system

Abstract

Provide a system. means for receiving input from a user regarding a destination; means for analyzing the input and generating follow-up questions based on the user's needs; means for presenting the follow-up question to the user and receiving a response from the user; means for analyzing the response and searching for a destination based on the user's needs; means for presenting the search results to a user; means for initiating navigation to a destination selected by the user; A system including:

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] Conventional car navigation systems require users to enter specific search terms, making it difficult to find the optimal destination that accurately reflects their needs. Furthermore, when users have complex needs, they must repeatedly change search criteria and re-search, which is inefficient. Furthermore, they are unable to provide search results that precisely reflect the user's wishes, resulting in a poor user experience. [Means for solving the problem]

[0005] To solve this problem, the present invention provides a system that receives destination-related input from a user, analyzes the input, and generates follow-up questions based on the user's needs. Specifically, based on the initial needs entered by the user, the system uses natural language processing to analyze the user's intent, generates follow-up questions, and presents them to the user. The system then receives and re-analyzes the user's additional answers, and searches the Internet or an internal database for destinations that meet the user's final needs. The search results are presented to the user, and navigation to the destination selected by the user begins. This process enables efficient and accurate navigation that reflects the user's specific needs.

[0006] "User" refers to a person who uses the system to search for destinations and navigate.

[0007] A "destination" is a place that a user wishes to visit.

[0008] "Input" refers to information, such as text or speech, that a user provides to a system.

[0009] "Analysis" is the process of understanding user input using techniques such as natural language processing and deriving appropriate questions and search criteria.

[0010] "Needs" refer to the specific conditions and desires that users are looking for.

[0011] "Additional questions" are questions generated by the system to understand the user's input more specifically.

[0012] An "answer" is information a user provides in response to a follow-up question.

[0013] "Search" is the process of searching the Internet or internal databases to obtain appropriate information based on a user's needs.

[0014] "Presentation" is the process of showing search results to the user.

[0015] "Navigation" is the process of guiding a user to a selected destination.

[0016] "Natural language processing" is a technology that uses computers to understand and analyze human language.

[0017] The "Internet" is a network of linked information from around the world that systems use to obtain information.

[0018] An "internal database" is a collection of information stored within the system, and is data that can be searched. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] This invention relates to an automobile navigation system that improves the efficiency of users' destination searches and provides optimal suggestions for specific needs. This system operates in cooperation with three entities: the user, the terminal, and the server.

[0041] System Overview

[0042] The system receives input from a user, the server analyzes the input to generate additional questions, and the terminal presents these questions to the user. Finally, the server gains a detailed understanding of the user's needs, searches for the most suitable destination, and presents it to the user via the terminal. Specific embodiments are described in detail below.

[0043] Program processing flow

[0044] 1. The user inputs the destination

[0045] The user launches a car navigation app and inputs their desired destination, for example, "I want to go to a nearby cafe," using text or voice input.

[0046] 2. The device sends the input to the server

[0047] The terminal sends the user's input to the server, where the input is transferred to the server as text data.

[0048] 3. The server parses the input

[0049] The server uses natural language processing (NLP) algorithms to analyze the user's input, for example extracting the keyword "cafe" and identifying specific areas and service details.

[0050] 4. The server generates a follow-up question

[0051] The server generates additional questions based on the user's input, such as "Which area is good?" or "Do you want a cafe that offers takeout?"

[0052] 5. The device presents the user with additional questions

[0053] The terminal displays the additional questions received from the server to the user, and the user provides specific answers to the displayed questions.

[0054] 6. The user answers any additional questions

[0055] The user enters the necessary information in response to the additional question. For example, the user answers, "A cafe in Shinjuku that offers takeout."

[0056] 7. The device sends the user's answer to the server

[0057] The terminal transmits the user's answer back to the server.

[0058] 8. The server analyzes the user's answers

[0059] The server then uses NLP again to analyze the user's responses and understand their detailed needs.

[0060] 9. The server searches for the destination

[0061] The server searches the internet and internal databases based on the user's needs to identify the best destination, for example, a cafe in the Shinjuku area that offers takeout.

[0062] 10. The server sends the search results to the device.

[0063] The server sends the search results it finds to the terminal, which prepares the results for display to the user.

[0064] 11. The device presents the search results to the user

[0065] The terminal displays the search results received from the server to the user, who then selects the location they want to go to from the displayed results.

[0066] 12. User selects destination

[0067] The user selects the desired destination from the presented results.

[0068] 13. The device begins navigation

[0069] The terminal sets the selected destination as a navigation destination and guides the user there.

[0070] Specific examples

[0071] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[0072] The user types, "I want to go to a nearby cafe."

[0073] The device sends input to the server

[0074] The server analyzes the keyword "cafe" and generates a follow-up question: "Which area is best?"

[0075] The device presents the question to the user

[0076] The user answers "Shinjuku area"

[0077] The device sends the answer to the server

[0078] The server searches for "cafes in the Shinjuku area" and sends the results to the device.

[0079] The device presents the search results to the user.

[0080] The user selects "Cafe A"

[0081] The device starts navigation to "Cafe A"

[0082] In this way, the present invention is a system that quickly and easily provides optimal destinations based on the user's needs.

[0083] The processing flow will be explained below.

[0084] Step 1:

[0085] A user launches a car navigation app and inputs their destination preference or request via text or voice. In this case, they might input, for example, "I want to go to a nearby cafe."

[0086] Step 2:

[0087] The device receives user input and sends the input text or voice data to the server, where the voice data is converted into text beforehand.

[0088] Step 3:

[0089] The server analyzes the received user input and uses natural language processing (NLP) algorithms to extract keywords such as "cafe" and "nearby" to understand the user's basic intent.

[0090] Step 4:

[0091] Based on the user's intent, the server generates additional questions to understand their needs in more detail, such as "Which area is good?" or "Do you want a cafe that offers takeout?"

[0092] Step 5:

[0093] The terminal displays the generated additional question to the user and requests an answer, and the user inputs a specific answer to the question displayed on the terminal screen.

[0094] Step 6:

[0095] The user inputs an answer to the additional question. For example, the user answers "a cafe in Shinjuku that offers takeout."

[0096] Step 7:

[0097] The terminal receives the user's response and transmits the data to the server.

[0098] Step 8:

[0099] The server then uses NLP to analyze the user's response, which clarifies that the user is looking for a "cafe in the Shinjuku area that offers takeout."

[0100] Step 9:

[0101] The server searches the internet or an internal database based on the user's specific needs, for example, to get a list of cafes in the Shinjuku area that offer takeout.

[0102] Step 10:

[0103] The server sends the search results to the device, which include details such as the cafe's name, location, and reviews.

[0104] Step 11:

[0105] The terminal receives the search results and displays them to the user, who can then select the location they want to go to from the displayed search results.

[0106] Step 12:

[0107] The user selects the desired cafe from the displayed results. For example, they select "Cafe A."

[0108] Step 13:

[0109] The device receives the user's selection and begins navigation to the selected destination. Using the device's navigation function, the device guides the user to "Cafe A."

[0110] In this way, the present invention is a system that sequentially analyzes user input, suggests optimal destinations, and provides navigation.

[0111] Example 1

[0112] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0113] Current automobile navigation systems have limitations in their ability to quickly and accurately satisfy a wide range of user needs. It is particularly difficult to accurately understand a user's wishes and suggest appropriate destinations when the user has detailed needs for specific locations. Conventional systems require users to manually select from a vast amount of information, making operation cumbersome. To solve this problem, there is a need for a navigation system that can accurately understand a user's vague wishes and automatically suggest optimal destinations.

[0114] 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.

[0115] In this invention, the server includes a means for receiving a destination-related input from a user, a means for analyzing the input and generating a follow-up question based on the user's needs, and a means for inputting a prompt sentence into a generative AI model to generate an appropriate follow-up question, thereby enabling the server to grasp the user's needs in detail and quickly and accurately present the optimal destination.

[0116] "User" refers to an individual who uses this system to receive guidance to a destination.

[0117] "Terminal" refers to an electronic device that acts as an interface to the system, receives user input, and presents information from the server to the user. Examples include smartphones and car navigation devices.

[0118] A "server" is a device that analyzes user destination input, generates follow-up questions based on needs, and processes the data to identify optimal destinations.

[0119] "Destination input" refers to information in the form of text or voice that expresses a user's preference regarding a location or facility they wish to visit.

[0120] "Natural language processing" refers to artificial intelligence technology that analyzes text and voice input from users and understands their content.

[0121] "Generative AI model" refers to artificial intelligence technology that automatically generates follow-up questions and suggestions to understand user needs.

[0122] A "prompt" is an instruction input to a generative AI model, serving as a starting point for generating questions and suggestions tailored to the user's specific needs.

[0123] "Additional questions" refer to questions that are generated by the server to understand the user's needs in more detail and presented to the user via the terminal.

[0124] "Internal database" refers to data stored within the system, including data storage used when searching for destinations.

[0125] "Network" refers to the communications infrastructure that serves as a transmission path for information, including the Internet and dedicated lines.

[0126] "Navigation" refers to the function of guiding the user to the optimal route to a selected destination.

[0127] This invention relates to an automobile navigation system that improves the efficiency of users' destination searches and provides optimal suggestions for specific needs. This system operates in cooperation with three entities: the user, the terminal, and the server.

[0128] The specific operation of the system will be explained below. A user inputs their destination preference using a car navigation application. In this case, the user can input their preference by text input or by using a voice recognition function. In the case of voice input, the voice is converted into text using a voice recognition module (e.g., a voice recognition API).

[0129] The terminal receives input from the user and sends it as text data to the server. The server uses natural language processing (NLP) algorithms (e.g., natural language processing APIs) to analyze the received text data. During the analysis, the server understands the user's intentions and extracts keywords.

[0130] The server then uses the generative AI model to generate additional questions based on the user's preferences, inputting the following prompt to the generative AI model: "The user wants to go to a nearby cafe. Please generate specific questions to understand the user's needs in more detail."

[0131] The generated follow-up question is presented to the user via the terminal. The user inputs a specific answer to the follow-up question. For example, the user might answer "a cafe in the Shinjuku area that offers takeout." This answer is then sent back to the server from the terminal.

[0132] The server then uses NLP to analyze the user's detailed needs and search for the appropriate destinations, using internet search APIs and internal databases. The server identifies the best destinations based on the user's needs and sends the results to the device.

[0133] The device presents the received search results to the user. The user selects a desired destination from the presented results. Finally, the device sets the selected destination in the navigation and launches a map application (e.g., a map application) to guide the user.

[0134] As a concrete example, if a user inputs "I want to go to a nearby cafe," the system will act as follows:

[0135] 1. The user types, "I want to go to a nearby cafe."

[0136] 2. The device sends the input to the server.

[0137] 3. The server parses the keyword "cafe" and generates a follow-up question: "Which area is good?"

[0138] 4. The device presents the question to the user.

[0139] 5. The user answers "Shinjuku area."

[0140] 6. The device sends the response to the server.

[0141] 7. The server searches for "cafes in the Shinjuku area" and sends the results to the device.

[0142] 8. The device presents the search results to the user.

[0143] 9. The user selects "Cafe A."

[0144] 10. The device begins navigation to "Cafe A."

[0145] In this way, the system can quickly and easily provide optimal destinations based on the user's needs. Specific actions allow users to search for and navigate to destinations without performing complex operations.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147] Step 1:

[0148] Input: The user enters their destination preference via text or voice.

[0149] How it works: A user launches a car navigation app and enters their desired destination. If they enter it via voice, it is converted to text using a speech recognition module (e.g., a speech recognition API), or they enter it directly into a text box.

[0150] Output: Text data about the user's destination.

[0151] Step 2:

[0152] Input: Text data about the user's destination.

[0153] Operation: The device receives the user's text data and sends it to the server. At this stage, it checks for errors and sends it to the server in the correct format.

[0154] Output: The user's input data is transmitted to the server.

[0155] Step 3:

[0156] Input: The user's text data received by the server.

[0157] How it works: The server uses natural language processing (NLP) algorithms to analyze the text data and extract keywords and user needs. Specifically, it uses an NLP API to identify keywords such as "cafe."

[0158] Output: Extracted keywords and analysis result data.

[0159] Step 4:

[0160] Input: Extracted keywords and analysis result data.

[0161] How it works: The server inputs a prompt statement into the generative AI model, which generates specific follow-up questions based on the user's needs. For example, the prompt might say, "The user wants to go to a nearby cafe. Please generate specific questions to understand the user's needs in more detail."

[0162] Output: The additional questions generated.

[0163] Step 5:

[0164] Input: The generated follow-up question.

[0165] Operation: The device receives additional questions from the server and displays them to the user. The questions are displayed in an interface that is easy for the user to understand. For example, the screen might say, "Which area is best?"

[0166] Output: The user sees a follow-up question.

[0167] Step 6:

[0168] Input: User's answer (text data).

[0169] How it works: The user enters a specific answer to the question displayed. For example, they might enter "cafes in the Shinjuku area that offer takeout."

[0170] Output: User's answer data (text).

[0171] Step 7:

[0172] Input: User response data (text).

[0173] Action: The device receives the user's answer and sends it back to the server, again verifying that the input data is in the correct format.

[0174] Output: The user's response data sent to the server.

[0175] Step 8:

[0176] Input: The user's answer data received by the server.

[0177] How it works: The server uses NLP to analyze the user's answers again and understand their specific needs, such as "Shinjuku area" and "takeout available."

[0178] Output: Detailed needs analysis result data.

[0179] Step 9:

[0180] Input: Detailed needs analysis result data.

[0181] How it works: The server uses the network (Internet search APIs or internal databases) to search for destinations. For example, it uses the Google® Maps API or a data store to search for "cafes in the Shinjuku area that offer takeout."

[0182] Output: Search result data.

[0183] Step 10:

[0184] Input: Search result data.

[0185] Operation: The server sends the search results to the device as text data.

[0186] Output: Search result data sent to the device.

[0187] Step 11:

[0188] Input: Search result data sent to the device.

[0189] Operation: The device presents the search results received from the server to the user. Specifically, a list of candidate cafes is displayed on the screen.

[0190] Output: User sees search results.

[0191] Step 12:

[0192] Input: The destination selected by the user.

[0193] Action: The user selects the desired destination from the list presented, for example, "Cafe A."

[0194] Output: Selected destination data.

[0195] Step 13:

[0196] Input: Selected destination data.

[0197] What it does: The device sets the selected destination for navigation and launches a map app. For example, it launches a map app and displays the route to "Cafe A."

[0198] Output: The user initiates navigation.

[0199] (Application example 1)

[0200] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0201] Conventional car navigation systems only suggest a single destination based on simple user input, making it difficult to suggest optimal destinations based on the user's specific needs. Furthermore, even when a user has specific requests, it is difficult to provide navigation that appropriately reflects those requests. Therefore, there is a demand for a navigation system that can quickly and accurately respond to diverse user needs.

[0202] 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.

[0203] In this invention, the server includes means for receiving a destination-related input from a user, means for analyzing the input and generating a follow-up question based on the user's needs, means for presenting the follow-up question to the user and receiving the user's answer, means for analyzing the answer and searching for a destination based on the user's needs, means for converting the destination into coordinates using a geographic information system and starting navigation in cooperation with a navigation system, means for presenting the search results to the user, and means for starting navigation to the destination selected by the user. This enables the server to suggest an optimal destination based on the user's specific needs and provide quick navigation.

[0204] "User" refers to a person who uses this system.

[0205] "Destination-related input" refers to information that expresses a user's desires regarding places they would like to visit.

[0206] "Means for parsing input" refers to a method or device for processing and understanding destination-related information provided by a user.

[0207] "Means for generating follow-up questions" refers to a method or device that creates questions to solicit further information based on the user's initial input.

[0208] "Means for presenting follow-up questions to a user" refers to a method or device for displaying generated follow-up questions to a user.

[0209] "Means for receiving a user's answer" refers to a method or device for receiving the answer provided by the user to the follow-up question.

[0210] "Means for analyzing answers" refers to a method or device for processing and understanding answers provided by users.

[0211] "Destination search means" refers to a method or device for identifying the best location based on a user's needs.

[0212] "Geographic information system" refers to a system for obtaining location information and displaying it on a map.

[0213] "Navigation system" refers to a system that provides directions to a specific destination.

[0214] The term "means for initiating navigation" refers to a method or device for executing guidance to a destination selected by a user.

[0215] This invention is a navigation system for autonomous vehicles that proposes optimal destinations based on the specific needs of the user and performs navigation. This system operates in cooperation with three entities: the user, the terminal (a smartphone or a device inside the autonomous vehicle), and the server.

[0216] Hardware and software used

[0217] Smartphone: Receives input from the user, presents additional questions, and displays final destination information.

[0218] Autonomous vehicle: Receives destination information and performs navigation.

[0219] Server: Uses natural language processing (NLP) to analyze user input, generate appropriate follow-up questions, and search for the final destination.

[0220] Specific software used is:

[0221] SpeechRecognition library: Converts voice input into text.

[0222] Requests library: Exchanges data with the server via HTTP communication.

[0223] Geopy library: Obtain geographic information and convert coordinates of destinations.

[0224] System Operation Overview

[0225] Receiving input from the user

[0226] First, the user inputs their destination using a smartphone or in-car device. The input method is either voice or text. For example, they might input, "I want to go to a cafe in the Shinjuku area that offers takeout."

[0227] Input analysis and generation of follow-up questions

[0228] The device sends the received input to the server, which analyzes it using natural language processing (NLP). Based on the analysis, the server generates additional questions (e.g., "Which area is good?") based on the user's needs.

[0229] Posting additional questions and receiving answers

[0230] The terminal displays the additional questions received from the server to the user and receives the user's answers. The user enters the information again (for example, "Shinjuku area"), which the terminal then sends to the server.

[0231] Searching for and presenting your final destination

[0232] The server analyzes the user's responses and searches the Internet or an internal database for the destination that best suits the user's needs. The server then sends the destination information obtained as a result of the search (e.g., "Cafe A in Shinjuku") to the terminal, which then presents it to the user.

[0233] Start navigation

[0234] When the user selects a desired destination from the displayed results, the device sends the information to the autonomous vehicle's navigation system and begins navigation. The destination is converted into coordinates using a geographic information system to ensure accurate navigation.

[0235] Specific examples

[0236] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[0237] The user inputs, "I want to go to a nearby cafe."

[0238] The device sends the input to the server, which analyzes the keyword "cafe" and generates a follow-up question: "Which area is better?"

[0239] The terminal displays this question to the user, and the user answers "Shinjuku area."

[0240] The terminal sends this response to the server, which then searches for "cafes in the Shinjuku area."

[0241] The terminal presents the search results to the user, and the user selects "Cafe A."

[0242] The device will begin navigation to "Cafe A," and the self-driving vehicle will guide the user to the destination.

[0243] Prompt Sentence Examples

[0244] I'd like to go to a nearby cafe. Are there any cafes you'd recommend in the Shinjuku area?

[0245] This allows the present invention to suggest optimal destinations and provide quick navigation based on the user's specific needs.

[0246] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0247] Step 1:

[0248] The user inputs the destination

[0249] Using a smartphone or in-car device, the user inputs their destination preferences by voice or text. A typical input might be, "I want to go to a nearby cafe." The input data is temporarily stored by the device and sent to the next processing step.

[0250] Step 2:

[0251] The device sends input to the server

[0252] The terminal sends the destination-related input received from the user to the server using HTTP communication. Specifically, the input is transferred to the server as text data and received by the server.

[0253] Step 3:

[0254] The server parses the input

[0255] The server uses natural language processing (NLP) algorithms to analyze the user's input, extract keywords (e.g., "cafe") from the text data, and process the data to understand the user's request. As a result of the analysis, appropriate follow-up questions are generated.

[0256] Step 4:

[0257] The server generates additional questions

[0258] Based on the analysis results, the server generates additional questions to understand the user's specific needs, such as "Which area is best?" This data is sent to the device in the next step.

[0259] Step 5:

[0260] The device presents the user with additional questions

[0261] The terminal displays the additional questions received from the server to the user. The user's answers to these questions clarify specific needs. Therefore, a process is performed to display the questions in text format on the display.

[0262] Step 6:

[0263] The user answers additional questions

[0264] The user inputs the necessary answers to the additional questions displayed on the terminal. For example, the user inputs the name of a specific area, such as "Shinjuku area." The input data is then saved on the terminal again.

[0265] Step 7:

[0266] The device sends the user's answer to the server

[0267] The terminal transfers the answer data to the user's follow-up question to the server using HTTP communication. The data is sent to the server in text format.

[0268] Step 8:

[0269] The server analyzes the user's answers

[0270] The server then uses natural language processing (NLP) algorithms to analyze the received user response data and identify detailed needs. Based on the analysis results, the server searches the internet and internal databases to process the data and identify the optimal destination.

[0271] Step 9:

[0272] The server searches for the destination

[0273] The server searches the Internet or an internal database for the best destination based on the analysis results, and retrieves information about the identified destination (e.g., "Cafe A in Shinjuku") from the database.

[0274] Step 10:

[0275] The server sends the search results to the device.

[0276] The server sends the identified destination information, including geographical information such as latitude and longitude, to the device, which receives this data and prepares it for the next processing step.

[0277] Step 11:

[0278] The device presents the search results to the user.

[0279] The terminal displays the destination information received from the server to the user, who then selects the desired destination from the presented search results.

[0280] Step 12:

[0281] The user selects a destination

[0282] The user selects the desired location from the search results displayed on the device. For example, they select "Cafe A." This selection data is used for the next navigation step.

[0283] Step 13:

[0284] The device begins navigation

[0285] The terminal sets the destination selected by the user in the navigation system and starts accurate route guidance using the GPS of the autonomous vehicle. The destination coordinates are obtained using the geographic information system and navigation is performed.

[0286] 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.

[0287] This invention relates to an automobile navigation system that streamlines user destination searches and provides optimal suggestions that take emotions into consideration. This system involves four entities: the user, the terminal, the server, and the emotion engine, all of which work together.

[0288] System Overview

[0289] The system receives input from a user, the server analyzes the input and generates additional questions, and the terminal presents these questions to the user. In parallel, an emotion engine recognizes the user's emotions and customizes the content of the questions and the presentation of search results accordingly. Finally, the server gains a detailed understanding of the user's needs and emotions, searches for the most suitable destination, and presents it to the user via the terminal. Specific embodiments are described in detail below.

[0290] Program processing flow

[0291] 1. The user inputs the destination

[0292] A user launches a car navigation app and inputs their destination preferences or requests via text or voice, and their emotions are inferred from the tone of their voice and the content of the text.

