal data based on the user's past behavioral and preference data, and suggests optimal content through dialogue with the user. The emotion engine recognizes the user's emotions and suggests content based on those emotions. For example, if a user says, "I'm feeling down today," the emotion engine will use that information to suggest a cafe that will lighten the user's mood.

[1596] Examples

[1597] For example, if a user uses a smartphone to type, "Please tell me recommended restaurants near my current location," the server will identify the current location based on map data and obtain weather information using the Weather API. After that, it will obtain restaurant information near the current location using the restaurant information API, and the generative AI model will suggest the most suitable restaurant based on the user's preferences and past ordering history.

[1598] Also, if a user types, "I'm feeling down today," the emotion engine will recognize that information and suggest cafes that will lighten the user's mood.

[1599] Examples of prompt statements

[1600] "What restaurants do you recommend near my current location?"

[1601] "I'm feeling down today, please suggest some place that will make me feel better."

[1602] In this way, a system can be created that makes optimal food delivery suggestions based on a user's current location and emotions.

[1603] The flow of the specific process in the application example 2 will be described with reference to FIG.

[1604] Step 1:

[1605] The user inputs a prompt into the terminal. For example, "Please tell me some recommended restaurants near my current location." This input becomes the starting point for the system's processing.

[1606] Step 2:

[1607] The terminal obtains the user's current location information. Using the smartphone's GPS function, it obtains the latitude and longitude information of the current location and sends it to the server. The input is the user's current location information, and the output is the latitude and longitude information sent to the server.

[1608] Step 3:

[1609] The server uses the Weather API to get weather information for the current location. The server sends latitude and longitude information to the Weather API to get current weather data. The input is latitude and longitude information, and the output is weather information.

[1610] Step 4:

[1611] The server uses the restaurant information API to obtain restaurant information around the current location. The server sends latitude and longitude information to the restaurant information API to obtain nearby restaurant data. The input is latitude and longitude information, and the output is restaurant information.

[1612] Step 5:

[1613] The server uses a generative AI model to make personalized restaurant suggestions based on the user's preferences and past ordering history. The server inputs the user's personal data and restaurant information into the generative AI model and selects the best restaurant. The input is the user's personal data and restaurant information, and the output is the best restaurant suggestion.

[1614] Step 6:

[1615] The server uses the emotion engine to recognize the user's emotion and suggest content based on the emotion. For example, if a user inputs "I'm feeling down today," the emotion engine uses that information to suggest a cafe that will lighten the mood. The input is the user's emotion information, and the output is content suggestions based on the emotion.

[1616] Step 7:

[1617] The server sends the final proposal to the device. The server integrates the output of the generative AI model and the emotion engine, and sends the best restaurant or cafe proposal to the user to the device. The input is the output of the generative AI model and the emotion engine, and the output is the proposal information sent to the user.

[1618] Step 8:

[1619] The terminal displays the suggested information received from the server to the user. The terminal displays the suggested restaurant or cafe information on the screen for the user to confirm. The input is the suggested information from the server, and the output is the information displayed to the user.

[1620] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1621] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1622] Another example of generative AI is Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1623] 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.

[1624] [Fourth embodiment]

[1625] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1626] 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.

[1627] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[1628] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1629] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[1630] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[1631] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54. The transmission and reception of various types of information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[1632] The control target 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, legs, etc. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, legs, etc. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1633] Fig. 8 shows an example of main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1634] 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.

[1635] 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.

[1636] In the robot 414, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50, and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1637] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1638] "Example 1"

[1639] One embodiment of the present invention is a system that connects map data, store data, and user personal data with a generative AI. This system provides the most enjoyable answer through dialogue with the user. Specifically, when a user asks, "Tell me about a recommended restaurant nearby," the generative AI provides an answer such as, "It seems you like Italian food. Here are some nearby Italian restaurants with good reviews," based on the user's past behavioral data and preference data.

[1640] "Example 2"

[1641] Another embodiment of the present invention is a system that suggests content that a user would enjoy most at a specified location or current location. This system suggests content taking into consideration information such as the user's current location, destination, time of day, and weather. Specifically, when a user asks "I'd like to go to lunch now" based on their current location, the system will make a suggestion such as "There's an Italian restaurant you might like, a five-minute walk from your current location" taking into consideration restaurant information around the current location, the user's preferences, and the current time of day.

