System

Abstract

To provide a system.SOLUTION: A system comprises: means for receiving an inquiry for transportation directions from a current location to a destination via chat or voice input; means for analyzing the received inquiry using pre-language processing technology, and generating the directions; means for translating the generated directions into a language used by a user; means for transmitting the translated directions; and means for recognizing emotions from the user's input, and providing additional information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] When foreigners visit Japan, they face challenges in choosing transportation and tourist destinations because they cannot understand the local language. Another challenge is that it is difficult to provide information tailored to the interests and preferences of individual tourists, and only general information can be provided. [Means for solving the problem]

[0005] This invention provides a means to generate transportation directions from a current location to a destination via chat or voice input and translate them into the user's language. It also provides a means to recommend nearby areas based on the user's behavioral and search history. It also includes a means to sell usage data or connect with other companies via API. This will eliminate the difficulties of travel and tourist destination selection for foreign tourists visiting Japan and enable the provision of information tailored to the interests and preferences of each individual tourist. [Brief explanation 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. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 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. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the disclosed technique will be described below with reference to the accompanying drawings.

[0008] do.

[0009] First, the terms used in the following description will be explained.

[0010] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0011] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0012] 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.

[0013] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0014] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0015] [First embodiment]

[0016] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] 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.

[0026] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0027] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0028] "Example 1"

[0029] One embodiment of the present invention is an application installed on a mobile device such as a smartphone or tablet. Through this application, a user can inquire about public transportation routes from their current location to their destination via chat or voice input. The application generates an optimal route based on the user's input, translates it into the user's language, and displays it. For example, if a user inputs, "What is the fastest route from Tokyo Station to Narita Airport?", the application generates the optimal route taking into account public transportation timetables and operating conditions, and displays it in the user's language.

[0030] "Example 2"

[0031] One embodiment of the present invention has a function to recommend nearby recommended information based on the user's behavioral history and search history. If the user visits a specific tourist spot or searches for a specific keyword, related recommended information is recommended based on that information. For example, if the user searches for "sushi," sushi restaurants near the user's current location are recommended.

[0032] "Example 3"

[0033] Furthermore, one embodiment of the present invention has the function of selling usage data or linking with other companies via API. Data such as user behavior history and search history is useful information in various industries, including tourism and advertising. This invention creates new business opportunities by providing this data in an appropriate format to third parties. For example, providing data on users who visited a specific tourist destination to the operator of the destination can support more effective marketing activities.

[0034] The processing flow of each embodiment will be described below.

[0035] "Example 1"

[0036] Step 1: A user launches an application installed on a mobile terminal.

[0037] Step 2: The user requests transportation directions from their current location to their destination via chat or voice input. For example, they might say, "What is the fastest route from Tokyo Station to Narita Airport?"

[0038] Step 3: The application generates an optimal route based on the user's input, taking into account public transport timetables and availability.

[0039] Step 4: Translate the generated route into the user's language.

[0040] Step 5: Display the translated route to the user in your application.

[0041] "Example 2"

[0042] Step 1: The user visits a specific tourist spot or searches for a specific keyword. Step 2: The application recommends related information based on the user's behavioral and search history.

[0043] Step 3: Display the recommended information to the user in the application.

[0044] "Example 3"

[0045] Step 1: The application collects data such as user behavior and search history.

[0046] Step 2: Provide the collected data to third parties in an appropriate manner, taking into consideration the protection of data privacy.

[0047] Step 3: The third party creates new business opportunities based on the provided data. For example, the third party may provide data on users who visited a particular tourist destination to the operator of the destination to support more effective marketing activities.

[0048] Example 1

[0049] 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 device 14 will be referred to as a "terminal."

[0050] Conventional transportation route guidance systems make it difficult for users to easily find the optimal route to their destination. Furthermore, they rarely translate information into the user's language, making them difficult to use for users who do not understand foreign languages. Furthermore, they lack personalized recommendations based on the user's behavioral and search history, resulting in low user convenience.

[0051] 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.

[0052] In this invention, the server includes means for receiving user input and analyzing it using natural language processing technology, means for generating an optimal route based on the analysis results, and means for translating the generated route into the user's language. This allows the user to easily find the optimal route to their destination, and since the route is displayed in the user's language, it can be used by users who do not understand foreign languages. In addition, personalized recommended information can be provided based on the user's behavioral history and search history, improving user convenience.

[0053] "User" means any person or entity that uses the System to request transportation directions.

[0054] "Input" refers to the provision of information by a user to the system via chat or voice.

[0055] "Natural language processing technology" refers to technology for analyzing user input and understanding its meaning.

[0056] "Analysis results" refers to information extracted from user input using natural language processing techniques.

[0057] An "optimal route" refers to the most efficient combination of transportation modes from a starting point to a destination.

[0058] "Translation" refers to converting the generated route into the user's language.

[0059] "Display" refers to providing the translated route visually on the user's terminal.

[0060] "Recommended information" refers to related information provided based on a user's behavioral history and search history.

[0061] "Behavior history" refers to a record of actions taken by a user in the past.

[0062] "Search history" refers to a record of searches a user has previously conducted.

[0063] "Personalization" refers to customizing information based on a user's individual characteristics and history.

[0064] "Usage Data" means information relating to the use of the System.

[0065] "API" refers to an interface for connecting with other systems and services.

[0066] This invention is a system that allows a user to inquire about transportation routes from their current location to a destination through an application installed on a mobile device such as a smartphone or tablet. Specific embodiments of this system are described below.

[0067] Hardware and software used

[0068] Hardware:

[0069] Mobile devices: smartphones, tablets

[0070] software:

[0071] Natural language processing software: Google® Cloud Natural Language API, IBM Watson® Natural Language Understanding

[0072] Route generation software: Google Maps API, OpenTripPlanner

[0073] Translation software: DeepL API, Google Translate API

[0074] System Operation

[0075] User Input

[0076] A user starts an application installed on a mobile device and uses chat or voice input to inquire about transportation routes from their current location to their destination. For example, they might input, "What is the fastest route from Tokyo Station to Narita Airport?"

[0077] Parsing input

[0078] The device receives user input and analyzes it through an application using natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding. The analysis extracts information such as the starting point, destination, and user intent.

[0079] Generate optimal routes

[0080] The server receives the analysis results sent from the device and generates the optimal route. The server uses route generation software such as Google Maps API or OpenTripPlanner. For example, it calculates the fastest route from Tokyo Station to Narita Airport and generates a route that combines multiple modes of transportation (trains, buses, etc.).

[0081] Route Translation

[0082] The server translates the generated route into the user's language using translation software such as the DeepL API or Google Translate API. For example, for an English-speaking user, the server generates a translation such as "Take the JR Yamanote Line from Tokyo Station to Nippori Station, then transfer to the Keisei Skyliner to Narita Airport."

[0083] View translated routes

[0084] The device receives the translated route sent from the server and displays it to the user. The display includes the translation results in the user's language. For example, the application screen might display the "fastest route from Tokyo Station to Narita Airport," along with specific transportation methods and travel times.

[0085] Specific examples

[0086] Example user input

[0087] A user types into the application, "What is the quickest route from Tokyo Station to Narita Airport?"

[0088] Prompt Sentence Examples

[0089] "The user inputs that they want to know the fastest route from Tokyo Station to Narita Airport. Please generate the optimal route taking into account public transport timetables and operation status, and display it in the user's language."

[0090] In this way, users can easily find the best route to their destination. It is also possible to provide personalized recommendations based on the user's behavioral and search history, improving user convenience.

[0091] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0092] Step 1:

[0093] User Input

[0094] A user starts an application installed on a mobile device and uses chat or voice input to inquire about transportation routes from their current location to their destination. For example, they might input, "Tell me the quickest route from Tokyo Station to Narita Airport." This input is sent to the device.

[0095] Input: User chat or voice input

[0096] Output: User input data

[0097] Step 2:

[0098] Parsing input

[0099] The device receives user input and analyzes it through an application using natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding. The analysis extracts information such as the starting point, destination, and user intent.

[0100] Input: User-entered data

[0101] Data processing: Analyzes input using natural language processing technology to extract origin, destination, and user intent.

[0102] Output: Analysis results (origin, destination, user intent)

[0103] Step 3:

[0104] Generate optimal routes

[0105] The server receives the analysis results sent from the device and generates the optimal route. The server uses route generation software such as Google Maps API or OpenTripPlanner. For example, it calculates the fastest route from Tokyo Station to Narita Airport and generates a route that combines multiple modes of transportation (trains, buses, etc.).

[0106] Input: Analysis results (origin, destination, user intent)

[0107] Data calculation: Calculate the optimal route using route generation software

[0108] Output: Optimal route

[0109] Step 4:

[0110] Route Translation

[0111] The server translates the generated route into the user's language using translation software such as the DeepL API or Google Translate API. For example, for an English-speaking user, the server generates a translation such as "Take the JR Yamanote Line from Tokyo Station to Nippori Station, then transfer to the Keisei Skyliner to Narita Airport."

[0112] Input: Best Route

[0113] Data processing: Translate the route into the user's language using translation software

[0114] Output: Translated route

[0115] Step 5:

[0116] View translated routes

[0117] The device receives the translated route sent from the server and displays it to the user. The display includes the translation results in the user's language. For example, the application screen might display the "fastest route from Tokyo Station to Narita Airport," along with specific transportation methods and travel times.

[0118] Input: Translated route

[0119] Data processing: formatting the translated route for display to the user

[0120] Output: Route displayed on the user's device

[0121] (Application example 1)

[0122] Next, a description will be given of Application Example 1 of Embodiment 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."

[0123] In order for food delivery workers to deliver efficiently, they need to quickly obtain the optimal route that takes into account traffic conditions, weather, and road congestion. However, current navigation systems have difficulty generating routes that fully consider these factors, resulting in reduced delivery efficiency. Furthermore, when delivery workers work in multilingual environments, route information is often not provided in a language that is easy for them to understand, creating communication barriers. A system that can solve these issues is needed.

[0124] 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.

[0125] In this invention, the server includes means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, means for selling usage data or linking with other companies via API, means for generating an optimal route taking into account traffic conditions, weather, and road congestion, and means for translating the generated route information into the delivery person's language and displaying it. This enables delivery people to efficiently obtain the optimal route and perform delivery work smoothly even in a multilingual environment.

[0126] "Current Location" refers to the location where the User is currently located.

[0127] "Destination" refers to the location the user is heading to.

[0128] "Transportation" refers to means of travel, including public transport and road transport.

[0129] "Chat" refers to a means of communication using text messages.

[0130] "Voice input" refers to a means of inputting information using voice.

[0131] "Translation" refers to the conversion of information written in one language into another language.

[0132] "Nearby recommendations" refers to information about interesting places and services near a user's current location or destination.

[0133] "Behavioral history" refers to a record of a user's past actions.

[0134] "Search history" refers to a record of searches a user has conducted in the past.

[0135] "Recommendation" refers to recommending specific information or services to a user.

[0136] "Usage Data" refers to data generated when you use the Application.

[0137] "API" stands for Application Programming Interface, and refers to an interface for exchanging functions and data between different software programs.

[0138] "Other companies" refers to companies other than the company that provides the application.

[0139] "Traffic conditions" refers to the current operating conditions of roads and public transport.

[0140] "Weather" refers to current weather conditions.

[0141] "Road congestion status" refers to the amount of traffic and congestion on the road.

[0142] An "optimal route" refers to the most efficient route to a destination, taking into account traffic conditions, weather, and road congestion.

[0143] "Delivery Person" refers to a person who delivers food to provide food delivery services.

[0144] A "multilingual environment" refers to an environment in which multiple languages ​​are used.

[0145] A system for implementing this invention includes an application installed on a mobile device such as a smartphone or tablet. Through this application, a user can inquire about transportation routes from their current location to their destination by chat or voice input. A specific embodiment of this system is described below.

[0146] Hardware and Software

[0147] Hardware: Smartphones, tablets

[0148] Software: Python, requests library, geopy library

[0149] System configuration

[0150] 1. User Input: Using an application installed on a smartphone or tablet, a user inputs their current location and destination via chat or voice input, for example, "What is the best route from my current location to my next delivery destination?"

[0151] 2. API request: The device sends a request to the traffic information API to obtain the optimal route from the current location to the destination, taking into account traffic conditions, weather, and road congestion.

[0152] 3. Route Generation: The server analyzes the data returned by the API and generates the optimal route. This route information is translated into the user's language.

[0153] 4. Information display: The terminal displays the generated route information to the user, allowing delivery personnel to efficiently obtain the optimal route and carry out delivery work smoothly even in a multilingual environment.

[0154] Specific examples

[0155] User input: "What is the best route from my current location to my next delivery?"

[0156] App output: "The best route is to go straight on Street A, turn right at intersection B, then take Street C."

[0157] This system allows delivery personnel to quickly obtain the optimal route taking into account traffic conditions, weather, and road congestion.In addition, route information is translated into the delivery personnel's language, allowing for smooth delivery operations even in multilingual environments.

[0158] Prompt Sentence Examples

[0159] User: What is the best route from my current location to my next delivery?

[0160] App: The best route is to go straight on Street A, turn right at intersection B, then take Street C.

[0161] In this way, a navigation app can be realized that helps food delivery personnel make deliveries efficiently.

[0162] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0163] Step 1:

[0164] The user uses an application installed on a smartphone or tablet to enter their current location and destination via chat or voice input.

[0165] Input: Current location and destination information (e.g., "What is the best route from my current location to my next delivery destination?")

[0166] Output: Input current location and destination information

[0167] What happens: The user launches the application and uses the chat box or voice input function to enter their current location and destination.

[0168] Step 2:

[0169] The device receives the entered current location and destination information and sends a request to the traffic information API.

[0170] Input: Current location and destination information

[0171] Output: Request to traffic information API

[0172] What happens: The device analyzes the input information and sends a request to the traffic information API endpoint, including parameters such as current location, destination, and language preference.

[0173] Step 3:

[0174] The server receives the response from the traffic information API and generates the optimal route.

[0175] Input: Response data from traffic information API (e.g., traffic conditions, weather, road congestion)

[0176] Output: Optimal route information

[0177] What it does: The server analyzes the data returned by the API and calculates the optimal route, taking into account traffic, weather, and road congestion. This calculation is done using a geographic information system (GIS) and route optimization algorithms.

[0178] Step 4:

[0179] The server translates the generated optimal route information into the user's language.

[0180] Input: Best route information, user language settings

[0181] Output: Translated route information

[0182] What it does: The server translates the route information into the user's preferred language using natural language processing (NLP) techniques and translation APIs.

[0183] Step 5:

[0184] The device displays the translated route information to the user.

[0185] Input: Translated route information

[0186] Output: Route information displayed to the user

[0187] What happens: The device receives the translated route information and displays it through its user interface (UI), including text and map displays.

[0188] Step 6:

[0189] The user confirms the route information displayed and begins delivery.

[0190] Input: Translated route information

[0191] Output: Start of delivery

[0192] What happens: The user reviews the route information displayed and follows the instructions to begin the delivery. The route information may be updated in real time during the delivery.

[0193] Example 2

[0194] Next, a description will be given of 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."

[0195] Conventional systems do not provide personalized recommendations based on users' behavioral and search histories, making it difficult to provide optimal information tailored to users' interests. Furthermore, the generated directions are sometimes not the optimal route, reducing user convenience.

[0196] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for generating transportation routes from the current location to the destination through chat or voice input and translating them into the user's language, a means for recommending nearby recommended information based on the user's behavior history and search history, a means for selling usage data or linking with other companies via API, a means for generating recommended information based on the user's interests using a generation AI model, and a means for providing the generated recommended information to the user terminal. This makes it possible to provide optimal recommended information personalized based on the user's behavior history and search history.

[0197] "Current location" refers to the location information of the user's current location.

[0198] "Destination" refers to the location information of the place where the user wants to go.

[0199] "Transportation directions" refers to the route from your current location to your destination, including public transportation, walking, driving, and other modes of transportation.

[0200] "Chat or voice input" refers to the means by which a user inputs information via text message or voice.

[0201] "User's preferred language" refers to the language used by the user and means that the system supports this language.

[0202] "Means for translating" refers to the function of converting input information into another language.

[0203] "Nearby recommendations" refers to information about nearby interesting places and services provided based on the user's current location and behavioral history.

[0204] "User behavior history" refers to a record of a user's past locations and actions.

[0205] "Search history" refers to a record of keywords or queries a user has searched for in the past.

[0206] "Recommendation means" refers to a function that suggests related information based on a user's behavioral history and search history.

[0207] "Usage data" refers to data such as user behavior history and search history.

[0208] "Means of collaboration with other companies through sales or APIs" refers to methods for sharing collected data with other companies or services.

[0209] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate information.

[0210] "Recommended information based on user interests" refers to information that is predicted to be of interest to the user based on an analysis of the user's behavioral history and search history.

[0211] "User terminal" refers to the device used by the user, including smartphones, tablets, PCs, etc.

[0212] This invention is a system that recommends nearby information based on a user's behavioral history and search history. The system is implemented using a server, user devices (smartphones, tablets, PCs, etc.), a database management system (e.g., MySQL (registered trademark)), a location information service (e.g., map API), and a generative AI model (e.g., GPT-4 (registered trademark)).

[0213] System configuration

[0214] 1. User Device:

[0215] Users use devices such as smartphones or PCs to search for specific keywords or visit specific tourist spots.

[0216] Example: A user searches for "sushi" on their smartphone.

[0217] 2. Sending data to the server:

[0218] The user's device collects the user's behavioral history and search history and sends them to the server.

[0219] Example: A smartphone sends the search keyword "sushi" and its current location to a server.

[0220] 3. Data storage:

[0221] The server stores the received data in a database.

[0222] Example: The server stores the search keyword "sushi" and the user's current location in a MySQL database.

[0223] 4. Data Analysis:

[0224] The server analyzes the stored data to identify the user's interests and concerns.

[0225] Example: The server extracts the keyword "sushi" from a database and determines that the user is interested in sushi.

[0226] 5. Generating recommendation information:

[0227] The server uses a generative AI model (e.g., GPT-4) to generate recommendations based on the user's interests.

[0228] Example: The server inputs the prompt sentence "If the user searches for 'sushi', please list recommended sushi restaurants near the current location" to GPT-4 and obtains the generated list of sushi restaurants.

[0229] 6. Providing Recommendations:

[0230] The server transmits the generated recommendation information to the user's terminal.

[0231] Example: The server sends the generated list of sushi restaurants to the user's smartphone, and the list of sushi restaurants is displayed on the smartphone.

[0232] Specific examples

[0233] User Action: User searches for "sushi" on their smartphone.

[0234] Device operation: The smartphone sends the search keyword "sushi" and its current location information to the server.

[0235] Server Action:

[0236] 1. The server stores the search keyword "sushi" and the user's current location information in a MySQL database.

[0237] 2. The server extracts the keyword "sushi" from the database and determines that the user is interested in sushi.

[0238] 3. The server inputs the prompt sentence "If the user searches for 'sushi,' please list recommended sushi restaurants near the current location" to GPT-4 and obtains the generated list of sushi restaurants.

[0239] 4. The server sends the generated list of sushi restaurants to the user's smartphone.

[0240] User pickup: A list of sushi restaurants near the user's current location will be displayed on the user's smartphone.

[0241] In this way, a system is realized that provides relevant recommended information based on a user's behavioral history and search history.

[0242] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0243] Step 1:

[0244] Users use devices such as smartphones and PCs to search for specific keywords or visit specific tourist spots.

[0245] Input: User searches for "sushi."

[0246] Specific action: The user types "sushi" into their smartphone and presses the search button.

[0247] Output: The search keyword "sushi" and your current location will be saved to your device.

[0248] Step 2:

[0249] The device collects the user's behavioral history and search history and sends them to the server.

[0250] Input: Search keyword "sushi" and your current location.

[0251] Specific operation: The smartphone sends the search keyword "sushi" and its current location information to the server.

[0252] Output: The server receives the search keyword "sushi" and the current location.

[0253] Step 3:

[0254] The server stores the received data in a database.

[0255] Input: Search keyword "sushi" and your current location.

[0256] Specific operation: The server saves the search keyword "sushi" and the user's current location information in a MySQL database.

[0257] Output: The search keyword "sushi" and current location information are saved in the database.

[0258] Step 4:

[0259] The server analyzes the stored data to identify the user's interests and concerns.

[0260] Input: Search keyword "sushi" saved in the database and your current location.

[0261] Specific operation: The server extracts the keyword "sushi" from the database and determines that the user is interested in sushi.

[0262] Output: The analysis result shows that the user is interested in sushi.

[0263] Step 5:

[0264] The server uses a generative AI model (e.g., GPT-4) to generate recommendations based on the user's interests.

[0265] Input: Analytics showing the user is interested in sushi and their current location.

[0266] Specific operation: The server inputs the prompt sentence to GPT-4: "If the user searches for 'sushi,' please list recommended sushi restaurants near the current location," and obtains the generated list of sushi restaurants.

[0267] Output: A list of recommended sushi restaurants near your current location.

[0268] Step 6:

[0269] The server transmits the generated recommendation information to the user terminal.

[0270] Input: A list of recommended sushi restaurants near your current location.

[0271] Specific operation: The server sends the generated list of sushi restaurants to the user's smartphone.

[0272] Output: A list of sushi restaurants will be displayed on the user's smartphone.

[0273] In this way, a system is realized that provides related recommended information based on a user's behavior history and search history.

[0274] (Application example 2)

[0275] 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."

[0276] Conventional recommendation systems provide insufficient information based on user behavioral history and search history, making it difficult to provide real-time recommendations based on the user's current location. Furthermore, personalized information based on the user's search history is limited, leaving a lack of means to improve user convenience.

[0277] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for generating transportation routes from the current location to the destination through chat or voice input and translating them into the user's language, a means for recommending nearby recommended information based on the user's behavioral history and search history, a means for selling usage data or linking with other companies via API, a means for obtaining recommended information around the current location based on the user's search history using an external API, and a means for displaying the obtained recommended information. This allows the user to obtain real-time recommended information based on their current location and provides personalized information based on their search history.

[0278] "Current location" refers to the location information of the user's current location.

[0279] "Destination" refers to the location information of the place where the user wants to go.

[0280] "Transportation directions" refers to route information for a user to travel from their current location to their destination.

[0281] "Chat or voice input" refers to the means by which a user inputs information using text messages or voice.

[0282] "User's preferred language" means the language used by the User.

[0283] "Means of translation" refers to the ability to convert information written in one language into another language.

[0284] "Recommended information in the area" refers to information about stores, tourist spots, etc. located near the user's current location.

[0285] "User behavior history" refers to a record of a user's past locations and actions.

[0286] "Search history" refers to a record of keywords and phrases a user has searched for in the past.

[0287] "Recommendation means" refers to the function of suggesting related information based on a user's behavioral history and search history.

[0288] "Usage data" refers to data such as user behavior history and search history.

[0289] "Means of selling or collaborating with other companies through APIs" refers to the ability to sell usage data to other companies or share data with other companies through APIs.