[0293] 2. The device sends the input to the server

[0294] The device receives user input and sends the input text or voice data to the server, where the voice data is converted into text beforehand.

[0295] 3. The server and emotion engine analyze the input

[0296] The server uses natural language processing (NLP) algorithms to analyze the user's input. In parallel, an emotion engine analyzes the user's input data to determine their emotional state, for example, whether they are stressed or relaxed.

[0297] 4. The server generates a follow-up question

[0298] Based on the user's intent, the server generates additional questions to understand their needs in more detail, such as "Which area is good?" or "Do you want a cafe that offers takeout?" These questions are adjusted based on the analysis results of the emotion engine.

[0299] 5. The device presents the user with additional questions

[0300] The device displays the generated follow-up questions to the user and asks for their answers, using a gentle tone and friendly expressions based on the emotion engine.

[0301] 6. The user answers any additional questions

[0302] The user inputs an answer to the additional question. For example, the user answers "a cafe in Shinjuku that offers takeout."

[0303] 7. The device sends the user's answer to the server

[0304] The terminal receives the user's response and transmits the data to the server.

[0305] 8. The server and emotion engine analyze the user's responses

[0306] The server then uses NLP to analyze the user's responses and understand their detailed needs. The emotion engine also simultaneously analyzes additional emotion information from the user's responses.

[0307] 9. The server searches for the destination

[0308] The server searches the internet or an internal database based on the user's detailed needs and emotional state, for example, to find cafes in the Shinjuku area that offer takeout and have an atmosphere that suits the user's emotional state.

[0309] 10. The server sends the search results to the device.

[0310] The server sends the search results to the device, which include details such as the cafe's name, location, and reviews.

[0311] 11. The device presents the search results to the user

[0312] The device receives the search results and displays them to the user, with the emotion engine providing prioritized results and customized display methods.

[0313] 12. User selects destination

[0314] The user selects the desired cafe from the displayed results. For example, they select "Cafe A."

[0315] 13. The device begins navigation

[0316] The device receives the user's selection and begins navigation to the selected destination. The emotion engine influences the device to provide a relaxing navigation voice and appropriate guidance.

[0317] Specific examples

[0318] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[0319] The user types, "I want to go to a nearby cafe."

[0320] The device sends input to the server

[0321] The server analyzes the keyword "cafe" and the emotion engine performs emotion analysis.

[0322] The server generates an additional question: "Which area is better?"

[0323] The device presents the user with a question (in this case, in a caring tone if the user is stressed)

[0324] The user answers "Shinjuku area"

[0325] The device sends the answer to the server

[0326] The server searches for "cafes in the Shinjuku area," and the emotion engine selects cafes that match the user's emotions.

[0327] The device presents search results to the user in an emotionally sensitive way.

[0328] The user selects "Cafe A"

[0329] The device will start navigating to "Cafe A" and provide a relaxing voice guidance.

[0330] In this way, by combining emotion recognition, the present invention is a system that quickly and easily provides the optimal destination based on the user's needs, realizing an excellent user experience that takes emotions into consideration.

[0331] The processing flow will be explained below.

[0332] Step 1:

[0333] A user launches a car navigation app and inputs their destination preference or request via text or voice. In this case, the user inputs, "I want to go to a nearby cafe."

[0334] Step 2:

[0335] The device receives user input and sends the input text or voice data to the server, where the voice data is converted into text beforehand.

[0336] Step 3:

[0337] The server analyzes the received user input and uses natural language processing (NLP) algorithms to extract keywords such as "cafe" and "nearby" to understand the user's basic intent.

[0338] Step 4:

[0339] Based on the user's intent, the server uses an emotion engine to analyze emotions from the user's speech text, such as determining stress levels, joy, or relaxation from the tone, speed, and phrasing of the speech.

[0340] Step 5:

[0341] Based on the user's input and emotional information, the server generates additional questions to understand their needs in more detail. For example, if the user is feeling stressed, the server generates questions in a gentle tone, such as "Which area would you like?" or "Would you like a cafe where you can relax?"

[0342] Step 6:

[0343] The device then displays the generated follow-up questions to the user and asks for their answers, adjusting the on-screen display and audio output to reflect the results of the emotion engine.

[0344] Step 7:

[0345] The user inputs an answer to the follow-up question, for example, "A relaxing cafe in Shinjuku."

[0346] Step 8:

[0347] The terminal receives the user's response and transmits the data to the server.

[0348] Step 9:

[0349] The server then uses NLP to analyze the user's responses and identify their specific needs. The emotion engine also simultaneously analyzes additional emotional information from the user's responses and reassess their stress level.

[0350] Step 10:

[0351] The server searches the internet or an internal database based on the user's detailed needs and emotional state, for example, to find cafes in the Shinjuku area that have a relaxing atmosphere.

[0352] Step 11:

[0353] The server sends the search results to the device, which include details such as the name, location, and reviews of each cafe.

[0354] Step 12:

[0355] The device receives the search results and displays them to the user, prioritizing the results based on the analysis results of the emotion engine and according to the user's emotional state.

[0356] Step 13:

[0357] The user selects the desired cafe from the search results presented. For example, they select "Cafe A."

[0358] Step 14:

[0359] The device receives the user's selection and begins navigation to the selected destination. The device reflects the emotion engine's data and uses voice guidance in a relaxing tone.

[0360] In this way, by combining emotion recognition, the present invention is a system that quickly and easily provides the optimal destination based on the user's needs, realizing an excellent user experience that takes emotions into consideration.

[0361] Example 2

[0362] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0363] Conventional car navigation systems can search for destinations based on user input, but they cannot take the user's emotions into account, making it difficult to provide optimal results that meet the emotional state and needs of each individual user. This has resulted in issues such as limited convenience and user experience.

[0364] 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.

[0365] In this invention, the server includes means for receiving a destination-related input from a user, means for analyzing the input and generating follow-up questions based on the user's needs and emotions, means for presenting the follow-up questions to the user and receiving the user's answers, means for analyzing the answers and searching for destinations based on the user's needs and emotions, means for presenting the search results to the user in a manner that takes emotions into consideration, and means for starting navigation to the destination selected by the user in a manner that takes emotions into consideration. This makes it possible to suggest optimal destinations based on the user's emotions and provide a more personalized navigation experience for each user.

[0366] "Means for receiving destination-related input from a user" refers to a system component for obtaining destination-related information entered by a user via text or voice.

[0367] "Means for analyzing the input and generating follow-up questions based on the user's needs and emotions" refers to a system component that uses a natural language processing algorithm and a sentiment analysis engine to understand the user's input content and emotions, and automatically create follow-up questions necessary to understand the user's detailed requirements.

[0368] "Means for presenting the additional question to the user and receiving the user's answer" refers to a system component for presenting a question generated by the server to the user and obtaining the user's answer thereto.

[0369] "Means for analyzing the answers and searching for destinations based on the user's needs and emotions" refers to a system component for re-analyzing the information and emotional state contained in the user's answers and searching for suitable destinations based thereon.

[0370] "Means for presenting the search results to the user in an emotionally sensitive manner" refers to a system component for presenting the search results to the user in an emotionally sensitive manner, such as by customizing and prioritizing the search results according to the user's emotional state.

[0371] The "means for initiating navigation to a destination selected by the user in an emotionally sensitive manner" refers to a system component for navigating to a destination selected by the user in a manner that adapts voice guidance and display methods to the user's emotional state.

[0372] MODE FOR CARRYING OUT THE INVENTION

[0373] This invention relates to an automobile navigation system that streamlines user destination searches and provides optimal suggestions that take emotions into consideration. This system operates in cooperation with four entities: the user, the terminal, the server, and the emotion engine.

[0374] System Overview

[0375] The system receives input from the user, analyzes the input, generates follow-up questions, and presents these questions to the user via the device. In parallel, an emotion engine recognizes the user's emotions and customizes the questions and search results accordingly. Finally, the server gains a detailed understanding of the user's needs and emotions, searches for the most suitable destination, and presents it to the user via the device.

[0376] Hardware and software used

[0377] The system uses the following hardware and software:

[0378] Terminals: Smartphones, tablets, in-car navigation systems, etc. These terminals are hardware that receives user input and communicates with the server.

[0379] Server: Cloud server or on-premise server. The server is the hardware and software that analyzes user input data and processes it in cooperation with the emotion engine.

[0380] Emotion engine: A software module for analyzing emotions from user input data. It implements an emotion recognition model including natural language processing (NLP) algorithms.

[0381] Natural Language Processing (NLP) algorithms: Software algorithms that analyze user input and understand their needs and intent.

[0382] Specific Embodiments

[0383] When a user launches a car navigation app and inputs their destination-related wishes or requests via text or voice, the device receives this input. For example, the user might say, "I want to go to a nearby cafe." The device then converts the voice data into text and sends it to the server. The server then uses a natural language processing (NLP) algorithm to analyze the user's input. In parallel, the emotion engine analyzes emotions from the input data, detecting, for example, the desire to relax.

[0384] Based on the analysis results, the server generates additional questions to understand the user's needs in more detail. For example, a question might be generated such as, "Which area are you looking for a cafe?" This question is adjusted based on the emotion engine's analysis results and presented to the user via the device. If the user answers "Shinjuku area," the device sends this answer to the server.

[0385] The server again uses NLP to analyze the user's answers and understand their detailed needs. The emotion engine also simultaneously analyzes additional emotional information from the user's answers. Based on this, the server uses the Internet or an internal database to search for a cafe that best suits the user's detailed needs and emotional state. For example, it searches for "a cafe where you can relax in the Shinjuku area."

[0386] The search results are sent from the server to the device, which then presents them to the user. The emotion engine applies a customized display method, prioritizing the most relaxing cafes. Once the user selects the desired cafe, the device begins navigation to the destination, providing relaxing voice and guidance based on the emotion engine.

[0387] Specific examples

[0388] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[0389] The user speaks, "I want to go to a nearby cafe."

[0390] The device converts the speech into text and sends it to the server.

[0391] The server analyzes the keyword "cafe" and the emotion engine performs emotion analysis.

[0392] The server generates a follow-up question: "Which area is better?"

[0393] The device presents the user with a question (displayed in a gentle tone to take emotions into consideration).

[0394] The user answers "Shinjuku area."

[0395] The device sends the response to the server.

[0396] The server searches for "cafes in the Shinjuku area," and the emotion engine selects cafes that match the user's emotions.

[0397] The device presents search results to the user using an emotion-sensitive display method.

[0398] The user selects "Cafe A."

[0399] The device will begin navigation to "Cafe A" (providing a relaxing navigation voice).

[0400] Prompt Sentence Examples

[0401] Here are some example prompts to input to a generative AI model:

[0402] "Please explain the specific processing steps of a car navigation system that allows users to search for destinations emotionally."

[0403] "Please explain in detail the program processing flow of a navigation system that analyzes emotions and makes optimal suggestions."

[0404] "Please provide a concrete example of a navigation system that utilizes user emotion recognition."

[0405] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0406] Step 1:

[0407] The user launches a car navigation app and enters their destination wishes and requests via text or voice.

[0408] Input: User text or voice input (e.g., "I want to go to a nearby cafe")

[0409] Output: In case of voice input, the data converted to text

[0410] Specific operation: The user performs an operation and speaks into the microphone of the car navigation app, saying, "I want to go to a nearby cafe." The app converts the speech into text.

[0411] Step 2:

[0412] The terminal sends the user's input to the server.

[0413] Input: User request in text format (e.g., "I want to go to a nearby cafe")

[0414] Output: User request data sent to the server

[0415] Specific operation: The terminal sends the user's text input as is to the server.

[0416] Step 3:

[0417] The server uses natural language processing (NLP) algorithms to parse the user's input.

[0418] Input: User's text request (e.g., "I want to go to a nearby cafe")

[0419] Output: Parsed request data (e.g. "cafe" and "nearby")

[0420] What it does: The server runs an NLP algorithm to parse the text request and extract the keywords "cafe" and "nearby."

[0421] Step 4:

[0422] In parallel, the emotion engine analyzes emotions from the user's input data.

[0423] Input: User's text request and voice tone data (e.g., "I want to go to a nearby cafe")

[0424] Output: Parsed emotion data (e.g. "Relaxed")

[0425] Specific operation: The emotion engine analyzes the user's text content and voice tone to detect when the user feels like relaxing.

[0426] Step 5:

[0427] The server generates additional questions to understand the specific needs.

[0428] Input: Parsed request data and sentiment data (e.g., "cafe," "nearby," "relax")

[0429] Output: Additional question data (e.g., "What area are you looking for a cafe in?")

[0430] Specific operation: Based on the analysis results, the server automatically generates an additional question: "In what area are you looking for a cafe?"

[0431] Step 6:

[0432] The terminal presents the generated follow-up questions to the user and receives answers.

[0433] Input: Additional question data (e.g., "What area cafe are you looking for?")

[0434] Output: User response data (e.g. "Shinjuku area")

[0435] Specific operation: The device presents the generated question to the user by voice or text, and the user answers "Shinjuku area."

[0436] Step 7:

[0437] The terminal sends the user's answer to the server.

[0438] Input: User response data (e.g., "Shinjuku area")

[0439] Output: Response data sent to the server

[0440] Specific operation: The terminal sends the user's answer "Shinjuku area" to the server.

[0441] Step 8:

[0442] The server then uses NLP again to analyze the user's responses and understand their detailed needs.

[0443] Input: User response data (e.g., "Shinjuku area")

[0444] Output: Analyzed detailed needs data (e.g. "Relaxing cafes in the Shinjuku area")

[0445] Specific operation: The server uses an NLP algorithm to analyze the "Shinjuku area" and understand detailed needs.

[0446] Step 9:

[0447] In parallel, the emotion engine parses additional emotion information from the user's responses.

[0448] Input: User response data and previous emotion data (e.g., "Shinjuku area" and "Relaxed")

[0449] Output: Updated emotion data (e.g., "I want to relax in the Shinjuku area")

[0450] Specific operation: The emotion engine analyzes the user's answer again and confirms that the user feels that they want to "relax in the Shinjuku area."

[0451] Step 10:

[0452] The server searches for destinations based on the user's specific needs and emotional state.

[0453] Input: Detailed needs data and emotion data (e.g., "A relaxing cafe in the Shinjuku area")

[0454] Output: Search result data (e.g. "Cafe A", "Cafe B", "Cafe C")

[0455] What happens: The server uses the Internet or an internal database to search for "relaxing cafes in the Shinjuku area" and retrieves the results.

[0456] Step 11:

[0457] The server sends the search results to the terminal.

[0458] Input: Search result data (e.g. "Cafe A", "Cafe B", "Cafe C")

[0459] Output: Search result data sent to the device

[0460] Specific operation: The server sends the search results to the terminal.

[0461] Step 12:

[0462] The terminal presents the search results to the user.

[0463] Input: Search result data (e.g. "Cafe A", "Cafe B", "Cafe C")

[0464] Output: Search results presented to the user (e.g., "Cafe A is prioritized as the most relaxing cafe")

[0465] Specific operation: The device presents search results to the user by voice or text, and prioritizes "Cafe A," a particularly relaxing place.

[0466] Step 13:

[0467] The user selects the desired cafe from the search results presented.

[0468] Input: User selection (e.g. "Cafe A")

[0469] Output: Selected cafe information (e.g. "Cafe A")

[0470] Specific operation: The user looks at the search results presented and selects "Cafe A."

[0471] Step 14:

[0472] The terminal receives the user's selection and initiates navigation to the selected destination.

[0473] Input: Selected cafe information (e.g. "Cafe A")

[0474] Output: Start navigation (e.g., route guidance to "Cafe A")

[0475] Specific operation: The device sets "Cafe A" as the destination and begins route guidance with a relaxing navigation voice.

[0476] (Application example 2)

[0477] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0478] Conventional car navigation systems can search for destinations based on the user's needs, but they are unable to consider the user's emotions. As a result, they do not provide suggestions or navigation appropriate to the user's emotional state, and the user experience is not sufficiently improved. Furthermore, the efficiency of destination searches is not sufficient, and users have to work hard to select a destination. To solve these issues, a system is needed that provides optimal destination suggestions and navigation according to the user's emotions.

[0479] 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.

[0480] In this invention, the server includes means for receiving a destination-related input from a user, means for analyzing the input and generating follow-up questions based on the user's needs, means for presenting the follow-up questions to the user and receiving the user's answers, means for analyzing the answers and searching for destinations based on the user's needs, means for presenting the search results to the user, means for starting navigation to the destination selected by the user, means for analyzing the user's emotions and customizing the questions and search results based on the emotions, and means for providing navigation voices and guidance text intended to relax the user or reduce stress based on the emotions. This enables optimal destination suggestions and navigation that take the user's emotional state into consideration, thereby improving the user experience compared to conventional methods.

[0481] "User destination input" refers to information describing a place or destination the user wishes to visit, which may be entered in text or voice format.

[0482] "Needs" refers to the wants and demands a user has for a particular destination, for example, a particular area or type of dining establishment.

[0483] "Follow-up questions" refer to additional inquiries generated to understand the user's needs more specifically.

[0484] "User Answers" refer to responses provided by users to follow-up questions that clarify the user's specific needs.

[0485] "Destination search means" refers to the function of finding an appropriate destination based on the user's needs and answers. This may use the Internet or an internal database.

[0486] "Search results" refers to candidate locations and related information obtained by a means of searching for a destination.

[0487] "Means for starting navigation" refers to a function that provides guidance to a destination selected by the user. It also shows the route to the destination.

[0488] "Means for analyzing emotions" refers to the ability to evaluate a user's emotional state from text or voice input. This typically involves the use of natural language processing or voice analysis.

[0489] "Search result customization" refers to the ability to adjust the results displayed based on the user's emotional state, in order to improve the user experience.

[0490] "Navigation voice and guidance" refers to the voice guidance and text display used when navigating to a destination. Emotionally-based, relaxing tones and stress-reducing content are provided.

[0491] "Intended for relaxation or stress reduction" refers to providing navigation that takes into account the user's current emotional state so that the user can reach their destination in a comfortable manner.

[0492] overview

[0493] This invention is a system that streamlines user destination searches and suggests optimal destinations taking emotions into consideration. The system aims to improve the user experience by customizing search results and navigation based on the user's requests and emotional state.

[0494] System Configuration

[0495] The system mainly consists of the following elements:

[0496] 1. Users

[0497] 2. Terminal

[0498] 3. Server

[0499] 4. Emotion Engine

[0500] Explanation of program processing

[0501] Receiving and parsing user input

[0502] The user speaks to the device or inputs text. If the input is speech, the device converts the input into text using speech recognition software (e.g., Google Speech Recognition). The device then sends this text data to the server.

[0503] Server analysis

[0504] The server analyzes the received user input using natural language processing (NLP) algorithms. At this time, an emotion engine analyzes the emotions from the user input data. The emotion engine utilizes commonly available natural language processing libraries (e.g., TextBlob).

[0505] Additional Question Generation

[0506] The server understands the user's needs and generates follow-up questions based on them. These questions are adjusted based on the analysis results of the emotion engine. For example, if the user is in a positive emotional state, a follow-up question such as "Do you have any dish recommendations?" will be generated.

[0507] Questions are presented by the device and answers are received from the user

[0508] The terminal presents the generated additional questions to the user and receives answers, which are then sent back to the server.

[0509] Optimal destination search by server

[0510] Based on the user's answers and the results of sentiment analysis, the server searches for the best destinations over the Internet or in an internal database, with specific dialogue prompts to refine the details.

[0511] Presenting search results and starting navigation

[0512] The device presents the search results to the user, customizing them based on the emotion engine. When the user selects a destination, the device begins navigation to the selected destination. The navigation voice and guidance are provided taking into account the user's emotional state.

[0513] Examples of concrete examples and prompts

[0514] For example, if a user speaks "I want pizza," the system will do the following:

[0515] User input: "I want pizza."

[0516] Server analysis results: Positive emotions

[0517] Follow-up question: "Do you have any food recommendations?"

[0518] User Answer: "I like Margherita."

[0519] Through these interactions, the server searches for the best restaurants and the terminal presents the results to the user.

[0520] Example prompt sentence:

[0521] If a user types, "I want pizza," the sentiment is positive. A follow-up question might be to ask the user, "Do you have any recommendations?" Find the best restaurant based on the user's answers to the following questions:

[0522] Hardware and software used

[0523] Speech recognition software: Google Speech Recognition

[0524] Natural Language Processing Library: TextBlob

[0525] Device: Smartphone or tablet

[0526] Server: Cloud services and internal servers

[0527] The present invention can improve the user experience by taking into consideration the user's emotions.

[0528] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0529] Step 1: The user speaks into the device or enters text manually.

[0530] Input: User voice or text input. Example: "I want pizza."

[0531] What happens: A user speaks or types text into their smartphone or tablet.

[0532] Step 2: Your device converts your voice to text.

[0533] Input: User's voice data.

[0534] What it does: Uses speech recognition software (e.g., Google Speech Recognition) to convert voice data into text.

[0535] Output: Text data. Example: "I want pizza."

[0536] Step 3: The device sends the text data to the server.

[0537] Input: Text data. "I want pizza."

[0538] What it does: The device sends the converted text data to a remote server via its internet connection.

[0539] Output: The text data sent to the server.

[0540] Step 4: The server parses the user's text input.

[0541] Input: User text input: "I want pizza."

[0542] What happens: The server uses Natural Language Processing (NLP) algorithms to analyze the text content. An emotion engine (e.g., TextBlob) evaluates the emotion.

[0543] Output: Analysis result. Example: Sentiment is positive.

[0544] Step 5: The server generates a follow-up question.

[0545] Input: The parsed input data, "I want pizza" and the sentiment analysis results.

[0546] What happens: The server generates a follow-up question based on the sentiment analysis results. For example, "Do you have any dish recommendations?"

[0547] Output: Additional questions.

[0548] Step 6: The terminal presents the user with a follow-up question.

[0549] Input: Follow-up question: "Do you have any dish recommendations?"

[0550] What happens: The device prompts the user with additional questions via voice or text.

[0551] Output: A question is presented to the user.

[0552] Step 7: The user answers any additional questions.

[0553] Input: A server-generated follow-up question: "Do you have any dish recommendations?"

[0554] Specific action: The user thinks and responds, for example, "I like Margherita."

[0555] Output: The user's answer.

[0556] Step 8: The terminal sends the user's answer to the server.

[0557] Input: The user's answer: "I like Margherita."

[0558] What happens: The device sends the user's answer to the server via an Internet connection.

[0559] Output: The response data sent to the server.

[0560] Step 9: The server re-analyzes the user's answers and emotional state.

[0561] Input: User's answer and sentiment-analyzed text: "I like Margherita" with positive sentiment.