[1642] The process flow of each embodiment will be described below.

[1643] "Example 1"

[1644] Step 1: The user asks the system a question. For example, "Tell me about some recommended restaurants nearby."

[1645] Step 2: The generative AI generates personal data based on the user's past behavioral data and preference data.

[1646] Step 3: Based on the personal data generated by the generative AI, the AI ​​provides the answer that the user will enjoy the most. Specifically, it provides an answer such as, "It seems that you like Italian food. Here are some good Italian restaurants nearby."

[1647] "Example 2"

[1648] Step 1: The user asks the system a question. For example, the user might say, "I want to go to lunch now" based on their current location.

[1649] Step 2: The system takes into account restaurant information around the current location, the user's preferences, and the current time of day.

[1650] Step 3: Based on the information taken into account, the system suggests the content the user will enjoy the most. For example, it might suggest, "There's an Italian restaurant you might like within a five-minute walk from your current location."

[1651] Example 1

[1652] Next, a description will be given of Example 1 of the embodiment 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1653] Conventional systems did not adequately collect and analyze data to provide users with the content they would most enjoy, and did not fully utilize user preferences or past behavioral data. As a result, they were unable to make optimal suggestions to users, resulting in a decline in satisfaction. In addition, it was difficult to make suggestions that took into account dynamic information such as the user's current location, time of day, and weather.

[1654] 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.

[1655] In this invention, the server includes a means for connecting map data, store data, and user's personal data to the generative AI model and providing the most enjoyable answer through dialogue with the user, a means for suggesting the most enjoyable content for the user at a specified location or current location, a means for receiving the user's inquiry, collecting necessary data, and sending a prompt to the generative AI model, and a means for receiving the answer from the generative AI model and displaying it to the user. This enables optimal suggestions based on the user's preferences and past behavioral data, and further enables suggestions that take into account dynamic information such as the current location, time of day, and weather.

[1656] "Map data" refers to data that includes geographical information, such as location information, topography, roads, and buildings.

[1657] "Store data" refers to data that includes information about each store, such as the store's name, location, business hours, ratings, and menu.

[1658] "User's personal data" refers to data that contains personal information about the user, including past behavioral data, preference data, evaluation data, etc.

[1659] A "generative AI model" is a model that uses artificial intelligence technology to generate and analyze data, and uses natural language processing and machine learning to generate optimal responses to user inquiries.

[1660] A "prompt sentence" is a sentence input to a generative AI model that contains the user's inquiry and related data.

[1661] The term "means for providing an answer that is most enjoyable to a user through dialogue" refers to a method or technology for providing an answer that is most enjoyable to a user through dialogue with the user.

[1662] "Means for suggesting content that a user would enjoy most at a specified location or current location" refers to methods and technologies for suggesting content that a user would enjoy most based on a location specified by the user or the current location.

[1663] "Means of collecting necessary data and sending prompt sentences to the generative AI model" refers to methods and technologies for collecting necessary data based on a user's inquiry and sending prompt sentences to the generative AI model based on that data.

[1664] "Means of receiving an answer from a generative AI model and displaying it to a user" refers to methods or technologies for displaying the answer received from a generative AI model to a user.

[1665] This invention is a system that connects map data, store data, and user personal data with a generative AI model and provides the most enjoyable answer through dialogue with the user. A specific embodiment of this system will be described below.

[1666] Hardware and Software Used

[1667] Server: Database server (MySQL), generative AI model (e.g., GPT-4)

[1668] Device: Smartphone (iOS, Android)

[1669] Software: Map app (Google Maps API), store database (Yelp API)

[1670] Program Processing

[1671] Server Processing

[1672] The server collects map data, store data, and the user's personal data, and connects this data to the generative AI model. When the server receives an inquiry from a user, it generates and sends a prompt sentence to the generative AI model. For example, if a user asks, "Tell me about some recommended restaurants nearby," the server refers to the user's past behavioral data and preference data, and sends a prompt sentence to the generative AI model to generate an answer such as, "It seems you like Italian food. Here are some nearby Italian restaurants with good reviews."