[0290] "External API" refers to an application programming interface provided by another system or service.

[0291] "Means of acquisition" refers to the function of acquiring the necessary information from an external API, etc.

[0292] "Means for displaying" refers to the function of visually presenting the acquired information to the user.

[0293] The system for implementing this invention has the function of generating transportation directions from a user's current location to a destination via chat or voice input and translating them into the user's language. It also has the function of recommending nearby information based on the user's behavioral history and search history. It also has the function of selling usage data or linking with other companies via API.

[0294] System configuration

[0295] The system consists of the following main components:

[0296] 1. User device: A device such as a smartphone or tablet on which the user chats or uses voice input.

[0297] 2. Server: Manages user behavioral history and search history and generates recommendation information.

[0298] 3. External API: Used to get recommendations around the user's current location.

[0299] Program processing

[0300] The server does the following:

[0301] 1. Obtaining current location: Obtaining current location information from the user's device, using GPS or Wi-Fi location services.

[0302] 2. Route Generation: Generate routes to a destination specified by the user via chat or voice input, using a map API (e.g., Google Maps API).

[0303] 3. Language Translation: The generated directions are translated into the user's language using a translation API (e.g., Google Translate API).

[0304] 4. Generating recommendation information: Based on the user's behavioral history and search history, recommended information around the current location is obtained from an external API and recommendation information is generated.

[0305] 5. Displaying information: Display the obtained recommended information on the user's device.

[0306] Hardware and software used

[0307] Hardware: smartphones, tablets, servers

[0308] Software: Map API (Google Maps API), Translation API (Google Translate API), Location Services (GPS, Wi-Fi)

[0309] Specific examples

[0310] For example, if a user searches for "cafe" and is currently in Tokyo, the system will:

[0311] 1. A user searches for "cafe" on their smartphone.

[0312] 2. The server obtains the user's current location (Tokyo).

[0313] 3. The server uses an external API to obtain information about cafes near the current location.

[0314] 4. The acquired cafe information is displayed on the user's smartphone.

[0315] Prompt Sentence Examples

[0316] User ID: user123

[0317] Search history: ["cafe", "sushi"]

[0318] Current location: Tokyo

[0319] By inputting this prompt into a generative AI model, the most appropriate recommendations can be provided to the user.

[0320] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0321] Step 1:

[0322] A user searches for "cafe" on their smartphone. The input is the keyword "cafe" that the user searched for, and the output is that this keyword is sent to the server.

[0323] Step 2:

[0324] The server obtains the user's current location information. The input is the location information (GPS data) sent from the user's smartphone, and the output is the user's current location (e.g., Tokyo). The server stores this location information in a database.

[0325] Step 3:

[0326] The server retrieves the user's search history from the database. The input is the user ID, and the output is the user's past search history (e.g., "cafe," "sushi"). The server prepares to generate recommendation information based on this history.

[0327] Step 4:

[0328] The server uses an external API to obtain information about cafes near the current location. The input is the user's current location (Tokyo) and the search keyword "cafe," and the output is a list of cafe information obtained from the external API. The server saves this list in a database.

[0329] Step 5:

[0330] The server sends the acquired cafe information to the user's smartphone. The input is a list of cafe information, and the output is the cafe information displayed on the user's smartphone. The user can select a cafe based on this information.

[0331] Step 6:

[0332] It displays detailed information about the cafe selected by the user. The input is the ID of the cafe selected by the user, and the output is detailed information about the cafe (address, opening hours, reviews, etc.). The user can visit the cafe based on this information.

[0333] Step 7:

[0334] The server updates the user's behavioral history. The input is information about the cafes the user visited, and the output is the updated behavioral history. The server stores this information in a database and uses it for future recommendations.

[0335] Example 3

[0336] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0337] Conventional systems have had difficulty effectively utilizing users' behavioral and search histories to provide personalized information. There were also issues with security when providing collected data to third parties, as well as the efficiency of data preprocessing and analysis. Furthermore, there was insufficient data provided to accurately cluster user behavior patterns and develop appropriate marketing strategies.

[0338] The identification process by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes a means for generating transportation routes from the current location to the destination through chat or voice input and translating the routes into the user's language, a means for recommending nearby recommended information based on the user's behavior history and search history, a means for selling usage data or linking with other companies via API, a means for collecting the user's behavior history and search history and storing them in a database, a means for preprocessing the stored data and converting it into a format suitable for analysis, a means for analyzing the preprocessed data and clustering user behavior patterns, and a means for providing the analysis results to a third party via API. This makes it possible to effectively utilize the user's behavior history and search history to provide personalized information and securely provide data to third parties.

[0339] "Current Location" refers to the location where the User is currently located.

[0340] "Destination" refers to the place where the user wants to go.

[0341] "Transportation directions" refers to the route from your current location to your destination.

[0342] "Chat or voice input" refers to the means by which a user inputs information by text or voice.

[0343] "User's preferred language" means the language used by the User.

[0344] "Translation" refers to the conversion of one language into another.

[0345] "Recommended information in the area" refers to useful information provided based on the user's current location and behavioral history.

[0346] "User behavior history" refers to a record of actions taken by a user in the past.

[0347] "Search history" refers to a record of searches a user has conducted in the past.

[0348] "Recommendation" refers to recommending specific information or products to users.

[0349] "Usage data" refers to data such as user behavior history and search history.

[0350] "Sales" refers to providing goods or services to others in exchange for payment.

[0351] "API" stands for Application Programming Interface, and refers to an interface for exchanging functions and data between different software programs.

[0352] "Collaboration with other companies" refers to working in cooperation with other companies or organizations.

[0353] A "database" refers to a system for efficiently storing, managing, and retrieving data.

[0354] "Preprocessing" refers to the initial processing of data to convert it into a form suitable for analysis.

[0355] "Analysis" refers to analyzing data to extract useful information.

[0356] "Clustering" refers to grouping data based on similarity.

[0357] "Third Party" refers to any individual or organization outside the system operator or user.

[0358] "Secure" means safe.

[0359] MODE FOR CARRYING OUT THE INVENTION

[0360] The present invention is a system that utilizes a user's behavior history and search history to provide personalized information and further provides this data to third parties. Specific embodiments of this system will be described below.

[0361] Data collection

[0362] The server collects the user's behavioral history and search history generated when the user uses the device to browse websites or use apps, including information on the user's searches for specific tourist attractions and the links they clicked.

[0363] Data storage

[0364] The server stores the collected data in a database, which can be an RDBMS or NoSQL database such as MySQL or MongoDB. The stored data includes user IDs, timestamps, search queries, clicked links, etc.

[0365] Data Preprocessing

[0366] The server preprocesses the stored data by cleaning and normalizing it and converting it into a format suitable for analysis, for example by removing duplicate data and imputing missing values.

[0367] Data analysis

[0368] The server then analyzes the preprocessed data using big data processing frameworks such as Hadoop or Spark, for example, using the K-means clustering algorithm to cluster user behavior patterns and identify user groups with specific interests.

[0369] Data provision

[0370] The server provides the analysis results to third parties via an API. The API is designed in a RESTful format and includes authentication functions such as OAuth, allowing data to be provided securely. For example, a tourism company could use this API to obtain data on users who visited a particular tourist destination.

[0371] Use of Data

[0372] Third parties may use the data provided to them to optimize their marketing strategies, for example, tourism operators may use data on users who have visited a particular tourist destination to target advertisements, thereby enabling them to conduct more effective marketing activities.

[0373] Specific examples

[0374] When a user searches for "Kyoto tourist spots" on their device, the search query and the clicked link are sent to the server. The server stores the user ID "12345," the timestamp "2023-10-01 10:00:00," the search query "Kyoto tourist spots," and the clicked link "https: / / example.com / kyoto-tourist-spots" in a database. The server then performs K-means clustering on the preprocessed data to classify users into "users interested in tourist spots" and "users interested in shopping." The server then provides the analysis results to the API endpoint "https: / / api.example.com / user-data." Tourist operators access this endpoint and retrieve the data using an authentication token. Based on the retrieved data, the tour operator delivers targeted advertisements to users who have visited specific tourist spots. For example, an advertisement for a Kyoto sightseeing tour is displayed to a user who is interested in "Kyoto tourist spots."

[0375] Prompt Sentence Examples

[0376] Examples of prompts to be input to a generative AI model include:

[0377] "Please explain how to analyze the interests of users who visit specific tourist spots based on user behavior history data and provide this information to tourist spot operators."

[0378] By using this prompt sentence, the generative AI model can generate detailed explanations about specific data analysis methods and API design methods. The flow of the specific processing in Example 3 will be described with reference to Fig. 15.

[0379] Step 1: Data collection

[0380] The server collects the behavioral history and search history generated when a user uses a device to browse websites or use apps. For example, if a user searches for "Kyoto tourist spots," the search query and information about the links clicked are sent to the server. The input is the user's search query and the links clicked, and the output is the collected behavioral history data.

[0381] Step 2: Save your data

[0382] The server stores the collected data in a database. The database uses an RDBMS or NoSQL database such as MySQL or MongoDB. Specifically, the user ID "12345", timestamp "2023-10-01 10:00:00", search query "Kyoto tourist spots", and clicked link "https: / / example.com / kyoto-tourist-spots" are stored. The input is the collected behavioral history data, and the output is the data stored in the database.

[0383] Step 3: Preprocessing the data

[0384] The server preprocesses the stored data. Specifically, it cleans and normalizes the data and converts it into a format suitable for analysis. For example, it removes duplicate data and imputes missing values. The input is the data stored in the database, and the output is the preprocessed data.

[0385] Step 4: Data analysis

[0386] The server analyzes the preprocessed data using a big data processing framework such as Hadoop or Spark. Specifically, it uses the K-means clustering algorithm to cluster user behavior patterns and identify user groups with specific interests. The input is the preprocessed data, and the output is the clustered user groups.

[0387] Step 5: Provide the data

[0388] The server provides the analysis results to third parties via an API. The API is designed in a RESTful format and includes authentication functions such as OAuth, allowing data to be provided securely. Specifically, tourism operators use this API to obtain data on users who have visited specific tourist destinations. The input is clustered user groups, and the output is data provided through the API.

[0389] Step 6: Use the data

[0390] Third parties use the provided data to optimize their marketing strategies. Specifically, a tourism company may run targeted advertisements based on data about users who have visited a particular tourist destination. For example, a user who is interested in "Kyoto tourist destinations" may be shown advertisements for sightseeing tours in Kyoto. The input is data provided through the API, and the output is an optimized marketing strategy.

[0391] (Application example 3)

[0392] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0393] In today's information society, there is a demand for effective utilization of users' behavioral and search histories to provide them with the most appropriate information and advertisements. However, conventional systems have been unable to fully utilize users' behavioral and search histories, resulting in low accuracy in providing personalized information and targeted advertisements. Furthermore, there have been issues with the difficulty of properly managing and linking data when providing user behavioral and search histories to third parties.

[0394] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0395] In this invention, the server includes means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, means for selling usage data or collaborating with other companies via API, and means for collecting the user's behavioral history and search history and providing advertisers with data for providing targeted advertisements via API, thereby making it possible to provide personalized information to users and improve the accuracy of targeted advertisements.

[0396] "Transportation routes" refers to route information for public transportation and roads used to travel from the current location to the destination.

[0397] "Chat or voice input" means a user inputs information into the system using text messages or voice.

[0398] "Means for translating into the user's preferred language" means the system's ability to translate information into the user's native language or language of choice.

[0399] "Nearby recommendations" are information about interesting places and services near the user's current location or destination.

[0400] "User behavior history" is a record of the places a user has visited and the services they have used in the past.

[0401] "Search history" is a record of keywords and phrases that a user has previously searched for on the Internet or in applications.

[0402] "Recommendation means" refers to the system's ability to suggest related information and services based on the user's behavioral history and search history.

[0403] "Usage data" refers to all data collected by the system, such as user behavior history and search history.

[0404] "Means of selling or collaborating with other companies through APIs" refers to methods for providing collected data to third parties, including selling data or collaborating through APIs.

[0405] "Targeted advertising" refers to advertising that is displayed based on specific user interests and behavior.

[0406] "Means provided via API" means a method of providing data to other systems or services through an application programming interface.

[0407] The system for carrying out the present invention is configured using a user's smartphone and a server. A specific embodiment of the system will be described below.

[0408] System configuration

[0409] The system consists of the following main components:

[0410] 1. User device: A mobile device such as a smartphone or tablet.

[0411] 2. Server: A server for data processing and API integration.

[0412] 3. Software: Python, Requests library, JSON library, etc.

[0413] Data collection and processing

[0414] The user's device collects the user's behavioral history and search history, including GPS data and search queries, and the collected data is sent to a server and stored in a database.

[0415] Data Translation and Recommendations

[0416] The server generates transportation directions from the user's current location to the destination, translates them into the user's language, and recommends nearby information based on the user's behavioral and search history.

[0417] Data sales and API integration

[0418] The server sells the collected usage data or connects with other companies via API, allowing third parties to use the data to create new business opportunities.

[0419] Providing targeted advertising

[0420] The server collects user behavior and search history and provides advertisers with data via API to deliver targeted ads, allowing them to display more effective ads to users.

[0421] Specific examples

[0422] For example, if a user visits tourist spot A, their behavioral history is collected and advertisements related to tourist spot A are displayed. Also, if a user searches for "restaurant B," coupon information for restaurant B is recommended based on their search history.

[0423] Prompt Sentence Examples

[0424] "Create a program that collects data and provides it to advertisers to show relevant ads based on the tourist destinations that users visit."

[0425] In this way, the system can improve the accuracy of providing personalized information and targeted advertising to users.

[0426] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0427] Step 1:

[0428] The user's device collects the user's behavioral history and search history.

[0429] Input: GPS data, search query

[0430] Data processing: Convert GPS data into location information and save search queries as text data.

[0431] Output: Behavioral history data, search history data

[0432] Step 2:

[0433] The user terminal transmits the collected behavioral history data and search history data to the server.

[0434] Input: Behavioral history data, search history data

[0435] Data calculation: Convert the data into JSON format and send it to the server.

[0436] Output: Data sent to the server

[0437] Step 3:

[0438] The server stores the received data in a database.

[0439] Input: JSON format behavior history data, search history data

[0440] Data processing: Convert the data into a database format and save it.

[0441] Output: Data stored in the database

[0442] Step 4:

[0443] The server generates transport directions from the user's current location to the destination and translates them into the user's language.

[0444] Input: current location, destination, user language

[0445] Data calculation: Obtaining transportation route information and calculating the optimal route. Translating the calculation results into the user's language.

[0446] Output: Translated directions

[0447] Step 5:

[0448] The server recommends nearby information based on the user's behavioral and search history.

[0449] Input: behavioral history data, search history data, current location

[0450] Data calculation: Analyzes behavioral and search history to extract relevant recommendations.

[0451] Output: Recommendation information

[0452] Step 6:

[0453] The server sells the collected usage data or connects with other companies via API.

[0454] Input: Usage data

[0455] Data processing: Converting data into a format that can be used by third parties.

[0456] Output: Data provided to third parties

[0457] Step 7:

[0458] The server collects user behavioral and search history and provides advertisers with data via an API to deliver targeted advertisements.

[0459] Input: Behavioral history data, search history data

[0460] Data calculation: Analyzes behavioral and search history to extract data suitable for targeted advertising.

[0461] Output: Targeted advertising data provided to advertisers

[0462] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0463] "Example 1"

[0464] One embodiment of the present invention is a system that incorporates an emotion engine that recognizes user emotions. This system recognizes emotions from the user's voice and text input and uses the results to adjust the information recommended. Specifically, when a user voice-inputs, "Tell me the fastest route from Tokyo Station to Narita Airport," if the tone and expression of the voice indicate that the user is in a hurry, the system not only provides the fastest route but also information on high-speed transportation such as taxis and helicopters. Similarly, when a user text-inputs, "Tell me about a good ramen restaurant nearby," if the expression in the text indicates that the user wants to relax, the system provides information on nearby ramen restaurants with a calm atmosphere.

[0465] "Example 2"

[0466] One embodiment of the present invention is a system that incorporates an emotion engine that recognizes user emotions. This system recognizes emotions from the user's voice and text input and uses the results to adjust the information recommended. Specifically, when a user voice-inputs, "Tell me the fastest route from Tokyo Station to Narita Airport," if the tone and expression of the voice indicate that the user is in a hurry, the system not only provides the fastest route but also information on high-speed transportation such as taxis and helicopters. Similarly, when a user text-inputs, "Tell me about a good ramen restaurant nearby," if the expression in the text indicates that the user wants to relax, the system provides information on nearby ramen restaurants with a calm atmosphere.

[0467] "Example 3"

[0468] One embodiment of the present invention is a system that incorporates an emotion engine that recognizes user emotions. This system recognizes emotions from the user's voice and text input and uses the results to adjust the information recommended. Specifically, when a user voice-inputs, "Tell me the fastest route from Tokyo Station to Narita Airport," if the tone and expression of the voice indicate that the user is in a hurry, the system not only provides the fastest route but also information on high-speed transportation such as taxis and helicopters. Similarly, when a user text-inputs, "Tell me about a good ramen restaurant nearby," if the expression in the text indicates that the user wants to relax, the system provides information on nearby ramen restaurants with a calm atmosphere.

[0469] The processing flow of each embodiment will be described below.

[0470] "Example 1"

[0471] Step 1: The user provides voice or text input to the system. For example, the user could provide voice input such as "What is the fastest route from Tokyo Station to Narita Airport?" or text input such as "What is a good ramen restaurant nearby?"

[0472] Step 2: The emotion engine recognizes emotions from the user's input, inferring the user's emotions from the tone and expressions of the voice, text expressions, etc.

[0473] Step 3: Based on the recognition results of the emotion engine, the system adjusts the information it recommends. For example, if the user feels they are in a hurry, it will not only provide the fastest route but also information on high-speed transportation such as taxis and helicopters. If the user feels they want to relax, it will provide information on nearby ramen restaurants with a calm atmosphere.

[0474] "Example 2"

[0475] Step 1: The user provides voice or text input to the system. For example, the user could provide voice input such as "What is the fastest route from Tokyo Station to Narita Airport?" or text input such as "What is a good ramen restaurant nearby?"

[0476] Step 2: The emotion engine recognizes emotions from the user's input, inferring the user's emotions from the tone and expressions of the voice, text expressions, etc.

[0477] Step 3: Based on the recognition results of the emotion engine, the system adjusts the information it recommends. For example, if the user feels they are in a hurry, it will not only provide the fastest route but also information on high-speed transportation such as taxis and helicopters. If the user feels they want to relax, it will provide information on nearby ramen restaurants with a calm atmosphere.

[0478] "Example 3"

[0479] Step 1: The user provides voice or text input to the system. For example, the user could provide voice input such as "What is the fastest route from Tokyo Station to Narita Airport?" or text input such as "What is a good ramen restaurant nearby?"

[0480] Step 2: The emotion engine recognizes emotions from the user's input, inferring the user's emotions from the tone and expressions of the voice, text expressions, etc.

[0481] Step 3: Based on the recognition results of the emotion engine, the system adjusts the information it recommends. For example, if the user feels they are in a hurry, it will not only provide the fastest route but also information on high-speed transportation such as taxis and helicopters. If the user feels they want to relax, it will provide information on nearby ramen restaurants with a calm atmosphere.

[0482] Example 1

[0483] 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 device 14 will be referred to as a "terminal."

[0484] Conventional transportation guidance systems can provide optimal routes based on user input, but they have the problem of being unable to respond flexibly to the user's emotions and circumstances. Furthermore, if the information provided is not translated into the user's language, it can be difficult for the user to understand. Furthermore, the lack of personalized recommendations based on the user's behavioral history and search history means that they are unable to fully meet the user's needs.

[0485] 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.

[0486] In this invention, the server includes a means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, a means for recommending nearby recommended information based on the user's behavioral history and search history, a means for selling usage data or linking with other companies via an interface, a means for recognizing emotions from the user's input and adjusting the information provided based on those emotions, and a means for translating the generated route information into the user's language. This allows for flexible responses according to the user's emotions and situation, and providing information in the user's language makes it easier to understand. It also enables personalized recommendations based on the user's behavioral history and search history.

[0487] "Current Location" refers to the location where the User is currently located.

[0488] "Destination" refers to the location the user wants to reach.

[0489] "Transportation" refers to means of transportation such as public transportation, taxis, and helicopters.

[0490] "Chat Input" refers to the method by which a User inputs information in text form.

[0491] "Voice input" refers to a method by which a user inputs information in the form of speech.

[0492] "Translation" refers to the conversion of information written in one language into another language.

[0493] "Nearby recommendations" refers to information about interesting places and services near a user's current location or destination.

[0494] "Behavioral history" refers to a record of a user's past actions and places visited.

[0495] "Search history" refers to a record of past search queries made by a user.

[0496] "Recommendation" refers to recommending specific information or services to a user.

[0497] "Usage Data" refers to data generated when a user uses the system.

[0498] An "interface" refers to a means of connection that allows different systems and services to work together.

[0499] "Emotion recognition" refers to analyzing and recognizing emotions from user input.

[0500] "Personalization" refers to providing information and services tailored to a user's individual needs and preferences.

[0501] "Optimal route" refers to the travel route that best suits the user's conditions.

[0502] MODE FOR CARRYING OUT THE INVENTION

[0503] This invention is a system that allows users to inquire about transportation routes from their current location to their destination by chat or voice input using an application installed on a mobile device such as a smartphone or tablet. Specific embodiments of this system are described below.

[0504] System configuration

[0505] Users install the application on their mobile devices, such as smartphones and tablets. This application generates the optimal route based on the user's input, translates it into the user's language, and displays it. It also incorporates an emotion engine that recognizes the user's emotions.

[0506] Hardware and software used

[0507] Mobile devices: smartphones, tablets

[0508] Server: Cloud Server

[0509] Map services: Google Maps API, OpenStreetMap

[0510] Translation services: Google Translate API, DeepL API

[0511] Speech recognition service: Google Speech-to-Text API

[0512] Emotion recognition services: IBM Watson Tone Analyzer, Microsoft® Azure® Text Analytics

[0513] Program processing

[0514] The user launches the application and makes a query via chat or voice input. For example, the user might type, "What is the fastest route from Tokyo Station to Narita Airport?" or speak the same via voice input. The device sends the user's text or voice data to the server. In the case of voice input, the device first uses the Google Speech-to-Text API to convert the speech to text.

[0515] The server analyzes the received text data to understand the user's request. It then uses the Google Maps API or OpenStreetMap to generate the optimal route, taking into account public transport timetables and operating conditions. The generated route information is translated into the user's language using the Google Translate API or DeepL API. The translated information is sent from the server to the device, which then displays it to the user.

[0516] Furthermore, the emotion engine recognizes emotions from user input. For example, if a user says, "Tell me the fastest route from Tokyo Station to Narita Airport," and the tone and expression of the voice indicate that the user is in a hurry, the server will not only provide the fastest route, but also information on high-speed transportation options such as taxis and helicopters.