[0562] Specific operation: The server again analyzes the response data using NLP and emotion engine to understand the user's detailed needs.

[0563] Output: Analyzed specific needs.

[0564] Step 10: The server searches for the destination.

[0565] Input: The parsed specific need. Example: "Pizza restaurant for positive users who like Margherita pizza."

[0566] What it does: The server searches the internet and its internal databases to find the destination that best suits your needs.

[0567] Output: Search results. Example: A list of pizza restaurants.

[0568] Step 11: The terminal presents the search results to the user.

[0569] Input: Search result data.

[0570] What it does: The device presents search results to the user via voice or text, with results sorted and customized based on sentiment.

[0571] Output: Presents search results to the user.

[0572] Step 12: The user selects a destination.

[0573] Input: The proposed search results.

[0574] Specific behavior: The user selects a destination from the presented options. Example: "Pizza Restaurant A."

[0575] Output: The selected destination.

[0576] Step 13: The device starts navigation.

[0577] Input: User selected destination data.

[0578] What happens: Your device will begin navigating to the selected destination, with audio and visual content customized based on your emotional state.

[0579] Output: Presents navigation information to the user.

[0580] 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.

[0581] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0582] 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.

[0583] [Second embodiment]

[0584] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0585] 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.

[0586] 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).

[0587] 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.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0596] This invention relates to an automobile navigation system that improves the efficiency of users' destination searches and provides optimal suggestions for specific needs. This system operates in cooperation with three entities: the user, the terminal, and the server.

[0597] System Overview

[0598] The system receives input from a user, the server analyzes the input to generate additional questions, and the terminal presents these questions to the user. Finally, the server gains a detailed understanding of the user's needs, searches for the most suitable destination, and presents it to the user via the terminal. Specific embodiments are described in detail below.

[0599] Program processing flow

[0600] 1. The user inputs the destination

[0601] The user launches a car navigation app and inputs their desired destination, for example, "I want to go to a nearby cafe," using text or voice input.

[0602] 2. The device sends the input to the server

[0603] The terminal sends the user's input to the server, where the input is transferred to the server as text data.

[0604] 3. The server parses the input

[0605] The server uses natural language processing (NLP) algorithms to analyze the user's input, for example extracting the keyword "cafe" and identifying specific areas and service details.

[0606] 4. The server generates a follow-up question

[0607] The server generates additional questions based on the user's input, such as "Which area is good?" or "Do you want a cafe that offers takeout?"

[0608] 5. The device presents the user with additional questions

[0609] The terminal displays the additional questions received from the server to the user, and the user provides specific answers to the displayed questions.

[0610] 6. The user answers any additional questions

[0611] The user enters the necessary information in response to the additional question. For example, the user answers, "A cafe in Shinjuku that offers takeout."

[0612] 7. The device sends the user's answer to the server

[0613] The terminal transmits the user's answer back to the server.

[0614] 8. The server analyzes the user's answers

[0615] The server then uses NLP again to analyze the user's responses and understand their detailed needs.

[0616] 9. The server searches for the destination

[0617] The server searches the internet and internal databases based on the user's needs to identify the best destination, for example, a cafe in the Shinjuku area that offers takeout.

[0618] 10. The server sends the search results to the device.

[0619] The server sends the search results it finds to the terminal, which prepares the results for display to the user.

[0620] 11. The device presents the search results to the user

[0621] The terminal displays the search results received from the server to the user, who then selects the location they want to go to from the displayed results.

[0622] 12. User selects destination

[0623] The user selects the desired destination from the presented results.

[0624] 13. The device begins navigation

[0625] The terminal sets the selected destination as a navigation destination and guides the user there.

[0626] Specific examples

[0627] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[0628] The user types, "I want to go to a nearby cafe."

[0629] The device sends input to the server

[0630] The server analyzes the keyword "cafe" and generates a follow-up question: "Which area is best?"

[0631] The device presents the question to the user

[0632] The user answers "Shinjuku area"

[0633] The device sends the answer to the server

[0634] The server searches for "cafes in the Shinjuku area" and sends the results to the device.

[0635] The device presents the search results to the user.

[0636] The user selects "Cafe A"

[0637] The device starts navigation to "Cafe A"

[0638] In this way, the present invention is a system that quickly and easily provides optimal destinations based on the user's needs.

[0639] The processing flow will be explained below.

[0640] Step 1:

[0641] A user launches a car navigation app and inputs their destination preference or request via text or voice. In this case, they might input, for example, "I want to go to a nearby cafe."

[0642] Step 2:

[0643] The device receives user input and sends the input text or voice data to the server, where the voice data is converted into text beforehand.

[0644] Step 3:

[0645] The server analyzes the received user input and uses natural language processing (NLP) algorithms to extract keywords such as "cafe" and "nearby" to understand the user's basic intent.

[0646] Step 4:

[0647] Based on the user's intent, the server generates additional questions to understand their needs in more detail, such as "Which area is good?" or "Do you want a cafe that offers takeout?"

[0648] Step 5:

[0649] The terminal displays the generated additional question to the user and requests an answer, and the user inputs a specific answer to the question displayed on the terminal screen.

[0650] Step 6:

[0651] The user inputs an answer to the additional question. For example, the user answers "a cafe in Shinjuku that offers takeout."

[0652] Step 7:

[0653] The terminal receives the user's response and transmits the data to the server.

[0654] Step 8:

[0655] The server then uses NLP to analyze the user's response, which clarifies that the user is looking for a "cafe in the Shinjuku area that offers takeout."

[0656] Step 9:

[0657] The server searches the internet or an internal database based on the user's specific needs, for example, to get a list of cafes in the Shinjuku area that offer takeout.

[0658] Step 10:

[0659] The server sends the search results to the device, which include details such as the cafe's name, location, and reviews.

[0660] Step 11:

[0661] The terminal receives the search results and displays them to the user, who can then select the location they want to go to from the displayed search results.

[0662] Step 12:

[0663] The user selects the desired cafe from the displayed results. For example, they select "Cafe A."

[0664] Step 13:

[0665] The device receives the user's selection and begins navigation to the selected destination. Using the device's navigation function, the device guides the user to "Cafe A."

[0666] In this way, the present invention is a system that sequentially analyzes user input, suggests optimal destinations, and provides navigation.

[0667] Example 1

[0668] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0669] Current automobile navigation systems have limitations in their ability to quickly and accurately satisfy a wide range of user needs. It is particularly difficult to accurately understand a user's wishes and suggest appropriate destinations when the user has detailed needs for specific locations. Conventional systems require users to manually select from a vast amount of information, making operation cumbersome. To solve this problem, there is a need for a navigation system that can accurately understand a user's vague wishes and automatically suggest optimal destinations.

[0670] 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.

[0671] In this invention, the server includes a means for receiving a destination-related input from a user, a means for analyzing the input and generating a follow-up question based on the user's needs, and a means for inputting a prompt sentence into a generative AI model to generate an appropriate follow-up question, thereby enabling the server to grasp the user's needs in detail and quickly and accurately present the optimal destination.

[0672] "User" refers to an individual who uses this system to receive guidance to a destination.

[0673] "Terminal" refers to an electronic device that acts as an interface to the system, receives user input, and presents information from the server to the user. Examples include smartphones and car navigation devices.

[0674] A "server" is a device that analyzes user destination input, generates follow-up questions based on needs, and processes the data to identify optimal destinations.

[0675] "Destination input" refers to information in the form of text or voice that expresses a user's preference regarding a location or facility they wish to visit.

[0676] "Natural language processing" refers to artificial intelligence technology that analyzes text and voice input from users and understands their content.

[0677] "Generative AI model" refers to artificial intelligence technology that automatically generates follow-up questions and suggestions to understand user needs.

[0678] A "prompt" is an instruction input to a generative AI model, serving as a starting point for generating questions and suggestions tailored to the user's specific needs.

[0679] "Additional questions" refer to questions that are generated by the server to understand the user's needs in more detail and presented to the user via the terminal.

[0680] "Internal database" refers to data stored within the system, including data storage used when searching for destinations.

[0681] "Network" refers to the communications infrastructure that serves as a transmission path for information, including the Internet and dedicated lines.

[0682] "Navigation" refers to the function of guiding the user to the optimal route to a selected destination.

[0683] This invention relates to an automobile navigation system that improves the efficiency of users' destination searches and provides optimal suggestions for specific needs. This system operates in cooperation with three entities: the user, the terminal, and the server.

[0684] The specific operation of the system will be explained below. A user inputs their destination preference using a car navigation application. In this case, the user can input their preference by text input or by using a voice recognition function. In the case of voice input, the voice is converted into text using a voice recognition module (e.g., a voice recognition API).

[0685] The terminal receives input from the user and sends it as text data to the server. The server uses natural language processing (NLP) algorithms (e.g., natural language processing APIs) to analyze the received text data. During the analysis, the server understands the user's intentions and extracts keywords.

[0686] The server then uses the generative AI model to generate additional questions based on the user's preferences, inputting the following prompt to the generative AI model: "The user wants to go to a nearby cafe. Please generate specific questions to understand the user's needs in more detail."

[0687] The generated follow-up question is presented to the user via the terminal. The user inputs a specific answer to the follow-up question. For example, the user might answer "a cafe in the Shinjuku area that offers takeout." This answer is then sent back to the server from the terminal.

[0688] The server then uses NLP to analyze the user's detailed needs and search for the appropriate destinations, using internet search APIs and internal databases. The server identifies the best destinations based on the user's needs and sends the results to the device.

[0689] The device presents the received search results to the user. The user selects a desired destination from the presented results. Finally, the device sets the selected destination in the navigation and launches a map application (e.g., a map application) to guide the user.

[0690] As a concrete example, if a user inputs "I want to go to a nearby cafe," the system will act as follows:

[0691] 1. The user types, "I want to go to a nearby cafe."

[0692] 2. The device sends the input to the server.

[0693] 3. The server parses the keyword "cafe" and generates a follow-up question: "Which area is good?"

[0694] 4. The device presents the question to the user.

[0695] 5. The user answers "Shinjuku area."

[0696] 6. The device sends the response to the server.

[0697] 7. The server searches for "cafes in the Shinjuku area" and sends the results to the device.

[0698] 8. The device presents the search results to the user.

[0699] 9. The user selects "Cafe A."

[0700] 10. The device begins navigation to "Cafe A."

[0701] In this way, the system can quickly and easily provide optimal destinations based on the user's needs. Specific actions allow users to search for and navigate to destinations without performing complex operations.

[0702] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0703] Step 1:

[0704] Input: The user enters their destination preference via text or voice.

[0705] How it works: A user launches a car navigation app and enters their desired destination. If they enter it via voice, it is converted to text using a speech recognition module (e.g., a speech recognition API), or they enter it directly into a text box.

[0706] Output: Text data about the user's destination.

[0707] Step 2:

[0708] Input: Text data about the user's destination.

[0709] Operation: The device receives the user's text data and sends it to the server. At this stage, it checks for errors and sends it to the server in the correct format.

[0710] Output: The user's input data is transmitted to the server.

[0711] Step 3:

[0712] Input: The user's text data received by the server.

[0713] How it works: The server uses natural language processing (NLP) algorithms to analyze the text data and extract keywords and user needs. Specifically, it uses an NLP API to identify keywords such as "cafe."

[0714] Output: Extracted keywords and analysis result data.

[0715] Step 4:

[0716] Input: Extracted keywords and analysis result data.

[0717] How it works: The server inputs a prompt statement into the generative AI model, which generates specific follow-up questions based on the user's needs. For example, the prompt might say, "The user wants to go to a nearby cafe. Please generate specific questions to understand the user's needs in more detail."

[0718] Output: The additional questions generated.

[0719] Step 5:

[0720] Input: The generated follow-up question.

[0721] Operation: The device receives additional questions from the server and displays them to the user. The questions are displayed in an interface that is easy for the user to understand. For example, the screen might say, "Which area is best?"

[0722] Output: The user sees a follow-up question.

[0723] Step 6:

[0724] Input: User's answer (text data).

[0725] How it works: The user enters a specific answer to the question displayed. For example, they might enter "cafes in the Shinjuku area that offer takeout."

[0726] Output: User's answer data (text).

[0727] Step 7:

[0728] Input: User response data (text).

[0729] Action: The device receives the user's answer and sends it back to the server, again verifying that the input data is in the correct format.

[0730] Output: The user's response data sent to the server.

[0731] Step 8:

[0732] Input: The user's answer data received by the server.

[0733] How it works: The server uses NLP to analyze the user's answers again and understand their specific needs, such as "Shinjuku area" and "takeout available."

[0734] Output: Detailed needs analysis result data.

[0735] Step 9:

[0736] Input: Detailed needs analysis result data.

[0737] How it works: The server uses the network (Internet search APIs or internal databases) to search for destinations. For example, it uses the Google Maps API or a data store to search for "cafes in the Shinjuku area that offer takeout."

[0738] Output: Search result data.

[0739] Step 10:

[0740] Input: Search result data.

[0741] Operation: The server sends the search results to the device as text data.

[0742] Output: Search result data sent to the device.

[0743] Step 11:

[0744] Input: Search result data sent to the device.

[0745] Operation: The device presents the search results received from the server to the user. Specifically, a list of candidate cafes is displayed on the screen.

[0746] Output: User sees search results.

[0747] Step 12:

[0748] Input: The destination selected by the user.

[0749] Action: The user selects the desired destination from the list presented, for example, "Cafe A."

[0750] Output: Selected destination data.

[0751] Step 13:

[0752] Input: Selected destination data.

[0753] What it does: The device sets the selected destination for navigation and launches a map app. For example, it launches a map app and displays the route to "Cafe A."

[0754] Output: The user initiates navigation.

[0755] (Application example 1)

[0756] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0757] Conventional car navigation systems only suggest a single destination based on simple user input, making it difficult to suggest optimal destinations based on the user's specific needs. Furthermore, even when a user has specific requests, it is difficult to provide navigation that appropriately reflects those requests. Therefore, there is a demand for a navigation system that can quickly and accurately respond to diverse user needs.

[0758] 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.

[0759] In this invention, the server includes means for receiving a destination-related input from a user, means for analyzing the input and generating a follow-up question based on the user's needs, means for presenting the follow-up question to the user and receiving the user's answer, means for analyzing the answer and searching for a destination based on the user's needs, means for converting the destination into coordinates using a geographic information system and starting navigation in cooperation with a navigation system, means for presenting the search results to the user, and means for starting navigation to the destination selected by the user. This enables the server to suggest an optimal destination based on the user's specific needs and provide quick navigation.

[0760] "User" refers to a person who uses this system.

[0761] "Destination-related input" refers to information that expresses a user's desires regarding places they would like to visit.

[0762] "Means for parsing input" refers to a method or device for processing and understanding destination-related information provided by a user.

[0763] "Means for generating follow-up questions" refers to a method or device that creates questions to solicit further information based on the user's initial input.

[0764] "Means for presenting follow-up questions to a user" refers to a method or device for displaying generated follow-up questions to a user.

[0765] "Means for receiving a user's answer" refers to a method or device for receiving the answer provided by the user to the follow-up question.

[0766] "Means for analyzing answers" refers to a method or device for processing and understanding answers provided by users.

[0767] "Destination search means" refers to a method or device for identifying the best location based on a user's needs.

[0768] "Geographic information system" refers to a system for obtaining location information and displaying it on a map.

[0769] "Navigation system" refers to a system that provides directions to a specific destination.

[0770] The term "means for initiating navigation" refers to a method or device for executing guidance to a destination selected by a user.

[0771] This invention is a navigation system for autonomous vehicles that proposes optimal destinations based on the specific needs of the user and performs navigation. This system operates in cooperation with three entities: the user, the terminal (a smartphone or a device inside the autonomous vehicle), and the server.

[0772] Hardware and software used

[0773] Smartphone: Receives input from the user, presents additional questions, and displays final destination information.

[0774] Autonomous vehicle: Receives destination information and performs navigation.

[0775] Server: Uses natural language processing (NLP) to analyze user input, generate appropriate follow-up questions, and search for the final destination.

[0776] Specific software used is:

[0777] SpeechRecognition library: Converts voice input into text.

[0778] Requests library: Exchanges data with the server via HTTP communication.

[0779] Geopy library: Obtain geographic information and convert coordinates of destinations.

[0780] System Operation Overview

[0781] Receiving input from the user

[0782] First, the user inputs their destination using a smartphone or in-car device. The input method is either voice or text. For example, they might input, "I want to go to a cafe in the Shinjuku area that offers takeout."

[0783] Input analysis and generation of follow-up questions

[0784] The device sends the received input to the server, which analyzes it using natural language processing (NLP). Based on the analysis, the server generates additional questions (e.g., "Which area is good?") based on the user's needs.

[0785] Posting additional questions and receiving answers

[0786] The terminal displays the additional questions received from the server to the user and receives the user's answers. The user enters the information again (for example, "Shinjuku area"), which the terminal then sends to the server.

[0787] Searching for and presenting your final destination

[0788] The server analyzes the user's responses and searches the Internet or an internal database for the destination that best suits the user's needs. The server then sends the destination information obtained as a result of the search (e.g., "Cafe A in Shinjuku") to the terminal, which then presents it to the user.

[0789] Start navigation

[0790] When the user selects a desired destination from the displayed results, the device sends the information to the autonomous vehicle's navigation system and begins navigation. The destination is converted into coordinates using a geographic information system to ensure accurate navigation.

[0791] Specific examples

[0792] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[0793] The user inputs, "I want to go to a nearby cafe."

[0794] The device sends the input to the server, which analyzes the keyword "cafe" and generates a follow-up question: "Which area is better?"

[0795] The terminal displays this question to the user, and the user answers "Shinjuku area."

[0796] The terminal sends this response to the server, which then searches for "cafes in the Shinjuku area."

[0797] The terminal presents the search results to the user, and the user selects "Cafe A."

[0798] The device will begin navigation to "Cafe A," and the self-driving vehicle will guide the user to the destination.

[0799] Prompt Sentence Examples

[0800] I'd like to go to a nearby cafe. Are there any cafes you'd recommend in the Shinjuku area?

[0801] This allows the present invention to suggest optimal destinations and provide quick navigation based on the user's specific needs.

[0802] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0803] Step 1:

[0804] The user inputs the destination

[0805] Using a smartphone or in-car device, the user inputs their destination preferences by voice or text. A typical input might be, "I want to go to a nearby cafe." The input data is temporarily stored by the device and sent to the next processing step.

[0806] Step 2:

[0807] The device sends input to the server

[0808] The terminal sends the destination-related input received from the user to the server using HTTP communication. Specifically, the input is transferred to the server as text data and received by the server.

[0809] Step 3:

[0810] The server parses the input

[0811] The server uses natural language processing (NLP) algorithms to analyze the user's input, extract keywords (e.g., "cafe") from the text data, and process the data to understand the user's request. As a result of the analysis, appropriate follow-up questions are generated.

[0812] Step 4:

[0813] The server generates additional questions

[0814] Based on the analysis results, the server generates additional questions to understand the user's specific needs, such as "Which area is best?" This data is sent to the device in the next step.

[0815] Step 5:

[0816] The device presents the user with additional questions

[0817] The terminal displays the additional questions received from the server to the user. The user's answers to these questions clarify specific needs. Therefore, a process is performed to display the questions in text format on the display.

[0818] Step 6:

[0819] The user answers additional questions

[0820] The user inputs the necessary answers to the additional questions displayed on the terminal. For example, the user inputs the name of a specific area, such as "Shinjuku area." The input data is then saved on the terminal again.

[0821] Step 7:

[0822] The device sends the user's answer to the server

[0823] The terminal transfers the answer data to the user's follow-up question to the server using HTTP communication. The data is sent to the server in text format.

[0824] Step 8:

[0825] The server analyzes the user's answers

[0826] The server then uses natural language processing (NLP) algorithms to analyze the received user response data and identify detailed needs. Based on the analysis results, the server searches the internet and internal databases to process the data and identify the optimal destination.

[0827] Step 9:

[0828] The server searches for the destination

[0829] The server searches the Internet or an internal database for the best destination based on the analysis results, and retrieves information about the identified destination (e.g., "Cafe A in Shinjuku") from the database.

[0830] Step 10:

[0831] The server sends the search results to the device.

[0832] The server sends the identified destination information, including geographical information such as latitude and longitude, to the device, which receives this data and prepares it for the next processing step.

[0833] Step 11:

[0834] The device presents the search results to the user.

[0835] The terminal displays the destination information received from the server to the user, who then selects the desired destination from the presented search results.

[0836] Step 12:

[0837] The user selects a destination

[0838] The user selects the desired location from the search results displayed on the device. For example, they select "Cafe A." This selection data is used for the next navigation step.

[0839] Step 13:

[0840] The device begins navigation

[0841] The terminal sets the destination selected by the user in the navigation system and starts accurate route guidance using the GPS of the autonomous vehicle. The destination coordinates are obtained using the geographic information system and navigation is performed.

[0842] 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.

[0843] This invention relates to an automobile navigation system that streamlines user destination searches and provides optimal suggestions that take emotions into consideration. This system involves four entities: the user, the terminal, the server, and the emotion engine, all of which work together.

[0844] System Overview

[0845] The system receives input from a user, the server analyzes the input and generates additional questions, and the terminal presents these questions to the user. In parallel, an emotion engine recognizes the user's emotions and customizes the content of the questions and the presentation of search results accordingly. Finally, the server gains a detailed understanding of the user's needs and emotions, searches for the most suitable destination, and presents it to the user via the terminal. Specific embodiments are described in detail below.

[0846] Program processing flow

[0847] 1. The user inputs the destination

[0848] A user launches a car navigation app and inputs their destination preferences or requests via text or voice, and their emotions are inferred from the tone of their voice and the content of the text.

[0849] 2. The device sends the input to the server

[0850] The device receives user input and sends the input text or voice data to the server, where the voice data is converted into text beforehand.

[0851] 3. The server and emotion engine analyze the input

[0852] The server uses natural language processing (NLP) algorithms to analyze the user's input. In parallel, an emotion engine analyzes the user's input data to determine their emotional state, for example, whether they are stressed or relaxed.

[0853] 4. The server generates a follow-up question

[0854] Based on the user's intent, the server generates additional questions to understand their needs in more detail, such as "Which area is good?" or "Do you want a cafe that offers takeout?" These questions are adjusted based on the analysis results of the emotion engine.

[0855] 5. The device presents the user with additional questions

[0856] The device displays the generated follow-up questions to the user and asks for their answers, using a gentle tone and friendly expressions based on the emotion engine.

[0857] 6. The user answers any additional questions

[0858] The user inputs an answer to the additional question. For example, the user answers "a cafe in Shinjuku that offers takeout."