[1673] Terminal handling

[1674] The terminal receives an inquiry from a user and transmits it to the server. The terminal receives the generated answer from the server and displays it to the user. For example, when a user uses a smartphone to input "Tell me about some recommended restaurants nearby," the terminal transmits this inquiry to the server, receives the answer from the server, and displays it on the screen.

[1675] User Actions

[1676] A user uses a terminal to make inquiries to the system. For example, if the user inputs "Tell me about some recommended restaurants nearby," the system will suggest the best restaurant based on the user's past behavioral data and preference data. The user can check the information on the suggested restaurant and visit it.

[1677] Examples

[1678] Examples of prompt statements

[1679] Example prompt text for when a user types "What are some good restaurants near me?":

[1680] User past behavior data:

[1681] Previously visited restaurants: Italian, French

[1682] Rated restaurants: Italian (5 stars), French (4 stars)

[1683] User Preference Data:

[1684] Favorite food: Italian

[1685] Disliked food: Chinese food

[1686] Prompt statement:

[1687] "Users seem to like Italian food. Can you recommend a good Italian restaurant nearby?"

[1688] By sending this prompt text to a generative AI model, the system can provide the user with the best restaurant suggestions.

[1689] Data processing and calculation

[1690] The server retrieves the user's past behavioral data and preference data from a MySQL database, and generates a prompt sentence based on this data. A generative AI model (e.g., GPT-4) analyzes this prompt sentence and generates an optimal answer. The generated answer is sent to the terminal via the server and displayed to the user.

[1691] In this way, it becomes possible to provide optimal suggestions based on the user's preferences and past behavioral data, and also to take into account dynamic information such as current location, time of day, and weather.

[1692] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1693] Step 1:

[1694] A user inputs a query into a terminal.

[1695] A user uses a device such as a smartphone or tablet to input a question to the system. For example, the user might type, "Tell me about some recommended restaurants nearby." The input data is then sent to the device's application.

[1696] Step 2:

[1697] The terminal sends a query to the server.

[1698] The terminal sends the query entered by the user to the server. At this time, the terminal transmits data to the server via the Internet. The input data includes the user ID and current location information. The output of the terminal is the query data that is sent to the server.

[1699] Step 3:

[1700] The server receives the query and collects the required data.

[1701] The server analyzes the inquiry received from the terminal and retrieves the user's past behavioral data and preference data from a database. For example, it retrieves the user's past restaurant visit history and rating data from a MySQL database. The input is the inquiry data from the terminal, and the output is the collected user data.

[1702] Step 4:

[1703] The server sends a prompt to the generative AI model.

[1704] The server generates a prompt sentence based on the collected data and sends it to the generative AI model. The prompt sentence includes the user's preferences and past behavioral data. For example, a prompt sentence may be generated that reads, "The user seems to like Italian food. Can you tell me about a nearby Italian restaurant with a good reputation?" The input is the collected user data, and the output is the generated prompt sentence.

[1705] Step 5:

[1706] The generative AI model generates an answer and returns it to the server.

[1707] The generative AI model generates the best answer for the user based on the prompt it receives. For example, it generates an answer such as, "It seems you like Italian food. Here are some good Italian restaurants nearby." The input is the prompt, and the output is the generated answer.

[1708] Step 6:

[1709] The server sends the generated answer to the terminal.

[1710] The server sends the answer received from the generative AI model to the terminal. At this time, the server converts the answer into an appropriate format and sends it. For example, it converts it into JSON format and sends it. The input is the generated answer, and the output is the data sent to the terminal.

[1711] Step 7:

[1712] The terminal displays the answer to the user.

[1713] The terminal displays the answer received from the server to the user. The user can check the suggested restaurant information on the terminal screen. The input is the answer data from the server, and the output is the information displayed to the user.

[1714] (Application example 1)

[1715] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1716] Conventional store recommendation systems do not fully consider the user's preferences or past behavior data, and can only make general recommendations, making it difficult for users to find stores that they will truly enjoy. In addition, real-time recommendations based on the user's current location are not made, resulting in low user convenience.

[1717] 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.