[0517] Specific examples

[0518] If a user types in text such as "Tell me about a good ramen restaurant nearby," the device sends this text data to the server. The server analyzes the text, and the emotion engine recognizes the user's desire to relax. The server uses the Google Maps API to search for nearby ramen restaurants with a relaxing atmosphere and translates the information into the user's language. The translated information is sent to the device, which then displays it to the user.

[0519] Prompt Sentence Examples

[0520] If a user says, "Tell me the fastest route from Tokyo Station to Narita Airport," and the tone and expression of the voice indicate that the user is in a hurry, the system will not only provide the fastest route, but also provide information on high-speed transportation options such as taxis and helicopters.

[0521] In this way, a system is realized that generates optimal routes based on user input and provides information according to emotions.

[0522] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0523] Step 1:

[0524] The user launches the application and makes a query via chat or voice input.

[0525] Input: The user types or speaks, "What is the fastest route from Tokyo Station to Narita Airport?"

[0526] What happens: A user launches the application on their smartphone or tablet and either types text into the chat box or speaks a question using the voice input feature.

[0527] Step 2:

[0528] The device sends the user's input to the server.

[0529] Input: The user's text or voice data.

[0530] Output: The text data sent to the server.

[0531] Specific operation: When the device receives voice input, it uses the Google Speech-to-Text API to convert the voice into text and sends the text data to the server.

[0532] Step 3:

[0533] The server analyzes the user's input and generates the optimal route.

[0534] Input: User text data.

[0535] Output: Optimal route information.

[0536] Specific operation: The server analyzes the received text data and uses the Google Maps API and OpenStreetMap to generate the optimal route taking into account public transport timetables and operating conditions.

[0537] Step 4:

[0538] The server translates the generated route information into the user's language.

[0539] Input: Optimal route information.

[0540] Output: The translated route information.

[0541] Specific operation: The server uses the Google Translate API or DeepL API to translate the generated route information into the user's language.

[0542] Step 5:

[0543] The server sends the translated information to the terminal.

[0544] Input: Translated route information.

[0545] Output: The translated route information sent to the device.

[0546] Specific operation: The server encrypts the translated route information and sends it to the device.

[0547] Step 6:

[0548] The device will then display the translated route information to the user.

[0549] Input: Translated route information.

[0550] Output: Route information displayed to the user.

[0551] What it does: The device will then display the translated route information on the application screen, allowing the user to view detailed directions to their destination.

[0552] Step 7:

[0553] The emotion engine recognizes emotions from user input and provides additional information as needed.

[0554] Input: The user's text or voice data.

[0555] Output: Additional information based on the sentiment.

[0556] How it works: The emotion engine analyzes the user's input to determine their emotions. For example, if the user feels they are in a hurry, it will provide information on the fastest route as well as other high-speed transportation options such as taxis and helicopters. It uses IBM Watson Tone Analyzer and Microsoft Azure Text Analytics to recognize emotions.

[0557] (Application example 1)

[0558] Next, a description will be given of Application Example 1 of Embodiment 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."

[0559] Conventional food delivery systems struggle to provide optimal delivery options based on the user's emotions and urgency. Furthermore, they lack the ability to analyze user input and generate appropriate prompts, limiting the user experience.

[0560] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for generating transportation routes from the current location to the destination via chat or voice input and translating them into the user's language, a means for recommending nearby recommended information based on the user's behavioral history and search history, a means for selling usage data or linking with other companies via API, a means for recognizing the user's emotions and providing the optimal delivery option based on those emotions, and a means for analyzing the user's input using a generative AI model and generating a prompt sentence. This makes it possible to provide the optimal delivery option according to the user's emotions and urgency, improving the user experience.

[0561] "Current Location" refers to the location where the User is currently located.

[0562] "Destination" refers to the location the user wants to reach.

[0563] "Transportation" refers to public transportation such as trains, buses, and taxis.

[0564] "Chat" refers to a means of communicating in real time using text messages.

[0565] "Voice input" refers to a means by which a user inputs information using voice.

[0566] "Translation" refers to the conversion of information written in one language into another language.

[0567] "Recommended Information" refers to useful information provided to users.

[0568] "Behavioral history" refers to a record of a user's past actions.

[0569] "Search history" refers to a record of searches a user has conducted in the past.

[0570] "Recommendation" refers to recommending appropriate information or products to users.

[0571] "Usage Data" refers to data generated when a user uses the system.

[0572] "API" stands for Application Programming Interface, and refers to an interface for exchanging functions and data between different software programs.

[0573] "Emotion recognition" refers to the technology of analyzing emotions from a user's voice or text.

[0574] "Delivery Options" means the delivery service options available to Users.

[0575] A "generative AI model" is a model that uses artificial intelligence to analyze data and generate new information or prompts.

[0576] A "prompt sentence" refers to a question or instruction sentence presented to the user.

[0577] The system for implementing this invention has the function of generating transportation directions from a user's current location to their destination via chat or voice input and translating them into the user's language. It also has the function of recommending nearby information based on the user's behavioral history and search history. Furthermore, it has the function of recognizing the user's emotions and providing the optimal delivery options based on those emotions.

[0578] Hardware and Software Configuration

[0579] 1. Hardware:

[0580] Mobile devices such as smartphones and tablets

[0581] server

[0582] 2. Software:

[0583] ANDROID(R) / iOS application

[0584] Emotion recognition engine (e.g. IBM Watson Tone Analyzer)

[0585] Delivery service API (e.g. Google Maps API)

[0586] Generative AI models (e.g., GPT-3 (registered trademark))

[0587] Data processing and calculation

[0588] 1. Getting user input:

[0589] The user uses a smartphone or tablet to voice-input "I want a nearby pizza delivered quickly."

[0590] Converts speech to text using the Google Speech-to-Text API.

[0591] 2. Emotion recognition:

[0592] The converted text is sent to IBM Watson Tone Analyzer to analyze the user's sentiment.

[0593] As a result, you perceive yourself as being in a hurry.

[0594] 3. Route generation and delivery options available:

[0595] Uses the Google Maps API to find the nearest pizza place based on the user's current location.

[0596] Select the pizza place that offers the fastest delivery.

[0597] 4. Information provision:

[0598] Displays information about selected pizza restaurants in the user's language.

[0599] It will display, "The fastest pizza place to deliver is XX. Would you like to order?"

[0600] Specific examples

[0601] If a user says "I want a nearby pizza delivered quickly," and the tone and expression of the voice indicate that the user is in a hurry, the system will provide information on pizza restaurants that can deliver the fastest. Similarly, if a user says "I want to order a relaxing dessert," and the expression in the text indicates that the user wants to relax, the system will provide information on nearby dessert restaurants with a calm atmosphere.

[0602] Prompt Sentence Examples

[0603] "I want pizza delivered quickly near me."

[0604] "I want to order a relaxing dessert."

[0605] In this way, it is possible to provide optimal delivery options according to the user's emotions and urgency, improving the user experience.

[0606] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0607] Step 1:

[0608] A user uses a smartphone or tablet to input voice commands. For example, they might say, "I want a nearby pizza delivered immediately." This voice data is then input into the device.

[0609] Step 2:

[0610] The device converts voice data to text data using the Google Speech-to-Text API. The input is voice data and the output is text data.

[0611] Step 3:

[0612] The device sends the converted text data to the IBM Watson Tone Analyzer to analyze the user's emotions. The input is text data, and the output is emotion data (e.g., "I'm in a hurry").

[0613] Step 4:

[0614] The server searches for the user's current location and the nearest pizza restaurant based on the emotion data using the Google Maps API. The input is the user's current location and emotion data, and the output is a list of the nearest pizza restaurants.

[0615] Step 5:

[0616] The server selects the pizza place that can deliver the fastest. The input is a list of the nearest pizza places, and the output is the information of the pizza place that can deliver the fastest.

[0617] Step 6:

[0618] The server translates the information about the selected pizza restaurant into the user's language and sends it to the terminal. The input is the pizza restaurant information, and the output is the translated pizza restaurant information.

[0619] Step 7:

[0620] The device displays the translated information about the pizza restaurant to the user. For example, it displays "The pizza restaurant with the fastest delivery time is ____. Would you like to order?" The input is the translated information about the pizza restaurant, and the output is what is displayed to the user.

[0621] In this way, it is possible to provide optimal delivery options according to the user's emotions and urgency, improving the user experience.

[0622] Example 2

[0623] Next, a description will be given of 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."

[0624] Conventional systems have the ability to recommend nearby information based on a user's behavioral and search history, but do not provide information that takes into account the user's emotions. As a result, they are unable to provide optimal information based on the user's current emotional state, resulting in a lack of improved user experience. Furthermore, there is a need for systems that provide appropriate information according to the situation, such as when the user is in a hurry or wants to relax.

[0625] 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.

[0626] In this invention, the server includes means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, means for selling usage data or linking with other companies via API, and means for recognizing emotions from the user's voice input or text input and adjusting the information to be recommended using the results. This makes it possible to provide optimal information according to the user's emotional state, which is expected to improve the user experience.

[0627] "Current Location" refers to the location where the User is currently located.

[0628] "Destination" refers to the place where the user wants to go.

[0629] "Transportation directions" refers to the route from your current location to your destination.

[0630] "Chat or voice input" refers to a method by which a user inputs information by text or voice.

[0631] "User's preferred language" means the language used by the User.

[0632] "Means of translation" refers to the method of converting from one language to another.

[0633] "Recommended information in the area" refers to useful information about the area around the user's current location.

[0634] "User behavior history" refers to a record of actions taken by a user in the past.

[0635] "Search history" refers to a record of keywords or queries a user has searched for in the past.

[0636] "Recommendation methods" refer to methods of suggesting appropriate information to users.

[0637] "Usage data" refers to data such as user behavior history and search history.

[0638] "Means of collaboration with other companies through sales or APIs" refers to ways in which usage data is shared with other companies.

[0639] "Voice input or text input" refers to the way in which a user inputs information by voice or text.

[0640] "Means for recognizing emotions" refers to a method for analyzing the user's emotional state.

[0641] "Means for adjusting recommended information" refers to methods for optimizing the information provided based on the user's emotional state.

[0642] MODE FOR CARRYING OUT THE INVENTION

[0643] The present invention is a system for providing optimal information based on a user's behavior history, search history, and emotional state. Specific embodiments of this system are described below.

[0644] Hardware and software used

[0645] Servers: Database servers, web servers

[0646] Devices: Smartphones, PCs

[0647] Software: Emotion recognition engine, recommendation engine, natural language processing (NLP) engine

[0648] Data processing and calculation

[0649] 1. Collecting user behavior and search history

[0650] User: Uses a smartphone or PC to visit a specific tourist spot or search for a specific keyword.

[0651] Specific action: A user searches for "sushi" on their smartphone.

[0652] Device: Records user behavior and search history and sends it to the server.

[0653] Specific operation: The smartphone sends the search keyword "sushi" to the server.

[0654] Server: Stores the received behavioral history and search history in a database.

[0655] Specific operation: The server saves the search keyword "sushi" in the database.

[0656] 2. Generating recommendation information

[0657] Server: Generates relevant recommendations based on the behavioral and search history stored in the database.

[0658] Specific operation: The server searches for sushi restaurants near the user's current location based on the search keyword "sushi."

[0659] Server: Uses a recommendation engine to select the best recommendations.

[0660] What it does: The recommendation engine generates a list of sushi restaurants near your current location.

[0661] 3. Emotion recognition

[0662] User: Use voice input or text input.

[0663] Specific operation: The user speaks, "What is the fastest route from Tokyo Station to Narita Airport?"

[0664] Device: Sends voice and text input to the emotion recognition engine.

[0665] What it does: Your smartphone sends voice data to an emotion recognition engine.

[0666] Server: The emotion recognition engine analyzes the user's emotions from the tone of voice and text expressions.

[0667] What it does: The emotion recognition engine analyzes the voice data and determines that the user is in a hurry.

[0668] 4. Adjusting information according to emotions

[0669] Server: Adjusts the recommendation information based on the results of the emotion recognition engine.

[0670] Specific behavior: When the server determines that a user is in a hurry, it provides information on the fastest route as well as high-speed transportation options such as taxis and helicopters.

[0671] On the device: Display tailored recommendations to the user.

[0672] What it does: Your smartphone will display information about the quickest routes and express transportation options to you.

[0673] Examples and prompts

[0674] Example 1: When a user searches for "sushi"

[0675] User: Searches for "sushi" on their smartphone.

[0676] Device: Sends search keywords to the server.

[0677] Server: Saves the search keywords in a database and uses a recommendation engine to search for sushi restaurants near the current location.

[0678] Server: The recommendation engine generates a list of sushi restaurants and sends it to the user.

[0679] Terminal: Show the user a list of sushi restaurants.

[0680] Example 2: When a user says, "What is the fastest route from Tokyo Station to Narita Airport?"

[0681] User: Uses voice input on a smartphone.

[0682] Device: Sends voice data to the emotion recognition engine.

[0683] Server: The emotion recognition engine analyzes the voice data and determines that the user is in a hurry.

[0684] Server: Generates information about the fastest routes as well as other high-speed transportation options such as taxis and helicopters and sends it to the user.

[0685] On the device: Show users the quickest route and express transport options.

[0686] Prompt Sentence Examples

[0687] "When a user searches for 'sushi,' recommend sushi restaurants near the user's current location."

[0688] "If a user says, 'What is the fastest route from Tokyo Station to Narita Airport?', the tone of the voice indicates that the user is in a hurry, so in addition to the fastest route, provide information on faster transportation options such as taxis and helicopters."

[0689] The above is a specific embodiment for carrying out the present invention.

[0690] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0691] Step 1:

[0692] Collection of user behavior and search history

[0693] User: Uses a smartphone or PC to visit a specific tourist spot or search for a specific keyword.

[0694] Specific action: A user searches for "sushi" on their smartphone.

[0695] Input: The search term "sushi" entered by the user.

[0696] Output: The search keywords are sent to the device.

[0697] Device: Records user behavior and search history and sends it to the server.

[0698] Specific operation: The smartphone sends the search keyword "sushi" to the server.

[0699] Input: User's search keyword "sushi".

[0700] Output: The search keywords are sent to the server.

[0701] Server: Stores the received behavioral history and search history in a database.

[0702] Specific operation: The server saves the search keyword "sushi" in the database.

[0703] Input: The search keyword "sushi" sent from the device.

[0704] Output: The search keywords are saved in the database.

[0705] Step 2:

[0706] Generating recommendation information

[0707] Server: Generates relevant recommendations based on the behavioral and search history stored in the database.

[0708] Specific operation: The server searches for sushi restaurants near the user's current location based on the search keyword "sushi."

[0709] Input: Search keyword "sushi" saved in the database.

[0710] Output: A list of sushi restaurants near your current location.

[0711] Server: Uses a recommendation engine to select the best recommendations.

[0712] What it does: The recommendation engine generates a list of sushi restaurants near your current location.

[0713] Input: A list of sushi restaurants near your current location.

[0714] Output: A list of the best sushi restaurants.

[0715] Step 3:

[0716] emotion recognition

[0717] User: Use voice input or text input.

[0718] Specific operation: The user speaks, "What is the fastest route from Tokyo Station to Narita Airport?"

[0719] Input: User speaks, "What is the fastest route from Tokyo Station to Narita Airport?"

[0720] Output: The audio data is sent to the device.

[0721] Device: Sends voice and text input to the emotion recognition engine.

[0722] What it does: Your smartphone sends voice data to an emotion recognition engine.

[0723] Input: User's voice data.

[0724] Output: The audio data is sent to the emotion recognition engine.

[0725] Server: The emotion recognition engine analyzes the user's emotions from the tone of voice and text expressions.

[0726] What it does: The emotion recognition engine analyzes the voice data and determines that the user is in a hurry.

[0727] Input: Audio data.

[0728] Output: User's emotional state (in a hurry).

[0729] Step 4:

[0730] Adjusting information according to emotions

[0731] Server: Adjusts the recommendation information based on the results of the emotion recognition engine.

[0732] Specific behavior: When the server determines that a user is in a hurry, it provides information on the fastest route as well as high-speed transportation options such as taxis and helicopters.

[0733] Input: User's emotional state (in a hurry), a list of the best sushi restaurants.

[0734] Output: Tailored recommendations (fastest routes and faster transport options).

[0735] On the device: Display tailored recommendations to the user.

[0736] What it does: Your smartphone will display information about the quickest routes and express transportation options to you.

[0737] Input: Tailored recommendation information.

[0738] Output: Information displayed to the user.

[0739] (Application example 2)

[0740] 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."

[0741] Conventional recommendation systems provide information based on a user's behavioral and search history, but they are unable to provide recommendations that take into account the user's emotions, making it difficult to provide information that is optimal for the user's current state or mood.In addition, there is a lack of real-time personalized information provided in physical stores that users visit, so there is a need to improve the user experience.

[0742] 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.

[0743] In this invention, the server includes means for generating transportation routes from the current location to the destination through chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, and means for recognizing emotions from the user's voice input or text input and adjusting the information to be recommended using the results. This makes it possible to provide personalized information based on the user's behavioral history and search history, as well as optimal information that takes the user's emotions into consideration.

[0744] "Current Location" refers to the location where the User is currently located.

[0745] "Destination" refers to the place where the user wants to go.

[0746] "Transportation" refers to the means used by users to travel, including trains, buses, taxis, helicopters, etc.

[0747] "Directions" refers to the route from the current location to the destination.

[0748] "Chat" refers to a means of communicating in real time using text messages.

[0749] "Voice input" refers to a means by which a user inputs information using voice.

[0750] "Translation" refers to the conversion of information written in one language into another language.

[0751] "Recommended information in the area" refers to information about stores and services located near the user's current location.

[0752] "Behavioral history" refers to a record of a user's past actions and places visited.

[0753] "Search history" refers to a record of keywords and phrases a user has searched for in the past.

[0754] "Recommendation" refers to recommending appropriate information or products to users.

[0755] "Emotion recognition" refers to determining a user's emotional state from their voice or text.

[0756] "Usage data" refers to data such as user behavior history, search history, and emotion recognition results.

[0757] "Sales" refers to providing goods or services to others in exchange for payment.

[0758] "API" stands for Application Program Interface and refers to a means of sharing functions and data between different software programs.

[0759] "Collaboration with other companies" refers to working with different companies or organizations to share services and data.

[0760] The system for carrying out the present invention is configured using a user's smartphone and a server. Specific embodiments will be described below.

[0761] System Configuration

[0762] 1. Hardware:

[0763] Smartphone: A mobile device carried by the user that is equipped with a microphone and keyboard for voice and text input.

[0764] Server: A central processing unit for data processing and generating recommendation information.

[0765] 2. Software:

[0766] EmotionRecognizer: A library for recognizing emotions from user voice and text input.

[0767] RecommendationEngine: An engine for generating recommendations based on user behavioral history and emotions.

[0768] Data processing and calculation

[0769] 1. Data Acquisition:

[0770] The server obtains the user's behavioral history and search history through the API, which allows it to understand the user's past behavioral patterns.

[0771] Example: https: / / api.example.com / user / {user_id} / history

[0772] 2. Emotion recognition:

[0773] The smartphone sends the user's voice and text input to the EmotionRecognizer to recognize emotions, which allows it to understand the user's current emotional state.

[0774] Example: EmotionRecognizer().recognize(input_text)

[0775] 3. Recommendation generation:

[0776] The server uses a recommendation engine to recommend appropriate products and services based on the user's behavioral history and emotion recognition results, thereby providing information that is optimal for the user's current state and mood.

[0777] Example: RecommendationEngine().recommend(user_history, emotion)

[0778] Specific examples

[0779] If a user voice-inputs "I'm tired," the server will use EmotionRecognizer to recognize the user's emotion as "fatigue," and then use RecommendationEngine to recommend relaxing cafes and massage parlors.

[0780] When a user searches for "sushi," the server recommends sushi restaurants near the user's current location based on the user's behavioral history and current location.

[0781] Prompt Sentence Examples

[0782] "Recommend places where users who feel tired can relax."

[0783] "Recommend sushi restaurants near the user's current location when they search for sushi."

[0784] In this way, it becomes possible to provide optimal information in real time based on the user's behavioral history and emotions.

[0785] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0786] Step 1:

[0787] A user uses a smartphone to input voice or text.

[0788] Input: User voice or text

[0789] Output: Input audio or text data

[0790] Specific action: The user speaks "I'm tired."

[0791] Step 2:

[0792] The terminal converts the input voice data into text data.

[0793] Input: Audio data

[0794] Output: Text data

[0795] Specific operation: Using the smartphone's voice recognition function, the spoken word "I'm tired" is converted into text.

[0796] Step 3:

[0797] The device sends the text data to EmotionRecognizer to recognize emotions.

[0798] Input: Text data

[0799] Output: Emotion data (e.g., fatigue)

[0800] Specific behavior: Enter the text "tired" into EmotionRecognizer and recognize the emotion as "fatigue."

[0801] Step 4:

[0802] The server obtains the user's behavioral history and search history through the API.

[0803] Input: User ID

[0804] Output: Behavioral history data and search history data

[0805] Specific operation: The server calls the API and obtains the user's past behavioral history and search history.

[0806] Step 5:

[0807] The server sends the behavioral history data, search history data, and emotion data to the Recommendation Engine to generate recommendation information.

[0808] Input: behavioral history data, search history data, emotion data

[0809] Output: Recommendation information (e.g., a list of relaxing cafes and massage parlors)

[0810] Specific operation: Input data into the Recommendation Engine and generate information on relaxing cafes and massage parlors.

[0811] Step 6:

[0812] The server transmits the generated recommendation information to the terminal.

[0813] Input: Recommendation information

[0814] Output: Recommendation information (displayed on the terminal)

[0815] Specific operation: The server sends recommendation information to the smartphone and displays it to the user.

[0816] Step 7:

[0817] The terminal displays the received recommendation information to the user.

[0818] Input: Recommendation information

[0819] Output: Information displayed to the user

[0820] Specific behavior: A list of relaxing cafes and massage parlors is displayed on the smartphone screen.

[0821] In this way, it becomes possible to provide optimal information in real time based on the user's behavioral history and emotions.

[0822] Example 3

[0823] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0824] Conventional systems provided recommendations based on user behavior and search history, and generated transportation routes, but they lacked the ability to provide information that took user sentiment into consideration. Furthermore, when providing collected data to third parties, they did not adequately analyze the data or provide it in an appropriate format. This limited the ability to improve user experience and create new business opportunities.

[0825] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0826] In this invention, the server includes means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, means for selling usage data or linking with other companies via API, means for recognizing emotions from the user's voice input or text input and using the results to adjust recommended information, means for collecting the user's behavioral history and search history and analyzing them using a data analysis tool, and means for providing data to third parties based on the analysis results. This makes it possible to provide information that takes the user's emotions into consideration, improving the user experience and creating new business opportunities.

[0827] "Current Location" refers to the location where the User is currently located.

[0828] "Destination" refers to the place where the user wants to go.