[0859] 7. The device sends the user's answer to the server

[0860] The terminal receives the user's response and transmits the data to the server.

[0861] 8. The server and emotion engine analyze the user's responses

[0862] The server then uses NLP to analyze the user's responses and understand their detailed needs. The emotion engine also simultaneously analyzes additional emotion information from the user's responses.

[0863] 9. The server searches for the destination

[0864] The server searches the internet or an internal database based on the user's detailed needs and emotional state, for example, to find cafes in the Shinjuku area that offer takeout and have an atmosphere that suits the user's emotional state.

[0865] 10. The server sends the search results to the device.

[0866] The server sends the search results to the device, which include details such as the cafe's name, location, and reviews.

[0867] 11. The device presents the search results to the user

[0868] The device receives the search results and displays them to the user, with the emotion engine providing prioritized results and customized display methods.

[0869] 12. User selects destination

[0870] The user selects the desired cafe from the displayed results. For example, they select "Cafe A."

[0871] 13. The device begins navigation

[0872] The device receives the user's selection and begins navigation to the selected destination. The emotion engine influences the device to provide a relaxing navigation voice and appropriate guidance.

[0873] Specific examples

[0874] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[0875] The user types, "I want to go to a nearby cafe."

[0876] The device sends input to the server

[0877] The server analyzes the keyword "cafe" and the emotion engine performs emotion analysis.

[0878] The server generates an additional question: "Which area is better?"

[0879] The device presents the user with a question (in this case, in a caring tone if the user is stressed)

[0880] The user answers "Shinjuku area"

[0881] The device sends the answer to the server

[0882] The server searches for "cafes in the Shinjuku area," and the emotion engine selects cafes that match the user's emotions.

[0883] The device presents search results to the user in an emotionally sensitive way.

[0884] The user selects "Cafe A"

[0885] The device will start navigating to "Cafe A" and provide a relaxing voice guidance.

[0886] In this way, by combining emotion recognition, the present invention is a system that quickly and easily provides the optimal destination based on the user's needs, realizing an excellent user experience that takes emotions into consideration.

[0887] The processing flow will be explained below.

[0888] Step 1:

[0889] A user launches a car navigation app and inputs their destination preference or request via text or voice. In this case, the user inputs, "I want to go to a nearby cafe."

[0890] Step 2:

[0891] The device receives user input and sends the input text or voice data to the server, where the voice data is converted into text beforehand.

[0892] Step 3:

[0893] The server analyzes the received user input and uses natural language processing (NLP) algorithms to extract keywords such as "cafe" and "nearby" to understand the user's basic intent.

[0894] Step 4:

[0895] Based on the user's intent, the server uses an emotion engine to analyze emotions from the user's speech text, such as determining stress levels, joy, or relaxation from the tone, speed, and phrasing of the speech.

[0896] Step 5:

[0897] Based on the user's input and emotional information, the server generates additional questions to understand their needs in more detail. For example, if the user is feeling stressed, the server generates questions in a gentle tone, such as "Which area would you like?" or "Would you like a cafe where you can relax?"

[0898] Step 6:

[0899] The device then displays the generated follow-up questions to the user and asks for their answers, adjusting the on-screen display and audio output to reflect the results of the emotion engine.

[0900] Step 7:

[0901] The user inputs an answer to the follow-up question, for example, "A relaxing cafe in Shinjuku."

[0902] Step 8:

[0903] The terminal receives the user's response and transmits the data to the server.

[0904] Step 9:

[0905] The server then uses NLP to analyze the user's responses and identify their specific needs. The emotion engine also simultaneously analyzes additional emotional information from the user's responses and reassess their stress level.

[0906] Step 10:

[0907] The server searches the internet or an internal database based on the user's detailed needs and emotional state, for example, to find cafes in the Shinjuku area that have a relaxing atmosphere.

[0908] Step 11:

[0909] The server sends the search results to the device, which include details such as the name, location, and reviews of each cafe.

[0910] Step 12:

[0911] The device receives the search results and displays them to the user, prioritizing the results based on the analysis results of the emotion engine and according to the user's emotional state.

[0912] Step 13:

[0913] The user selects the desired cafe from the search results presented. For example, they select "Cafe A."

[0914] Step 14:

[0915] The device receives the user's selection and begins navigation to the selected destination. The device reflects the emotion engine's data and uses voice guidance in a relaxing tone.

[0916] In this way, by combining emotion recognition, the present invention is a system that quickly and easily provides the optimal destination based on the user's needs, realizing an excellent user experience that takes emotions into consideration.

[0917] Example 2

[0918] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0919] Conventional car navigation systems can search for destinations based on user input, but they cannot take the user's emotions into account, making it difficult to provide optimal results that meet the emotional state and needs of each individual user. This has resulted in issues such as limited convenience and user experience.

[0920] 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.

[0921] In this invention, the server includes means for receiving a destination-related input from a user, means for analyzing the input and generating follow-up questions based on the user's needs and emotions, means for presenting the follow-up questions to the user and receiving the user's answers, means for analyzing the answers and searching for destinations based on the user's needs and emotions, means for presenting the search results to the user in a manner that takes emotions into consideration, and means for starting navigation to the destination selected by the user in a manner that takes emotions into consideration. This makes it possible to suggest optimal destinations based on the user's emotions and provide a more personalized navigation experience for each user.

[0922] "Means for receiving destination-related input from a user" refers to a system component for obtaining destination-related information entered by a user via text or voice.

[0923] "Means for analyzing the input and generating follow-up questions based on the user's needs and emotions" refers to a system component that uses a natural language processing algorithm and a sentiment analysis engine to understand the user's input content and emotions, and automatically create follow-up questions necessary to understand the user's detailed requirements.

[0924] "Means for presenting the additional question to the user and receiving the user's answer" refers to a system component for presenting a question generated by the server to the user and obtaining the user's answer thereto.

[0925] "Means for analyzing the answers and searching for destinations based on the user's needs and emotions" refers to a system component for re-analyzing the information and emotional state contained in the user's answers and searching for suitable destinations based thereon.

[0926] "Means for presenting the search results to the user in an emotionally sensitive manner" refers to a system component for presenting the search results to the user in an emotionally sensitive manner, such as by customizing and prioritizing the search results according to the user's emotional state.

[0927] The "means for initiating navigation to a destination selected by the user in an emotionally sensitive manner" refers to a system component for navigating to a destination selected by the user in a manner that adapts voice guidance and display methods to the user's emotional state.

[0928] MODE FOR CARRYING OUT THE INVENTION

[0929] This invention relates to an automobile navigation system that streamlines user destination searches and provides optimal suggestions that take emotions into consideration. This system operates in cooperation with four entities: the user, the terminal, the server, and the emotion engine.

[0930] System Overview

[0931] The system receives input from the user, analyzes the input, generates follow-up questions, and presents these questions to the user via the device. In parallel, an emotion engine recognizes the user's emotions and customizes the questions and search results accordingly. Finally, the server gains a detailed understanding of the user's needs and emotions, searches for the most suitable destination, and presents it to the user via the device.

[0932] Hardware and software used

[0933] The system uses the following hardware and software:

[0934] Terminals: Smartphones, tablets, in-car navigation systems, etc. These terminals are hardware that receives user input and communicates with the server.

[0935] Server: Cloud server or on-premise server. The server is the hardware and software that analyzes user input data and processes it in cooperation with the emotion engine.

[0936] Emotion engine: A software module for analyzing emotions from user input data. It implements an emotion recognition model including natural language processing (NLP) algorithms.

[0937] Natural Language Processing (NLP) algorithms: Software algorithms that analyze user input and understand their needs and intent.

[0938] Specific Embodiments

[0939] When a user launches a car navigation app and inputs their destination-related wishes or requests via text or voice, the device receives this input. For example, the user might say, "I want to go to a nearby cafe." The device then converts the voice data into text and sends it to the server. The server then uses a natural language processing (NLP) algorithm to analyze the user's input. In parallel, the emotion engine analyzes emotions from the input data, detecting, for example, the desire to relax.

[0940] Based on the analysis results, the server generates additional questions to understand the user's needs in more detail. For example, a question might be generated such as, "Which area are you looking for a cafe?" This question is adjusted based on the emotion engine's analysis results and presented to the user via the device. If the user answers "Shinjuku area," the device sends this answer to the server.

[0941] The server again uses NLP to analyze the user's answers and understand their detailed needs. The emotion engine also simultaneously analyzes additional emotional information from the user's answers. Based on this, the server uses the Internet or an internal database to search for a cafe that best suits the user's detailed needs and emotional state. For example, it searches for "a cafe where you can relax in the Shinjuku area."

[0942] The search results are sent from the server to the device, which then presents them to the user. The emotion engine applies a customized display method, prioritizing the most relaxing cafes. Once the user selects the desired cafe, the device begins navigation to the destination, providing relaxing voice and guidance based on the emotion engine.

[0943] Specific examples

[0944] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[0945] The user speaks, "I want to go to a nearby cafe."

[0946] The device converts the speech into text and sends it to the server.

[0947] The server analyzes the keyword "cafe" and the emotion engine performs emotion analysis.

[0948] The server generates a follow-up question: "Which area is better?"

[0949] The device presents the user with a question (displayed in a gentle tone to take emotions into consideration).

[0950] The user answers "Shinjuku area."

[0951] The device sends the response to the server.

[0952] The server searches for "cafes in the Shinjuku area," and the emotion engine selects cafes that match the user's emotions.

[0953] The device presents search results to the user using an emotion-sensitive display method.

[0954] The user selects "Cafe A."

[0955] The device will begin navigation to "Cafe A" (providing a relaxing navigation voice).

[0956] Prompt Sentence Examples

[0957] Here are some example prompts to input to a generative AI model:

[0958] "Please explain the specific processing steps of a car navigation system that allows users to search for destinations emotionally."

[0959] "Please explain in detail the program processing flow of a navigation system that analyzes emotions and makes optimal suggestions."

[0960] "Please provide a concrete example of a navigation system that utilizes user emotion recognition."

[0961] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0962] Step 1:

[0963] The user launches a car navigation app and enters their destination wishes and requests via text or voice.

[0964] Input: User text or voice input (e.g., "I want to go to a nearby cafe")

[0965] Output: In case of voice input, the data converted to text

[0966] Specific operation: The user performs an operation and speaks into the microphone of the car navigation app, saying, "I want to go to a nearby cafe." The app converts the speech into text.

[0967] Step 2:

[0968] The terminal sends the user's input to the server.

[0969] Input: User request in text format (e.g., "I want to go to a nearby cafe")

[0970] Output: User request data sent to the server

[0971] Specific operation: The terminal sends the user's text input as is to the server.

[0972] Step 3:

[0973] The server uses natural language processing (NLP) algorithms to parse the user's input.

[0974] Input: User's text request (e.g., "I want to go to a nearby cafe")

[0975] Output: Parsed request data (e.g. "cafe" and "nearby")

[0976] What it does: The server runs an NLP algorithm to parse the text request and extract the keywords "cafe" and "nearby."

[0977] Step 4:

[0978] In parallel, the emotion engine analyzes emotions from the user's input data.

[0979] Input: User's text request and voice tone data (e.g., "I want to go to a nearby cafe")

[0980] Output: Parsed emotion data (e.g. "Relaxed")

[0981] Specific operation: The emotion engine analyzes the user's text content and voice tone to detect when the user feels like relaxing.

[0982] Step 5:

[0983] The server generates additional questions to understand the specific needs.

[0984] Input: Parsed request data and sentiment data (e.g., "cafe," "nearby," "relax")

[0985] Output: Additional question data (e.g., "What area are you looking for a cafe in?")

[0986] Specific operation: Based on the analysis results, the server automatically generates an additional question: "In what area are you looking for a cafe?"

[0987] Step 6:

[0988] The terminal presents the generated follow-up questions to the user and receives answers.

[0989] Input: Additional question data (e.g., "What area cafe are you looking for?")

[0990] Output: User response data (e.g. "Shinjuku area")

[0991] Specific operation: The device presents the generated question to the user by voice or text, and the user answers "Shinjuku area."

[0992] Step 7:

[0993] The terminal sends the user's answer to the server.

[0994] Input: User response data (e.g., "Shinjuku area")

[0995] Output: Response data sent to the server

[0996] Specific operation: The terminal sends the user's answer "Shinjuku area" to the server.

[0997] Step 8:

[0998] The server then uses NLP again to analyze the user's responses and understand their detailed needs.

[0999] Input: User response data (e.g., "Shinjuku area")

[1000] Output: Analyzed detailed needs data (e.g. "Relaxing cafes in the Shinjuku area")

[1001] Specific operation: The server uses an NLP algorithm to analyze the "Shinjuku area" and understand detailed needs.

[1002] Step 9:

[1003] In parallel, the emotion engine parses additional emotion information from the user's responses.

[1004] Input: User response data and previous emotion data (e.g., "Shinjuku area" and "Relaxed")

[1005] Output: Updated emotion data (e.g., "I want to relax in the Shinjuku area")

[1006] Specific operation: The emotion engine analyzes the user's answer again and confirms that the user feels that they want to "relax in the Shinjuku area."

[1007] Step 10:

[1008] The server searches for destinations based on the user's specific needs and emotional state.

[1009] Input: Detailed needs data and emotion data (e.g., "A relaxing cafe in the Shinjuku area")

[1010] Output: Search result data (e.g. "Cafe A", "Cafe B", "Cafe C")

[1011] What happens: The server uses the Internet or an internal database to search for "relaxing cafes in the Shinjuku area" and retrieves the results.

[1012] Step 11:

[1013] The server sends the search results to the terminal.

[1014] Input: Search result data (e.g. "Cafe A", "Cafe B", "Cafe C")

[1015] Output: Search result data sent to the device

[1016] Specific operation: The server sends the search results to the terminal.

[1017] Step 12:

[1018] The terminal presents the search results to the user.

[1019] Input: Search result data (e.g. "Cafe A", "Cafe B", "Cafe C")

[1020] Output: Search results presented to the user (e.g., "Cafe A is prioritized as the most relaxing cafe")

[1021] Specific operation: The device presents search results to the user by voice or text, and prioritizes "Cafe A," a particularly relaxing place.

[1022] Step 13:

[1023] The user selects the desired cafe from the search results presented.

[1024] Input: User selection (e.g. "Cafe A")

[1025] Output: Selected cafe information (e.g. "Cafe A")

[1026] Specific operation: The user looks at the search results presented and selects "Cafe A."

[1027] Step 14:

[1028] The terminal receives the user's selection and initiates navigation to the selected destination.

[1029] Input: Selected cafe information (e.g. "Cafe A")

[1030] Output: Start navigation (e.g., route guidance to "Cafe A")

[1031] Specific operation: The device sets "Cafe A" as the destination and begins route guidance with a relaxing navigation voice.

[1032] (Application example 2)

[1033] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1034] Conventional car navigation systems can search for destinations based on the user's needs, but they are unable to consider the user's emotions. As a result, they do not provide suggestions or navigation appropriate to the user's emotional state, and the user experience is not sufficiently improved. Furthermore, the efficiency of destination searches is not sufficient, and users have to work hard to select a destination. To solve these issues, a system is needed that provides optimal destination suggestions and navigation according to the user's emotions.

[1035] 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.

[1036] In this invention, the server includes means for receiving a destination-related input from a user, means for analyzing the input and generating follow-up questions based on the user's needs, means for presenting the follow-up questions to the user and receiving the user's answers, means for analyzing the answers and searching for destinations based on the user's needs, means for presenting the search results to the user, means for starting navigation to the destination selected by the user, means for analyzing the user's emotions and customizing the questions and search results based on the emotions, and means for providing navigation voices and guidance text intended to relax the user or reduce stress based on the emotions. This enables optimal destination suggestions and navigation that take the user's emotional state into consideration, thereby improving the user experience compared to conventional methods.

[1037] "User destination input" refers to information describing a place or destination the user wishes to visit, which may be entered in text or voice format.

[1038] "Needs" refers to the wants and demands a user has for a particular destination, for example, a particular area or type of dining establishment.

[1039] "Follow-up questions" refer to additional inquiries generated to understand the user's needs more specifically.

[1040] "User Answers" refer to responses provided by users to follow-up questions that clarify the user's specific needs.

[1041] "Destination search means" refers to the function of finding an appropriate destination based on the user's needs and answers. This may use the Internet or an internal database.

[1042] "Search results" refers to candidate locations and related information obtained by a means of searching for a destination.

[1043] "Means for starting navigation" refers to a function that provides guidance to a destination selected by the user. It also shows the route to the destination.

[1044] "Means for analyzing emotions" refers to the ability to evaluate a user's emotional state from text or voice input. This typically involves the use of natural language processing or voice analysis.

[1045] "Search result customization" refers to the ability to adjust the results displayed based on the user's emotional state, in order to improve the user experience.

[1046] "Navigation voice and guidance" refers to the voice guidance and text display used when navigating to a destination. Emotionally-based, relaxing tones and stress-reducing content are provided.

[1047] "Intended for relaxation or stress reduction" refers to providing navigation that takes into account the user's current emotional state so that the user can reach their destination in a comfortable manner.

[1048] overview

[1049] This invention is a system that streamlines user destination searches and suggests optimal destinations taking emotions into consideration. The system aims to improve the user experience by customizing search results and navigation based on the user's requests and emotional state.

[1050] System Configuration

[1051] The system mainly consists of the following elements:

[1052] 1. Users

[1053] 2. Terminal

[1054] 3. Server

[1055] 4. Emotion Engine

[1056] Explanation of program processing

[1057] Receiving and parsing user input

[1058] The user speaks to the device or inputs text. If the input is speech, the device converts the input into text using speech recognition software (e.g., Google Speech Recognition). The device then sends this text data to the server.

[1059] Server analysis

[1060] The server analyzes the received user input using natural language processing (NLP) algorithms. At this time, an emotion engine analyzes the emotions from the user input data. The emotion engine utilizes commonly available natural language processing libraries (e.g., TextBlob).

[1061] Additional Question Generation

[1062] The server understands the user's needs and generates follow-up questions based on them. These questions are adjusted based on the analysis results of the emotion engine. For example, if the user is in a positive emotional state, a follow-up question such as "Do you have any dish recommendations?" will be generated.

[1063] Questions are presented by the device and answers are received from the user

[1064] The terminal presents the generated additional questions to the user and receives answers, which are then sent back to the server.

[1065] Optimal destination search by server

[1066] Based on the user's answers and the results of sentiment analysis, the server searches for the best destinations over the Internet or in an internal database, with specific dialogue prompts to refine the details.

[1067] Presenting search results and starting navigation

[1068] The device presents the search results to the user, customizing them based on the emotion engine. When the user selects a destination, the device begins navigation to the selected destination. The navigation voice and guidance are provided taking into account the user's emotional state.

[1069] Examples of concrete examples and prompts

[1070] For example, if a user speaks "I want pizza," the system will do the following:

[1071] User input: "I want pizza."

[1072] Server analysis results: Positive emotions

[1073] Follow-up question: "Do you have any food recommendations?"

[1074] User Answer: "I like Margherita."

[1075] Through these interactions, the server searches for the best restaurants and the terminal presents the results to the user.

[1076] Example prompt sentence:

[1077] If a user types, "I want pizza," the sentiment is positive. A follow-up question might be to ask the user, "Do you have any recommendations?" Find the best restaurant based on the user's answers to the following questions:

[1078] Hardware and software used

[1079] Speech recognition software: Google Speech Recognition

[1080] Natural Language Processing Library: TextBlob

[1081] Device: Smartphone or tablet

[1082] Server: Cloud services and internal servers

[1083] The present invention can improve the user experience by taking into consideration the user's emotions.

[1084] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1085] Step 1: The user speaks into the device or enters text manually.

[1086] Input: User voice or text input. Example: "I want pizza."

[1087] What happens: A user speaks or types text into their smartphone or tablet.

[1088] Step 2: Your device converts your voice to text.

[1089] Input: User's voice data.

[1090] What it does: Uses speech recognition software (e.g., Google Speech Recognition) to convert voice data into text.

[1091] Output: Text data. Example: "I want pizza."

[1092] Step 3: The device sends the text data to the server.

[1093] Input: Text data. "I want pizza."

[1094] What it does: The device sends the converted text data to a remote server via its internet connection.

[1095] Output: The text data sent to the server.

[1096] Step 4: The server parses the user's text input.

[1097] Input: User text input: "I want pizza."

[1098] What happens: The server uses Natural Language Processing (NLP) algorithms to analyze the text content. An emotion engine (e.g., TextBlob) evaluates the emotion.

[1099] Output: Analysis result. Example: Sentiment is positive.

[1100] Step 5: The server generates a follow-up question.

[1101] Input: The parsed input data, "I want pizza" and the sentiment analysis results.

[1102] What happens: The server generates a follow-up question based on the sentiment analysis results. For example, "Do you have any dish recommendations?"

[1103] Output: Additional questions.

[1104] Step 6: The terminal presents the user with a follow-up question.

[1105] Input: Follow-up question: "Do you have any dish recommendations?"

[1106] What happens: The device prompts the user with additional questions via voice or text.

[1107] Output: A question is presented to the user.

[1108] Step 7: The user answers any additional questions.

[1109] Input: A server-generated follow-up question: "Do you have any dish recommendations?"

[1110] Specific action: The user thinks and responds, for example, "I like Margherita."

[1111] Output: The user's answer.

[1112] Step 8: The terminal sends the user's answer to the server.

[1113] Input: The user's answer: "I like Margherita."

[1114] What happens: The device sends the user's answer to the server via an Internet connection.

[1115] Output: The response data sent to the server.

[1116] Step 9: The server re-analyzes the user's answers and emotional state.

[1117] Input: User's answer and sentiment-analyzed text: "I like Margherita" with positive sentiment.

[1118] Specific operation: The server again analyzes the response data using NLP and emotion engine to understand the user's detailed needs.

[1119] Output: Analyzed specific needs.

[1120] Step 10: The server searches for the destination.

[1121] Input: The parsed specific need. Example: "Pizza restaurant for positive users who like Margherita pizza."

[1122] What it does: The server searches the internet and its internal databases to find the destination that best suits your needs.

[1123] Output: Search results. Example: A list of pizza restaurants.

[1124] Step 11: The terminal presents the search results to the user.

[1125] Input: Search result data.

[1126] What it does: The device presents search results to the user via voice or text, with results sorted and customized based on sentiment.

[1127] Output: Presents search results to the user.

[1128] Step 12: The user selects a destination.

[1129] Input: The proposed search results.

[1130] Specific behavior: The user selects a destination from the presented options. Example: "Pizza Restaurant A."

[1131] Output: The selected destination.

[1132] Step 13: The device starts navigation.