[1718] In this invention, the server includes a means for connecting map data, store data, and user's personal data to generative AI and providing the most enjoyable answer through dialogue with the user, a means for proposing content that the user will enjoy most at a specified location or current location, and a means for searching for stores within a certain radius from the user's current location and recommending the most suitable store based on the user's preferences. This enables personalized store recommendations that take into account the user's preferences and past behavioral data, making it possible to find the most suitable store in real time.

[1719] "Map Data" means data that includes geographic location information and provides detailed information about specific points or regions.

[1720] "Store data" is data that includes information about each store, providing details such as the store's name, location, opening hours, and ratings.

[1721] "User's personal data" refers to data that contains information about the individual user, including preferences, past behavioral history, current location information, etc.

[1722] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide optimal answers through dialogue with users.

[1723] "Means for providing the most enjoyable answers through dialogue" are means that have the function of providing information and suggestions that users will find most enjoyable through communication with the users.

[1724] "Means for suggesting content that the user will enjoy most at a specified location or current location" refers to means having a function for suggesting content that the user will enjoy based on a location specified by the user or current location information.

[1725] The "means for searching for stores within a certain radius from the user's current location" refers to a means having a function for searching for stores within a certain distance based on the user's current location.

[1726] A "means for recommending the most suitable store based on the user's preferences" is a means having a function for selecting and recommending the most suitable store to a user based on the user's preference data.

[1727] The system for implementing this invention connects map data, store data, and user's personal data with generative AI, and provides the most enjoyable answer through dialogue with the user. A specific embodiment of this system is described below.

[1728] System Configuration

[1729] The system includes a server, a user's smartphone, and a generative AI model. The server manages map data, store data, and the user's personal data, and processes the data in cooperation with the generative AI model. The user's smartphone functions as a dialogue interface, receives input from the user, and communicates with the server.

[1730] Program Processing

[1731] The server processes the data in the following steps:

[1732] 1. Data Acquisition: The server acquires the user's current location information, past behavior data, and preference data, including GPS data, past search history, and rating data.

[1733] 2. Store search: The server uses map services such as Google Maps API to search for stores within a certain radius of the user's current location.

[1734] 3. Data analysis: The server uses a generative AI model to analyze the acquired data and select the most suitable store based on the user's preferences.

[1735] 4. Generation of recommendation results: The server uses a generative AI model to generate a list of stores that are most suitable for the user and sends it to the smartphone.

[1736] Hardware and software used

[1737] Hardware: Smartphones, servers

[1738] Software: Python, Google Maps API, generative AI model

[1739] Examples

[1740] When a user asks their smartphone, "Can you recommend a nearby cafe?" the system operates as follows.

[1741] 1. The server obtains that the user's current location is "Shibuya-ku, Tokyo."

[1742] 2. The server learns that the user's preferences are "cafe" and "Italian."

[1743] 3. The server uses the Google Maps API to search for cafes in Shibuya Ward.

[1744] 4. The server uses a generative AI model to filter the search results to cafes that match the user's preferences.

[1745] 5. The server generates a list of the best cafes and sends it to the smartphone.

[1746] Examples of prompt statements

[1747] "Please tell me about some recommended cafes nearby. I like cafes and Italian food. I'm currently located in Shibuya Ward, Tokyo."

[1748] The flow of the specific process in the application example 1 will be described with reference to FIG.

[1749] Step 1:

[1750] The server acquires the user's current location information.

[1751] Input: GPS data from the user's smartphone

[1752] Output: User's current location (e.g. Shibuya-ku, Tokyo)

[1753] Specific operation: The server receives the GPS data sent from the smartphone and determines the user's current location.

[1754] Step 2:

[1755] The server acquires the user's past behavioral data and preference data.

[1756] Input: User's past search history, rating data, preference data

[1757] Output: User's past behavioral data and preference data (e.g., likes cafes and Italian food)

[1758] Specific operations: The server retrieves the user's past behavioral history and preference data from the database and prepares it for analysis.

[1759] Step 3:

[1760] The server uses the Google Maps API to search for stores within a certain radius of the user's current location.