[0829] "Transportation" refers to the means used by users to travel, including trains, buses, taxis, airplanes, etc.

[0830] "Chat" refers to a text-based means of communication.

[0831] "Voice input" refers to a means by which a user inputs information using voice.

[0832] "Translation" refers to the conversion of information expressed in one language into another.

[0833] "Nearby recommended information" refers to useful nearby information provided based on the user's current location and behavioral history.

[0834] "Behavioral history" refers to a record of a user's past actions.

[0835] "Search history" refers to a record of searches a user has conducted in the past.

[0836] "Recommendation" refers to suggesting appropriate information or options to users.

[0837] "Usage data" refers to data such as user behavior history and search history.

[0838] "Sales" refers to providing goods or services to others in exchange for payment.

[0839] "API" stands for Application Programming Interface, and refers to an interface for exchanging functions and data between different software programs.

[0840] "Other companies" refers to companies or organizations other than the company providing the system.

[0841] "Emotion recognition" refers to analyzing and identifying emotions from a user's voice or text.

[0842] "Data analysis tools" refers to software and algorithms used to analyze collected data.

[0843] "Third Party" refers to any individual or organization other than the user or system provider.

[0844] MODE FOR CARRYING OUT THE INVENTION

[0845] This invention is a system that provides recommendation information based on a user's behavioral history and search history, generates transportation routes, provides information using emotion recognition, and provides collected data to third parties. Specific embodiments of this system are described below.

[0846] Hardware and software used

[0847] Hardware: devices such as smartphones, tablets, and PCs

[0848] Software: Emotion recognition engine (e.g., Google Cloud Natural Language API, IBM Watson), data analysis tools (e.g., Python Pandas, Scikit-learn), API integration tools

[0849] System configuration

[0850] 1. Data Collection

[0851] Users use devices such as smartphones and PCs to browse websites and use apps.

[0852] The device collects the user's behavioral history and search history in real time and sends it to the server.

[0853] 2. Data Analysis

[0854] The server stores the received data in a database.

[0855] The server analyzes the data using data analysis tools to identify user behavior patterns and interests.

[0856] 3. Emotion recognition

[0857] The user inputs voice or text.

[0858] The device sends these inputs to an emotion recognition engine.

[0859] The server uses an emotion recognition engine to analyze the user's emotions from voice and text, and adjusts the recommendation information based on the results.

[0860] 4. Information provision

[0861] The server adjusts the recommendation information according to the user's emotional state and sends it to the terminal.

[0862] Based on the analysis results, the server provides data to third parties such as tourism operators and advertising agencies.

[0863] Specific examples

[0864] The user speaks, "What is the fastest route from Tokyo Station to Narita Airport?"

[0865] The terminal transmits the voice data to the server.

[0866] The server uses an emotion recognition engine to determine that the user is in a hurry.

[0867] The server provides the fastest routes as well as taxi and helicopter information.

[0868] Prompt Sentence Examples

[0869] Data provision prompt: "How can I collect user behavior history and provide it to tourism providers?"

[0870] Emotion recognition engine prompt: "How can I provide the fastest route if I sense the user is in a hurry?"

[0871] This system analyzes the user's behavior history and emotions and provides appropriate information to improve the user experience and create new business opportunities. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0872] Program processing flow

[0873] Step 1: Data collection

[0874] Users use devices such as smartphones and PCs to browse websites and use apps.

[0875] Input: User behavior and search history

[0876] The device collects this data in real time and sends it to the server.

[0877] Output: Collected behavioral and search history data

[0878] Step 2: Save data

[0879] The server stores the received data in a database.

[0880] Input: Collected behavioral and search history data

[0881] Output: Data stored in the database

[0882] Step 3: Data analysis

[0883] The server analyzes the data using data analysis tools (e.g., Python's Pandas or Scikit-learn).

[0884] Input: Activity and search history data stored in a database

[0885] The server processes and calculates the data to identify the user's behavioral patterns and interests.

[0886] Output: Analysis results (user behavior patterns and interests)

[0887] Step 4: Emotion Recognition

[0888] The user inputs voice or text.

[0889] Input: User voice and text data

[0890] The device sends these inputs to an emotion recognition engine.

[0891] The server uses an emotion recognition engine (e.g., Google Cloud Natural Language API or IBM Watson) to analyze the user's emotions from voice and text.

[0892] Output: Emotion analysis results

[0893] Step 5: Adjust your recommendations

[0894] The server adjusts the recommendation information according to the user's emotional state based on the results of emotion analysis.

[0895] Input: Sentiment analysis results, user behavior patterns and interests

[0896] The server selects appropriate information and generates recommendation information.

[0897] Output: Adjusted recommendation information

[0898] Step 6: Provide information

[0899] The server transmits the adjusted recommendation information to the terminal.

[0900] Input: Adjusted recommendation information

[0901] The terminal displays the information to the user.

[0902] Output: Recommendation information provided to the user

[0903] Step 7: Provide data

[0904] Based on the analysis results, the server provides data to third parties such as tourism operators and advertising agencies.

[0905] Input: Analysis results

[0906] The server converts the data into an appropriate format and provides it to third parties via an API.

[0907] Output: Data provided to third parties

[0908] In this way, the system analyzes the user's behavioral history and emotions at each step and provides appropriate information, thereby improving the user experience and creating new business opportunities.

[0909] (Application example 3)

[0910] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0911] While conventional systems can recommend information based on a user's behavioral history and search history, they have the problem of being unable to recommend advertisements that take into account the user's emotional state. They also lack the functionality to analyze the effectiveness of advertisements and provide the collected data to third parties. This makes it difficult to provide advertisements that best meet the user's needs, and they are unable to maximize the effectiveness of advertisements.

[0912] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0913] In this invention, the server includes means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, means for selling usage data or linking with other companies via API, means for recognizing emotions from the user's voice input or text input and recommending optimal advertisements based on those emotions, and means for analyzing the effectiveness of advertisements and providing the collected data to third parties. This makes it possible to recommend optimal advertisements based on the user's emotional state, maximizing the effectiveness of advertisements.

[0914] "Current Location" refers to the location where the User is currently located.

[0915] "Destination" refers to the place where the user wants to go.

[0916] "Transportation directions" refers to the route from your current location to your destination.

[0917] "Chat or voice input" refers to the way in which a user inputs information by text message or voice.

[0918] "User's preferred language" means the language used by the User.

[0919] "Means of translation" refers to the method of converting from one language to another.

[0920] "Recommended information in the area" refers to useful information provided based on the user's current location and behavioral history.

[0921] "User behavior history" refers to a record of a user's past actions and the places they have visited.

[0922] "Search history" refers to a record of keywords and phrases a user has searched for in the past.

[0923] "Recommendation methods" refer to methods of suggesting appropriate information or advertisements to users.

[0924] "Usage data" refers to data such as user behavior history and search history.

[0925] "Means of selling or collaborating with other companies through APIs" refers to methods of selling collected data to other companies or sharing data through APIs.

[0926] "User voice or text input" refers to the way in which a user inputs information by voice or text.

[0927] "Means for recognizing emotions" refers to a method for analyzing and recognizing emotions from user input.

[0928] "Means for recommending optimal ads" refers to a method that suggests the most suitable ads based on the user's emotional state.

[0929] "Means for analyzing the effectiveness of advertising" refers to a method for evaluating how effective the displayed advertisements were.

[0930] "Means of providing collected data to third parties" refers to the methods of providing collected data to other companies or organizations.

[0931] A system for implementing this invention has the function of generating transportation routes from a user's current location to a destination and translating them into the user's language. It also has the function of recommending nearby information based on the user's behavioral and search history, and selling usage data or linking with other companies via API. It also has the function of recognizing emotions from the user's voice or text input and recommending optimal advertisements based on those emotions. It is also possible to analyze the effectiveness of advertisements and provide the collected data to third parties.

[0932] Hardware and software used

[0933] Hardware

[0934] Smartphone (microphone, internet connection)

[0935] software

[0936] Python

[0937] speech_recognition library (speech recognition)

[0938] textblob library (sentiment analysis)

[0939] requests library (API requests)

[0940] Processing flow

[0941] Acquiring voice input

[0942] The server uses the speech_recognition library to convert the user's voice input into text. When the user inputs voice through the smartphone's microphone, the voice is converted into text data.

[0943] Emotion recognition

[0944] The server uses the textblob library to analyze the sentiment of the converted text, recognize the user's emotional state from the text data, and select appropriate advertisements based on that sentiment.

[0945] Ad recommendations

[0946] The server recommends the most appropriate advertisement based on the user's emotional state: for example, if the user feels like relaxing, it will display an advertisement for a relaxing travel plan.

[0947] Data provision

[0948] The server analyzes the effectiveness of the advertisements and provides the collected data to third parties, which allows us to maximize the effectiveness of the advertisements and provide the collected data to other companies and organizations.

[0949] Specific examples

[0950] If a user voice-inputs, "Tell me a good ramen restaurant nearby," and the tone and expression of the voice indicate that the user wants to relax, the system will display ads for relaxing travel plans.

[0951] Prompt Sentence Examples

[0952] If a user says, "Tell me a good ramen restaurant nearby," and the tone and expression of the voice suggest that the user wants to relax, create a program that displays ads for relaxing travel plans.

[0953] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0954] Step 1:

[0955] The user inputs voice through the smartphone's microphone, and the input voice data is captured by the smartphone's microphone.

[0956] Step 2:

[0957] The device converts the captured audio data into text data using the speech_recognition library, where audio data is given as input and text data is obtained as output.

[0958] Step 3:

[0959] The server performs sentiment analysis on the converted text data using the textblob library. The text data is given as input and the emotional state (positive, negative, neutral) is obtained as output.

[0960] Step 4:

[0961] The server selects the most appropriate advertisement based on the emotional state. The emotional state is given as input, and the most appropriate advertisement is obtained as output. For example, if the user feels like relaxing, an advertisement for a relaxing travel plan is selected.

[0962] Step 5:

[0963] The server sends the selected advertisement to the user's smartphone for display, and the selected advertisement is given as input and displayed on the smartphone screen.

[0964] Step 6:

[0965] The server collects user response data to analyze the effectiveness of the advertisement. User response data (number of clicks, duration of visit, etc.) is given as input, and the results of the advertisement effectiveness analysis are obtained as output.

[0966] Step 7:

[0967] The server provides the collected data to a third party. The results of the analysis of the effectiveness of the advertisement are given as input, and the data to be provided to the third party is obtained as output.

[0968] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0969] 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 engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0970] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0971] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0972] [Second embodiment]

[0973] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0974] 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.

[0975] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0976] 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.

[0977] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0978] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0979] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0980] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0981] 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.

[0982] 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.

[0983] 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.

[0984] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0985] "Example 1"

[0986] One embodiment of the present invention is an application installed on a mobile device such as a smartphone or tablet. Through this application, a user can inquire about public transportation routes from their current location to their destination via chat or voice input. The application generates an optimal route based on the user's input, translates it into the user's language, and displays it. For example, if a user inputs, "What is the fastest route from Tokyo Station to Narita Airport?", the application generates the optimal route taking into account public transportation timetables and operating conditions, and displays it in the user's language.

[0987] "Example 2"

[0988] One embodiment of the present invention has a function to recommend nearby recommended information based on the user's behavioral history and search history. If the user visits a specific tourist spot or searches for a specific keyword, related recommended information is recommended based on that information. For example, if the user searches for "sushi," sushi restaurants near the user's current location are recommended.

[0989] "Example 3"

[0990] Furthermore, one embodiment of the present invention has the function of selling usage data or linking with other companies via API. Data such as user behavior history and search history is useful information in various industries, including tourism and advertising. This invention creates new business opportunities by providing this data in an appropriate format to third parties. For example, providing data on users who visited a specific tourist destination to the operator of the destination can support more effective marketing activities.

[0991] The processing flow of each embodiment will be described below.

[0992] "Example 1"

[0993] Step 1: A user launches an application installed on a mobile terminal.

[0994] Step 2: The user requests transportation directions from their current location to their destination via chat or voice input. For example, they might say, "What is the fastest route from Tokyo Station to Narita Airport?"

[0995] Step 3: The application generates an optimal route based on the user's input, taking into account public transport timetables and availability.

[0996] Step 4: Translate the generated route into the user's language.

[0997] Step 5: Display the translated route to the user in your application.

[0998] "Example 2"

[0999] Step 1: The user visits a specific tourist spot or searches for a specific keyword. Step 2: The application recommends related information based on the user's behavioral and search history.

[1000] Step 3: Display the recommended information to the user in the application.

[1001] "Example 3"

[1002] Step 1: The application collects data such as user behavior and search history.

[1003] Step 2: Provide the collected data to third parties in an appropriate manner, taking into consideration the protection of data privacy.

[1004] Step 3: The third party creates new business opportunities based on the provided data. For example, the third party may provide data on users who visited a particular tourist destination to the operator of the destination to support more effective marketing activities.

[1005] Example 1

[1006] 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."

[1007] Conventional transportation route guidance systems make it difficult for users to easily find the optimal route to their destination. Furthermore, they rarely translate information into the user's language, making them difficult to use for users who do not understand foreign languages. Furthermore, they lack personalized recommendations based on the user's behavioral and search history, resulting in low user convenience.

[1008] 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.

[1009] In this invention, the server includes means for receiving user input and analyzing it using natural language processing technology, means for generating an optimal route based on the analysis results, and means for translating the generated route into the user's language. This allows the user to easily find the optimal route to their destination, and since the route is displayed in the user's language, it can be used by users who do not understand foreign languages. In addition, personalized recommended information can be provided based on the user's behavioral history and search history, improving user convenience.

[1010] "User" means any person or entity that uses the System to request transportation directions.

[1011] "Input" refers to the provision of information by a user to the system via chat or voice.

[1012] "Natural language processing technology" refers to technology for analyzing user input and understanding its meaning.

[1013] "Analysis results" refers to information extracted from user input using natural language processing techniques.

[1014] An "optimal route" refers to the most efficient combination of transportation modes from a starting point to a destination.

[1015] "Translation" refers to converting the generated route into the user's language.

[1016] "Display" refers to providing the translated route visually on the user's terminal.

[1017] "Recommended information" refers to related information provided based on a user's behavioral history and search history.

[1018] "Behavior history" refers to a record of actions taken by a user in the past.

[1019] "Search history" refers to a record of searches a user has previously conducted.

[1020] "Personalization" refers to customizing information based on a user's individual characteristics and history.

[1021] "Usage Data" means information relating to the use of the System.

[1022] "API" refers to an interface for connecting with other systems and services.

[1023] This invention is a system that allows a user to inquire about transportation routes from their current location to a destination through an application installed on a mobile device such as a smartphone or tablet. Specific embodiments of this system are described below.

[1024] Hardware and software used

[1025] Hardware:

[1026] Mobile devices: smartphones, tablets

[1027] software:

[1028] Natural language processing software: Google Cloud Natural Language API, IBM Watson Natural Language Understanding

[1029] Route generation software: Google Maps API, OpenTripPlanner

[1030] Translation software: DeepL API, Google Translate API

[1031] System Operation

[1032] User Input

[1033] A user starts an application installed on a mobile device and uses chat or voice input to inquire about transportation routes from their current location to their destination. For example, they might input, "What is the fastest route from Tokyo Station to Narita Airport?"

[1034] Parsing input

[1035] The device receives user input and analyzes it through an application using natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding. The analysis extracts information such as the starting point, destination, and user intent.

[1036] Generate optimal routes

[1037] The server receives the analysis results sent from the device and generates the optimal route. The server uses route generation software such as Google Maps API or OpenTripPlanner. For example, it calculates the fastest route from Tokyo Station to Narita Airport and generates a route that combines multiple modes of transportation (trains, buses, etc.).

[1038] Route Translation

[1039] The server translates the generated route into the user's language using translation software such as the DeepL API or Google Translate API. For example, for an English-speaking user, the server generates a translation such as "Take the JR Yamanote Line from Tokyo Station to Nippori Station, then transfer to the Keisei Skyliner to Narita Airport."

[1040] View translated routes

[1041] The device receives the translated route sent from the server and displays it to the user. The display includes the translation results in the user's language. For example, the application screen might display the "fastest route from Tokyo Station to Narita Airport," along with specific transportation methods and travel times.

[1042] Specific examples

[1043] Example user input

[1044] A user types into the application, "What is the quickest route from Tokyo Station to Narita Airport?"

[1045] Prompt Sentence Examples

[1046] "The user inputs that they want to know the fastest route from Tokyo Station to Narita Airport. Please generate the optimal route taking into account public transport timetables and operation status, and display it in the user's language."

[1047] In this way, users can easily find the best route to their destination. It is also possible to provide personalized recommendations based on the user's behavioral and search history, improving user convenience.

[1048] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1049] Step 1:

[1050] User Input

[1051] A user starts an application installed on a mobile device and uses chat or voice input to inquire about transportation routes from their current location to their destination. For example, they might input, "Tell me the quickest route from Tokyo Station to Narita Airport." This input is sent to the device.

[1052] Input: User chat or voice input

[1053] Output: User input data

[1054] Step 2:

[1055] Parsing input

[1056] The device receives user input and analyzes it through an application using natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding. The analysis extracts information such as the starting point, destination, and user intent.

[1057] Input: User-entered data

[1058] Data processing: Analyzes input using natural language processing technology to extract origin, destination, and user intent.

[1059] Output: Analysis results (origin, destination, user intent)

[1060] Step 3:

[1061] Generate optimal routes

[1062] The server receives the analysis results sent from the device and generates the optimal route. The server uses route generation software such as Google Maps API or OpenTripPlanner. For example, it calculates the fastest route from Tokyo Station to Narita Airport and generates a route that combines multiple modes of transportation (trains, buses, etc.).

[1063] Input: Analysis results (origin, destination, user intent)

[1064] Data calculation: Calculate the optimal route using route generation software

[1065] Output: Optimal route

[1066] Step 4:

[1067] Route Translation

[1068] The server translates the generated route into the user's language using translation software such as the DeepL API or Google Translate API. For example, for an English-speaking user, the server generates a translation such as "Take the JR Yamanote Line from Tokyo Station to Nippori Station, then transfer to the Keisei Skyliner to Narita Airport."

[1069] Input: Best Route

[1070] Data processing: Translate the route into the user's language using translation software

[1071] Output: Translated route

[1072] Step 5:

[1073] View translated routes

[1074] The device receives the translated route sent from the server and displays it to the user. The display includes the translation results in the user's language. For example, the application screen might display the "fastest route from Tokyo Station to Narita Airport," along with specific transportation methods and travel times.

[1075] Input: Translated route

[1076] Data processing: formatting the translated route for display to the user

[1077] Output: Route displayed on the user's device

[1078] (Application example 1)

[1079] 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."

[1080] In order for food delivery workers to deliver efficiently, they need to quickly obtain the optimal route that takes into account traffic conditions, weather, and road congestion. However, current navigation systems have difficulty generating routes that fully consider these factors, resulting in reduced delivery efficiency. Furthermore, when delivery workers work in multilingual environments, route information is often not provided in a language that is easy for them to understand, creating communication barriers. A system that can solve these issues is needed.

[1081] 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.

[1082] In this invention, the server includes means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, means for selling usage data or linking with other companies via API, means for generating an optimal route taking into account traffic conditions, weather, and road congestion, and means for translating the generated route information into the delivery person's language and displaying it. This enables delivery people to efficiently obtain the optimal route and perform delivery work smoothly even in a multilingual environment.

[1083] "Current Location" refers to the location where the User is currently located.

[1084] "Destination" refers to the location the user is heading to.

[1085] "Transportation" refers to means of travel, including public transport and road transport.

[1086] "Chat" refers to a means of communication using text messages.

[1087] "Voice input" refers to a means of inputting information using voice.

[1088] "Translation" refers to the conversion of information written in one language into another language.

[1089] "Nearby recommendations" refers to information about interesting places and services near a user's current location or destination.

[1090] "Behavioral history" refers to a record of a user's past actions.

[1091] "Search history" refers to a record of searches a user has conducted in the past.

[1092] "Recommendation" refers to recommending specific information or services to a user.

[1093] "Usage Data" refers to data generated when you use the Application.

[1094] "API" stands for Application Programming Interface, and refers to an interface for exchanging functions and data between different software programs.

[1095] "Other companies" refers to companies other than the company that provides the application.

[1096] "Traffic conditions" refers to the current operating conditions of roads and public transport.

[1097] "Weather" refers to current weather conditions.

[1098] "Road congestion status" refers to the amount of traffic and congestion on the road.

[1099] An "optimal route" refers to the most efficient route to a destination, taking into account traffic conditions, weather, and road congestion.

[1100] "Delivery Person" refers to a person who delivers food to provide food delivery services.

[1101] A "multilingual environment" refers to an environment in which multiple languages ​​are used.

[1102] A system for implementing this invention includes an application installed on a mobile device such as a smartphone or tablet. Through this application, a user can inquire about transportation routes from their current location to their destination by chat or voice input. A specific embodiment of this system is described below.

[1103] Hardware and Software

[1104] Hardware: Smartphones, tablets

[1105] Software: Python, requests library, geopy library

[1106] System configuration

[1107] 1. User Input: Using an application installed on a smartphone or tablet, a user inputs their current location and destination via chat or voice input, for example, "What is the best route from my current location to my next delivery destination?"

[1108] 2. API request: The device sends a request to the traffic information API to obtain the optimal route from the current location to the destination, taking into account traffic conditions, weather, and road congestion.

[1109] 3. Route Generation: The server analyzes the data returned by the API and generates the optimal route. This route information is translated into the user's language.

[1110] 4. Information display: The terminal displays the generated route information to the user, allowing delivery personnel to efficiently obtain the optimal route and carry out delivery work smoothly even in a multilingual environment.

[1111] Specific examples

[1112] User input: "What is the best route from my current location to my next delivery?"

[1113] App output: "The best route is to go straight on Street A, turn right at intersection B, then take Street C."

[1114] This system allows delivery personnel to quickly obtain the optimal route taking into account traffic conditions, weather, and road congestion.In addition, route information is translated into the delivery personnel's language, allowing for smooth delivery operations even in multilingual environments.

[1115] Prompt Sentence Examples

[1116] User: What is the best route from my current location to my next delivery?

[1117] App: The best route is to go straight on Street A, turn right at intersection B, then take Street C.

[1118] In this way, a navigation app can be realized that helps food delivery personnel make deliveries efficiently.

[1119] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1120] Step 1:

[1121] The user uses an application installed on a smartphone or tablet to enter their current location and destination via chat or voice input.

[1122] Input: Current location and destination information (e.g., "What is the best route from my current location to my next delivery destination?")

[1123] Output: Input current location and destination information

[1124] What happens: The user launches the application and uses the chat box or voice input function to enter their current location and destination.

[1125] Step 2:

[1126] The device receives the entered current location and destination information and sends a request to the traffic information API.

[1127] Input: Current location and destination information

[1128] Output: Request to traffic information API

[1129] What happens: The device analyzes the input information and sends a request to the traffic information API endpoint, including parameters such as current location, destination, and language preference.