[1133] Input: User selected destination data.

[1134] What happens: Your device will begin navigating to the selected destination, with audio and visual content customized based on your emotional state.

[1135] Output: Presents navigation information to the user.

[1136] 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.

[1137] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1138] 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.

[1139] [Third embodiment]

[1140] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1141] 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.

[1142] 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).

[1143] 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.

[1144] 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.

[1145] 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).

[1146] 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.

[1147] 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.

[1148] 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.

[1149] 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.

[1150] 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.

[1151] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1152] This invention relates to an automobile navigation system that improves the efficiency of users' destination searches and provides optimal suggestions for specific needs. This system operates in cooperation with three entities: the user, the terminal, and the server.

[1153] System Overview

[1154] The system receives input from a user, the server analyzes the input to generate additional questions, and the terminal presents these questions to the user. Finally, the server gains a detailed understanding of the user's needs, searches for the most suitable destination, and presents it to the user via the terminal. Specific embodiments are described in detail below.

[1155] Program processing flow

[1156] 1. The user inputs the destination

[1157] The user launches a car navigation app and inputs their desired destination, for example, "I want to go to a nearby cafe," using text or voice input.

[1158] 2. The device sends the input to the server

[1159] The terminal sends the user's input to the server, where the input is transferred to the server as text data.

[1160] 3. The server parses the input

[1161] The server uses natural language processing (NLP) algorithms to analyze the user's input, for example extracting the keyword "cafe" and identifying specific areas and service details.

[1162] 4. The server generates a follow-up question

[1163] The server generates additional questions based on the user's input, such as "Which area is good?" or "Do you want a cafe that offers takeout?"

[1164] 5. The device presents the user with additional questions

[1165] The terminal displays the additional questions received from the server to the user, and the user provides specific answers to the displayed questions.

[1166] 6. The user answers any additional questions

[1167] The user enters the necessary information in response to the additional question. For example, the user answers, "A cafe in Shinjuku that offers takeout."

[1168] 7. The device sends the user's answer to the server

[1169] The terminal transmits the user's answer back to the server.

[1170] 8. The server analyzes the user's answers

[1171] The server then uses NLP again to analyze the user's responses and understand their detailed needs.

[1172] 9. The server searches for the destination

[1173] The server searches the internet and internal databases based on the user's needs to identify the best destination, for example, a cafe in the Shinjuku area that offers takeout.

[1174] 10. The server sends the search results to the device.

[1175] The server sends the search results it finds to the terminal, which prepares the results for display to the user.

[1176] 11. The device presents the search results to the user

[1177] The terminal displays the search results received from the server to the user, who then selects the location they want to go to from the displayed results.

[1178] 12. User selects destination

[1179] The user selects the desired destination from the presented results.

[1180] 13. The device begins navigation

[1181] The terminal sets the selected destination as a navigation destination and guides the user there.

[1182] Specific examples

[1183] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[1184] The user types, "I want to go to a nearby cafe."

[1185] The device sends input to the server

[1186] The server analyzes the keyword "cafe" and generates a follow-up question: "Which area is best?"

[1187] The device presents the question to the user

[1188] The user answers "Shinjuku area"

[1189] The device sends the answer to the server

[1190] The server searches for "cafes in the Shinjuku area" and sends the results to the device.

[1191] The device presents the search results to the user.

[1192] The user selects "Cafe A"

[1193] The device starts navigation to "Cafe A"

[1194] In this way, the present invention is a system that quickly and easily provides optimal destinations based on the user's needs.

[1195] The processing flow will be explained below.

[1196] Step 1:

[1197] A user launches a car navigation app and inputs their destination preference or request via text or voice. In this case, they might input, for example, "I want to go to a nearby cafe."

[1198] Step 2:

[1199] The device receives user input and sends the input text or voice data to the server, where the voice data is converted into text beforehand.

[1200] Step 3:

[1201] The server analyzes the received user input and uses natural language processing (NLP) algorithms to extract keywords such as "cafe" and "nearby" to understand the user's basic intent.

[1202] Step 4:

[1203] Based on the user's intent, the server generates additional questions to understand their needs in more detail, such as "Which area is good?" or "Do you want a cafe that offers takeout?"

[1204] Step 5:

[1205] The terminal displays the generated additional question to the user and requests an answer, and the user inputs a specific answer to the question displayed on the terminal screen.

[1206] Step 6:

[1207] The user inputs an answer to the additional question. For example, the user answers "a cafe in Shinjuku that offers takeout."

[1208] Step 7:

[1209] The terminal receives the user's response and transmits the data to the server.

[1210] Step 8:

[1211] The server then uses NLP to analyze the user's response, which clarifies that the user is looking for a "cafe in the Shinjuku area that offers takeout."

[1212] Step 9:

[1213] The server searches the internet or an internal database based on the user's specific needs, for example, to get a list of cafes in the Shinjuku area that offer takeout.

[1214] Step 10:

[1215] The server sends the search results to the device, which include details such as the cafe's name, location, and reviews.

[1216] Step 11:

[1217] The terminal receives the search results and displays them to the user, who can then select the location they want to go to from the displayed search results.

[1218] Step 12:

[1219] The user selects the desired cafe from the displayed results. For example, they select "Cafe A."

[1220] Step 13:

[1221] The device receives the user's selection and begins navigation to the selected destination. Using the device's navigation function, the device guides the user to "Cafe A."

[1222] In this way, the present invention is a system that sequentially analyzes user input, suggests optimal destinations, and provides navigation.

[1223] Example 1

[1224] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1225] Current automobile navigation systems have limitations in their ability to quickly and accurately satisfy a wide range of user needs. It is particularly difficult to accurately understand a user's wishes and suggest appropriate destinations when the user has detailed needs for specific locations. Conventional systems require users to manually select from a vast amount of information, making operation cumbersome. To solve this problem, there is a need for a navigation system that can accurately understand a user's vague wishes and automatically suggest optimal destinations.

[1226] 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.

[1227] In this invention, the server includes a means for receiving a destination-related input from a user, a means for analyzing the input and generating a follow-up question based on the user's needs, and a means for inputting a prompt sentence into a generative AI model to generate an appropriate follow-up question, thereby enabling the server to grasp the user's needs in detail and quickly and accurately present the optimal destination.

[1228] "User" refers to an individual who uses this system to receive guidance to a destination.

[1229] "Terminal" refers to an electronic device that acts as an interface to the system, receives user input, and presents information from the server to the user. Examples include smartphones and car navigation devices.

[1230] A "server" is a device that analyzes user destination input, generates follow-up questions based on needs, and processes the data to identify optimal destinations.

[1231] "Destination input" refers to information in the form of text or voice that expresses a user's preference regarding a location or facility they wish to visit.

[1232] "Natural language processing" refers to artificial intelligence technology that analyzes text and voice input from users and understands their content.

[1233] "Generative AI model" refers to artificial intelligence technology that automatically generates follow-up questions and suggestions to understand user needs.

[1234] A "prompt" is an instruction input to a generative AI model, serving as a starting point for generating questions and suggestions tailored to the user's specific needs.

[1235] "Additional questions" refer to questions that are generated by the server to understand the user's needs in more detail and presented to the user via the terminal.

[1236] "Internal database" refers to data stored within the system, including data storage used when searching for destinations.

[1237] "Network" refers to the communications infrastructure that serves as a transmission path for information, including the Internet and dedicated lines.

[1238] "Navigation" refers to the function of guiding the user to the optimal route to a selected destination.

[1239] This invention relates to an automobile navigation system that improves the efficiency of users' destination searches and provides optimal suggestions for specific needs. This system operates in cooperation with three entities: the user, the terminal, and the server.

[1240] The specific operation of the system will be explained below. A user inputs their destination preference using a car navigation application. In this case, the user can input their preference by text input or by using a voice recognition function. In the case of voice input, the voice is converted into text using a voice recognition module (e.g., a voice recognition API).

[1241] The terminal receives input from the user and sends it as text data to the server. The server uses natural language processing (NLP) algorithms (e.g., natural language processing APIs) to analyze the received text data. During the analysis, the server understands the user's intentions and extracts keywords.

[1242] The server then uses the generative AI model to generate additional questions based on the user's preferences, inputting the following prompt to the generative AI model: "The user wants to go to a nearby cafe. Please generate specific questions to understand the user's needs in more detail."

[1243] The generated follow-up question is presented to the user via the terminal. The user inputs a specific answer to the follow-up question. For example, the user might answer "a cafe in the Shinjuku area that offers takeout." This answer is then sent back to the server from the terminal.

[1244] The server then uses NLP to analyze the user's detailed needs and search for the appropriate destinations, using internet search APIs and internal databases. The server identifies the best destinations based on the user's needs and sends the results to the device.

[1245] The device presents the received search results to the user. The user selects a desired destination from the presented results. Finally, the device sets the selected destination in the navigation and launches a map application (e.g., a map application) to guide the user.

[1246] As a concrete example, if a user inputs "I want to go to a nearby cafe," the system will act as follows:

[1247] 1. The user types, "I want to go to a nearby cafe."

[1248] 2. The device sends the input to the server.

[1249] 3. The server parses the keyword "cafe" and generates a follow-up question: "Which area is good?"

[1250] 4. The device presents the question to the user.

[1251] 5. The user answers "Shinjuku area."

[1252] 6. The device sends the response to the server.

[1253] 7. The server searches for "cafes in the Shinjuku area" and sends the results to the device.

[1254] 8. The device presents the search results to the user.

[1255] 9. The user selects "Cafe A."

[1256] 10. The device begins navigation to "Cafe A."

[1257] In this way, the system can quickly and easily provide optimal destinations based on the user's needs. Specific actions allow users to search for and navigate to destinations without performing complex operations.

[1258] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1259] Step 1:

[1260] Input: The user enters their destination preference via text or voice.

[1261] How it works: A user launches a car navigation app and enters their desired destination. If they enter it via voice, it is converted to text using a speech recognition module (e.g., a speech recognition API), or they enter it directly into a text box.

[1262] Output: Text data about the user's destination.

[1263] Step 2:

[1264] Input: Text data about the user's destination.

[1265] Operation: The device receives the user's text data and sends it to the server. At this stage, it checks for errors and sends it to the server in the correct format.

[1266] Output: The user's input data is transmitted to the server.

[1267] Step 3:

[1268] Input: The user's text data received by the server.

[1269] How it works: The server uses natural language processing (NLP) algorithms to analyze the text data and extract keywords and user needs. Specifically, it uses an NLP API to identify keywords such as "cafe."

[1270] Output: Extracted keywords and analysis result data.

[1271] Step 4:

[1272] Input: Extracted keywords and analysis result data.

[1273] How it works: The server inputs a prompt statement into the generative AI model, which generates specific follow-up questions based on the user's needs. For example, the prompt might say, "The user wants to go to a nearby cafe. Please generate specific questions to understand the user's needs in more detail."

[1274] Output: The additional questions generated.

[1275] Step 5:

[1276] Input: The generated follow-up question.

[1277] Operation: The device receives additional questions from the server and displays them to the user. The questions are displayed in an interface that is easy for the user to understand. For example, the screen might say, "Which area is best?"

[1278] Output: The user sees a follow-up question.

[1279] Step 6:

[1280] Input: User's answer (text data).

[1281] How it works: The user enters a specific answer to the question displayed. For example, they might enter "cafes in the Shinjuku area that offer takeout."

[1282] Output: User's answer data (text).

[1283] Step 7:

[1284] Input: User response data (text).

[1285] Action: The device receives the user's answer and sends it back to the server, again verifying that the input data is in the correct format.

[1286] Output: The user's response data sent to the server.

[1287] Step 8:

[1288] Input: The user's answer data received by the server.

[1289] How it works: The server uses NLP to analyze the user's answers again and understand their specific needs, such as "Shinjuku area" and "takeout available."

[1290] Output: Detailed needs analysis result data.

[1291] Step 9:

[1292] Input: Detailed needs analysis result data.

[1293] How it works: The server uses the network (Internet search APIs or internal databases) to search for destinations. For example, it uses the Google Maps API or a data store to search for "cafes in the Shinjuku area that offer takeout."

[1294] Output: Search result data.

[1295] Step 10:

[1296] Input: Search result data.

[1297] Operation: The server sends the search results to the device as text data.

[1298] Output: Search result data sent to the device.

[1299] Step 11:

[1300] Input: Search result data sent to the device.

[1301] Operation: The device presents the search results received from the server to the user. Specifically, a list of candidate cafes is displayed on the screen.

[1302] Output: User sees search results.

[1303] Step 12:

[1304] Input: The destination selected by the user.

[1305] Action: The user selects the desired destination from the list presented, for example, "Cafe A."

[1306] Output: Selected destination data.

[1307] Step 13:

[1308] Input: Selected destination data.

[1309] What it does: The device sets the selected destination for navigation and launches a map app. For example, it launches a map app and displays the route to "Cafe A."

[1310] Output: The user initiates navigation.

[1311] (Application example 1)

[1312] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1313] Conventional car navigation systems only suggest a single destination based on simple user input, making it difficult to suggest optimal destinations based on the user's specific needs. Furthermore, even when a user has specific requests, it is difficult to provide navigation that appropriately reflects those requests. Therefore, there is a demand for a navigation system that can quickly and accurately respond to diverse user needs.

[1314] 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.

[1315] In this invention, the server includes means for receiving a destination-related input from a user, means for analyzing the input and generating a follow-up question based on the user's needs, means for presenting the follow-up question to the user and receiving the user's answer, means for analyzing the answer and searching for a destination based on the user's needs, means for converting the destination into coordinates using a geographic information system and starting navigation in cooperation with a navigation system, means for presenting the search results to the user, and means for starting navigation to the destination selected by the user. This enables the server to suggest an optimal destination based on the user's specific needs and provide quick navigation.

[1316] "User" refers to a person who uses this system.

[1317] "Destination-related input" refers to information that expresses a user's desires regarding places they would like to visit.

[1318] "Means for parsing input" refers to a method or device for processing and understanding destination-related information provided by a user.

[1319] "Means for generating follow-up questions" refers to a method or device that creates questions to solicit further information based on the user's initial input.

[1320] "Means for presenting follow-up questions to a user" refers to a method or device for displaying generated follow-up questions to a user.

[1321] "Means for receiving a user's answer" refers to a method or device for receiving the answer provided by the user to the follow-up question.

[1322] "Means for analyzing answers" refers to a method or device for processing and understanding answers provided by users.

[1323] "Destination search means" refers to a method or device for identifying the best location based on a user's needs.

[1324] "Geographic information system" refers to a system for obtaining location information and displaying it on a map.

[1325] "Navigation system" refers to a system that provides directions to a specific destination.

[1326] The term "means for initiating navigation" refers to a method or device for executing guidance to a destination selected by a user.

[1327] This invention is a navigation system for autonomous vehicles that proposes optimal destinations based on the specific needs of the user and performs navigation. This system operates in cooperation with three entities: the user, the terminal (a smartphone or a device inside the autonomous vehicle), and the server.

[1328] Hardware and software used

[1329] Smartphone: Receives input from the user, presents additional questions, and displays final destination information.

[1330] Autonomous vehicle: Receives destination information and performs navigation.

[1331] Server: Uses natural language processing (NLP) to analyze user input, generate appropriate follow-up questions, and search for the final destination.

[1332] Specific software used is:

[1333] SpeechRecognition library: Converts voice input into text.

[1334] Requests library: Exchanges data with the server via HTTP communication.

[1335] Geopy library: Obtain geographic information and convert coordinates of destinations.

[1336] System Operation Overview

[1337] Receiving input from the user

[1338] First, the user inputs their destination using a smartphone or in-car device. The input method is either voice or text. For example, they might input, "I want to go to a cafe in the Shinjuku area that offers takeout."

[1339] Input analysis and generation of follow-up questions

[1340] The device sends the received input to the server, which analyzes it using natural language processing (NLP). Based on the analysis, the server generates additional questions (e.g., "Which area is good?") based on the user's needs.

[1341] Posting additional questions and receiving answers

[1342] The terminal displays the additional questions received from the server to the user and receives the user's answers. The user enters the information again (for example, "Shinjuku area"), which the terminal then sends to the server.

[1343] Searching for and presenting your final destination

[1344] The server analyzes the user's responses and searches the Internet or an internal database for the destination that best suits the user's needs. The server then sends the destination information obtained as a result of the search (e.g., "Cafe A in Shinjuku") to the terminal, which then presents it to the user.

[1345] Start navigation

[1346] When the user selects a desired destination from the displayed results, the device sends the information to the autonomous vehicle's navigation system and begins navigation. The destination is converted into coordinates using a geographic information system to ensure accurate navigation.

[1347] Specific examples

[1348] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[1349] The user inputs, "I want to go to a nearby cafe."

[1350] The device sends the input to the server, which analyzes the keyword "cafe" and generates a follow-up question: "Which area is better?"

[1351] The terminal displays this question to the user, and the user answers "Shinjuku area."

[1352] The terminal sends this response to the server, which then searches for "cafes in the Shinjuku area."

[1353] The terminal presents the search results to the user, and the user selects "Cafe A."

[1354] The device will begin navigation to "Cafe A," and the self-driving vehicle will guide the user to the destination.

[1355] Prompt Sentence Examples

[1356] I'd like to go to a nearby cafe. Are there any cafes you'd recommend in the Shinjuku area?

[1357] This allows the present invention to suggest optimal destinations and provide quick navigation based on the user's specific needs.

[1358] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1359] Step 1:

[1360] The user inputs the destination

[1361] Using a smartphone or in-car device, the user inputs their destination preferences by voice or text. A typical input might be, "I want to go to a nearby cafe." The input data is temporarily stored by the device and sent to the next processing step.

[1362] Step 2:

[1363] The device sends input to the server

[1364] The terminal sends the destination-related input received from the user to the server using HTTP communication. Specifically, the input is transferred to the server as text data and received by the server.

[1365] Step 3:

[1366] The server parses the input

[1367] The server uses natural language processing (NLP) algorithms to analyze the user's input, extract keywords (e.g., "cafe") from the text data, and process the data to understand the user's request. As a result of the analysis, appropriate follow-up questions are generated.

[1368] Step 4:

[1369] The server generates additional questions

[1370] Based on the analysis results, the server generates additional questions to understand the user's specific needs, such as "Which area is best?" This data is sent to the device in the next step.

[1371] Step 5:

[1372] The device presents the user with additional questions

[1373] The terminal displays the additional questions received from the server to the user. The user's answers to these questions clarify specific needs. Therefore, a process is performed to display the questions in text format on the display.

[1374] Step 6:

[1375] The user answers additional questions

[1376] The user inputs the necessary answers to the additional questions displayed on the terminal. For example, the user inputs the name of a specific area, such as "Shinjuku area." The input data is then saved on the terminal again.

[1377] Step 7:

[1378] The device sends the user's answer to the server

[1379] The terminal transfers the answer data to the user's follow-up question to the server using HTTP communication. The data is sent to the server in text format.

[1380] Step 8:

[1381] The server analyzes the user's answers

[1382] The server then uses natural language processing (NLP) algorithms to analyze the received user response data and identify detailed needs. Based on the analysis results, the server searches the internet and internal databases to process the data and identify the optimal destination.

[1383] Step 9:

[1384] The server searches for the destination

[1385] The server searches the Internet or an internal database for the best destination based on the analysis results, and retrieves information about the identified destination (e.g., "Cafe A in Shinjuku") from the database.

[1386] Step 10:

[1387] The server sends the search results to the device.

[1388] The server sends the identified destination information, including geographical information such as latitude and longitude, to the device, which receives this data and prepares it for the next processing step.

[1389] Step 11:

[1390] The device presents the search results to the user.

[1391] The terminal displays the destination information received from the server to the user, who then selects the desired destination from the presented search results.

[1392] Step 12:

[1393] The user selects a destination

[1394] The user selects the desired location from the search results displayed on the device. For example, they select "Cafe A." This selection data is used for the next navigation step.

[1395] Step 13:

[1396] The device begins navigation

[1397] The terminal sets the destination selected by the user in the navigation system and starts accurate route guidance using the GPS of the autonomous vehicle. The destination coordinates are obtained using the geographic information system and navigation is performed.

[1398] 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.

[1399] This invention relates to an automobile navigation system that streamlines user destination searches and provides optimal suggestions that take emotions into consideration. This system involves four entities: the user, the terminal, the server, and the emotion engine, all of which work together.

[1400] System Overview

[1401] The system receives input from a user, the server analyzes the input and generates additional questions, and the terminal presents these questions to the user. In parallel, an emotion engine recognizes the user's emotions and customizes the content of the questions and the presentation of search results accordingly. Finally, the server gains a detailed understanding of the user's needs and emotions, searches for the most suitable destination, and presents it to the user via the terminal. Specific embodiments are described in detail below.

[1402] Program processing flow

[1403] 1. The user inputs the destination

[1404] A user launches a car navigation app and inputs their destination preferences or requests via text or voice, and their emotions are inferred from the tone of their voice and the content of the text.

[1405] 2. The device sends the input to the server

[1406] The device receives user input and sends the input text or voice data to the server, where the voice data is converted into text beforehand.

[1407] 3. The server and emotion engine analyze the input

[1408] The server uses natural language processing (NLP) algorithms to analyze the user's input. In parallel, an emotion engine analyzes the user's input data to determine their emotional state, for example, whether they are stressed or relaxed.

[1409] 4. The server generates a follow-up question

[1410] Based on the user's intent, the server generates additional questions to understand their needs in more detail, such as "Which area is good?" or "Do you want a cafe that offers takeout?" These questions are adjusted based on the analysis results of the emotion engine.

[1411] 5. The device presents the user with additional questions

[1412] The device displays the generated follow-up questions to the user and asks for their answers, using a gentle tone and friendly expressions based on the emotion engine.

[1413] 6. The user answers any additional questions

[1414] The user inputs an answer to the additional question. For example, the user answers "a cafe in Shinjuku that offers takeout."

[1415] 7. The device sends the user's answer to the server

[1416] The terminal receives the user's response and transmits the data to the server.

[1417] 8. The server and emotion engine analyze the user's responses

[1418] The server then uses NLP to analyze the user's responses and understand their detailed needs. The emotion engine also simultaneously analyzes additional emotion information from the user's responses.

[1419] 9. The server searches for the destination

[1420] The server searches the internet or an internal database based on the user's detailed needs and emotional state, for example, to find cafes in the Shinjuku area that offer takeout and have an atmosphere that suits the user's emotional state.

[1421] 10. The server sends the search results to the device.

[1422] The server sends the search results to the device, which include details such as the cafe's name, location, and reviews.

[1423] 11. The device presents the search results to the user

[1424] The device receives the search results and displays them to the user, with the emotion engine providing prioritized results and customized display methods.