[1761] Input: User's current location, search radius (e.g. 1500 meters)

[1762] Output: A list of stores as search results

[1763] Specific operation: The server sends a request to the Google Maps API specifying the user's current location and search radius, and receives the returned list of stores.

[1764] Step 4:

[1765] The server uses a generative AI model to analyze the acquired data and select the most suitable store based on the user's preferences.

[1766] Input: Store list, user preference data

[1767] Output: A list of optimal stores that match the user's preferences

[1768] Specific operation: The server inputs the store list and user preference data into the generative AI model, and the AI ​​model filters the most suitable stores based on the preferences.

[1769] Step 5:

[1770] The server uses a generative AI model to generate a list of stores that are best suited for the user and sends it to the user's smartphone.

[1771] Input: Optimal store list

[1772] Output: A list of recommended stores displayed on the user's smartphone

[1773] Specific operation: The server sends the optimal store list generated by the generative AI model to the user's smartphone and displays it on the smartphone screen.

[1774] Example 2

[1775] Next, a second embodiment of the second embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1776] Conventional content suggestion systems have difficulty in suggesting optimal content by comprehensively considering various information such as the user's current location, preferences, time of day, and weather. As a result, it has been difficult to provide content that users can truly enjoy, and improving user satisfaction has been an issue.

[1777] 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.

[1778] In this invention, the server includes a means for acquiring user's current location information, a means for acquiring weather information, a means for acquiring time zone information, a means for acquiring user's preference data, a means for analyzing the acquired data, a means for generating content using a generative AI model, a means for transmitting the generated content to the user's terminal, and a means for displaying the content on the user's terminal. This makes it possible to propose content that the user will enjoy most by comprehensively considering the user's various information.

[1779] The "means for acquiring the user's current location information" is a function for acquiring location information from the user's terminal.

[1780] "Means for acquiring weather information" is a function for acquiring current weather data from an external weather information service.

[1781] The "means for acquiring time zone information" is a function for acquiring current time zone data from a system clock or an external time information providing service.

[1782] "Means for acquiring user preference data" refers to a function for acquiring a user's past behavior history and preference data from a database.

[1783] The "means for analyzing acquired data" refers to a data analysis function for identifying the most suitable content for a user based on various collected data.

[1784] A "means for generating content using a generative AI model" is a function for using a generative AI model to generate content to be suggested to users.

[1785] The "means for transmitting the generated content to the user's terminal" is a function for transmitting the generated content to the user's terminal via a network.

[1786] The "means for displaying content on a user's terminal" is a function for displaying received content on the screen of the user's terminal.

[1787] This invention is a system that proposes optimal content by comprehensively considering information such as the user's current location, preferences, time of day, weather, etc. This system is realized using a server, a terminal, and a generative AI model.

[1788] First, the server uses the GPS function of the terminal to obtain the user's current location information. Specifically, the terminal uses the built-in GPS module to obtain the user's current location and transmits the information to the server.

[1789] The server then uses an external weather information service (e.g., OpenWeatherMap API) to obtain weather information. The server retrieves current weather data based on the user's current location.

[1790] Additionally, the server uses the system clock to obtain time zone information. The server obtains the current time zone data and combines it with other data for analysis.

[1791] The server uses a database (e.g., MySQL) to obtain user preference data. The server obtains the user's past behavior history and preference data from the database and uses it for analysis.

[1792] The server performs data analysis based on the various data acquired. This analysis uses machine learning models (e.g. TensorFlow). The server identifies the most suitable content for the user based on the collected data.

[1793] The server then uses a generative AI model (e.g., GPT-3) to generate content to suggest to the user, taking into account the user's current location, preferences, time of day, weather, etc.

[1794] The generated content is sent from the server to the user's device. The device displays the received content to the user. For example, the application screen may display, "There's an Italian restaurant you might like, just a five-minute walk from your current location."

[1795] Examples:

[1796] When a user inputs "I want to go to lunch now" into the terminal, the following specific actions are taken:

[1797] 1. The user opens the application on their smartphone and types, "I want to go to lunch now."

[1798] 2. The device uses its built-in GPS function to obtain its current location, "Shibuya Ward, Tokyo."

[1799] 3. The server uses the OpenWeatherMap API to obtain weather information such as "sunny, temperature 25 degrees."