[1130] Step 3:

[1131] The server receives the response from the traffic information API and generates the optimal route.

[1132] Input: Response data from traffic information API (e.g., traffic conditions, weather, road congestion)

[1133] Output: Optimal route information

[1134] What it does: The server analyzes the data returned by the API and calculates the optimal route, taking into account traffic, weather, and road congestion. This calculation is done using a geographic information system (GIS) and route optimization algorithms.

[1135] Step 4:

[1136] The server translates the generated optimal route information into the user's language.

[1137] Input: Best route information, user language settings

[1138] Output: Translated route information

[1139] What it does: The server translates the route information into the user's preferred language using natural language processing (NLP) techniques and translation APIs.

[1140] Step 5:

[1141] The device displays the translated route information to the user.

[1142] Input: Translated route information

[1143] Output: Route information displayed to the user

[1144] What happens: The device receives the translated route information and displays it through its user interface (UI), including text and map displays.

[1145] Step 6:

[1146] The user confirms the route information displayed and begins delivery.

[1147] Input: Translated route information

[1148] Output: Start of delivery

[1149] What happens: The user reviews the route information displayed and follows the instructions to begin the delivery. The route information may be updated in real time during the delivery.

[1150] Example 2

[1151] Next, a description will be given of 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."

[1152] Conventional systems do not provide personalized recommendations based on users' behavioral and search histories, making it difficult to provide optimal information tailored to users' interests. Furthermore, the generated directions are sometimes not the optimal route, reducing user convenience.

[1153] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for generating transportation routes from the current location to the destination through chat or voice input and translating them into the user's language, a means for recommending nearby recommended information based on the user's behavior history and search history, a means for selling usage data or linking with other companies via API, a means for generating recommended information based on the user's interests using a generation AI model, and a means for providing the generated recommended information to the user terminal. This makes it possible to provide optimal recommended information personalized based on the user's behavior history and search history.

[1154] "Current location" refers to the location information of the user's current location.

[1155] "Destination" refers to the location information of the place where the user wants to go.

[1156] "Transportation directions" refers to the route from your current location to your destination, including public transportation, walking, driving, and other modes of transportation.

[1157] "Chat or voice input" refers to the means by which a user inputs information via text message or voice.

[1158] "User's preferred language" refers to the language used by the user and means that the system supports this language.

[1159] "Means for translating" refers to the function of converting input information into another language.

[1160] "Nearby recommendations" refers to information about nearby interesting places and services provided based on the user's current location and behavioral history.

[1161] "User behavior history" refers to a record of a user's past locations and actions.

[1162] "Search history" refers to a record of keywords or queries a user has searched for in the past.

[1163] "Recommendation means" refers to a function that suggests related information based on a user's behavioral history and search history.

[1164] "Usage data" refers to data such as user behavior history and search history.

[1165] "Means of collaboration with other companies through sales or APIs" refers to methods for sharing collected data with other companies or services.

[1166] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate information.

[1167] "Recommended information based on user interests" refers to information that is predicted to be of interest to the user based on an analysis of the user's behavioral history and search history.

[1168] "User terminal" refers to the device used by the user, including smartphones, tablets, PCs, etc.

[1169] This invention is a system that recommends nearby information based on a user's behavioral history and search history. The system is implemented using a server, user devices (smartphones, tablets, PCs, etc.), a database management system (e.g., MySQL), a location information service (e.g., map API), and a generative AI model (e.g., GPT-4).

[1170] System configuration

[1171] 1. User Device:

[1172] Users use devices such as smartphones or PCs to search for specific keywords or visit specific tourist spots.

[1173] Example: A user searches for "sushi" on their smartphone.

[1174] 2. Sending data to the server:

[1175] The user's device collects the user's behavioral history and search history and sends them to the server.

[1176] Example: A smartphone sends the search keyword "sushi" and its current location to a server.

[1177] 3. Data storage:

[1178] The server stores the received data in a database.

[1179] Example: The server stores the search keyword "sushi" and the user's current location in a MySQL database.

[1180] 4. Data Analysis:

[1181] The server analyzes the stored data to identify the user's interests and concerns.

[1182] Example: The server extracts the keyword "sushi" from a database and determines that the user is interested in sushi.

[1183] 5. Generating recommendation information:

[1184] The server uses a generative AI model (e.g., GPT-4) to generate recommendations based on the user's interests.

[1185] Example: The server inputs the prompt sentence "If the user searches for 'sushi', please list recommended sushi restaurants near the current location" to GPT-4 and obtains the generated list of sushi restaurants.

[1186] 6. Providing Recommendations:

[1187] The server transmits the generated recommendation information to the user's terminal.

[1188] Example: The server sends the generated list of sushi restaurants to the user's smartphone, and the list of sushi restaurants is displayed on the smartphone.

[1189] Specific examples

[1190] User Action: User searches for "sushi" on their smartphone.

[1191] Device operation: The smartphone sends the search keyword "sushi" and its current location information to the server.

[1192] Server Action:

[1193] 1. The server stores the search keyword "sushi" and the user's current location information in a MySQL database.

[1194] 2. The server extracts the keyword "sushi" from the database and determines that the user is interested in sushi.

[1195] 3. The server inputs the prompt sentence "If the user searches for 'sushi,' please list recommended sushi restaurants near the current location" to GPT-4 and obtains the generated list of sushi restaurants.

[1196] 4. The server sends the generated list of sushi restaurants to the user's smartphone.

[1197] User pickup: A list of sushi restaurants near the user's current location will be displayed on the user's smartphone.

[1198] In this way, a system is realized that provides relevant recommended information based on a user's behavioral history and search history.

[1199] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1200] Step 1:

[1201] Users use devices such as smartphones and PCs to search for specific keywords or visit specific tourist spots.

[1202] Input: User searches for "sushi."

[1203] Specific action: The user types "sushi" into their smartphone and presses the search button.

[1204] Output: The search keyword "sushi" and your current location will be saved to your device.

[1205] Step 2:

[1206] The device collects the user's behavioral history and search history and sends them to the server.

[1207] Input: Search keyword "sushi" and your current location.

[1208] Specific operation: The smartphone sends the search keyword "sushi" and its current location information to the server.

[1209] Output: The server receives the search keyword "sushi" and the current location.

[1210] Step 3:

[1211] The server stores the received data in a database.

[1212] Input: Search keyword "sushi" and your current location.

[1213] Specific operation: The server saves the search keyword "sushi" and the user's current location information in a MySQL database.

[1214] Output: The search keyword "sushi" and current location information are saved in the database.

[1215] Step 4:

[1216] The server analyzes the stored data to identify the user's interests and concerns.

[1217] Input: Search keyword "sushi" saved in the database and your current location.

[1218] Specific operation: The server extracts the keyword "sushi" from the database and determines that the user is interested in sushi.

[1219] Output: The analysis result shows that the user is interested in sushi.

[1220] Step 5:

[1221] The server uses a generative AI model (e.g., GPT-4) to generate recommendations based on the user's interests.

[1222] Input: Analytics showing the user is interested in sushi and their current location.

[1223] Specific operation: The server inputs the prompt sentence to GPT-4: "If the user searches for 'sushi,' please list recommended sushi restaurants near the current location," and obtains the generated list of sushi restaurants.

[1224] Output: A list of recommended sushi restaurants near your current location.

[1225] Step 6:

[1226] The server transmits the generated recommendation information to the user terminal.

[1227] Input: A list of recommended sushi restaurants near your current location.

[1228] Specific operation: The server sends the generated list of sushi restaurants to the user's smartphone.

[1229] Output: A list of sushi restaurants will be displayed on the user's smartphone.

[1230] In this way, a system is realized that provides related recommended information based on a user's behavior history and search history.

[1231] (Application example 2)

[1232] 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."

[1233] Conventional recommendation systems provide insufficient information based on user behavioral history and search history, making it difficult to provide real-time recommendations based on the user's current location. Furthermore, personalized information based on the user's search history is limited, leaving a lack of means to improve user convenience.

[1234] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for generating transportation routes from the current location to the destination through chat or voice input and translating them into the user's language, a means for recommending nearby recommended information based on the user's behavioral history and search history, a means for selling usage data or linking with other companies via API, a means for obtaining recommended information around the current location based on the user's search history using an external API, and a means for displaying the obtained recommended information. This allows the user to obtain real-time recommended information based on their current location and provides personalized information based on their search history.

[1235] "Current location" refers to the location information of the user's current location.

[1236] "Destination" refers to the location information of the place where the user wants to go.

[1237] "Transportation directions" refers to route information for a user to travel from their current location to their destination.

[1238] "Chat or voice input" refers to the means by which a user inputs information using text messages or voice.

[1239] "User's preferred language" means the language used by the User.

[1240] "Means of translation" refers to the ability to convert information written in one language into another language.

[1241] "Recommended information in the area" refers to information about stores, tourist spots, etc. located near the user's current location.

[1242] "User behavior history" refers to a record of a user's past locations and actions.

[1243] "Search history" refers to a record of keywords and phrases a user has searched for in the past.

[1244] "Recommendation means" refers to the function of suggesting related information based on a user's behavioral history and search history.

[1245] "Usage data" refers to data such as user behavior history and search history.

[1246] "Means of selling or collaborating with other companies through APIs" refers to the ability to sell usage data to other companies or share data with other companies through APIs.

[1247] "External API" refers to an application programming interface provided by another system or service.

[1248] "Means of acquisition" refers to the function of acquiring the necessary information from an external API, etc.

[1249] "Means for displaying" refers to the function of visually presenting the acquired information to the user.

[1250] The system for implementing this invention has the function of generating transportation directions from a user's current location to a destination via chat or voice input and translating them into the user's language. It also has the function of recommending nearby information based on the user's behavioral history and search history. It also has the function of selling usage data or linking with other companies via API.

[1251] System configuration

[1252] The system consists of the following main components:

[1253] 1. User device: A device such as a smartphone or tablet on which the user chats or uses voice input.

[1254] 2. Server: Manages user behavioral history and search history and generates recommendation information.

[1255] 3. External API: Used to get recommendations around the user's current location.

[1256] Program processing

[1257] The server does the following:

[1258] 1. Obtaining current location: Obtaining current location information from the user's device, using GPS or Wi-Fi location services.

[1259] 2. Route Generation: Generate routes to a destination specified by the user via chat or voice input, using a map API (e.g., Google Maps API).

[1260] 3. Language Translation: The generated directions are translated into the user's language using a translation API (e.g., Google Translate API).

[1261] 4. Generating recommendation information: Based on the user's behavioral history and search history, recommended information around the current location is obtained from an external API and recommendation information is generated.

[1262] 5. Displaying information: Display the obtained recommended information on the user's device.

[1263] Hardware and software used

[1264] Hardware: smartphones, tablets, servers

[1265] Software: Map API (Google Maps API), Translation API (Google Translate API), Location Services (GPS, Wi-Fi)

[1266] Specific examples

[1267] For example, if a user searches for "cafe" and is currently in Tokyo, the system will:

[1268] 1. A user searches for "cafe" on their smartphone.

[1269] 2. The server obtains the user's current location (Tokyo).

[1270] 3. The server uses an external API to obtain information about cafes near the current location.

[1271] 4. The acquired cafe information is displayed on the user's smartphone.

[1272] Prompt Sentence Examples

[1273] User ID: user123

[1274] Search history: ["cafe", "sushi"]

[1275] Current location: Tokyo

[1276] By inputting this prompt into a generative AI model, the most appropriate recommendations can be provided to the user.

[1277] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1278] Step 1:

[1279] A user searches for "cafe" on their smartphone. The input is the keyword "cafe" that the user searched for, and the output is that this keyword is sent to the server.

[1280] Step 2:

[1281] The server obtains the user's current location information. The input is the location information (GPS data) sent from the user's smartphone, and the output is the user's current location (e.g., Tokyo). The server stores this location information in a database.

[1282] Step 3:

[1283] The server retrieves the user's search history from the database. The input is the user ID, and the output is the user's past search history (e.g., "cafe," "sushi"). The server prepares to generate recommendation information based on this history.

[1284] Step 4:

[1285] The server uses an external API to obtain information about cafes near the current location. The input is the user's current location (Tokyo) and the search keyword "cafe," and the output is a list of cafe information obtained from the external API. The server saves this list in a database.

[1286] Step 5:

[1287] The server sends the acquired cafe information to the user's smartphone. The input is a list of cafe information, and the output is the cafe information displayed on the user's smartphone. The user can select a cafe based on this information.

[1288] Step 6:

[1289] It displays detailed information about the cafe selected by the user. The input is the ID of the cafe selected by the user, and the output is detailed information about the cafe (address, opening hours, reviews, etc.). The user can visit the cafe based on this information.

[1290] Step 7:

[1291] The server updates the user's behavioral history. The input is information about the cafes the user visited, and the output is the updated behavioral history. The server stores this information in a database and uses it for future recommendations.

[1292] Example 3

[1293] Next, a description will be given of Example 3 of Form Example 3. 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."

[1294] Conventional systems have had difficulty effectively utilizing users' behavioral and search histories to provide personalized information. There were also issues with security when providing collected data to third parties, as well as the efficiency of data preprocessing and analysis. Furthermore, there was insufficient data provided to accurately cluster user behavior patterns and develop appropriate marketing strategies.

[1295] The identification process by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes a means for generating transportation routes from the current location to the destination through chat or voice input and translating the routes into the user's language, a means for recommending nearby recommended information based on the user's behavior history and search history, a means for selling usage data or linking with other companies via API, a means for collecting the user's behavior history and search history and storing them in a database, a means for preprocessing the stored data and converting it into a format suitable for analysis, a means for analyzing the preprocessed data and clustering user behavior patterns, and a means for providing the analysis results to a third party via API. This makes it possible to effectively utilize the user's behavior history and search history to provide personalized information and securely provide data to third parties.

[1296] "Current Location" refers to the location where the User is currently located.

[1297] "Destination" refers to the place where the user wants to go.

[1298] "Transportation directions" refers to the route from your current location to your destination.

[1299] "Chat or voice input" refers to the means by which a user inputs information by text or voice.

[1300] "User's preferred language" means the language used by the User.

[1301] "Translation" refers to the conversion of one language into another.

[1302] "Recommended information in the area" refers to useful information provided based on the user's current location and behavioral history.

[1303] "User behavior history" refers to a record of actions taken by a user in the past.

[1304] "Search history" refers to a record of searches a user has conducted in the past.

[1305] "Recommendation" refers to recommending specific information or products to users.

[1306] "Usage data" refers to data such as user behavior history and search history.

[1307] "Sales" refers to providing goods or services to others in exchange for payment.

[1308] "API" stands for Application Programming Interface, and refers to an interface for exchanging functions and data between different software programs.

[1309] "Collaboration with other companies" refers to working in cooperation with other companies or organizations.

[1310] A "database" refers to a system for efficiently storing, managing, and retrieving data.

[1311] "Preprocessing" refers to the initial processing of data to convert it into a form suitable for analysis.

[1312] "Analysis" refers to analyzing data to extract useful information.

[1313] "Clustering" refers to grouping data based on similarity.

[1314] "Third Party" refers to any individual or organization outside the system operator or user.

[1315] "Secure" means safe.

[1316] MODE FOR CARRYING OUT THE INVENTION

[1317] The present invention is a system that utilizes a user's behavior history and search history to provide personalized information and further provides this data to third parties. Specific embodiments of this system will be described below.

[1318] Data collection

[1319] The server collects the user's behavioral history and search history generated when the user uses the device to browse websites or use apps, including information on the user's searches for specific tourist attractions and the links they clicked.

[1320] Data storage

[1321] The server stores the collected data in a database, which can be an RDBMS or NoSQL database such as MySQL or MongoDB. The stored data includes user IDs, timestamps, search queries, clicked links, etc.

[1322] Data Preprocessing

[1323] The server preprocesses the stored data by cleaning and normalizing it and converting it into a format suitable for analysis, for example by removing duplicate data and imputing missing values.

[1324] Data analysis

[1325] The server then analyzes the preprocessed data using big data processing frameworks such as Hadoop or Spark, for example, using the K-means clustering algorithm to cluster user behavior patterns and identify user groups with specific interests.

[1326] Data provision

[1327] The server provides the analysis results to third parties via an API. The API is designed in a RESTful format and includes authentication functions such as OAuth, allowing data to be provided securely. For example, a tourism company could use this API to obtain data on users who visited a particular tourist destination.

[1328] Use of Data

[1329] Third parties may use the data provided to them to optimize their marketing strategies, for example, tourism operators may use data on users who have visited a particular tourist destination to target advertisements, thereby enabling them to conduct more effective marketing activities.

[1330] Specific examples

[1331] When a user searches for "Kyoto tourist spots" on their device, the search query and the clicked link are sent to the server. The server stores the user ID "12345," the timestamp "2023-10-01 10:00:00," the search query "Kyoto tourist spots," and the clicked link "https: / / example.com / kyoto-tourist-spots" in a database. The server then performs K-means clustering on the preprocessed data to classify users into "users interested in tourist spots" and "users interested in shopping." The server then provides the analysis results to the API endpoint "https: / / api.example.com / user-data." Tourist operators access this endpoint and retrieve the data using an authentication token. Based on the retrieved data, the tour operator delivers targeted advertisements to users who have visited specific tourist spots. For example, an advertisement for a Kyoto sightseeing tour is displayed to a user who is interested in "Kyoto tourist spots."

[1332] Prompt Sentence Examples

[1333] Examples of prompts to be input to a generative AI model include:

[1334] "Please explain how to analyze the interests of users who visit specific tourist spots based on user behavior history data and provide this information to tourist spot operators."

[1335] By using this prompt sentence, the generative AI model can generate detailed explanations about specific data analysis methods and API design methods. The flow of the specific processing in Example 3 will be described with reference to Fig. 15.

[1336] Step 1: Data collection

[1337] The server collects the behavioral history and search history generated when a user uses a device to browse websites or use apps. For example, if a user searches for "Kyoto tourist spots," the search query and information about the links clicked are sent to the server. The input is the user's search query and the links clicked, and the output is the collected behavioral history data.

[1338] Step 2: Save your data

[1339] The server stores the collected data in a database. The database uses an RDBMS or NoSQL database such as MySQL or MongoDB. Specifically, the user ID "12345", timestamp "2023-10-01 10:00:00", search query "Kyoto tourist spots", and clicked link "https: / / example.com / kyoto-tourist-spots" are stored. The input is the collected behavioral history data, and the output is the data stored in the database.

[1340] Step 3: Preprocessing the data

[1341] The server preprocesses the stored data. Specifically, it cleans and normalizes the data and converts it into a format suitable for analysis. For example, it removes duplicate data and imputes missing values. The input is the data stored in the database, and the output is the preprocessed data.

[1342] Step 4: Data analysis

[1343] The server analyzes the preprocessed data using a big data processing framework such as Hadoop or Spark. Specifically, it uses the K-means clustering algorithm to cluster user behavior patterns and identify user groups with specific interests. The input is the preprocessed data, and the output is the clustered user groups.

[1344] Step 5: Provide the data

[1345] The server provides the analysis results to third parties via an API. The API is designed in a RESTful format and includes authentication functions such as OAuth, allowing data to be provided securely. Specifically, tourism operators use this API to obtain data on users who have visited specific tourist destinations. The input is clustered user groups, and the output is data provided through the API.

[1346] Step 6: Use the data

[1347] Third parties use the provided data to optimize their marketing strategies. Specifically, a tourism company may run targeted advertisements based on data about users who have visited a particular tourist destination. For example, a user who is interested in "Kyoto tourist destinations" may be shown advertisements for sightseeing tours in Kyoto. The input is data provided through the API, and the output is an optimized marketing strategy.

[1348] (Application example 3)

[1349] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1350] In today's information society, there is a demand for effective utilization of users' behavioral and search histories to provide them with the most appropriate information and advertisements. However, conventional systems have been unable to fully utilize users' behavioral and search histories, resulting in low accuracy in providing personalized information and targeted advertisements. Furthermore, there have been issues with the difficulty of properly managing and linking data when providing user behavioral and search histories to third parties.

[1351] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1352] In this invention, the server includes means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, means for selling usage data or collaborating with other companies via API, and means for collecting the user's behavioral history and search history and providing advertisers with data for providing targeted advertisements via API, thereby making it possible to provide personalized information to users and improve the accuracy of targeted advertisements.

[1353] "Transportation routes" refers to route information for public transportation and roads used to travel from the current location to the destination.

[1354] "Chat or voice input" means a user inputs information into the system using text messages or voice.

[1355] "Means for translating into the user's preferred language" means the system's ability to translate information into the user's native language or language of choice.

[1356] "Nearby recommendations" are information about interesting places and services near the user's current location or destination.

[1357] "User behavior history" is a record of the places a user has visited and the services they have used in the past.

[1358] "Search history" is a record of keywords and phrases that a user has previously searched for on the Internet or in applications.

[1359] "Recommendation means" refers to the system's ability to suggest related information and services based on the user's behavioral history and search history.

[1360] "Usage data" refers to all data collected by the system, such as user behavior history and search history.

[1361] "Means of selling or collaborating with other companies through APIs" refers to methods for providing collected data to third parties, including selling data or collaborating through APIs.

[1362] "Targeted advertising" refers to advertising that is displayed based on specific user interests and behavior.

[1363] "Means provided via API" means a method of providing data to other systems or services through an application programming interface.

[1364] The system for carrying out the present invention is configured using a user's smartphone and a server. A specific embodiment of the system will be described below.

[1365] System configuration

[1366] The system consists of the following main components:

[1367] 1. User device: A mobile device such as a smartphone or tablet.

[1368] 2. Server: A server for data processing and API integration.

[1369] 3. Software: Python, Requests library, JSON library, etc.

[1370] Data collection and processing

[1371] The user's device collects the user's behavioral history and search history, including GPS data and search queries, and the collected data is sent to a server and stored in a database.

[1372] Data Translation and Recommendations

[1373] The server generates transportation directions from the user's current location to the destination, translates them into the user's language, and recommends nearby information based on the user's behavioral and search history.

[1374] Data sales and API integration

[1375] The server sells the collected usage data or connects with other companies via API, allowing third parties to use the data to create new business opportunities.

[1376] Providing targeted advertising

[1377] The server collects user behavior and search history and provides advertisers with data via API to deliver targeted ads, allowing them to display more effective ads to users.

[1378] Specific examples

[1379] For example, if a user visits tourist spot A, their behavioral history is collected and advertisements related to tourist spot A are displayed. Also, if a user searches for "restaurant B," coupon information for restaurant B is recommended based on their search history.

[1380] Prompt Sentence Examples

[1381] "Create a program that collects data and provides it to advertisers to show relevant ads based on the tourist destinations that users visit."

[1382] In this way, the system can improve the accuracy of providing personalized information and targeted advertising to users.

[1383] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1384] Step 1:

[1385] The user's device collects the user's behavioral history and search history.

[1386] Input: GPS data, search query

[1387] Data processing: Convert GPS data into location information and save search queries as text data.