[1425] 12. User selects destination

[1426] The user selects the desired cafe from the displayed results. For example, they select "Cafe A."

[1427] 13. The device begins navigation

[1428] The device receives the user's selection and begins navigation to the selected destination. The emotion engine influences the device to provide a relaxing navigation voice and appropriate guidance.

[1429] Specific examples

[1430] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[1431] The user types, "I want to go to a nearby cafe."

[1432] The device sends input to the server

[1433] The server analyzes the keyword "cafe" and the emotion engine performs emotion analysis.

[1434] The server generates an additional question: "Which area is better?"

[1435] The device presents the user with a question (in this case, in a caring tone if the user is stressed)

[1436] The user answers "Shinjuku area"

[1437] The device sends the answer to the server

[1438] The server searches for "cafes in the Shinjuku area," and the emotion engine selects cafes that match the user's emotions.

[1439] The device presents search results to the user in an emotionally sensitive way.

[1440] The user selects "Cafe A"

[1441] The device will start navigating to "Cafe A" and provide a relaxing voice guidance.

[1442] In this way, by combining emotion recognition, the present invention is a system that quickly and easily provides the optimal destination based on the user's needs, realizing an excellent user experience that takes emotions into consideration.

[1443] The processing flow will be explained below.

[1444] Step 1:

[1445] A user launches a car navigation app and inputs their destination preference or request via text or voice. In this case, the user inputs, "I want to go to a nearby cafe."

[1446] Step 2:

[1447] The device receives user input and sends the input text or voice data to the server, where the voice data is converted into text beforehand.

[1448] Step 3:

[1449] The server analyzes the received user input and uses natural language processing (NLP) algorithms to extract keywords such as "cafe" and "nearby" to understand the user's basic intent.

[1450] Step 4:

[1451] Based on the user's intent, the server uses an emotion engine to analyze emotions from the user's speech text, such as determining stress levels, joy, or relaxation from the tone, speed, and phrasing of the speech.

[1452] Step 5:

[1453] Based on the user's input and emotional information, the server generates additional questions to understand their needs in more detail. For example, if the user is feeling stressed, the server generates questions in a gentle tone, such as "Which area would you like?" or "Would you like a cafe where you can relax?"

[1454] Step 6:

[1455] The device then displays the generated follow-up questions to the user and asks for their answers, adjusting the on-screen display and audio output to reflect the results of the emotion engine.

[1456] Step 7:

[1457] The user inputs an answer to the follow-up question, for example, "A relaxing cafe in Shinjuku."

[1458] Step 8:

[1459] The terminal receives the user's response and transmits the data to the server.

[1460] Step 9:

[1461] The server then uses NLP to analyze the user's responses and identify their specific needs. The emotion engine also simultaneously analyzes additional emotional information from the user's responses and reassess their stress level.

[1462] Step 10:

[1463] The server searches the internet or an internal database based on the user's detailed needs and emotional state, for example, to find cafes in the Shinjuku area that have a relaxing atmosphere.

[1464] Step 11:

[1465] The server sends the search results to the device, which include details such as the name, location, and reviews of each cafe.

[1466] Step 12:

[1467] The device receives the search results and displays them to the user, prioritizing the results based on the analysis results of the emotion engine and according to the user's emotional state.

[1468] Step 13:

[1469] The user selects the desired cafe from the search results presented. For example, they select "Cafe A."

[1470] Step 14:

[1471] The device receives the user's selection and begins navigation to the selected destination. The device reflects the emotion engine's data and uses voice guidance in a relaxing tone.

[1472] In this way, by combining emotion recognition, the present invention is a system that quickly and easily provides the optimal destination based on the user's needs, realizing an excellent user experience that takes emotions into consideration.

[1473] Example 2

[1474] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1475] Conventional car navigation systems can search for destinations based on user input, but they cannot take the user's emotions into account, making it difficult to provide optimal results that meet the emotional state and needs of each individual user. This has resulted in issues such as limited convenience and user experience.

[1476] 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.

[1477] In this invention, the server includes means for receiving a destination-related input from a user, means for analyzing the input and generating follow-up questions based on the user's needs and emotions, means for presenting the follow-up questions to the user and receiving the user's answers, means for analyzing the answers and searching for destinations based on the user's needs and emotions, means for presenting the search results to the user in a manner that takes emotions into consideration, and means for starting navigation to the destination selected by the user in a manner that takes emotions into consideration. This makes it possible to suggest optimal destinations based on the user's emotions and provide a more personalized navigation experience for each user.

[1478] "Means for receiving destination-related input from a user" refers to a system component for obtaining destination-related information entered by a user via text or voice.

[1479] "Means for analyzing the input and generating follow-up questions based on the user's needs and emotions" refers to a system component that uses a natural language processing algorithm and a sentiment analysis engine to understand the user's input content and emotions, and automatically create follow-up questions necessary to understand the user's detailed requirements.

[1480] "Means for presenting the additional question to the user and receiving the user's answer" refers to a system component for presenting a question generated by the server to the user and obtaining the user's answer thereto.

[1481] "Means for analyzing the answers and searching for destinations based on the user's needs and emotions" refers to a system component for re-analyzing the information and emotional state contained in the user's answers and searching for suitable destinations based thereon.

[1482] "Means for presenting the search results to the user in an emotionally sensitive manner" refers to a system component for presenting the search results to the user in an emotionally sensitive manner, such as by customizing and prioritizing the search results according to the user's emotional state.

[1483] The "means for initiating navigation to a destination selected by the user in an emotionally sensitive manner" refers to a system component for navigating to a destination selected by the user in a manner that adapts voice guidance and display methods to the user's emotional state.

[1484] MODE FOR CARRYING OUT THE INVENTION

[1485] This invention relates to an automobile navigation system that streamlines user destination searches and provides optimal suggestions that take emotions into consideration. This system operates in cooperation with four entities: the user, the terminal, the server, and the emotion engine.

[1486] System Overview

[1487] The system receives input from the user, analyzes the input, generates follow-up questions, and presents these questions to the user via the device. In parallel, an emotion engine recognizes the user's emotions and customizes the questions and search results accordingly. Finally, the server gains a detailed understanding of the user's needs and emotions, searches for the most suitable destination, and presents it to the user via the device.

[1488] Hardware and software used

[1489] The system uses the following hardware and software:

[1490] Terminals: Smartphones, tablets, in-car navigation systems, etc. These terminals are hardware that receives user input and communicates with the server.

[1491] Server: Cloud server or on-premise server. The server is the hardware and software that analyzes user input data and processes it in cooperation with the emotion engine.

[1492] Emotion engine: A software module for analyzing emotions from user input data. It implements an emotion recognition model including natural language processing (NLP) algorithms.

[1493] Natural Language Processing (NLP) algorithms: Software algorithms that analyze user input and understand their needs and intent.

[1494] Specific Embodiments

[1495] When a user launches a car navigation app and inputs their destination-related wishes or requests via text or voice, the device receives this input. For example, the user might say, "I want to go to a nearby cafe." The device then converts the voice data into text and sends it to the server. The server then uses a natural language processing (NLP) algorithm to analyze the user's input. In parallel, the emotion engine analyzes emotions from the input data, detecting, for example, the desire to relax.

[1496] Based on the analysis results, the server generates additional questions to understand the user's needs in more detail. For example, a question might be generated such as, "Which area are you looking for a cafe?" This question is adjusted based on the emotion engine's analysis results and presented to the user via the device. If the user answers "Shinjuku area," the device sends this answer to the server.

[1497] The server again uses NLP to analyze the user's answers and understand their detailed needs. The emotion engine also simultaneously analyzes additional emotional information from the user's answers. Based on this, the server uses the Internet or an internal database to search for a cafe that best suits the user's detailed needs and emotional state. For example, it searches for "a cafe where you can relax in the Shinjuku area."

[1498] The search results are sent from the server to the device, which then presents them to the user. The emotion engine applies a customized display method, prioritizing the most relaxing cafes. Once the user selects the desired cafe, the device begins navigation to the destination, providing relaxing voice and guidance based on the emotion engine.

[1499] Specific examples

[1500] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[1501] The user speaks, "I want to go to a nearby cafe."

[1502] The device converts the speech into text and sends it to the server.

[1503] The server analyzes the keyword "cafe" and the emotion engine performs emotion analysis.

[1504] The server generates a follow-up question: "Which area is better?"

[1505] The device presents the user with a question (displayed in a gentle tone to take emotions into consideration).

[1506] The user answers "Shinjuku area."

[1507] The device sends the response to the server.

[1508] The server searches for "cafes in the Shinjuku area," and the emotion engine selects cafes that match the user's emotions.

[1509] The device presents search results to the user using an emotion-sensitive display method.

[1510] The user selects "Cafe A."

[1511] The device will begin navigation to "Cafe A" (providing a relaxing navigation voice).

[1512] Prompt Sentence Examples

[1513] Here are some example prompts to input to a generative AI model:

[1514] "Please explain the specific processing steps of a car navigation system that allows users to search for destinations emotionally."

[1515] "Please explain in detail the program processing flow of a navigation system that analyzes emotions and makes optimal suggestions."

[1516] "Please provide a concrete example of a navigation system that utilizes user emotion recognition."

[1517] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1518] Step 1:

[1519] The user launches a car navigation app and enters their destination wishes and requests via text or voice.

[1520] Input: User text or voice input (e.g., "I want to go to a nearby cafe")

[1521] Output: In case of voice input, the data converted to text

[1522] Specific operation: The user performs an operation and speaks into the microphone of the car navigation app, saying, "I want to go to a nearby cafe." The app converts the speech into text.

[1523] Step 2:

[1524] The terminal sends the user's input to the server.

[1525] Input: User request in text format (e.g., "I want to go to a nearby cafe")

[1526] Output: User request data sent to the server

[1527] Specific operation: The terminal sends the user's text input as is to the server.

[1528] Step 3:

[1529] The server uses natural language processing (NLP) algorithms to parse the user's input.

[1530] Input: User's text request (e.g., "I want to go to a nearby cafe")

[1531] Output: Parsed request data (e.g. "cafe" and "nearby")

[1532] What it does: The server runs an NLP algorithm to parse the text request and extract the keywords "cafe" and "nearby."

[1533] Step 4:

[1534] In parallel, the emotion engine analyzes emotions from the user's input data.

[1535] Input: User's text request and voice tone data (e.g., "I want to go to a nearby cafe")

[1536] Output: Parsed emotion data (e.g. "Relaxed")

[1537] Specific operation: The emotion engine analyzes the user's text content and voice tone to detect when the user feels like relaxing.

[1538] Step 5:

[1539] The server generates additional questions to understand the specific needs.

[1540] Input: Parsed request data and sentiment data (e.g., "cafe," "nearby," "relax")

[1541] Output: Additional question data (e.g., "What area are you looking for a cafe in?")

[1542] Specific operation: Based on the analysis results, the server automatically generates an additional question: "In what area are you looking for a cafe?"

[1543] Step 6:

[1544] The terminal presents the generated follow-up questions to the user and receives answers.

[1545] Input: Additional question data (e.g., "What area cafe are you looking for?")

[1546] Output: User response data (e.g. "Shinjuku area")

[1547] Specific operation: The device presents the generated question to the user by voice or text, and the user answers "Shinjuku area."

[1548] Step 7:

[1549] The terminal sends the user's answer to the server.

[1550] Input: User response data (e.g., "Shinjuku area")

[1551] Output: Response data sent to the server

[1552] Specific operation: The terminal sends the user's answer "Shinjuku area" to the server.

[1553] Step 8:

[1554] The server then uses NLP again to analyze the user's responses and understand their detailed needs.

[1555] Input: User response data (e.g., "Shinjuku area")

[1556] Output: Analyzed detailed needs data (e.g. "Relaxing cafes in the Shinjuku area")

[1557] Specific operation: The server uses an NLP algorithm to analyze the "Shinjuku area" and understand detailed needs.

[1558] Step 9:

[1559] In parallel, the emotion engine parses additional emotion information from the user's responses.

[1560] Input: User response data and previous emotion data (e.g., "Shinjuku area" and "Relaxed")

[1561] Output: Updated emotion data (e.g., "I want to relax in the Shinjuku area")

[1562] Specific operation: The emotion engine analyzes the user's answer again and confirms that the user feels that they want to "relax in the Shinjuku area."

[1563] Step 10:

[1564] The server searches for destinations based on the user's specific needs and emotional state.

[1565] Input: Detailed needs data and emotion data (e.g., "A relaxing cafe in the Shinjuku area")

[1566] Output: Search result data (e.g. "Cafe A", "Cafe B", "Cafe C")

[1567] What happens: The server uses the Internet or an internal database to search for "relaxing cafes in the Shinjuku area" and retrieves the results.

[1568] Step 11:

[1569] The server sends the search results to the terminal.

[1570] Input: Search result data (e.g. "Cafe A", "Cafe B", "Cafe C")

[1571] Output: Search result data sent to the device

[1572] Specific operation: The server sends the search results to the terminal.

[1573] Step 12:

[1574] The terminal presents the search results to the user.

[1575] Input: Search result data (e.g. "Cafe A", "Cafe B", "Cafe C")

[1576] Output: Search results presented to the user (e.g., "Cafe A is prioritized as the most relaxing cafe")

[1577] Specific operation: The device presents search results to the user by voice or text, and prioritizes "Cafe A," a particularly relaxing place.

[1578] Step 13:

[1579] The user selects the desired cafe from the search results presented.

[1580] Input: User selection (e.g. "Cafe A")

[1581] Output: Selected cafe information (e.g. "Cafe A")

[1582] Specific operation: The user looks at the search results presented and selects "Cafe A."

[1583] Step 14:

[1584] The terminal receives the user's selection and initiates navigation to the selected destination.

[1585] Input: Selected cafe information (e.g. "Cafe A")

[1586] Output: Start navigation (e.g., route guidance to "Cafe A")

[1587] Specific operation: The device sets "Cafe A" as the destination and begins route guidance with a relaxing navigation voice.

[1588] (Application example 2)

[1589] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1590] Conventional car navigation systems can search for destinations based on the user's needs, but they are unable to consider the user's emotions. As a result, they do not provide suggestions or navigation appropriate to the user's emotional state, and the user experience is not sufficiently improved. Furthermore, the efficiency of destination searches is not sufficient, and users have to work hard to select a destination. To solve these issues, a system is needed that provides optimal destination suggestions and navigation according to the user's emotions.

[1591] 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.

[1592] In this invention, the server includes means for receiving a destination-related input from a user, means for analyzing the input and generating follow-up questions based on the user's needs, means for presenting the follow-up questions to the user and receiving the user's answers, means for analyzing the answers and searching for destinations based on the user's needs, means for presenting the search results to the user, means for starting navigation to the destination selected by the user, means for analyzing the user's emotions and customizing the questions and search results based on the emotions, and means for providing navigation voices and guidance text intended to relax the user or reduce stress based on the emotions. This enables optimal destination suggestions and navigation that take the user's emotional state into consideration, thereby improving the user experience compared to conventional methods.

[1593] "User destination input" refers to information describing a place or destination the user wishes to visit, which may be entered in text or voice format.

[1594] "Needs" refers to the wants and demands a user has for a particular destination, for example, a particular area or type of dining establishment.

[1595] "Follow-up questions" refer to additional inquiries generated to understand the user's needs more specifically.

[1596] "User Answers" refer to responses provided by users to follow-up questions that clarify the user's specific needs.

[1597] "Destination search means" refers to the function of finding an appropriate destination based on the user's needs and answers. This may use the Internet or an internal database.

[1598] "Search results" refers to candidate locations and related information obtained by a means of searching for a destination.

[1599] "Means for starting navigation" refers to a function that provides guidance to a destination selected by the user. It also shows the route to the destination.

[1600] "Means for analyzing emotions" refers to the ability to evaluate a user's emotional state from text or voice input. This typically involves the use of natural language processing or voice analysis.

[1601] "Search result customization" refers to the ability to adjust the results displayed based on the user's emotional state, in order to improve the user experience.

[1602] "Navigation voice and guidance" refers to the voice guidance and text display used when navigating to a destination. Emotionally-based, relaxing tones and stress-reducing content are provided.

[1603] "Intended for relaxation or stress reduction" refers to providing navigation that takes into account the user's current emotional state so that the user can reach their destination in a comfortable manner.

[1604] overview

[1605] This invention is a system that streamlines user destination searches and suggests optimal destinations taking emotions into consideration. The system aims to improve the user experience by customizing search results and navigation based on the user's requests and emotional state.

[1606] System Configuration

[1607] The system mainly consists of the following elements:

[1608] 1. Users

[1609] 2. Terminal

[1610] 3. Server

[1611] 4. Emotion Engine

[1612] Explanation of program processing

[1613] Receiving and parsing user input

[1614] The user speaks to the device or inputs text. If the input is speech, the device converts the input into text using speech recognition software (e.g., Google Speech Recognition). The device then sends this text data to the server.

[1615] Server analysis

[1616] The server analyzes the received user input using natural language processing (NLP) algorithms. At this time, an emotion engine analyzes the emotions from the user input data. The emotion engine utilizes commonly available natural language processing libraries (e.g., TextBlob).

[1617] Additional Question Generation

[1618] The server understands the user's needs and generates follow-up questions based on them. These questions are adjusted based on the analysis results of the emotion engine. For example, if the user is in a positive emotional state, a follow-up question such as "Do you have any dish recommendations?" will be generated.

[1619] Questions are presented by the device and answers are received from the user

[1620] The terminal presents the generated additional questions to the user and receives answers, which are then sent back to the server.

[1621] Optimal destination search by server

[1622] Based on the user's answers and the results of sentiment analysis, the server searches for the best destinations over the Internet or in an internal database, with specific dialogue prompts to refine the details.

[1623] Presenting search results and starting navigation

[1624] The device presents the search results to the user, customizing them based on the emotion engine. When the user selects a destination, the device begins navigation to the selected destination. The navigation voice and guidance are provided taking into account the user's emotional state.

[1625] Examples of concrete examples and prompts

[1626] For example, if a user speaks "I want pizza," the system will do the following:

[1627] User input: "I want pizza."

[1628] Server analysis results: Positive emotions

[1629] Follow-up question: "Do you have any food recommendations?"

[1630] User Answer: "I like Margherita."

[1631] Through these interactions, the server searches for the best restaurants and the terminal presents the results to the user.

[1632] Example prompt sentence:

[1633] If a user types, "I want pizza," the sentiment is positive. A follow-up question might be to ask the user, "Do you have any recommendations?" Find the best restaurant based on the user's answers to the following questions:

[1634] Hardware and software used

[1635] Speech recognition software: Google Speech Recognition

[1636] Natural Language Processing Library: TextBlob

[1637] Device: Smartphone or tablet

[1638] Server: Cloud services and internal servers

[1639] The present invention can improve the user experience by taking into consideration the user's emotions.

[1640] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1641] Step 1: The user speaks into the device or enters text manually.

[1642] Input: User voice or text input. Example: "I want pizza."

[1643] What happens: A user speaks or types text into their smartphone or tablet.

[1644] Step 2: Your device converts your voice to text.

[1645] Input: User's voice data.

[1646] What it does: Uses speech recognition software (e.g., Google Speech Recognition) to convert voice data into text.

[1647] Output: Text data. Example: "I want pizza."

[1648] Step 3: The device sends the text data to the server.

[1649] Input: Text data. "I want pizza."

[1650] What it does: The device sends the converted text data to a remote server via its internet connection.

[1651] Output: The text data sent to the server.

[1652] Step 4: The server parses the user's text input.

[1653] Input: User text input: "I want pizza."

[1654] What happens: The server uses Natural Language Processing (NLP) algorithms to analyze the text content. An emotion engine (e.g., TextBlob) evaluates the emotion.

[1655] Output: Analysis result. Example: Sentiment is positive.

[1656] Step 5: The server generates a follow-up question.

[1657] Input: The parsed input data, "I want pizza" and the sentiment analysis results.

[1658] What happens: The server generates a follow-up question based on the sentiment analysis results. For example, "Do you have any dish recommendations?"

[1659] Output: Additional questions.

[1660] Step 6: The terminal presents the user with a follow-up question.

[1661] Input: Follow-up question: "Do you have any dish recommendations?"

[1662] What happens: The device prompts the user with additional questions via voice or text.

[1663] Output: A question is presented to the user.

[1664] Step 7: The user answers any additional questions.

[1665] Input: A server-generated follow-up question: "Do you have any dish recommendations?"

[1666] Specific action: The user thinks and responds, for example, "I like Margherita."

[1667] Output: The user's answer.

[1668] Step 8: The terminal sends the user's answer to the server.

[1669] Input: The user's answer: "I like Margherita."

[1670] What happens: The device sends the user's answer to the server via an Internet connection.

[1671] Output: The response data sent to the server.

[1672] Step 9: The server re-analyzes the user's answers and emotional state.

[1673] Input: User's answer and sentiment-analyzed text: "I like Margherita" with positive sentiment.

[1674] Specific operation: The server again analyzes the response data using NLP and emotion engine to understand the user's detailed needs.

[1675] Output: Analyzed specific needs.

[1676] Step 10: The server searches for the destination.

[1677] Input: The parsed specific need. Example: "Pizza restaurant for positive users who like Margherita pizza."

[1678] What it does: The server searches the internet and its internal databases to find the destination that best suits your needs.

[1679] Output: Search results. Example: A list of pizza restaurants.

[1680] Step 11: The terminal presents the search results to the user.

[1681] Input: Search result data.

[1682] What it does: The device presents search results to the user via voice or text, with results sorted and customized based on sentiment.

[1683] Output: Presents search results to the user.

[1684] Step 12: The user selects a destination.

[1685] Input: The proposed search results.

[1686] Specific behavior: The user selects a destination from the presented options. Example: "Pizza Restaurant A."

[1687] Output: The selected destination.

[1688] Step 13: The device starts navigation.

[1689] Input: User selected destination data.

[1690] What happens: Your device will begin navigating to the selected destination, with audio and visual content customized based on your emotional state.

[1691] Output: Presents navigation information to the user.

[1692] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1693] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1694] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1695] [Fourth embodiment]

[1696] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1697] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1698] 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).

[1699] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1700] 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.

[1701] 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).

[1702] 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.

[1703] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1704] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1705] 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.

[1706] 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.

[1707] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1708] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1709] This invention relates to an automobile navigation system that improves the efficiency of users' destination searches and provides optimal suggestions for specific needs. This system operates in cooperation with three entities: the user, the terminal, and the server.