[1800] 4. The server obtains the time zone information for "12:30 PM" from the system clock.

[1801] 5. The server retrieves the user's preference data, such as "I like Italian food," from the database.

[1802] 6. The server uses TensorFlow to identify "Italian restaurants within a 5-minute walk from the current location."

[1803] 7. The server uses GPT-3 to generate suggestions such as, "There's an Italian restaurant you might like within a five-minute walk from your current location."

[1804] 8. The server sends the generated proposal to the user's device.

[1805] 9. The device will display the message on the application screen: "There's an Italian restaurant you might like, just a five-minute walk from your current location."

[1806] In this way, it is possible to comprehensively consider the user's various information and propose content that the user will enjoy most.

[1807] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1808] Step 1:

[1809] The user types a prompt into the terminal.

[1810] Specific actions: The user opens an application on their smartphone and types, "I want to go to lunch now."

[1811] Input: User prompt text

[1812] Output: The prompt is sent to the terminal.

[1813] Step 2:

[1814] The device obtains location information.

[1815] Specific operation: The device uses the built-in GPS function to obtain the user's current location information. For example, the device obtains the current location information of "Shibuya-ku, Tokyo."

[1816] Input: GPS data

[1817] Output: Current location is saved on the device

[1818] Step 3:

[1819] The device sends its current location information to the server.

[1820] Specific operation: The device sends the obtained location information to the server.

[1821] Input: Current location

[1822] Output: Current location is sent to the server

[1823] Step 4:

[1824] The server obtains weather information.

[1825] Specific operation: The server obtains weather information for the user's current location using an external weather information service (e.g., OpenWeatherMap API). For example, it obtains weather information such as "sunny, temperature 25 degrees."

[1826] Input: Current location

[1827] Output: Weather information is stored on the server.

[1828] Step 5:

[1829] The server obtains the time zone information.

[1830] Specific behavior: The server uses the system clock to obtain the current time zone information, for example, "12:30 PM."

[1831] Input: System clock data

[1832] Output: Time zone information is saved on the server

[1833] Step 6:

[1834] The server obtains the user's preference data.

[1835] Specific operation: The server retrieves the user's past behavior history and preference data from a database (e.g., MySQL). For example, it retrieves preference data such as "I like Italian food."

[1836] Input: User ID

[1837] Output: Preference data is stored on the server

[1838] Step 7:

[1839] The server analyzes the obtained data.

[1840] Specific operation: The server uses a machine learning model (e.g. TensorFlow) to identify the most suitable content for the user based on the collected current location information, weather information, time zone information, and preference data. For example, it identifies "Italian restaurants within a 5-minute walk from the current location."

[1841] Input: Current location, weather, time zone, preference data

[1842] Output: The analysis results are stored on the server.

[1843] Step 8:

[1844] The server generates content using a generative AI model.

[1845] Specific operation: The server uses a generative AI model (e.g. GPT-3) to generate content to suggest to the user. For example, it generates suggestions such as "There is an Italian restaurant you might like within a five-minute walk from your current location."

[1846] Input: Analysis results

[1847] Output: The generated content is saved on the server.

[1848] Step 9:

[1849] The server transmits the generated content to the terminal.

[1850] Specific operation: The server sends the generated proposal to the user's device.

[1851] Input: Generated content

[1852] Output: Content is sent to the device

[1853] Step 10:

[1854] The terminal displays the content to the user.

[1855] Specific operation: The device displays the received content to the user. For example, the application screen displays "There is an Italian restaurant you might like, just a five-minute walk from your current location."

[1856] Input: Generated content

[1857] Output: The content is displayed on the device screen.

[1858] (Application example 2)

[1859] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal".

[1860] Conventional content recommendation systems do not take into consideration the user's current location, time of day, weather, and preferences, making it difficult to provide users with the content they would most enjoy. In addition, there is a lack of interactive recommendation systems using smartphones or smart glasses, which makes it difficult for users to find the best restaurants and delivery options in real time.

[1861] 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.