[1388] Output: Behavioral history data, search history data

[1389] Step 2:

[1390] The user terminal transmits the collected behavioral history data and search history data to the server.

[1391] Input: Behavioral history data, search history data

[1392] Data calculation: Convert the data into JSON format and send it to the server.

[1393] Output: Data sent to the server

[1394] Step 3:

[1395] The server stores the received data in a database.

[1396] Input: JSON format behavior history data, search history data

[1397] Data processing: Convert the data into a database format and save it.

[1398] Output: Data stored in the database

[1399] Step 4:

[1400] The server generates transport directions from the user's current location to the destination and translates them into the user's language.

[1401] Input: current location, destination, user language

[1402] Data calculation: Obtaining transportation route information and calculating the optimal route. Translating the calculation results into the user's language.

[1403] Output: Translated directions

[1404] Step 5:

[1405] The server recommends nearby information based on the user's behavioral and search history.

[1406] Input: behavioral history data, search history data, current location

[1407] Data calculation: Analyzes behavioral and search history to extract relevant recommendations.

[1408] Output: Recommendation information

[1409] Step 6:

[1410] The server sells the collected usage data or connects with other companies via API.

[1411] Input: Usage data

[1412] Data processing: Converting data into a format that can be used by third parties.

[1413] Output: Data provided to third parties

[1414] Step 7:

[1415] The server collects user behavioral and search history and provides advertisers with data via an API to deliver targeted advertisements.

[1416] Input: Behavioral history data, search history data

[1417] Data calculation: Analyzes behavioral and search history to extract data suitable for targeted advertising.

[1418] Output: Targeted advertising data provided to advertisers

[1419] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1420] "Example 1"

[1421] One embodiment of the present invention is a system that incorporates an emotion engine that recognizes user emotions. This system recognizes emotions from the user's voice and text input and uses the results to adjust the information recommended. Specifically, when a user voice-inputs, "Tell me the fastest route from Tokyo Station to Narita Airport," if the tone and expression of the voice indicate that the user is in a hurry, the system not only provides the fastest route but also information on high-speed transportation such as taxis and helicopters. Similarly, when a user text-inputs, "Tell me about a good ramen restaurant nearby," if the expression in the text indicates that the user wants to relax, the system provides information on nearby ramen restaurants with a calm atmosphere.

[1422] "Example 2"

[1423] One embodiment of the present invention is a system that incorporates an emotion engine that recognizes user emotions. This system recognizes emotions from the user's voice and text input and uses the results to adjust the information recommended. Specifically, when a user voice-inputs, "Tell me the fastest route from Tokyo Station to Narita Airport," if the tone and expression of the voice indicate that the user is in a hurry, the system not only provides the fastest route but also information on high-speed transportation such as taxis and helicopters. Similarly, when a user text-inputs, "Tell me about a good ramen restaurant nearby," if the expression in the text indicates that the user wants to relax, the system provides information on nearby ramen restaurants with a calm atmosphere.

[1424] "Example 3"

[1425] One embodiment of the present invention is a system that incorporates an emotion engine that recognizes user emotions. This system recognizes emotions from the user's voice and text input and uses the results to adjust the information recommended. Specifically, when a user voice-inputs, "Tell me the fastest route from Tokyo Station to Narita Airport," if the tone and expression of the voice indicate that the user is in a hurry, the system not only provides the fastest route but also information on high-speed transportation such as taxis and helicopters. Similarly, when a user text-inputs, "Tell me about a good ramen restaurant nearby," if the expression in the text indicates that the user wants to relax, the system provides information on nearby ramen restaurants with a calm atmosphere.

[1426] The processing flow of each embodiment will be described below.

[1427] "Example 1"

[1428] Step 1: The user provides voice or text input to the system. For example, the user could provide voice input such as "What is the fastest route from Tokyo Station to Narita Airport?" or text input such as "What is a good ramen restaurant nearby?"

[1429] Step 2: The emotion engine recognizes emotions from the user's input, inferring the user's emotions from the tone and expressions of the voice, text expressions, etc.

[1430] Step 3: Based on the recognition results of the emotion engine, the system adjusts the information it recommends. For example, if the user feels they are in a hurry, it will not only provide the fastest route but also information on high-speed transportation such as taxis and helicopters. If the user feels they want to relax, it will provide information on nearby ramen restaurants with a calm atmosphere.

[1431] "Example 2"

[1432] Step 1: The user provides voice or text input to the system. For example, the user could provide voice input such as "What is the fastest route from Tokyo Station to Narita Airport?" or text input such as "What is a good ramen restaurant nearby?"

[1433] Step 2: The emotion engine recognizes emotions from the user's input, inferring the user's emotions from the tone and expressions of the voice, text expressions, etc.

[1434] Step 3: Based on the recognition results of the emotion engine, the system adjusts the information it recommends. For example, if the user feels they are in a hurry, it will not only provide the fastest route but also information on high-speed transportation such as taxis and helicopters. If the user feels they want to relax, it will provide information on nearby ramen restaurants with a calm atmosphere.

[1435] "Example 3"

[1436] Step 1: The user provides voice or text input to the system. For example, the user could provide voice input such as "What is the fastest route from Tokyo Station to Narita Airport?" or text input such as "What is a good ramen restaurant nearby?"

[1437] Step 2: The emotion engine recognizes emotions from the user's input, inferring the user's emotions from the tone and expressions of the voice, text expressions, etc.

[1438] Step 3: Based on the recognition results of the emotion engine, the system adjusts the information it recommends. For example, if the user feels they are in a hurry, it will not only provide the fastest route but also information on high-speed transportation such as taxis and helicopters. If the user feels they want to relax, it will provide information on nearby ramen restaurants with a calm atmosphere.

[1439] Example 1

[1440] 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."

[1441] Conventional transportation guidance systems can provide optimal routes based on user input, but they have the problem of being unable to respond flexibly to the user's emotions and circumstances. Furthermore, if the information provided is not translated into the user's language, it can be difficult for the user to understand. Furthermore, the lack of personalized recommendations based on the user's behavioral history and search history means that they are unable to fully meet the user's needs.

[1442] 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.

[1443] In this invention, the server includes a means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, a means for recommending nearby recommended information based on the user's behavioral history and search history, a means for selling usage data or linking with other companies via an interface, a means for recognizing emotions from the user's input and adjusting the information provided based on those emotions, and a means for translating the generated route information into the user's language. This allows for flexible responses according to the user's emotions and situation, and providing information in the user's language makes it easier to understand. It also enables personalized recommendations based on the user's behavioral history and search history.

[1444] "Current Location" refers to the location where the User is currently located.

[1445] "Destination" refers to the location the user wants to reach.

[1446] "Transportation" refers to means of transportation such as public transportation, taxis, and helicopters.

[1447] "Chat Input" refers to the method by which a User inputs information in text form.

[1448] "Voice input" refers to a method by which a user inputs information in the form of speech.

[1449] "Translation" refers to the conversion of information written in one language into another language.

[1450] "Nearby recommendations" refers to information about interesting places and services near a user's current location or destination.

[1451] "Behavioral history" refers to a record of a user's past actions and places visited.

[1452] "Search history" refers to a record of past search queries made by a user.

[1453] "Recommendation" refers to recommending specific information or services to a user.

[1454] "Usage Data" refers to data generated when a user uses the system.

[1455] An "interface" refers to a means of connection that allows different systems and services to work together.

[1456] "Emotion recognition" refers to analyzing and recognizing emotions from user input.

[1457] "Personalization" refers to providing information and services tailored to a user's individual needs and preferences.

[1458] "Optimal route" refers to the travel route that best suits the user's conditions.

[1459] MODE FOR CARRYING OUT THE INVENTION

[1460] This invention is a system that allows users to inquire about transportation routes from their current location to their destination by chat or voice input using an application installed on a mobile device such as a smartphone or tablet. Specific embodiments of this system are described below.

[1461] System configuration

[1462] Users install the application on their mobile devices, such as smartphones and tablets. This application generates the optimal route based on the user's input, translates it into the user's language, and displays it. It also incorporates an emotion engine that recognizes the user's emotions.

[1463] Hardware and software used

[1464] Mobile devices: smartphones, tablets

[1465] Server: Cloud Server

[1466] Map services: Google Maps API, OpenStreetMap

[1467] Translation services: Google Translate API, DeepL API

[1468] Speech recognition service: Google Speech-to-Text API

[1469] Emotion recognition services: IBM Watson Tone Analyzer, Microsoft Azure Text Analytics

[1470] Program processing

[1471] The user launches the application and makes a query via chat or voice input. For example, the user might type, "What is the fastest route from Tokyo Station to Narita Airport?" or speak the same via voice input. The device sends the user's text or voice data to the server. In the case of voice input, the device first uses the Google Speech-to-Text API to convert the speech to text.

[1472] The server analyzes the received text data to understand the user's request. It then uses the Google Maps API or OpenStreetMap to generate the optimal route, taking into account public transport timetables and operating conditions. The generated route information is translated into the user's language using the Google Translate API or DeepL API. The translated information is sent from the server to the device, which then displays it to the user.

[1473] Furthermore, the emotion engine recognizes emotions from user input. For example, if a user says, "Tell me the fastest route from Tokyo Station to Narita Airport," and the tone and expression of the voice indicate that the user is in a hurry, the server will not only provide the fastest route, but also information on high-speed transportation options such as taxis and helicopters.

[1474] Specific examples

[1475] If a user types in text such as "Tell me about a good ramen restaurant nearby," the device sends this text data to the server. The server analyzes the text, and the emotion engine recognizes the user's desire to relax. The server uses the Google Maps API to search for nearby ramen restaurants with a relaxing atmosphere and translates the information into the user's language. The translated information is sent to the device, which then displays it to the user.

[1476] Prompt Sentence Examples

[1477] If a user says, "Tell me the fastest route from Tokyo Station to Narita Airport," and the tone and expression of the voice indicate that the user is in a hurry, the system will not only provide the fastest route, but also provide information on high-speed transportation options such as taxis and helicopters.

[1478] In this way, a system is realized that generates optimal routes based on user input and provides information according to emotions.

[1479] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1480] Step 1:

[1481] The user launches the application and makes a query via chat or voice input.

[1482] Input: The user types or speaks, "What is the fastest route from Tokyo Station to Narita Airport?"

[1483] What happens: A user launches the application on their smartphone or tablet and either types text into the chat box or speaks a question using the voice input feature.

[1484] Step 2:

[1485] The device sends the user's input to the server.

[1486] Input: The user's text or voice data.

[1487] Output: The text data sent to the server.

[1488] Specific operation: When the device receives voice input, it uses the Google Speech-to-Text API to convert the voice into text and sends the text data to the server.

[1489] Step 3:

[1490] The server analyzes the user's input and generates the optimal route.

[1491] Input: User text data.

[1492] Output: Optimal route information.

[1493] Specific operation: The server analyzes the received text data and uses the Google Maps API and OpenStreetMap to generate the optimal route taking into account public transport timetables and operating conditions.

[1494] Step 4:

[1495] The server translates the generated route information into the user's language.

[1496] Input: Optimal route information.

[1497] Output: The translated route information.

[1498] Specific operation: The server uses the Google Translate API or DeepL API to translate the generated route information into the user's language.

[1499] Step 5:

[1500] The server sends the translated information to the terminal.

[1501] Input: Translated route information.

[1502] Output: The translated route information sent to the device.

[1503] Specific operation: The server encrypts the translated route information and sends it to the device.

[1504] Step 6:

[1505] The device will then display the translated route information to the user.

[1506] Input: Translated route information.

[1507] Output: Route information displayed to the user.

[1508] What it does: The device will then display the translated route information on the application screen, allowing the user to view detailed directions to their destination.

[1509] Step 7:

[1510] The emotion engine recognizes emotions from user input and provides additional information as needed.

[1511] Input: The user's text or voice data.

[1512] Output: Additional information based on the sentiment.

[1513] How it works: The emotion engine analyzes the user's input to determine their emotions. For example, if the user feels they are in a hurry, it will provide information on the fastest route as well as other high-speed transportation options such as taxis and helicopters. It uses IBM Watson Tone Analyzer and Microsoft Azure Text Analytics to recognize emotions.

[1514] (Application example 1)

[1515] 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."

[1516] Conventional food delivery systems struggle to provide optimal delivery options based on the user's emotions and urgency. Furthermore, they lack the ability to analyze user input and generate appropriate prompts, limiting the user experience.

[1517] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a means for generating transportation routes from the current location to the destination via chat or voice input and translating them into the user's language, a means for recommending nearby recommended information based on the user's behavioral history and search history, a means for selling usage data or linking with other companies via API, a means for recognizing the user's emotions and providing the optimal delivery option based on those emotions, and a means for analyzing the user's input using a generative AI model and generating a prompt sentence. This makes it possible to provide the optimal delivery option according to the user's emotions and urgency, improving the user experience.

[1518] "Current Location" refers to the location where the User is currently located.

[1519] "Destination" refers to the location the user wants to reach.

[1520] "Transportation" refers to public transportation such as trains, buses, and taxis.

[1521] "Chat" refers to a means of communicating in real time using text messages.

[1522] "Voice input" refers to a means by which a user inputs information using voice.

[1523] "Translation" refers to the conversion of information written in one language into another language.

[1524] "Recommended Information" refers to useful information provided to users.

[1525] "Behavioral history" refers to a record of a user's past actions.

[1526] "Search history" refers to a record of searches a user has conducted in the past.

[1527] "Recommendation" refers to recommending appropriate information or products to users.

[1528] "Usage Data" refers to data generated when a user uses the system.

[1529] "API" stands for Application Programming Interface, and refers to an interface for exchanging functions and data between different software programs.

[1530] "Emotion recognition" refers to the technology of analyzing emotions from a user's voice or text.

[1531] "Delivery Options" means the delivery service options available to Users.

[1532] A "generative AI model" is a model that uses artificial intelligence to analyze data and generate new information or prompts.

[1533] A "prompt sentence" refers to a question or instruction sentence presented to the user.

[1534] The system for implementing this invention has the function of generating transportation directions from a user's current location to their destination via chat or voice input and translating them into the user's language. It also has the function of recommending nearby information based on the user's behavioral history and search history. Furthermore, it has the function of recognizing the user's emotions and providing the optimal delivery options based on those emotions.

[1535] Hardware and Software Configuration

[1536] 1. Hardware:

[1537] Mobile devices such as smartphones and tablets

[1538] server

[1539] 2. Software:

[1540] Android / iOS applications

[1541] Emotion recognition engine (e.g. IBM Watson Tone Analyzer)

[1542] Delivery service API (e.g. Google Maps API)

[1543] Generative AI models (e.g., GPT-3)

[1544] Data processing and calculation

[1545] 1. Getting user input:

[1546] The user uses a smartphone or tablet to voice-input "I want a nearby pizza delivered quickly."

[1547] Converts speech to text using the Google Speech-to-Text API.

[1548] 2. Emotion recognition:

[1549] The converted text is sent to IBM Watson Tone Analyzer to analyze the user's sentiment.

[1550] As a result, you perceive yourself as being in a hurry.

[1551] 3. Route generation and delivery options available:

[1552] Uses the Google Maps API to find the nearest pizza place based on the user's current location.

[1553] Select the pizza place that offers the fastest delivery.

[1554] 4. Information provision:

[1555] Displays information about selected pizza restaurants in the user's language.

[1556] It will display, "The fastest pizza place to deliver is XX. Would you like to order?"

[1557] Specific examples

[1558] If a user says "I want a nearby pizza delivered quickly," and the tone and expression of the voice indicate that the user is in a hurry, the system will provide information on pizza restaurants that can deliver the fastest. Similarly, if a user says "I want to order a relaxing dessert," and the expression in the text indicates that the user wants to relax, the system will provide information on nearby dessert restaurants with a calm atmosphere.

[1559] Prompt Sentence Examples

[1560] "I want pizza delivered quickly near me."

[1561] "I want to order a relaxing dessert."

[1562] In this way, it is possible to provide optimal delivery options according to the user's emotions and urgency, improving the user experience.

[1563] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1564] Step 1:

[1565] A user uses a smartphone or tablet to input voice commands. For example, they might say, "I want a nearby pizza delivered immediately." This voice data is then input into the device.

[1566] Step 2:

[1567] The device converts voice data to text data using the Google Speech-to-Text API. The input is voice data and the output is text data.

[1568] Step 3:

[1569] The device sends the converted text data to the IBM Watson Tone Analyzer to analyze the user's emotions. The input is text data, and the output is emotion data (e.g., "I'm in a hurry").

[1570] Step 4:

[1571] The server searches for the user's current location and the nearest pizza restaurant based on the emotion data using the Google Maps API. The input is the user's current location and emotion data, and the output is a list of the nearest pizza restaurants.

[1572] Step 5:

[1573] The server selects the pizza place that can deliver the fastest. The input is a list of the nearest pizza places, and the output is the information of the pizza place that can deliver the fastest.

[1574] Step 6:

[1575] The server translates the information about the selected pizza restaurant into the user's language and sends it to the terminal. The input is the pizza restaurant information, and the output is the translated pizza restaurant information.

[1576] Step 7:

[1577] The device displays the translated information about the pizza restaurant to the user. For example, it displays "The pizza restaurant with the fastest delivery time is ____. Would you like to order?" The input is the translated information about the pizza restaurant, and the output is what is displayed to the user.

[1578] In this way, it is possible to provide optimal delivery options according to the user's emotions and urgency, improving the user experience.

[1579] Example 2

[1580] Next, a description will be given of 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."

[1581] Conventional systems have the ability to recommend nearby information based on a user's behavioral and search history, but do not provide information that takes into account the user's emotions. As a result, they are unable to provide optimal information based on the user's current emotional state, resulting in a lack of improved user experience. Furthermore, there is a need for systems that provide appropriate information according to the situation, such as when the user is in a hurry or wants to relax.

[1582] 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.

[1583] In this invention, the server includes means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, means for selling usage data or linking with other companies via API, and means for recognizing emotions from the user's voice input or text input and adjusting the information to be recommended using the results. This makes it possible to provide optimal information according to the user's emotional state, which is expected to improve the user experience.

[1584] "Current Location" refers to the location where the User is currently located.

[1585] "Destination" refers to the place where the user wants to go.

[1586] "Transportation directions" refers to the route from your current location to your destination.

[1587] "Chat or voice input" refers to a method by which a user inputs information by text or voice.

[1588] "User's preferred language" means the language used by the User.

[1589] "Means of translation" refers to the method of converting from one language to another.

[1590] "Recommended information in the area" refers to useful information about the area around the user's current location.

[1591] "User behavior history" refers to a record of actions taken by a user in the past.

[1592] "Search history" refers to a record of keywords or queries a user has searched for in the past.

[1593] "Recommendation methods" refer to methods of suggesting appropriate information to users.

[1594] "Usage data" refers to data such as user behavior history and search history.

[1595] "Means of collaboration with other companies through sales or APIs" refers to ways in which usage data is shared with other companies.

[1596] "Voice input or text input" refers to the way in which a user inputs information by voice or text.

[1597] "Means for recognizing emotions" refers to a method for analyzing the user's emotional state.

[1598] "Means for adjusting recommended information" refers to methods for optimizing the information provided based on the user's emotional state.

[1599] MODE FOR CARRYING OUT THE INVENTION

[1600] The present invention is a system for providing optimal information based on a user's behavior history, search history, and emotional state. Specific embodiments of this system are described below.

[1601] Hardware and software used

[1602] Servers: Database servers, web servers

[1603] Devices: Smartphones, PCs

[1604] Software: Emotion recognition engine, recommendation engine, natural language processing (NLP) engine

[1605] Data processing and calculation

[1606] 1. Collecting user behavior and search history

[1607] User: Uses a smartphone or PC to visit a specific tourist spot or search for a specific keyword.

[1608] Specific action: A user searches for "sushi" on their smartphone.

[1609] Device: Records user behavior and search history and sends it to the server.

[1610] Specific operation: The smartphone sends the search keyword "sushi" to the server.

[1611] Server: Stores the received behavioral history and search history in a database.

[1612] Specific operation: The server saves the search keyword "sushi" in the database.

[1613] 2. Generating recommendation information

[1614] Server: Generates relevant recommendations based on the behavioral and search history stored in the database.

[1615] Specific operation: The server searches for sushi restaurants near the user's current location based on the search keyword "sushi."

[1616] Server: Uses a recommendation engine to select the best recommendations.

[1617] What it does: The recommendation engine generates a list of sushi restaurants near your current location.

[1618] 3. Emotion recognition

[1619] User: Use voice input or text input.

[1620] Specific operation: The user speaks, "What is the fastest route from Tokyo Station to Narita Airport?"

[1621] Device: Sends voice and text input to the emotion recognition engine.

[1622] What it does: Your smartphone sends voice data to an emotion recognition engine.

[1623] Server: The emotion recognition engine analyzes the user's emotions from the tone of voice and text expressions.

[1624] What it does: The emotion recognition engine analyzes the voice data and determines that the user is in a hurry.

[1625] 4. Adjusting information according to emotions

[1626] Server: Adjusts the recommendation information based on the results of the emotion recognition engine.

[1627] Specific behavior: When the server determines that a user is in a hurry, it provides information on the fastest route as well as high-speed transportation options such as taxis and helicopters.

[1628] On the device: Display tailored recommendations to the user.

[1629] What it does: Your smartphone will display information about the quickest routes and express transportation options to you.

[1630] Examples and prompts

[1631] Example 1: When a user searches for "sushi"

[1632] User: Searches for "sushi" on their smartphone.

[1633] Device: Sends search keywords to the server.

[1634] Server: Saves the search keywords in a database and uses a recommendation engine to search for sushi restaurants near the current location.

[1635] Server: The recommendation engine generates a list of sushi restaurants and sends it to the user.

[1636] Terminal: Show the user a list of sushi restaurants.

[1637] Example 2: When a user says, "What is the fastest route from Tokyo Station to Narita Airport?"

[1638] User: Uses voice input on a smartphone.

[1639] Device: Sends voice data to the emotion recognition engine.

[1640] Server: The emotion recognition engine analyzes the voice data and determines that the user is in a hurry.

[1641] Server: Generates information about the fastest routes as well as other high-speed transportation options such as taxis and helicopters and sends it to the user.

[1642] On the device: Show users the quickest route and express transport options.

[1643] Prompt Sentence Examples

[1644] "When a user searches for 'sushi,' recommend sushi restaurants near the user's current location."

[1645] "If a user says, 'What is the fastest route from Tokyo Station to Narita Airport?', the tone of the voice indicates that the user is in a hurry, so in addition to the fastest route, provide information on faster transportation options such as taxis and helicopters."

[1646] The above is a specific embodiment for carrying out the present invention.

[1647] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1648] Step 1:

[1649] Collection of user behavior and search history

[1650] User: Uses a smartphone or PC to visit a specific tourist spot or search for a specific keyword.

[1651] Specific action: A user searches for "sushi" on their smartphone.

[1652] Input: The search term "sushi" entered by the user.