[1710] System Overview

[1711] The system receives input from a user, the server analyzes the input to generate additional questions, and the terminal presents these questions to the user. Finally, the server gains a detailed understanding of the user's needs, searches for the most suitable destination, and presents it to the user via the terminal. Specific embodiments are described in detail below.

[1712] Program processing flow

[1713] 1. The user inputs the destination

[1714] The user launches a car navigation app and inputs their desired destination, for example, "I want to go to a nearby cafe," using text or voice input.

[1715] 2. The device sends the input to the server

[1716] The terminal sends the user's input to the server, where the input is transferred to the server as text data.

[1717] 3. The server parses the input

[1718] The server uses natural language processing (NLP) algorithms to analyze the user's input, for example extracting the keyword "cafe" and identifying specific areas and service details.

[1719] 4. The server generates a follow-up question

[1720] The server generates additional questions based on the user's input, such as "Which area is good?" or "Do you want a cafe that offers takeout?"

[1721] 5. The device presents the user with additional questions

[1722] The terminal displays the additional questions received from the server to the user, and the user provides specific answers to the displayed questions.

[1723] 6. The user answers any additional questions

[1724] The user enters the necessary information in response to the additional question. For example, the user answers, "A cafe in Shinjuku that offers takeout."

[1725] 7. The device sends the user's answer to the server

[1726] The terminal transmits the user's answer back to the server.

[1727] 8. The server analyzes the user's answers

[1728] The server then uses NLP again to analyze the user's responses and understand their detailed needs.

[1729] 9. The server searches for the destination

[1730] The server searches the internet and internal databases based on the user's needs to identify the best destination, for example, a cafe in the Shinjuku area that offers takeout.

[1731] 10. The server sends the search results to the device.

[1732] The server sends the search results it finds to the terminal, which prepares the results for display to the user.

[1733] 11. The device presents the search results to the user

[1734] The terminal displays the search results received from the server to the user, who then selects the location they want to go to from the displayed results.

[1735] 12. User selects destination

[1736] The user selects the desired destination from the presented results.

[1737] 13. The device begins navigation

[1738] The terminal sets the selected destination as a navigation destination and guides the user there.

[1739] Specific examples

[1740] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[1741] The user types, "I want to go to a nearby cafe."

[1742] The device sends input to the server

[1743] The server analyzes the keyword "cafe" and generates a follow-up question: "Which area is best?"

[1744] The device presents the question to the user

[1745] The user answers "Shinjuku area"

[1746] The device sends the answer to the server

[1747] The server searches for "cafes in the Shinjuku area" and sends the results to the device.

[1748] The device presents the search results to the user.

[1749] The user selects "Cafe A"

[1750] The device starts navigation to "Cafe A"

[1751] In this way, the present invention is a system that quickly and easily provides optimal destinations based on the user's needs.

[1752] The processing flow will be explained below.

[1753] Step 1:

[1754] A user launches a car navigation app and inputs their destination preference or request via text or voice. In this case, they might input, for example, "I want to go to a nearby cafe."

[1755] Step 2:

[1756] The device receives user input and sends the input text or voice data to the server, where the voice data is converted into text beforehand.

[1757] Step 3:

[1758] The server analyzes the received user input and uses natural language processing (NLP) algorithms to extract keywords such as "cafe" and "nearby" to understand the user's basic intent.

[1759] Step 4:

[1760] Based on the user's intent, the server generates additional questions to understand their needs in more detail, such as "Which area is good?" or "Do you want a cafe that offers takeout?"

[1761] Step 5:

[1762] The terminal displays the generated additional question to the user and requests an answer, and the user inputs a specific answer to the question displayed on the terminal screen.

[1763] Step 6:

[1764] The user inputs an answer to the additional question. For example, the user answers "a cafe in Shinjuku that offers takeout."

[1765] Step 7:

[1766] The terminal receives the user's response and transmits the data to the server.

[1767] Step 8:

[1768] The server then uses NLP to analyze the user's response, which clarifies that the user is looking for a "cafe in the Shinjuku area that offers takeout."

[1769] Step 9:

[1770] The server searches the internet or an internal database based on the user's specific needs, for example, to get a list of cafes in the Shinjuku area that offer takeout.

[1771] Step 10:

[1772] The server sends the search results to the device, which include details such as the cafe's name, location, and reviews.

[1773] Step 11:

[1774] The terminal receives the search results and displays them to the user, who can then select the location they want to go to from the displayed search results.

[1775] Step 12:

[1776] The user selects the desired cafe from the displayed results. For example, they select "Cafe A."

[1777] Step 13:

[1778] The device receives the user's selection and begins navigation to the selected destination. Using the device's navigation function, the device guides the user to "Cafe A."

[1779] In this way, the present invention is a system that sequentially analyzes user input, suggests optimal destinations, and provides navigation.

[1780] Example 1

[1781] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1782] Current automobile navigation systems have limitations in their ability to quickly and accurately satisfy a wide range of user needs. It is particularly difficult to accurately understand a user's wishes and suggest appropriate destinations when the user has detailed needs for specific locations. Conventional systems require users to manually select from a vast amount of information, making operation cumbersome. To solve this problem, there is a need for a navigation system that can accurately understand a user's vague wishes and automatically suggest optimal destinations.

[1783] 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.

[1784] In this invention, the server includes a means for receiving a destination-related input from a user, a means for analyzing the input and generating a follow-up question based on the user's needs, and a means for inputting a prompt sentence into a generative AI model to generate an appropriate follow-up question, thereby enabling the server to grasp the user's needs in detail and quickly and accurately present the optimal destination.

[1785] "User" refers to an individual who uses this system to receive guidance to a destination.

[1786] "Terminal" refers to an electronic device that acts as an interface to the system, receives user input, and presents information from the server to the user. Examples include smartphones and car navigation devices.

[1787] A "server" is a device that analyzes user destination input, generates follow-up questions based on needs, and processes the data to identify optimal destinations.

[1788] "Destination input" refers to information in the form of text or voice that expresses a user's preference regarding a location or facility they wish to visit.

[1789] "Natural language processing" refers to artificial intelligence technology that analyzes text and voice input from users and understands their content.

[1790] "Generative AI model" refers to artificial intelligence technology that automatically generates follow-up questions and suggestions to understand user needs.

[1791] A "prompt" is an instruction input to a generative AI model, serving as a starting point for generating questions and suggestions tailored to the user's specific needs.

[1792] "Additional questions" refer to questions that are generated by the server to understand the user's needs in more detail and presented to the user via the terminal.

[1793] "Internal database" refers to data stored within the system, including data storage used when searching for destinations.

[1794] "Network" refers to the communications infrastructure that serves as a transmission path for information, including the Internet and dedicated lines.

[1795] "Navigation" refers to the function of guiding the user to the optimal route to a selected destination.

[1796] This invention relates to an automobile navigation system that improves the efficiency of users' destination searches and provides optimal suggestions for specific needs. This system operates in cooperation with three entities: the user, the terminal, and the server.

[1797] The specific operation of the system will be explained below. A user inputs their destination preference using a car navigation application. In this case, the user can input their preference by text input or by using a voice recognition function. In the case of voice input, the voice is converted into text using a voice recognition module (e.g., a voice recognition API).

[1798] The terminal receives input from the user and sends it as text data to the server. The server uses natural language processing (NLP) algorithms (e.g., natural language processing APIs) to analyze the received text data. During the analysis, the server understands the user's intentions and extracts keywords.

[1799] The server then uses the generative AI model to generate additional questions based on the user's preferences, inputting the following prompt to the generative AI model: "The user wants to go to a nearby cafe. Please generate specific questions to understand the user's needs in more detail."

[1800] The generated follow-up question is presented to the user via the terminal. The user inputs a specific answer to the follow-up question. For example, the user might answer "a cafe in the Shinjuku area that offers takeout." This answer is then sent back to the server from the terminal.

[1801] The server then uses NLP to analyze the user's detailed needs and search for the appropriate destinations, using internet search APIs and internal databases. The server identifies the best destinations based on the user's needs and sends the results to the device.

[1802] The device presents the received search results to the user. The user selects a desired destination from the presented results. Finally, the device sets the selected destination in the navigation and launches a map application (e.g., a map application) to guide the user.

[1803] As a concrete example, if a user inputs "I want to go to a nearby cafe," the system will act as follows:

[1804] 1. The user types, "I want to go to a nearby cafe."

[1805] 2. The device sends the input to the server.

[1806] 3. The server parses the keyword "cafe" and generates a follow-up question: "Which area is good?"

[1807] 4. The device presents the question to the user.

[1808] 5. The user answers "Shinjuku area."

[1809] 6. The device sends the response to the server.

[1810] 7. The server searches for "cafes in the Shinjuku area" and sends the results to the device.

[1811] 8. The device presents the search results to the user.

[1812] 9. The user selects "Cafe A."

[1813] 10. The device begins navigation to "Cafe A."

[1814] In this way, the system can quickly and easily provide optimal destinations based on the user's needs. Specific actions allow users to search for and navigate to destinations without performing complex operations.

[1815] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1816] Step 1:

[1817] Input: The user enters their destination preference via text or voice.

[1818] How it works: A user launches a car navigation app and enters their desired destination. If they enter it via voice, it is converted to text using a speech recognition module (e.g., a speech recognition API), or they enter it directly into a text box.

[1819] Output: Text data about the user's destination.

[1820] Step 2:

[1821] Input: Text data about the user's destination.

[1822] Operation: The device receives the user's text data and sends it to the server. At this stage, it checks for errors and sends it to the server in the correct format.

[1823] Output: The user's input data is transmitted to the server.

[1824] Step 3:

[1825] Input: The user's text data received by the server.

[1826] How it works: The server uses natural language processing (NLP) algorithms to analyze the text data and extract keywords and user needs. Specifically, it uses an NLP API to identify keywords such as "cafe."

[1827] Output: Extracted keywords and analysis result data.

[1828] Step 4:

[1829] Input: Extracted keywords and analysis result data.

[1830] How it works: The server inputs a prompt statement into the generative AI model, which generates specific follow-up questions based on the user's needs. For example, the prompt might say, "The user wants to go to a nearby cafe. Please generate specific questions to understand the user's needs in more detail."

[1831] Output: The additional questions generated.

[1832] Step 5:

[1833] Input: The generated follow-up question.

[1834] Operation: The device receives additional questions from the server and displays them to the user. The questions are displayed in an interface that is easy for the user to understand. For example, the screen might say, "Which area is best?"

[1835] Output: The user sees a follow-up question.

[1836] Step 6:

[1837] Input: User's answer (text data).

[1838] How it works: The user enters a specific answer to the question displayed. For example, they might enter "cafes in the Shinjuku area that offer takeout."

[1839] Output: User's answer data (text).

[1840] Step 7:

[1841] Input: User response data (text).

[1842] Action: The device receives the user's answer and sends it back to the server, again verifying that the input data is in the correct format.

[1843] Output: The user's response data sent to the server.

[1844] Step 8:

[1845] Input: The user's answer data received by the server.

[1846] How it works: The server uses NLP to analyze the user's answers again and understand their specific needs, such as "Shinjuku area" and "takeout available."

[1847] Output: Detailed needs analysis result data.

[1848] Step 9:

[1849] Input: Detailed needs analysis result data.

[1850] How it works: The server uses the network (Internet search APIs or internal databases) to search for destinations. For example, it uses the Google Maps API or a data store to search for "cafes in the Shinjuku area that offer takeout."

[1851] Output: Search result data.

[1852] Step 10:

[1853] Input: Search result data.

[1854] Operation: The server sends the search results to the device as text data.

[1855] Output: Search result data sent to the device.

[1856] Step 11:

[1857] Input: Search result data sent to the device.

[1858] Operation: The device presents the search results received from the server to the user. Specifically, a list of candidate cafes is displayed on the screen.

[1859] Output: User sees search results.

[1860] Step 12:

[1861] Input: The destination selected by the user.

[1862] Action: The user selects the desired destination from the list presented, for example, "Cafe A."

[1863] Output: Selected destination data.

[1864] Step 13:

[1865] Input: Selected destination data.

[1866] What it does: The device sets the selected destination for navigation and launches a map app. For example, it launches a map app and displays the route to "Cafe A."

[1867] Output: The user initiates navigation.

[1868] (Application example 1)

[1869] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1870] Conventional car navigation systems only suggest a single destination based on simple user input, making it difficult to suggest optimal destinations based on the user's specific needs. Furthermore, even when a user has specific requests, it is difficult to provide navigation that appropriately reflects those requests. Therefore, there is a demand for a navigation system that can quickly and accurately respond to diverse user needs.

[1871] 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.

[1872] In this invention, the server includes means for receiving a destination-related input from a user, means for analyzing the input and generating a follow-up question based on the user's needs, means for presenting the follow-up question to the user and receiving the user's answer, means for analyzing the answer and searching for a destination based on the user's needs, means for converting the destination into coordinates using a geographic information system and starting navigation in cooperation with a navigation system, means for presenting the search results to the user, and means for starting navigation to the destination selected by the user. This enables the server to suggest an optimal destination based on the user's specific needs and provide quick navigation.

[1873] "User" refers to a person who uses this system.

[1874] "Destination-related input" refers to information that expresses a user's desires regarding places they would like to visit.

[1875] "Means for parsing input" refers to a method or device for processing and understanding destination-related information provided by a user.

[1876] "Means for generating follow-up questions" refers to a method or device that creates questions to solicit further information based on the user's initial input.

[1877] "Means for presenting follow-up questions to a user" refers to a method or device for displaying generated follow-up questions to a user.

[1878] "Means for receiving a user's answer" refers to a method or device for receiving the answer provided by the user to the follow-up question.

[1879] "Means for analyzing answers" refers to a method or device for processing and understanding answers provided by users.

[1880] "Destination search means" refers to a method or device for identifying the best location based on a user's needs.

[1881] "Geographic information system" refers to a system for obtaining location information and displaying it on a map.

[1882] "Navigation system" refers to a system that provides directions to a specific destination.

[1883] The term "means for initiating navigation" refers to a method or device for executing guidance to a destination selected by a user.

[1884] This invention is a navigation system for autonomous vehicles that proposes optimal destinations based on the specific needs of the user and performs navigation. This system operates in cooperation with three entities: the user, the terminal (a smartphone or a device inside the autonomous vehicle), and the server.

[1885] Hardware and software used

[1886] Smartphone: Receives input from the user, presents additional questions, and displays final destination information.

[1887] Autonomous vehicle: Receives destination information and performs navigation.

[1888] Server: Uses natural language processing (NLP) to analyze user input, generate appropriate follow-up questions, and search for the final destination.

[1889] Specific software used is:

[1890] SpeechRecognition library: Converts voice input into text.

[1891] Requests library: Exchanges data with the server via HTTP communication.

[1892] Geopy library: Obtain geographic information and convert coordinates of destinations.

[1893] System Operation Overview

[1894] Receiving input from the user

[1895] First, the user inputs their destination using a smartphone or in-car device. The input method is either voice or text. For example, they might input, "I want to go to a cafe in the Shinjuku area that offers takeout."

[1896] Input analysis and generation of follow-up questions

[1897] The device sends the received input to the server, which analyzes it using natural language processing (NLP). Based on the analysis, the server generates additional questions (e.g., "Which area is good?") based on the user's needs.

[1898] Posting additional questions and receiving answers

[1899] The terminal displays the additional questions received from the server to the user and receives the user's answers. The user enters the information again (for example, "Shinjuku area"), which the terminal then sends to the server.

[1900] Searching for and presenting your final destination

[1901] The server analyzes the user's responses and searches the Internet or an internal database for the destination that best suits the user's needs. The server then sends the destination information obtained as a result of the search (e.g., "Cafe A in Shinjuku") to the terminal, which then presents it to the user.

[1902] Start navigation

[1903] When the user selects a desired destination from the displayed results, the device sends the information to the autonomous vehicle's navigation system and begins navigation. The destination is converted into coordinates using a geographic information system to ensure accurate navigation.

[1904] Specific examples

[1905] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[1906] The user inputs, "I want to go to a nearby cafe."

[1907] The device sends the input to the server, which analyzes the keyword "cafe" and generates a follow-up question: "Which area is better?"

[1908] The terminal displays this question to the user, and the user answers "Shinjuku area."

[1909] The terminal sends this response to the server, which then searches for "cafes in the Shinjuku area."

[1910] The terminal presents the search results to the user, and the user selects "Cafe A."

[1911] The device will begin navigation to "Cafe A," and the self-driving vehicle will guide the user to the destination.

[1912] Prompt Sentence Examples

[1913] I'd like to go to a nearby cafe. Are there any cafes you'd recommend in the Shinjuku area?

[1914] This allows the present invention to suggest optimal destinations and provide quick navigation based on the user's specific needs.

[1915] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1916] Step 1:

[1917] The user inputs the destination

[1918] Using a smartphone or in-car device, the user inputs their destination preferences by voice or text. A typical input might be, "I want to go to a nearby cafe." The input data is temporarily stored by the device and sent to the next processing step.

[1919] Step 2:

[1920] The device sends input to the server

[1921] The terminal sends the destination-related input received from the user to the server using HTTP communication. Specifically, the input is transferred to the server as text data and received by the server.

[1922] Step 3:

[1923] The server parses the input

[1924] The server uses natural language processing (NLP) algorithms to analyze the user's input, extract keywords (e.g., "cafe") from the text data, and process the data to understand the user's request. As a result of the analysis, appropriate follow-up questions are generated.

[1925] Step 4:

[1926] The server generates additional questions

[1927] Based on the analysis results, the server generates additional questions to understand the user's specific needs, such as "Which area is best?" This data is sent to the device in the next step.

[1928] Step 5:

[1929] The device presents the user with additional questions

[1930] The terminal displays the additional questions received from the server to the user. The user's answers to these questions clarify specific needs. Therefore, a process is performed to display the questions in text format on the display.

[1931] Step 6:

[1932] The user answers additional questions

[1933] The user inputs the necessary answers to the additional questions displayed on the terminal. For example, the user inputs the name of a specific area, such as "Shinjuku area." The input data is then saved on the terminal again.

[1934] Step 7:

[1935] The device sends the user's answer to the server

[1936] The terminal transfers the answer data to the user's follow-up question to the server using HTTP communication. The data is sent to the server in text format.

[1937] Step 8:

[1938] The server analyzes the user's answers

[1939] The server then uses natural language processing (NLP) algorithms to analyze the received user response data and identify detailed needs. Based on the analysis results, the server searches the internet and internal databases to process the data and identify the optimal destination.

[1940] Step 9:

[1941] The server searches for the destination

[1942] The server searches the Internet or an internal database for the best destination based on the analysis results, and retrieves information about the identified destination (e.g., "Cafe A in Shinjuku") from the database.

[1943] Step 10:

[1944] The server sends the search results to the device.

[1945] The server sends the identified destination information, including geographical information such as latitude and longitude, to the device, which receives this data and prepares it for the next processing step.

[1946] Step 11:

[1947] The device presents the search results to the user.

[1948] The terminal displays the destination information received from the server to the user, who then selects the desired destination from the presented search results.

[1949] Step 12:

[1950] The user selects a destination

[1951] The user selects the desired location from the search results displayed on the device. For example, they select "Cafe A." This selection data is used for the next navigation step.

[1952] Step 13:

[1953] The device begins navigation

[1954] The terminal sets the destination selected by the user in the navigation system and starts accurate route guidance using the GPS of the autonomous vehicle. The destination coordinates are obtained using the geographic information system and navigation is performed.

[1955] 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.

[1956] This invention relates to an automobile navigation system that streamlines user destination searches and provides optimal suggestions that take emotions into consideration. This system involves four entities: the user, the terminal, the server, and the emotion engine, all of which work together.

[1957] System Overview

[1958] The system receives input from a user, the server analyzes the input and generates additional questions, and the terminal presents these questions to the user. In parallel, an emotion engine recognizes the user's emotions and customizes the content of the questions and the presentation of search results accordingly. Finally, the server gains a detailed understanding of the user's needs and emotions, searches for the most suitable destination, and presents it to the user via the terminal. Specific embodiments are described in detail below.

[1959] Program processing flow

[1960] 1. The user inputs the destination

[1961] A user launches a car navigation app and inputs their destination preferences or requests via text or voice, and their emotions are inferred from the tone of their voice and the content of the text.

[1962] 2. The device sends the input to the server

[1963] The device receives user input and sends the input text or voice data to the server, where the voice data is converted into text beforehand.

[1964] 3. The server and emotion engine analyze the input

[1965] The server uses natural language processing (NLP) algorithms to analyze the user's input. In parallel, an emotion engine analyzes the user's input data to determine their emotional state, for example, whether they are stressed or relaxed.

[1966] 4. The server generates a follow-up question

[1967] Based on the user's intent, the server generates additional questions to understand their needs in more detail, such as "Which area is good?" or "Do you want a cafe that offers takeout?" These questions are adjusted based on the analysis results of the emotion engine.

[1968] 5. The device presents the user with additional questions

[1969] The device displays the generated follow-up questions to the user and asks for their answers, using a gentle tone and friendly expressions based on the emotion engine.

[1970] 6. The user answers any additional questions

[1971] The user inputs an answer to the additional question. For example, the user answers "a cafe in Shinjuku that offers takeout."

[1972] 7. The device sends the user's answer to the server

[1973] The terminal receives the user's response and transmits the data to the server.

[1974] 8. The server and emotion engine analyze the user's responses

[1975] The server then uses NLP to analyze the user's responses and understand their detailed needs. The emotion engine also simultaneously analyzes additional emotion information from the user's responses.

[1976] 9. The server searches for the destination

[1977] The server searches the internet or an internal database based on the user's detailed needs and emotional state, for example, to find cafes in the Shinjuku area that offer takeout and have an atmosphere that suits the user's emotional state.

[1978] 10. The server sends the search results to the device.

[1979] The server sends the search results to the device, which include details such as the cafe's name, location, and reviews.

[1980] 11. The device presents the search results to the user

[1981] The device receives the search results and displays them to the user, with the emotion engine providing prioritized results and customized display methods.

[1982] 12. User selects destination

[1983] The user selects the desired cafe from the displayed results. For example, they select "Cafe A."

[1984] 13. The device begins navigation

[1985] The device receives the user's selection and begins navigation to the selected destination. The emotion engine influences the device to provide a relaxing navigation voice and appropriate guidance.

[1986] Specific examples

[1987] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[1988] The user types, "I want to go to a nearby cafe."

[1989] The device sends input to the server

[1990] The server analyzes the keyword "cafe" and the emotion engine performs emotion analysis.

[1991] The server generates an additional question: "Which area is better?"

[1992] The device presents the user with a question (in this case, in a caring tone if the user is stressed)

[1993] The user answers "Shinjuku area"

[1994] The device sends the answer to the server

[1995] The server searches for "cafes in the Shinjuku area," and the emotion engine selects cafes that match the user's emotions.