[1862] In this invention, the server includes a means for connecting map data, store data, and user's personal data to a generative AI and providing the most enjoyable answer through dialogue with the user, a means for suggesting the most enjoyable content for the user at a specified location or current location, a means for suggesting optimal restaurants and delivery options taking into consideration the user's current location, time zone, weather, and preferences, and a means for making suggestions based on the content of a question posed by the user using a smartphone or smart glasses. This enables the user to find the optimal restaurants and delivery options in real time.

[1863] "Map Data" means data that includes geographic location information and is used to identify a User's current location and destination.

[1864] "Store data" refers to data that includes information about stores such as restaurants and retail outlets, providing details such as store location, opening hours, and menu.

[1865] "User's personal data" refers to data that includes personal information such as the user's preferences and past behavioral history, and is used to make optimal suggestions to the user.

[1866] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide optimal answers through dialogue with users.

[1867] "Means for providing the most enjoyable answers through dialogue" refers to methods and technologies for providing content or information that users will find most enjoyable through dialogue with the user.

[1868] "Means for suggesting content that a user will enjoy most at a specified location or current location" refers to a method or technology for suggesting content that a user will enjoy based on a location specified by the user or the current location.

[1869] "Means for suggesting optimal restaurants and delivery options taking into consideration a user's current location, time of day, weather, and preferences" refers to methods and technologies for suggesting optimal restaurants and delivery options based on information such as a user's current location, time of day, weather, and preferences.

[1870] "Means for making suggestions based on questions posed by a user using a smartphone or smart glasses" refers to a method or technology for making optimal suggestions based on questions posed by a user using a smartphone or smart glasses.

[1871] A system for implementing this invention connects map data, store data, and user personal data with generative AI, and provides the most enjoyable answer through dialogue with the user. The system includes a means for suggesting the most enjoyable content for the user at a specified location or current location. The system also includes a means for suggesting optimal restaurants and delivery options taking into account the user's current location, time of day, weather, and preferences, and a means for making suggestions based on the content of a question posed by the user using a smartphone or smart glasses.

[1872] The server first obtains the user's personal data, including the user's preferences and past behavioral history. Next, the server obtains information such as the user's current location, time of day, and weather. Based on this information, the server uses generative AI to suggest optimal restaurants and delivery options.

[1873] Specifically, the server performs the following process. First, it obtains the user's current location and compares map data with store data. Next, it creates a list of the user's favorite restaurants based on the user's preference data. It also takes into account the current time of day and weather to make optimal suggestions. For example, if it is raining, it will give priority to delivery options.

[1874] The system makes suggestions based on questions posed by users using a smartphone or smart glasses. If a user says, "I'd like to go to lunch now," the system will suggest the most suitable restaurant, taking into account the user's current location, preferences, and time of day. For example, the system might suggest, "There's an Italian restaurant you might like, just a five-minute walk from your current location. They also offer delivery."

[1875] The hardware used is a smartphone or smart glasses, and the software uses Python. API requests are used to obtain data, and the standard Python library is used to process and calculate the data.

[1876] As a concrete example, the prompt sentence to be input to the generative AI model is shown below.

[1877] User ID: user123

[1878] Current location: Tokyo Station

[1879] Preferences: Italian, Price range: Medium, Allergies: Nuts

[1880] Weather: Sunny

[1881] If you enter this prompt, you will get the following suggestions:

[1882] There is an Italian restaurant you might like within a 5-minute walk from your current location, and they also offer delivery.

[1883] This way, users can find the best dining and delivery options in real time.

[1884] The flow of the specific process in the application example 2 will be described with reference to FIG...

Claims

1. A means to connect map data, store data, and user personal data with a generative AI model and provide the most enjoyable answers through dialogue with users, A means for suggesting content that the user will most enjoy at a specified location or a current location; means for receiving a user inquiry, collecting necessary data, and sending a prompt to the generative AI model; A means for receiving a response from the generative AI model and displaying it to a user; Including, The system includes a means for displaying to the user store data that the user will find most enjoyable based on the prompt text.

2. The system according to claim 1 , wherein the generative AI model generates the personal data based on the user's past behavioral data and preference data.

3. The system according to claim 1 , wherein the content suggesting means suggests the content taking into consideration information on the user's current location, destination, time zone, and weather.

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