[1653] Output: The search keywords are sent to the device.

[1654] Device: Records user behavior and search history and sends it to the server.

[1655] Specific operation: The smartphone sends the search keyword "sushi" to the server.

[1656] Input: User's search keyword "sushi".

[1657] Output: The search keywords are sent to the server.

[1658] Server: Stores the received behavioral history and search history in a database.

[1659] Specific operation: The server saves the search keyword "sushi" in the database.

[1660] Input: The search keyword "sushi" sent from the device.

[1661] Output: The search keywords are saved in the database.

[1662] Step 2:

[1663] Generating recommendation information

[1664] Server: Generates relevant recommendations based on the behavioral and search history stored in the database.

[1665] Specific operation: The server searches for sushi restaurants near the user's current location based on the search keyword "sushi."

[1666] Input: Search keyword "sushi" saved in the database.

[1667] Output: A list of sushi restaurants near your current location.

[1668] Server: Uses a recommendation engine to select the best recommendations.

[1669] What it does: The recommendation engine generates a list of sushi restaurants near your current location.

[1670] Input: A list of sushi restaurants near your current location.

[1671] Output: A list of the best sushi restaurants.

[1672] Step 3:

[1673] emotion recognition

[1674] User: Use voice input or text input.

[1675] Specific operation: The user speaks, "What is the fastest route from Tokyo Station to Narita Airport?"

[1676] Input: User speaks, "What is the fastest route from Tokyo Station to Narita Airport?"

[1677] Output: The audio data is sent to the device.

[1678] Device: Sends voice and text input to the emotion recognition engine.

[1679] What it does: Your smartphone sends voice data to an emotion recognition engine.

[1680] Input: User's voice data.

[1681] Output: The audio data is sent to the emotion recognition engine.

[1682] Server: The emotion recognition engine analyzes the user's emotions from the tone of voice and text expressions.

[1683] What it does: The emotion recognition engine analyzes the voice data and determines that the user is in a hurry.

[1684] Input: Audio data.

[1685] Output: User's emotional state (in a hurry).

[1686] Step 4:

[1687] Adjusting information according to emotions

[1688] Server: Adjusts the recommendation information based on the results of the emotion recognition engine.

[1689] Specific behavior: When the server determines that a user is in a hurry, it provides information on the fastest route as well as high-speed transportation options such as taxis and helicopters.

[1690] Input: User's emotional state (in a hurry), a list of the best sushi restaurants.

[1691] Output: Tailored recommendations (fastest routes and faster transport options).

[1692] On the device: Display tailored recommendations to the user.

[1693] What it does: Your smartphone will display information about the quickest routes and express transportation options to you.

[1694] Input: Tailored recommendation information.

[1695] Output: Information displayed to the user.

[1696] (Application example 2)

[1697] 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."

[1698] Conventional recommendation systems provide information based on a user's behavioral and search history, but they are unable to provide recommendations that take into account the user's emotions, making it difficult to provide information that is optimal for the user's current state or mood.In addition, there is a lack of real-time personalized information provided in physical stores that users visit, so there is a need to improve the user experience.

[1699] 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.

[1700] In this invention, the server includes means for generating transportation routes from the current location to the destination through chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, and means for recognizing emotions from the user's voice input or text input and adjusting the information to be recommended using the results. This makes it possible to provide personalized information based on the user's behavioral history and search history, as well as optimal information that takes the user's emotions into consideration.

[1701] "Current Location" refers to the location where the User is currently located.

[1702] "Destination" refers to the place where the user wants to go.

[1703] "Transportation" refers to the means used by users to travel, including trains, buses, taxis, helicopters, etc.

[1704] "Directions" refers to the route from the current location to the destination.

[1705] "Chat" refers to a means of communicating in real time using text messages.

[1706] "Voice input" refers to a means by which a user inputs information using voice.

[1707] "Translation" refers to the conversion of information written in one language into another language.

[1708] "Recommended information in the area" refers to information about stores and services located near the user's current location.

[1709] "Behavioral history" refers to a record of a user's past actions and places visited.

[1710] "Search history" refers to a record of keywords and phrases a user has searched for in the past.

[1711] "Recommendation" refers to recommending appropriate information or products to users.

[1712] "Emotion recognition" refers to determining a user's emotional state from their voice or text.

[1713] "Usage data" refers to data such as user behavior history, search history, and emotion recognition results.

[1714] "Sales" refers to providing goods or services to others in exchange for payment.

[1715] "API" stands for Application Program Interface and refers to a means of sharing functions and data between different software programs.

[1716] "Collaboration with other companies" refers to working with different companies or organizations to share services and data.

[1717] The system for carrying out the present invention is configured using a user's smartphone and a server. Specific embodiments will be described below.

[1718] System Configuration

[1719] 1. Hardware:

[1720] Smartphone: A mobile device carried by the user that is equipped with a microphone and keyboard for voice and text input.

[1721] Server: A central processing unit for data processing and generating recommendation information.

[1722] 2. Software:

[1723] EmotionRecognizer: A library for recognizing emotions from user voice and text input.

[1724] RecommendationEngine: An engine for generating recommendations based on user behavioral history and emotions.

[1725] Data processing and calculation

[1726] 1. Data Acquisition:

[1727] The server obtains the user's behavioral history and search history through the API, which allows it to understand the user's past behavioral patterns.

[1728] Example: https: / / api.example.com / user / {user_id} / history

[1729] 2. Emotion recognition:

[1730] The smartphone sends the user's voice and text input to the EmotionRecognizer to recognize emotions, which allows it to understand the user's current emotional state.

[1731] Example: EmotionRecognizer().recognize(input_text)

[1732] 3. Recommendation generation:

[1733] The server uses a recommendation engine to recommend appropriate products and services based on the user's behavioral history and emotion recognition results, thereby providing information that is optimal for the user's current state and mood.

[1734] Example: RecommendationEngine().recommend(user_history, emotion)

[1735] Specific examples

[1736] If a user voice-inputs "I'm tired," the server will use EmotionRecognizer to recognize the user's emotion as "fatigue," and then use RecommendationEngine to recommend relaxing cafes and massage parlors.

[1737] When a user searches for "sushi," the server recommends sushi restaurants near the user's current location based on the user's behavioral history and current location.

[1738] Prompt Sentence Examples

[1739] "Recommend places where users who feel tired can relax."

[1740] "Recommend sushi restaurants near the user's current location when they search for sushi."

[1741] In this way, it becomes possible to provide optimal information in real time based on the user's behavioral history and emotions.

[1742] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1743] Step 1:

[1744] A user uses a smartphone to input voice or text.

[1745] Input: User voice or text

[1746] Output: Input audio or text data

[1747] Specific action: The user speaks "I'm tired."

[1748] Step 2:

[1749] The terminal converts the input voice data into text data.

[1750] Input: Audio data

[1751] Output: Text data

[1752] Specific operation: Using the smartphone's voice recognition function, the spoken word "I'm tired" is converted into text.

[1753] Step 3:

[1754] The device sends the text data to EmotionRecognizer to recognize emotions.

[1755] Input: Text data

[1756] Output: Emotion data (e.g., fatigue)

[1757] Specific behavior: Enter the text "tired" into EmotionRecognizer and recognize the emotion as "fatigue."

[1758] Step 4:

[1759] The server obtains the user's behavioral history and search history through the API.

[1760] Input: User ID

[1761] Output: Behavioral history data and search history data

[1762] Specific operation: The server calls the API and obtains the user's past behavioral history and search history.

[1763] Step 5:

[1764] The server sends the behavioral history data, search history data, and emotion data to the Recommendation Engine to generate recommendation information.

[1765] Input: behavioral history data, search history data, emotion data

[1766] Output: Recommendation information (e.g., a list of relaxing cafes and massage parlors)

[1767] Specific operation: Input data into the Recommendation Engine and generate information on relaxing cafes and massage parlors.

[1768] Step 6:

[1769] The server transmits the generated recommendation information to the terminal.

[1770] Input: Recommendation information

[1771] Output: Recommendation information (displayed on the terminal)

[1772] Specific operation: The server sends recommendation information to the smartphone and displays it to the user.

[1773] Step 7:

[1774] The terminal displays the received recommendation information to the user.

[1775] Input: Recommendation information

[1776] Output: Information displayed to the user

[1777] Specific behavior: A list of relaxing cafes and massage parlors is displayed on the smartphone screen.

[1778] In this way, it becomes possible to provide optimal information in real time based on the user's behavioral history and emotions.

[1779] Example 3

[1780] Next, a description will be given of Example 3 of Form Example 3. 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."

[1781] Conventional systems provided recommendations based on user behavior and search history, and generated transportation routes, but they lacked the ability to provide information that took user sentiment into consideration. Furthermore, when providing collected data to third parties, they did not adequately analyze the data or provide it in an appropriate format. This limited the ability to improve user experience and create new business opportunities.

[1782] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1783] In this invention, the server includes means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, means for selling usage data or linking with other companies via API, means for recognizing emotions from the user's voice input or text input and using the results to adjust recommended information, means for collecting the user's behavioral history and search history and analyzing them using a data analysis tool, and means for providing data to third parties based on the analysis results. This makes it possible to provide information that takes the user's emotions into consideration, improving the user experience and creating new business opportunities.

[1784] "Current Location" refers to the location where the User is currently located.

[1785] "Destination" refers to the place where the user wants to go.

[1786] "Transportation" refers to the means used by users to travel, including trains, buses, taxis, airplanes, etc.

[1787] "Chat" refers to a text-based means of communication.

[1788] "Voice input" refers to a means by which a user inputs information using voice.

[1789] "Translation" refers to the conversion of information expressed in one language into another.

[1790] "Nearby recommended information" refers to useful nearby information provided based on the user's current location and behavioral history.

[1791] "Behavioral history" refers to a record of a user's past actions.

[1792] "Search history" refers to a record of searches a user has conducted in the past.

[1793] "Recommendation" refers to suggesting appropriate information or options to users.

[1794] "Usage data" refers to data such as user behavior history and search history.

[1795] "Sales" refers to providing goods or services to others in exchange for payment.

[1796] "API" stands for Application Programming Interface, and refers to an interface for exchanging functions and data between different software programs.

[1797] "Other companies" refers to companies or organizations other than the company providing the system.

[1798] "Emotion recognition" refers to analyzing and identifying emotions from a user's voice or text.

[1799] "Data analysis tools" refers to software and algorithms used to analyze collected data.

[1800] "Third Party" refers to any individual or organization other than the user or system provider.

[1801] MODE FOR CARRYING OUT THE INVENTION

[1802] This invention is a system that provides recommendation information based on a user's behavioral history and search history, generates transportation routes, provides information using emotion recognition, and provides collected data to third parties. Specific embodiments of this system are described below.

[1803] Hardware and software used

[1804] Hardware: devices such as smartphones, tablets, and PCs

[1805] Software: Emotion recognition engine (e.g., Google Cloud Natural Language API, IBM Watson), data analysis tools (e.g., Python Pandas, Scikit-learn), API integration tools

[1806] System configuration

[1807] 1. Data Collection

[1808] Users use devices such as smartphones and PCs to browse websites and use apps.

[1809] The device collects the user's behavioral history and search history in real time and sends it to the server.

[1810] 2. Data Analysis

[1811] The server stores the received data in a database.

[1812] The server analyzes the data using data analysis tools to identify user behavior patterns and interests.

[1813] 3. Emotion recognition

[1814] The user inputs voice or text.

[1815] The device sends these inputs to an emotion recognition engine.

[1816] The server uses an emotion recognition engine to analyze the user's emotions from voice and text, and adjusts the recommendation information based on the results.

[1817] 4. Information provision

[1818] The server adjusts the recommendation information according to the user's emotional state and sends it to the terminal.

[1819] Based on the analysis results, the server provides data to third parties such as tourism operators and advertising agencies.

[1820] Specific examples

[1821] The user speaks, "What is the fastest route from Tokyo Station to Narita Airport?"

[1822] The terminal transmits the voice data to the server.

[1823] The server uses an emotion recognition engine to determine that the user is in a hurry.

[1824] The server provides the fastest routes as well as taxi and helicopter information.

[1825] Prompt Sentence Examples

[1826] Data provision prompt: "How can I collect user behavior history and provide it to tourism providers?"

[1827] Emotion recognition engine prompt: "How can I provide the fastest route if I sense the user is in a hurry?"

[1828] This system analyzes the user's behavior history and emotions and provides appropriate information to improve the user experience and create new business opportunities. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1829] Program processing flow

[1830] Step 1: Data collection

[1831] Users use devices such as smartphones and PCs to browse websites and use apps.

[1832] Input: User behavior and search history

[1833] The device collects this data in real time and sends it to the server.

[1834] Output: Collected behavioral and search history data

[1835] Step 2: Save data

[1836] The server stores the received data in a database.

[1837] Input: Collected behavioral and search history data

[1838] Output: Data stored in the database

[1839] Step 3: Data analysis

[1840] The server analyzes the data using data analysis tools (e.g., Python's Pandas or Scikit-learn).

[1841] Input: Activity and search history data stored in a database

[1842] The server processes and calculates the data to identify the user's behavioral patterns and interests.

[1843] Output: Analysis results (user behavior patterns and interests)

[1844] Step 4: Emotion Recognition

[1845] The user inputs voice or text.

[1846] Input: User voice and text data

[1847] The device sends these inputs to an emotion recognition engine.

[1848] The server uses an emotion recognition engine (e.g., Google Cloud Natural Language API or IBM Watson) to analyze the user's emotions from voice and text.

[1849] Output: Emotion analysis results

[1850] Step 5: Adjust your recommendations

[1851] The server adjusts the recommendation information according to the user's emotional state based on the results of emotion analysis.

[1852] Input: Sentiment analysis results, user behavior patterns and interests

[1853] The server selects appropriate information and generates recommendation information.

[1854] Output: Adjusted recommendation information

[1855] Step 6: Provide information

[1856] The server transmits the adjusted recommendation information to the terminal.

[1857] Input: Adjusted recommendation information

[1858] The terminal displays the information to the user.

[1859] Output: Recommendation information provided to the user

[1860] Step 7: Provide data

[1861] Based on the analysis results, the server provides data to third parties such as tourism operators and advertising agencies.

[1862] Input: Analysis results

[1863] The server converts the data into an appropriate format and provides it to third parties via an API.

[1864] Output: Data provided to third parties

[1865] In this way, the system analyzes the user's behavioral history and emotions at each step and provides appropriate information, thereby improving the user experience and creating new business opportunities.

[1866] (Application example 3)

[1867] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1868] While conventional systems can recommend information based on a user's behavioral history and search history, they have the problem of being unable to recommend advertisements that take into account the user's emotional state. They also lack the functionality to analyze the effectiveness of advertisements and provide the collected data to third parties. This makes it difficult to provide advertisements that best meet the user's needs, and they are unable to maximize the effectiveness of advertisements.

[1869] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1870] In this invention, the server includes means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, means for selling usage data or linking with other companies via API, means for recognizing emotions from the user's voice input or text input and recommending optimal advertisements based on those emotions, and means for analyzing the effectiveness of advertisements and providing the collected data to third parties. This makes it possible to recommend optimal advertisements based on the user's emotional state, maximizing the effectiveness of advertisements.

[1871] "Current Location" refers to the location where the User is currently located.

[1872] "Destination" refers to the place where the user wants to go.

[1873] "Transportation directions" refers to the route from your current location to your destination.

[1874] "Chat or voice input" refers to the way in which a user inputs information by text message or voice.

[1875] "User's preferred language" means the language used by the User.

[1876] "Means of translation" refers to the method of converting from one language to another.

[1877] "Recommended information in the area" refers to useful information provided based on the user's current location and behavioral history.

[1878] "User behavior history" refers to a record of a user's past actions and the places they have visited.

[1879] "Search history" refers to a record of keywords and phrases a user has searched for in the past.

[1880] "Recommendation methods" refer to methods of suggesting appropriate information or advertisements to users.

[1881] "Usage data" refers to data such as user behavior history and search history.

[1882] "Means of selling or collaborating with other companies through APIs" refers to methods of selling collected data to other companies or sharing data through APIs.

[1883] "User voice or text input" refers to the way in which a user inputs information by voice or text.

[1884] "Means for recognizing emotions" refers to a method for analyzing and recognizing emotions from user input.

[1885] "Means for recommending optimal ads" refers to a method that suggests the most suitable ads based on the user's emotional state.

[1886] "Means for analyzing the effectiveness of advertising" refers to a method for evaluating how effective the displayed advertisements were.

[1887] "Means of providing collected data to third parties" refers to the methods of providing collected data to other companies or organizations.

[1888] A system for implementing this invention has the function of generating transportation routes from a user's current location to a destination and translating them into the user's language. It also has the function of recommending nearby information based on the user's behavioral and search history, and selling usage data or linking with other companies via API. It also has the function of recognizing emotions from the user's voice or text input and recommending optimal advertisements based on those emotions. It is also possible to analyze the effectiveness of advertisements and provide the collected data to third parties.

[1889] Hardware and software used

[1890] Hardware

[1891] Smartphone (microphone, internet connection)

[1892] software

[1893] Python

[1894] speech_recognition library (speech recognition)

[1895] textblob library (sentiment analysis)

[1896] requests library (API requests)

[1897] Processing flow

[1898] Acquiring voice input

[1899] The server uses the speech_recognition library to convert the user's voice input into text. When the user inputs voice through the smartphone's microphone, the voice is converted into text data.

[1900] Emotion recognition

[1901] The server uses the textblob library to analyze the sentiment of the converted text, recognize the user's emotional state from the text data, and select appropriate advertisements based on that sentiment.

[1902] Ad recommendations

[1903] The server recommends the most appropriate advertisement based on the user's emotional state: for example, if the user feels like relaxing, it will display an advertisement for a relaxing travel plan.

[1904] Data provision

[1905] The server analyzes the effectiveness of the advertisements and provides the collected data to third parties, which allows us to maximize the effectiveness of the advertisements and provide the collected data to other companies and organizations.

[1906] Specific examples

[1907] If a user voice-inputs, "Tell me a good ramen restaurant nearby," and the tone and expression of the voice indicate that the user wants to relax, the system will display ads for relaxing travel plans.

[1908] Prompt Sentence Examples

[1909] If a user says, "Tell me a good ramen restaurant nearby," and the tone and expression of the voice suggest that the user wants to relax, create a program that displays ads for relaxing travel plans.

[1910] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1911] Step 1:

[1912] The user inputs voice through the smartphone's microphone, and the input voice data is captured by the smartphone's microphone.

[1913] Step 2:

[1914] The device converts the captured audio data into text data using the speech_recognition library, where audio data is given as input and text data is obtained as output.

[1915] Step 3:

[1916] The server performs sentiment analysis on the converted text data using the textblob library. The text data is given as input and the emotional state (positive, negative, neutral) is obtained as output.

[1917] Step 4:

[1918] The server selects the most appropriate advertisement based on the emotional state. The emotional state is given as input, and the most appropriate advertisement is obtained as output. For example, if the user feels like relaxing, an advertisement for a relaxing travel plan is selected.

[1919] Step 5:

[1920] The server sends the selected advertisement to the user's smartphone for display, and the selected advertisement is given as input and displayed on the smartphone screen.

[1921] Step 6:

[1922] The server collects user response data to analyze the effectiveness of the advertisement. User response data (number of clicks, duration of visit, etc.) is given as input, and the results of the advertisement effectiveness analysis are obtained as output.

[1923] Step 7:

[1924] The server provides the collected data to a third party. The results of the analysis of the effectiveness of the advertisement are given as input, and the data to be provided to the third party is obtained as output.

[1925] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1926] 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 performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1927] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1928] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1929] [Third embodiment]

[1930] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1931] 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.

[1932] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1933] 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.

[1934] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1935] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1936] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1937] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1938] 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.

[1939] 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.

[1940] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1941] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1942] "Example 1"

[1943] One embodiment of the present invention is an application installed on a mobile device such as a smartphone or tablet. Through this application, a user can inquire about public transportation routes from their current location to their destination via chat or voice input. The application generates an optimal route based on the user's input, translates it into the user's language, and displays it. For example, if a user inputs, "What is the fastest route from Tokyo Station to Narita Airport?", the application generates the optimal route taking into account public transportation timetables and operating conditions, and displays it in the user's language.

[1944] "Example 2"

[1945] One embodiment of the present invention has a function to recommend nearby recommended information based on the user's behavioral history and search history. If the user visits a specific tourist spot or searches for a specific keyword, related recommended information is recommended based on that information. For example, if the user searches for "sushi," sushi restaurants near the user's current location are recommended.

[1946] "Example 3"

[1947] Furthermore, one embodiment of the present invention has the function of selling usage data or linking with other companies via API. Data such as user behavior history and search history is useful information in various industries, including tourism and advertising. This invention creates new business opportunities by providing this data in an appropriate format to third parties. For example, providing data on users who visited a specific tourist destination to the operator of the destination can support more effective marketing activities.

[1948] The processing flow of each embodiment will be described below.

[1949] "Example 1"

[1950] Step 1: A user launches an application installed on a mobile terminal.

[1951] Step 2: The user requests transportation directions from their current location to their destination via chat or voice input. For example, they might say, "What is the fastest route from Tokyo Station to Narita Airport?"

[1952] Step 3: The application generates an optimal route based on the user's input, taking into account public transport timetables and availability.

[1953] Step 4: Translate the generated route into the user's language.

[1954] Step 5: Display the translated route to the user in your application.

[1955] "Example 2"

[1956] Step 1: The user visits a specific tourist spot or searches for a specific keyword. Step 2: The application recommends related information based on the user's behavioral and search history.

[1957] Step 3: Display the recommended information to the user in the application.

[1958] "Example 3"

[1959] Step 1: The application collects data such as user behavior and search history.

[1960] Step 2: Provide the collected data to third parties in an appropriate manner, taking into consideration the protection of data privacy.

[1961] Step 3: The third party creates new business opportunities based on the provided data. For example, the third party may provide data on users who visited a particular tourist destination to the operator of the destination to support more effective marketing activities.

[1962] Example 1

[1963] 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 headset type terminal 314 will be referred to as a "terminal."

[1964] Conventional transportation route guidance systems make it difficult for users to easily find the optimal route to their destination. Furthermore, they rarely translate information into the user's language, making them difficult to use for users who do not understand foreign languages. Furthermore, they lack personalized recommendations based on the user's behavioral and search history, resulting in low user convenience.

[1965] 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.

[1966] In this invention, the server includes means for receiving user input and analyzing it using natural language processing technology, means for generating an optimal route based on the analysis results, and means for translating the generated route into the user's language. This allows the user to easily find the optimal route to their destination, and since the route is displayed in the user's language, it can be used by users who do not understand foreign languages. In addition, personalized recommended information can be provided based on the user's behavioral history and search history, improving user convenience.

[1967] "User" means any person or entity that uses the System to request transportation directions.

[1968] "Input" refers to the provision of information by a user to the system via chat or voice.

[1969] "Natural language processing technology" refers to technology for analyzing user input and understanding its meaning.