[1996] The device presents search results to the user in an emotionally sensitive way.

[1997] The user selects "Cafe A"

[1998] The device will start navigating to "Cafe A" and provide a relaxing voice guidance.

[1999] In this way, by combining emotion recognition, the present invention is a system that quickly and easily provides the optimal destination based on the user's needs, realizing an excellent user experience that takes emotions into consideration.

[2000] The processing flow will be explained below.

[2001] Step 1:

[2002] A user launches a car navigation app and inputs their destination preference or request via text or voice. In this case, the user inputs, "I want to go to a nearby cafe."

[2003] Step 2:

[2004] The device receives user input and sends the input text or voice data to the server, where the voice data is converted into text beforehand.

[2005] Step 3:

[2006] The server analyzes the received user input and uses natural language processing (NLP) algorithms to extract keywords such as "cafe" and "nearby" to understand the user's basic intent.

[2007] Step 4:

[2008] Based on the user's intent, the server uses an emotion engine to analyze emotions from the user's speech text, such as determining stress levels, joy, or relaxation from the tone, speed, and phrasing of the speech.

[2009] Step 5:

[2010] Based on the user's input and emotional information, the server generates additional questions to understand their needs in more detail. For example, if the user is feeling stressed, the server generates questions in a gentle tone, such as "Which area would you like?" or "Would you like a cafe where you can relax?"

[2011] Step 6:

[2012] The device then displays the generated follow-up questions to the user and asks for their answers, adjusting the on-screen display and audio output to reflect the results of the emotion engine.

[2013] Step 7:

[2014] The user inputs an answer to the follow-up question, for example, "A relaxing cafe in Shinjuku."

[2015] Step 8:

[2016] The terminal receives the user's response and transmits the data to the server.

[2017] Step 9:

[2018] The server then uses NLP to analyze the user's responses and identify their specific needs. The emotion engine also simultaneously analyzes additional emotional information from the user's responses and reassess their stress level.

[2019] Step 10:

[2020] The server searches the internet or an internal database based on the user's detailed needs and emotional state, for example, to find cafes in the Shinjuku area that have a relaxing atmosphere.

[2021] Step 11:

[2022] The server sends the search results to the device, which include details such as the name, location, and reviews of each cafe.

[2023] Step 12:

[2024] The device receives the search results and displays them to the user, prioritizing the results based on the analysis results of the emotion engine and according to the user's emotional state.

[2025] Step 13:

[2026] The user selects the desired cafe from the search results presented. For example, they select "Cafe A."

[2027] Step 14:

[2028] The device receives the user's selection and begins navigation to the selected destination. The device reflects the emotion engine's data and uses voice guidance in a relaxing tone.

[2029] In this way, by combining emotion recognition, the present invention is a system that quickly and easily provides the optimal destination based on the user's needs, realizing an excellent user experience that takes emotions into consideration.

[2030] Example 2

[2031] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2032] Conventional car navigation systems can search for destinations based on user input, but they cannot take the user's emotions into account, making it difficult to provide optimal results that meet the emotional state and needs of each individual user. This has resulted in issues such as limited convenience and user experience.

[2033] 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.

[2034] In this invention, the server includes means for receiving a destination-related input from a user, means for analyzing the input and generating follow-up questions based on the user's needs and emotions, means for presenting the follow-up questions to the user and receiving the user's answers, means for analyzing the answers and searching for destinations based on the user's needs and emotions, means for presenting the search results to the user in a manner that takes emotions into consideration, and means for starting navigation to the destination selected by the user in a manner that takes emotions into consideration. This makes it possible to suggest optimal destinations based on the user's emotions and provide a more personalized navigation experience for each user.

[2035] "Means for receiving destination-related input from a user" refers to a system component for obtaining destination-related information entered by a user via text or voice.

[2036] "Means for analyzing the input and generating follow-up questions based on the user's needs and emotions" refers to a system component that uses a natural language processing algorithm and a sentiment analysis engine to understand the user's input content and emotions, and automatically create follow-up questions necessary to understand the user's detailed requirements.

[2037] "Means for presenting the additional question to the user and receiving the user's answer" refers to a system component for presenting a question generated by the server to the user and obtaining the user's answer thereto.

[2038] "Means for analyzing the answers and searching for destinations based on the user's needs and emotions" refers to a system component for re-analyzing the information and emotional state contained in the user's answers and searching for suitable destinations based thereon.

[2039] "Means for presenting the search results to the user in an emotionally sensitive manner" refers to a system component for presenting the search results to the user in an emotionally sensitive manner, such as by customizing and prioritizing the search results according to the user's emotional state.

[2040] The "means for initiating navigation to a destination selected by the user in an emotionally sensitive manner" refers to a system component for navigating to a destination selected by the user in a manner that adapts voice guidance and display methods to the user's emotional state.

[2041] MODE FOR CARRYING OUT THE INVENTION

[2042] This invention relates to an automobile navigation system that streamlines user destination searches and provides optimal suggestions that take emotions into consideration. This system operates in cooperation with four entities: the user, the terminal, the server, and the emotion engine.

[2043] System Overview

[2044] The system receives input from the user, analyzes the input, generates follow-up questions, and presents these questions to the user via the device. In parallel, an emotion engine recognizes the user's emotions and customizes the questions and search results accordingly. Finally, the server gains a detailed understanding of the user's needs and emotions, searches for the most suitable destination, and presents it to the user via the device.

[2045] Hardware and software used

[2046] The system uses the following hardware and software:

[2047] Terminals: Smartphones, tablets, in-car navigation systems, etc. These terminals are hardware that receives user input and communicates with the server.

[2048] Server: Cloud server or on-premise server. The server is the hardware and software that analyzes user input data and processes it in cooperation with the emotion engine.

[2049] Emotion engine: A software module for analyzing emotions from user input data. It implements an emotion recognition model including natural language processing (NLP) algorithms.

[2050] Natural Language Processing (NLP) algorithms: Software algorithms that analyze user input and understand their needs and intent.

[2051] Specific Embodiments

[2052] When a user launches a car navigation app and inputs their destination-related wishes or requests via text or voice, the device receives this input. For example, the user might say, "I want to go to a nearby cafe." The device then converts the voice data into text and sends it to the server. The server then uses a natural language processing (NLP) algorithm to analyze the user's input. In parallel, the emotion engine analyzes emotions from the input data, detecting, for example, the desire to relax.

[2053] Based on the analysis results, the server generates additional questions to understand the user's needs in more detail. For example, a question might be generated such as, "Which area are you looking for a cafe?" This question is adjusted based on the emotion engine's analysis results and presented to the user via the device. If the user answers "Shinjuku area," the device sends this answer to the server.

[2054] The server again uses NLP to analyze the user's answers and understand their detailed needs. The emotion engine also simultaneously analyzes additional emotional information from the user's answers. Based on this, the server uses the Internet or an internal database to search for a cafe that best suits the user's detailed needs and emotional state. For example, it searches for "a cafe where you can relax in the Shinjuku area."

[2055] The search results are sent from the server to the device, which then presents them to the user. The emotion engine applies a customized display method, prioritizing the most relaxing cafes. Once the user selects the desired cafe, the device begins navigation to the destination, providing relaxing voice and guidance based on the emotion engine.

[2056] Specific examples

[2057] For example, if a user types "I want to go to a nearby cafe," the system will do the following:

[2058] The user speaks, "I want to go to a nearby cafe."

[2059] The device converts the speech into text and sends it to the server.

[2060] The server analyzes the keyword "cafe" and the emotion engine performs emotion analysis.

[2061] The server generates a follow-up question: "Which area is better?"

[2062] The device presents the user with a question (displayed in a gentle tone to take emotions into consideration).

[2063] The user answers "Shinjuku area."

[2064] The device sends the response to the server.

[2065] The server searches for "cafes in the Shinjuku area," and the emotion engine selects cafes that match the user's emotions.

[2066] The device presents search results to the user using an emotion-sensitive display method.

[2067] The user selects "Cafe A."

[2068] The device will begin navigation to "Cafe A" (providing a relaxing navigation voice).

[2069] Prompt Sentence Examples

[2070] Here are some example prompts to input to a generative AI model:

[2071] "Please explain the specific processing steps of a car navigation system that allows users to search for destinations emotionally."

[2072] "Please explain in detail the program processing flow of a navigation system that analyzes emotions and makes optimal suggestions."

[2073] "Please provide a concrete example of a navigation system that utilizes user emotion recognition."

[2074] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2075] Step 1:

[2076] The user launches a car navigation app and enters their destination wishes and requests via text or voice.

[2077] Input: User text or voice input (e.g., "I want to go to a nearby cafe")

[2078] Output: In case of voice input, the data converted to text

[2079] Specific operation: The user performs an operation and speaks into the microphone of the car navigation app, saying, "I want to go to a nearby cafe." The app converts the speech into text.

[2080] Step 2:

[2081] The terminal sends the user's input to the server.

[2082] Input: User request in text format (e.g., "I want to go to a nearby cafe")

[2083] Output: User request data sent to the server

[2084] Specific operation: The terminal sends the user's text input as is to the server.

[2085] Step 3:

[2086] The server uses natural language processing (NLP) algorithms to parse the user's input.

[2087] Input: User's text request (e.g., "I want to go to a nearby cafe")

[2088] Output: Parsed request data (e.g. "cafe" and "nearby")

[2089] What it does: The server runs an NLP algorithm to parse the text request and extract the keywords "cafe" and "nearby."

[2090] Step 4:

[2091] In parallel, the emotion engine analyzes emotions from the user's input data.

[2092] Input: User's text request and voice tone data (e.g., "I want to go to a nearby cafe")

[2093] Output: Parsed emotion data (e.g. "Relaxed")

[2094] Specific operation: The emotion engine analyzes the user's text content and voice tone to detect when the user feels like relaxing.

[2095] Step 5:

[2096] The server generates additional questions to understand the specific needs.

[2097] Input: Parsed request data and sentiment data (e.g., "cafe," "nearby," "relax")

[2098] Output: Additional question data (e.g., "What area are you looking for a cafe in?")

[2099] Specific operation: Based on the analysis results, the server automatically generates an additional question: "In what area are you looking for a cafe?"

[2100] Step 6:

[2101] The terminal presents the generated follow-up questions to the user and receives answers.

[2102] Input: Additional question data (e.g., "What area cafe are you looking for?")

[2103] Output: User response data (e.g. "Shinjuku area")

[2104] Specific operation: The device presents the generated question to the user by voice or text, and the user answers "Shinjuku area."

[2105] Step 7:

[2106] The terminal sends the user's answer to the server.

[2107] Input: User response data (e.g., "Shinjuku area")

[2108] Output: Response data sent to the server

[2109] Specific operation: The terminal sends the user's answer "Shinjuku area" to the server.

[2110] Step 8:

[2111] The server then uses NLP again to analyze the user's responses and understand their detailed needs.

[2112] Input: User response data (e.g., "Shinjuku area")

[2113] Output: Analyzed detailed needs data (e.g. "Relaxing cafes in the Shinjuku area")

[2114] Specific operation: The server uses an NLP algorithm to analyze the "Shinjuku area" and understand detailed needs.

[2115] Step 9:

[2116] In parallel, the emotion engine parses additional emotion information from the user's responses.

[2117] Input: User response data and previous emotion data (e.g., "Shinjuku area" and "Relaxed")

[2118] Output: Updated emotion data (e.g., "I want to relax in the Shinjuku area")

[2119] Specific operation: The emotion engine analyzes the user's answer again and confirms that the user feels that they want to "relax in the Shinjuku area."

[2120] Step 10:

[2121] The server searches for destinations based on the user's specific needs and emotional state.

[2122] Input: Detailed needs data and emotion data (e.g., "A relaxing cafe in the Shinjuku area")

[2123] Output: Search result data (e.g. "Cafe A", "Cafe B", "Cafe C")

[2124] What happens: The server uses the Internet or an internal database to search for "relaxing cafes in the Shinjuku area" and retrieves the results.

[2125] Step 11:

[2126] The server sends the search results to the terminal.

[2127] Input: Search result data (e.g. "Cafe A", "Cafe B", "Cafe C")

[2128] Output: Search result data sent to the device

[2129] Specific operation: The server sends the search results to the terminal.

[2130] Step 12:

[2131] The terminal presents the search results to the user.

[2132] Input: Search result data (e.g. "Cafe A", "Cafe B", "Cafe C")

[2133] Output: Search results presented to the user (e.g., "Cafe A is prioritized as the most relaxing cafe")

[2134] Specific operation: The device presents search results to the user by voice or text, and prioritizes "Cafe A," a particularly relaxing place.

[2135] Step 13:

[2136] The user selects the desired cafe from the search results presented.

[2137] Input: User selection (e.g. "Cafe A")

[2138] Output: Selected cafe information (e.g. "Cafe A")

[2139] Specific operation: The user looks at the search results presented and selects "Cafe A."

[2140] Step 14:

[2141] The terminal receives the user's selection and initiates navigation to the selected destination.

[2142] Input: Selected cafe information (e.g. "Cafe A")

[2143] Output: Start navigation (e.g., route guidance to "Cafe A")

[2144] Specific operation: The device sets "Cafe A" as the destination and begins route guidance with a relaxing navigation voice.

[2145] (Application example 2)

[2146] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2147] Conventional car navigation systems can search for destinations based on the user's needs, but they are unable to consider the user's emotions. As a result, they do not provide suggestions or navigation appropriate to the user's emotional state, and the user experience is not sufficiently improved. Furthermore, the efficiency of destination searches is not sufficient, and users have to work hard to select a destination. To solve these issues, a system is needed that provides optimal destination suggestions and navigation according to the user's emotions.

[2148] 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.

[2149] In this invention, the server includes means for receiving a destination-related input from a user, means for analyzing the input and generating follow-up questions based on the user's needs, means for presenting the follow-up questions to the user and receiving the user's answers, means for analyzing the answers and searching for destinations based on the user's needs, means for presenting the search results to the user, means for starting navigation to the destination selected by the user, means for analyzing the user's emotions and customizing the questions and search results based on the emotions, and means for providing navigation voices and guidance text intended to relax the user or reduce stress based on the emotions. This enables optimal destination suggestions and navigation that take the user's emotional state into consideration, thereby improving the user experience compared to conventional methods.

[2150] "User destination input" refers to information describing a place or destination the user wishes to visit, which may be entered in text or voice format.

[2151] "Needs" refers to the wants and demands a user has for a particular destination, for example, a particular area or type of dining establishment.

[2152] "Follow-up questions" refer to additional inquiries generated to understand the user's needs more specifically.

[2153] "User Answers" refer to responses provided by users to follow-up questions that clarify the user's specific needs.

[2154] "Destination search means" refers to the function of finding an appropriate destination based on the user's needs and answers. This may use the Internet or an internal database.

[2155] "Search results" refers to candidate locations and related information obtained by a means of searching for a destination.

[2156] "Means for starting navigation" refers to a function that provides guidance to a destination selected by the user. It also shows the route to the destination.

[2157] "Means for analyzing emotions" refers to the ability to evaluate a user's emotional state from text or voice input. This typically involves the use of natural language processing or voice analysis.

[2158] "Search result customization" refers to the ability to adjust the results displayed based on the user's emotional state, in order to improve the user experience.

[2159] "Navigation voice and guidance" refers to the voice guidance and text display used when navigating to a destination. Emotionally-based, relaxing tones and stress-reducing content are provided.

[2160] "Intended for relaxation or stress reduction" refers to providing navigation that takes into account the user's current emotional state so that the user can reach their destination in a comfortable manner.

[2161] overview

[2162] This invention is a system that streamlines user destination searches and suggests optimal destinations taking emotions into consideration. The system aims to improve the user experience by customizing search results and navigation based on the user's requests and emotional state.

[2163] System Configuration

[2164] The system mainly consists of the following elements:

[2165] 1. Users

[2166] 2. Terminal

[2167] 3. Server

[2168] 4. Emotion Engine

[2169] Explanation of program processing

[2170] Receiving and parsing user input

[2171] The user speaks to the device or inputs text. If the input is speech, the device converts the input into text using speech recognition software (e.g., Google Speech Recognition). The device then sends this text data to the server.

[2172] Server analysis

[2173] The server analyzes the received user input using natural language processing (NLP) algorithms. At this time, an emotion engine analyzes the emotions from the user input data. The emotion engine utilizes commonly available natural language processing libraries (e.g., TextBlob).

[2174] Additional Question Generation

[2175] The server understands the user's needs and generates follow-up questions based on them. These questions are adjusted based on the analysis results of the emotion engine. For example, if the user is in a positive emotional state, a follow-up question such as "Do you have any dish recommendations?" will be generated.

[2176] Questions are presented by the device and answers are received from the user

[2177] The terminal presents the generated additional questions to the user and receives answers, which are then sent back to the server.

[2178] Optimal destination search by server

[2179] Based on the user's answers and the results of sentiment analysis, the server searches for the best destinations over the Internet or in an internal database, with specific dialogue prompts to refine the details.

[2180] Presenting search results and starting navigation

[2181] The device presents the search results to the user, customizing them based on the emotion engine. When the user selects a destination, the device begins navigation to the selected destination. The navigation voice and guidance are provided taking into account the user's emotional state.

[2182] Examples of concrete examples and prompts

[2183] For example, if a user speaks "I want pizza," the system will do the following:

[2184] User input: "I want pizza."

[2185] Server analysis results: Positive emotions

[2186] Follow-up question: "Do you have any food recommendations?"

[2187] User Answer: "I like Margherita."

[2188] Through these interactions, the server searches for the best restaurants and the terminal presents the results to the user.

[2189] Example prompt sentence:

[2190] If a user types, "I want pizza," the sentiment is positive. A follow-up question might be to ask the user, "Do you have any recommendations?" Find the best restaurant based on the user's answers to the following questions:

[2191] Hardware and software used

[2192] Speech recognition software: Google Speech Recognition

[2193] Natural Language Processing Library: TextBlob

[2194] Device: Smartphone or tablet

[2195] Server: Cloud services and internal servers

[2196] The present invention can improve the user experience by taking into consideration the user's emotions.

[2197] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2198] Step 1: The user speaks into the device or enters text manually.

[2199] Input: User voice or text input. Example: "I want pizza."

[2200] What happens: A user speaks or types text into their smartphone or tablet.

[2201] Step 2: Your device converts your voice to text.

[2202] Input: User's voice data.

[2203] What it does: Uses speech recognition software (e.g., Google Speech Recognition) to convert voice data into text.

[2204] Output: Text data. Example: "I want pizza."

[2205] Step 3: The device sends the text data to the server.

[2206] Input: Text data. "I want pizza."

[2207] What it does: The device sends the converted text data to a remote server via its internet connection.

[2208] Output: The text data sent to the server.

[2209] Step 4: The server parses the user's text input.

[2210] Input: User text input: "I want pizza."

[2211] What happens: The server uses Natural Language Processing (NLP) algorithms to analyze the text content. An emotion engine (e.g., TextBlob) evaluates the emotion.

[2212] Output: Analysis result. Example: Sentiment is positive.

[2213] Step 5: The server generates a follow-up question.

[2214] Input: The parsed input data, "I want pizza" and the sentiment analysis results.

[2215] What happens: The server generates a follow-up question based on the sentiment analysis results. For example, "Do you have any dish recommendations?"

[2216] Output: Additional questions.

[2217] Step 6: The terminal presents the user with a follow-up question.

[2218] Input: Follow-up question: "Do you have any dish recommendations?"

[2219] What happens: The device prompts the user with additional questions via voice or text.

[2220] Output: A question is presented to the user.

[2221] Step 7: The user answers any additional questions.

[2222] Input: A server-generated follow-up question: "Do you have any dish recommendations?"

[2223] Specific action: The user thinks and responds, for example, "I like Margherita."

[2224] Output: The user's answer.

[2225] Step 8: The terminal sends the user's answer to the server.

[2226] Input: The user's answer: "I like Margherita."

[2227] What happens: The device sends the user's answer to the server via an Internet connection.

[2228] Output: The response data sent to the server.

[2229] Step 9: The server re-analyzes the user's answers and emotional state.

[2230] Input: User's answer and sentiment-analyzed text: "I like Margherita" with positive sentiment.

[2231] Specific operation: The server again analyzes the response data using NLP and emotion engine to understand the user's detailed needs.

[2232] Output: Analyzed specific needs.

[2233] Step 10: The server searches for the destination.

[2234] Input: The parsed specific need. Example: "Pizza restaurant for positive users who like Margherita pizza."

[2235] What it does: The server searches the internet and its internal databases to find the destination that best suits your needs.

[2236] Output: Search results. Example: A list of pizza restaurants.

[2237] Step 11: The terminal presents the search results to the user.

[2238] Input: Search result data.

[2239] What it does: The device presents search results to the user via voice or text, with results sorted and customized based on sentiment.

[2240] Output: Presents search results to the user.

[2241] Step 12: The user selects a destination.

[2242] Input: The proposed search results.

[2243] Specific behavior: The user selects a destination from the presented options. Example: "Pizza Restaurant A."

[2244] Output: The selected destination.

[2245] Step 13: The device starts navigation.

[2246] Input: User selected destination data.

[2247] What happens: Your device will begin navigating to the selected destination, with audio and visual content customized based on your emotional state.

[2248] Output: Presents navigation information to the user.

[2249] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2250] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2251] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2252] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2253] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2254] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2255] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2256] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2257] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2258] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2259] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2260] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2261] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2262] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2263] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2264] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2265] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2266] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2267] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2268] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2269] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2270] The following is further disclosed regarding the above embodiment.

[2271] (Claim 1)

[2272] means for receiving input from a user regarding a destination;

[2273] means for analyzing the input and generating follow-up questions based on the user's needs;

[2274] means for presenting the follow-up question to the user and receiving a response from the user;

[2275] means for analyzing the response and searching for a destination based on the user's needs;

[2276] means for presenting the search results to a user;

[2277] means for initiating navigation to a destination selected by the user;

[2278] A system including:

[2279] (Claim 2)

[2280] 10. The system of claim 1, further comprising means for analyzing the destination-r...

Claims

1. means for receiving input from a user regarding a destination; means for analyzing the input and generating follow-up questions based on the user's needs; means for presenting the follow-up question to the user and receiving a response from the user; means for analyzing the response and searching for a destination based on the user's needs; means for presenting the search results to a user; means for initiating navigation to a destination selected by the user; A system including:

2. The system of claim 1 further comprising means for analyzing the destination-related input using natural language processing.

3. 2. The system of claim 1, wherein the means for searching for a destination searches for a destination using the Internet or an internal database.

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