[1970] "Analysis results" refers to information extracted from user input using natural language processing techniques.

[1971] An "optimal route" refers to the most efficient combination of transportation modes from a starting point to a destination.

[1972] "Translation" refers to converting the generated route into the user's language.

[1973] "Display" refers to providing the translated route visually on the user's terminal.

[1974] "Recommended information" refers to related information provided based on a user's behavioral history and search history.

[1975] "Behavior history" refers to a record of actions taken by a user in the past.

[1976] "Search history" refers to a record of searches a user has previously conducted.

[1977] "Personalization" refers to customizing information based on a user's individual characteristics and history.

[1978] "Usage Data" means information relating to the use of the System.

[1979] "API" refers to an interface for connecting with other systems and services.

[1980] This invention is a system that allows a user to inquire about transportation routes from their current location to a destination through an application installed on a mobile device such as a smartphone or tablet. Specific embodiments of this system are described below.

[1981] Hardware and software used

[1982] Hardware:

[1983] Mobile devices: smartphones, tablets

[1984] software:

[1985] Natural language processing software: Google Cloud Natural Language API, IBM Watson Natural Language Understanding

[1986] Route generation software: Google Maps API, OpenTripPlanner

[1987] Translation software: DeepL API, Google Translate API

[1988] System Operation

[1989] User Input

[1990] A user starts an application installed on a mobile device and uses chat or voice input to inquire about transportation routes from their current location to their destination. For example, they might input, "What is the fastest route from Tokyo Station to Narita Airport?"

[1991] Parsing input

[1992] The device receives user input and analyzes it through an application using natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding. The analysis extracts information such as the starting point, destination, and user intent.

[1993] Generate optimal routes

[1994] The server receives the analysis results sent from the device and generates the optimal route. The server uses route generation software such as Google Maps API or OpenTripPlanner. For example, it calculates the fastest route from Tokyo Station to Narita Airport and generates a route that combines multiple modes of transportation (trains, buses, etc.).

[1995] Route Translation

[1996] The server translates the generated route into the user's language using translation software such as the DeepL API or Google Translate API. For example, for an English-speaking user, the server generates a translation such as "Take the JR Yamanote Line from Tokyo Station to Nippori Station, then transfer to the Keisei Skyliner to Narita Airport."

[1997] View translated routes

[1998] The device receives the translated route sent from the server and displays it to the user. The display includes the translation results in the user's language. For example, the application screen might display the "fastest route from Tokyo Station to Narita Airport," along with specific transportation methods and travel times.

[1999] Specific examples

[2000] Example user input

[2001] A user types into the application, "What is the quickest route from Tokyo Station to Narita Airport?"

[2002] Prompt Sentence Examples

[2003] "The user inputs that they want to know the fastest route from Tokyo Station to Narita Airport. Please generate the optimal route taking into account public transport timetables and operation status, and display it in the user's language."

[2004] In this way, users can easily find the best route to their destination. It is also possible to provide personalized recommendations based on the user's behavioral and search history, improving user convenience.

[2005] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2006] Step 1:

[2007] User Input

[2008] A user starts an application installed on a mobile device and uses chat or voice input to inquire about transportation routes from their current location to their destination. For example, they might input, "Tell me the quickest route from Tokyo Station to Narita Airport." This input is sent to the device.

[2009] Input: User chat or voice input

[2010] Output: User input data

[2011] Step 2:

[2012] Parsing input

[2013] The device receives user input and analyzes it through an application using natural language processing technologies such as Google Cloud Natural Language API and IBM Watson Natural Language Understanding. The analysis extracts information such as the starting point, destination, and user intent.

[2014] Input: User-entered data

[2015] Data processing: Analyzes input using natural language processing technology to extract origin, destination, and user intent.

[2016] Output: Analysis results (origin, destination, user intent)

[2017] Step 3:

[2018] Generate optimal routes

[2019] The server receives the analysis results sent from the device and generates the optimal route. The server uses route generation software such as Google Maps API or OpenTripPlanner. For example, it calculates the fastest route from Tokyo Station to Narita Airport and generates a route that combines multiple modes of transportation (trains, buses, etc.).

[2020] Input: Analysis results (origin, destination, user intent)

[2021] Data calculation: Calculate the optimal route using route generation software

[2022] Output: Optimal route

[2023] Step 4:

[2024] Route Translation

[2025] The server translates the generated route into the user's language using translation software such as the DeepL API or Google Translate API. For example, for an English-speaking user, the server generates a translation such as "Take the JR Yamanote Line from Tokyo Station to Nippori Station, then transfer to the Keisei Skyliner to Narita Airport."

[2026] Input: Best Route

[2027] Data processing: Translate the route into the user's language using translation software

[2028] Output: Translated route

[2029] Step 5:

[2030] View translated routes

[2031] The device receives the translated route sent from the server and displays it to the user. The display includes the translation results in the user's language. For example, the application screen might display the "fastest route from Tokyo Station to Narita Airport," along with specific transportation methods and travel times.

[2032] Input: Translated route

[2033] Data processing: formatting the translated route for display to the user

[2034] Output: Route displayed on the user's device

[2035] (Application example 1)

[2036] 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."

[2037] In order for food delivery workers to deliver efficiently, they need to quickly obtain the optimal route that takes into account traffic conditions, weather, and road congestion. However, current navigation systems have difficulty generating routes that fully consider these factors, resulting in reduced delivery efficiency. Furthermore, when delivery workers work in multilingual environments, route information is often not provided in a language that is easy for them to understand, creating communication barriers. A system that can solve these issues is needed.

[2038] 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.

[2039] In this invention, the server includes means for generating transportation directions from the current location to the destination via chat or voice input and translating them into the user's language, means for recommending nearby recommended information based on the user's behavioral history and search history, means for selling usage data or linking with other companies via API, means for generating an optimal route taking into account traffic conditions, weather, and road congestion, and means for translating the generated route information into the delivery person's language and displaying it. This enables delivery people to efficiently obtain the optimal route and perform delivery work smoothly even in a multilingual environment.

[2040] "Current Location" refers to the location where the User is currently located.

[2041] "Destination" refers to the location the user is heading to.

[2042] "Transportation" refers to means of travel, including public transport and road transport.

[2043] "Chat" refers to a means of communication using text messages.

[2044] "Voice input" refers to a means of inputting information using voice.

[2045] "Translation" refers to the conversion of information written in one language into another language.

[2046] "Nearby recommendations" refers to information about interesting places and services near a user's current location or destination.

[2047] "Behavioral history" refers to a record of a user's past actions.

[2048] "Search history" refers to a record of searches a user has conducted in the past.

[2049] "Recommendation" refers to recommending specific information or services to a user.

[2050] "Usage Data" refers to data generated when you use the Application.

[2051] "API" stands for Application Programming Interface, and refers to an interface for exchanging functions and data between different software programs.

[2052] "Other companies" refers to companies other than the company that provides the application.

[2053] "Traffic conditions" refers to the current operating conditions of roads and public transport.

[2054] "Weather" refers to current weather conditions.

[2055] "Road congestion status" refers to the amount of traffic and congestion on the road.

[2056] An "optimal route" refers to the most efficient route to a destination, taking into account traffic conditions, weather, and road congestion.

[2057] "Delivery Person" refers to a person who delivers food to provide food delivery services.

[2058] A "multilingual environment" refers to an environment in which multiple languages ​​are used.

[2059] A system for implementing this invention includes an application installed on a mobile device such as a smartphone or tablet. Through this application, a user can inquire about transportation routes from their current location to their destination by chat or voice input. A specific embodiment of this system is described below.

[2060] Hardware and Software

[2061] Hardware: Smartphones, tablets

[2062] Software: Python, requests library, geopy library

[2063] System configuration

[2064] 1. User Input: Using an application installed on a smartphone or tablet, a user inputs their current location and destination via chat or voice input, for example, "What is the best route from my current location to my next delivery destination?"

[2065] 2. API request: The device sends a request to the traffic information API to obtain the optimal route from the current location to the destination, taking into account traffic conditions, weather, and road congestion.

[2066] 3. Route Generation: The server analyzes the data returned by the API and generates the optimal route. This route information is translated into the user's language.

[2067] 4. Information display: The terminal displays the generated route information to the user, allowing delivery personnel to efficiently obtain the optimal route and carry out delivery work smoothly even in a multilingual environment.

[2068] Specific examples

[2069] User input: "What is the best route from my current location to my next delivery?"

[2070] App output: "The best route is to go straight on Street A, turn right at intersection B, then take Street C."

[2071] This system allows delivery personnel to quickly obtain the optimal route taking into account traffic conditions, weather, and road congestion.In addition, route information is translated into the delivery personnel's language, allowing for smooth delivery operations even in multilingual environments.

[2072] Prompt Sentence Examples

[2073] User: What is the best route from my current location to my next delivery?

[2074] App: The best route is to go straight on Street A, turn right at intersection B, then take Street C.

[2075] In this way, a navigation app can be realized that helps food delivery personnel make deliveries efficiently.

[2076] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2077] Step 1:

[2078] The user uses an application installed on a smartphone or tablet to enter their current location and destination via chat or voice input.

[2079] Input: Current location and destination information (e.g., "What is the best route from my current location to my next delivery destination?")

[2080] Output: Input current location and destination information

[2081] What happens: The user launches the application and uses the chat box or voice input function to enter their current location and destination.

[2082] Step 2:

[2083] The device receives the entered current location and destination information and sends a request to the traffic information API.

[2084] Input: Current location and destination information

[2085] Output: Request to traffic information API

[2086] What happens: The device analyzes the input information and sends a request to the traffic information API endpoint, including parameters such as current location, destination, and language preference.

[2087] Step 3:

[2088] The server receives the response from the traffic information API and generates the optimal route.

[2089] Input: Response data from traffic information API (e.g., traffic conditions, weather, road congestion)

[2090] Output: Optimal route information

[2091] What it does: The server analyzes the data returned by the API and calculates the optimal route, taking into account traffic, weather, and road congestion. This calculation is done using a geographic information system (GIS) and route optimization algorithms.

[2092] Step 4:

[2093] The server translates the generated optimal route information into the user's language.

[2094] Input: Best route information, user language settings

[2095] Output: Translated route information

[2096] What it does: The server translates the route information into the user's preferred language using natural language processing (NLP) techniques and translation APIs.

[2097] Step 5:

[2098] The device displays the translated route information to the user.

[2099] Input: Translated route information

[2100] Output: Route information displayed to the user

[2101] What happens: The device receives the translated route information and displays it through its user interface (UI), including text and map displays.

[2102] Step 6:

[2103] The user confirms the route information displayed and begins delivery.

[2104] Input: Translated route information

[2105] Output: Start of delivery

[2106] What happens: The user reviews the route information displayed and follows the instructions to begin the delivery. The route information may be updated in real time during the delivery.

[2107] Example 2

[2108] Next, a description will be given of 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."

[2109] Conventional systems do not provide personalized recommendations based on users' behavioral and search histories, making it difficult to provide optimal information tailored to users' interests. Furthermore, the generated directions are sometimes not the optimal route, reducing user convenience.

[2110] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for generating transportation routes from the current location to the destination through chat or voice input and translating them into the user's language, a means for recommending nearby recommended information based on the user's behavior history and search history, a means for selling usage data or linking with other companies via API, a means for generating recommended information based on the user's interests using a generation AI model, and a means for providing the generated recommended information to the user terminal. This makes it possible to provide optimal recommended information personalized based on the user's behavior history and search history.

[2111] "Current location" refers to the location information of the user's current location.

[2112] "Destination" refers to the location information of the place where the user wants to go.

[2113] "Transportation directions" refers to the route from your current location to your destination, including public transportation, walking, driving, and other modes of transportation.

[2114] "Chat or voice input" refers to the means by which a user inputs information via text message or voice.

[2115] "User's preferred language" refers to the language used by the user and means that the system supports this language.

[2116] "Means for translating" refers to the function of converting input information into another language.

[2117] "Nearby recommendations" refers to information about nearby interesting places and services provided based on the user's current location and behavioral history.

[2118] "User behavior history" refers to a record of a user's past locations and actions.

[2119] "Search history" refers to a record of keywords or queries a user has searched for in the past.

[2120] "Recommendation means" refers to a function that suggests related information based on a user's behavioral history and search history.

[2121] "Usage data" refers to data such as user behavior history and search history.

[2122] "Means of collaboration with other companies through sales or APIs" refers to methods for sharing collected data with other companies or services.

[2123] A "generative AI model" refers to an algorithm or system that uses artificial intelligence to analyze data and generate information.

[2124] "Recommended information based on user interests" refers to information that is predicted to be of interest to the user based on an analysis of the user's behavioral history and search history.

[2125] "User terminal" refers to the device used by the user, including smartphones, tablets, PCs, etc.

[2126] This invention is a system that recommends nearby information based on a user's behavioral history and search history. The system is implemented using a server, user devices (smartphones, tablets, PCs, etc.), a database management system (e.g., MySQL), a location information service (e.g., map API), and a generative AI model (e.g., GPT-4).

[2127] System configuration

[2128] 1. User Device:

[2129] Users use devices such as smartphones or PCs to search for specific keywords or visit specific tourist spots.

[2130] Example: A user searches for "sushi" on their smartphone.

[2131] 2. Sending data to the server:

[2132] The user's device collects the user's behavioral history and search history and sends them to the server.

[2133] Example: A smartphone sends the search keyword "sushi" and its current location to a server.

[2134] 3. Data storage:

[2135] The server stores the received data in a database.

[2136] Example: The server stores the search keyword "sushi" and the user's current location in a MySQL database.

[2137] 4. Data Analysis:

[2138] The server analyzes the stored data to identify the user's interests and concerns.

[2139] Example: The server extracts the keyword "sushi" from a database and determines that the user is interested in sushi.

[2140] 5. Generating recommendation information:

[2141] The server uses a generative AI model (e.g., GPT-4) to generate recommendations based on the user's interests.

[2142] Example: The server inputs the prompt sentence "If the user searches for 'sushi', please list recommended sushi restaurants near the current location" to GPT-4 and obtains the generated list of sushi restaurants.

[2143] 6. Providing Recommendations:

[2144] The server transmits the generated recommendation information to the user's terminal.

[2145] Example: The server sends the generated list of sushi restaurants to the user's smartphone, and the list of sushi restaurants is displayed on the smartphone.

[2146] Specific examples

[2147] User Action: User searches for "sushi" on their smartphone.

[2148] Device operation: The smartphone sends the search keyword "sushi" and its current location information to the server.

[2149] Server Action:

[2150] 1. The server stores the search keyword "sushi" and the user's current location information in a MySQL database.

[2151] 2. The server extracts the keyword "sushi" from the database and determines that the user is interested in sushi.

[2152] 3. The server inputs the prompt sentence "If the user searches for 'sushi,' please list recommended sushi restaurants near the current location" to GPT-4 and obtains the generated list of sushi restaurants.

[2153] 4. The server sends the generated list of sushi restaurants to the user's smartphone.

[2154] User pickup: A list of sushi restaurants near the user's current location will be displayed on the user's smartphone.

[2155] In this way, a system is realized that provides relevant recommended information based on a user's behavioral history and search history.

[2156] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2157] Step 1:

[2158] Users use devices such as smartphones and PCs to search for specific keywords or visit specific tourist spots.

[2159] Input: User searches for "sushi."

[2160] Specific action: The user types "sushi" into their smartphone and presses the search button.

[2161] Output: The search keyword "sushi" and your current location will be saved to your device.

[2162] Step 2:

[2163] The device collects the user's behavioral history and search history and sends them to the server.

[2164] Input: Search keyword "sushi" and your current location.

[2165] Specific operation: The smartphone sends the search keyword "sushi" and its current location information to the server.

[2166] Output: The server receives the search keyword "sushi" and the current location.

[2167] Step 3:

[2168] The server stores the received data in a database.

[2169] Input: Search keyword "sushi" and your current location.

[2170] Specific operation: The server saves the search keyword "sushi" and the user's current location information in a MySQL database.

[2171] Output: The search keyword "sushi" and current location information are saved in the database.

[2172] Step 4:

[2173] The server analyzes the stored data to identify the user's interests and concerns.

[2174] Input: Search keyword "sushi" saved in the database and your current location.

[2175] Specific operation: The server extracts the keyword "sushi" from the database and determines that the user is interested in sushi.

[2176] Output: The analysis result shows that the user is interested in sushi.

[2177] Step 5:

[2178] The server uses a generative AI model (e.g., GPT-4) to generate recommendations based on the user's interests.

[2179] Input: Analytics showing the user is interested in sushi and their current location.

[2180] Specific operation: The server inputs the prompt sentence to GPT-4: "If the user searches for 'sushi,' please list recommended sushi restaurants near the current location," and obtains the generated list of sushi restaurants.

[2181] Output: A list of recommended sushi restaurants near your current location.

[2182] Step 6:

[2183] The server transmits the generated recommendation information to the user terminal.

[2184] Input: A list of recommended sushi restaurants near your current location.

[2185] Specific operation: The server sends the generated list of sushi restaurants to the user's smartphone.

[2186] Output: A list of sushi restaurants will be displayed on the user's smartphone.

[2187] In this way, a system is realized that provides related recommended information based on a user's behavior history and search history.

[2188] (Application example 2)

[2189] 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."

[2190] Conventional recommendation systems provide insufficient information based on user behavioral history and search history, making it difficult to provide real-time recommendations based on the user's current location. Furthermore, personalized information based on the user's search history is limited, leaving a lack of means to improve user convenience.

[2191] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for generating transportation routes from the current location to the destination through chat or voice input and translating them into the user's language, a means for recommending nearby recommended information based on the user's behavioral history and search history, a means for selling usage data or linking with other companies via API, a means for obtaining recommended information around the current location based on the user's search history using an external API, and a means for displaying the obtained recommended information. This allows the user to obtain real-time recommended information based on their current location and provides personalized information based on their search history.

[2192] "Current location" refers to the location information of the user's current location.

[2193] "Destination" refers to the location information of the place where the user wants to go.

[2194] "Transportation directions" refers to route information for a user to travel from their current location to their destination.

[2195] "Chat or voice input" refers to the means by which a user inputs information using text messages or voice.

[2196] "User's preferred language" means the language used by the User.

[2197] "Means of translation" refers to the ability to convert information written in one language into another language.

[2198] "Recommended information in the area" refers to information about stores, tourist spots, etc. located near the user's current location.

[2199] "User behavior history" refers to a record of a user's past locations and actions.

[2200] "Search history" refers to a record of keywords and phrases a user has searched for in the past.

[2201] "Recommendation means" refers to the function of suggesting related information based on a user's behavioral history and search history.

[2202] "Usage data" refers to data such as user behavior history and search history.

[2203] "Means of selling or collaborating with other companies through APIs" refers to the ability to sell usage data to other companies or share data with other companies through APIs.

[2204] "External API" refers to an application programming interface provided by another system or service.

[2205] "Means of acquisition" refers to the function of acquiring the necessary information from an external API, etc.

[2206] "Means for displaying" refers to the function of visually presenting the acquired information to the user.

[2207] The system for implementing this invention has the function of generating transportation directions from a user's current location to a destination via chat or voice input and translating them into the user's language. It also has the function of recommending nearby information based on the user's behavioral history and search history. It also has the function of selling usage data or linking with other companies via API.

[2208] System configuration

[2209] The system consists of the following main components:

[2210] 1. User device: A device such as a smartphone or tablet on which the user chats or uses voice input.

[2211] 2. Server: Manages user behavioral history and search history and generates recommendation information.

[2212] 3. External API: Used to get recommendations around the user's current location.

[2213] Program processing

[2214] The server does the following:

[2215] 1. Obtaining current location: Obtaining current location information from the user's device, using GPS or Wi-Fi location services.

[2216] 2. Route Generation: Generate routes to a destination specified by the user via chat or voice input, using a map API (e.g., Google Maps API).

[2217] 3. Language Translation: The generated directions are translated into the user's language using a translation API (e.g., Google Translate API).

[2218] 4. Generating recommendation information: Based on the user's behavioral history and search history, recommended information around the current location is obtained from an external API and recommendation information is generated.

[2219] 5. Displaying information: Display the obtained recommended information on the user's device.

[2220] Hardware and software used

[2221] Hardware: smartphones, tablets, servers

[2222] Software: Map API (Google Maps API), Translation API (Google Translate API), Location Services (GPS, Wi-Fi)

[2223] Specific examples

[2224] For example, if a user searches for "cafe" and is currently in Tokyo, the system will:

[2225] 1. A user searches for "cafe" on their smartphone.

[2226] 2. The server obtains the user's current location (Tokyo).

[2227] 3. The server uses an external API to obtain information about cafes near the current location.

[2228] 4. The acquired cafe information is displayed on the user's smartphone.

[2229] Prompt Sentence Examples

[2230] User ID: user123

[2231] Search history: ["cafe", "sushi"]

[2232] Current location: Tokyo

[2233] By inputting this prompt into a generative AI model, the most appropriate recommendations can be provided to the user.

[2234] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2235] Step 1:

[2236] A user searches for "cafe" on their smartphone. The input is the keyword "cafe" that the user searched for, and the output is that this keyword is sent to the server.

[2237] Step 2:

[2238] The server obtains the user's current location information. The input is the location information (GPS data) sent from the user's smartphone, and the output is the user's current location (e.g., Tokyo). The server stores this location information in a database.

[2239] Step 3:

[2240] The server retrieves the user's search history from the database. The input is the user ID, and the output is the user's past search history (e.g., "cafe," "sushi"). The server prepares to generate recommendation information based on this history.

[2241] Step 4:

[2242] The server uses an external API to obtain information about cafes near the current location. The input is the user's current location (Tokyo) and the search keyword "cafe," and the output is a list of cafe information obtained from the external API. The server saves this list in a database.

[2243] Step 5:

[2244] The server sends the acquired cafe information to the user's smartphone. The input is a list of cafe information, and the output is the cafe information displayed on the user's smartphone. The user can select a cafe based on this information.

[2245] Step 6:

[2246] It displays detailed information about the cafe selected by the user. The input is the ID of the cafe selected by the user, and the output is detailed information about the cafe (address, opening hours, reviews, etc.). The user can visit the cafe based on this information.

[2247] Step 7:

[2248] The server updates the user's behavioral history. The input is information about the cafes the user visited, and the output is the updated behavioral history. The server stores this information in a database and uses it for future recommendations.

[2249] Example 3

[2250] Next, a third embodiment of the third embodiment 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."

[2251] Conventional systems have had difficulty effectively utilizing user...

Claims

1. A means to accept inquiries about transportation routes from the current location to the destination via chat or voice input, means for analyzing the received query using a pre-processing technique and generating the route; A means for translating the generated route into a user's language; means for transmitting the translated directions; A means for recognizing emotions from user input and providing additional information; and A system including:

2. The system according to claim 1 , further comprising a means for recommending nearby recommended information based on a user's behavior history and search history.

3. 3. The system of claim 2, wherein the recommended information is personalized based on a user's behavioral history and search history.

Images