system

Abstract

Provide a system. [Solution] A means for receiving captured image data; A means for analyzing the received image data to identify the location where the image was taken; means for retrieving information relating to the identified location from a database; A means for generating a description in natural language based on the acquired information; A means for converting the generated description into audio data; means for playing audio data; 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] In modern tourism, it is common to read guidebooks or listen to tour guides to learn about the detailed history and highlights of tourist destinations. However, these methods provide limited information and can make it difficult for tourists to access the specific information that interests them. They also often place a high degree of dependency on tour guides, restricting their freedom of movement. Furthermore, in regions where language barriers exist, language problems can arise, making it difficult for tourists to fully understand the information. There is a need for technology that can solve these issues and enable tourists to easily obtain detailed information without the need for a guide. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a system including a means for receiving captured image data, a means for analyzing the received image data to identify the location where the image was taken, a means for retrieving information related to the identified location from a database, a means for generating a natural language description based on the retrieved information, a means for converting the generated description into audio data, and a means for playing back the audio data. This allows tourists to hear audio information about the detailed history and highlights of a location simply by taking a photo, enabling them to obtain interesting information without the need for a guide. Furthermore, the use of a convolutional neural network improves the accuracy of identifying the location where the image was taken, and high-quality audio guidance is provided using speech synthesis technology. This system allows tourists to efficiently obtain a wealth of information while moving freely.

[0006] "Photographed image data" refers to digital data of photographs taken by a user at tourist spots or the like.

[0007] "Means for receiving" refers to the equipment and software used to receive and process image data captured by the user.

[0008] "Means for analyzing and identifying the location of the photograph" refers to the technical means for analyzing and identifying the location based on the image data, primarily using convolutional neural networks (CNN).

[0009] "Means for retrieving information from a database" refers to the technology used to search and retrieve information related to the identified location (such as its history or points of interest) from a stored database.

[0010] "Means for generating explanatory text in natural language" refers to technology that automatically creates explanatory text in a sentence format that is easy for humans to understand based on acquired information.

[0011] "Means for converting into audio data" refers to the technology used to convert the generated natural language description into audio data, primarily using text-to-speech (TTS) technology.

[0012] "Means for playing audio data" refers to a device or software for playing the converted audio data so that the user can hear it. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] The present invention relates to a tourism application that analyzes image data of tourist spots taken by users and provides detailed information about the locations by voice. This system functions as follows between a server, a terminal, and a user.

[0035] System Overview

[0036] Users upload photos they take at tourist spots to the app. This image data is sent from the device to the server. The server uses a Convolutional Neural Network (CNN) to analyze the image and identify the location where the photo was taken. The server then retrieves relevant information about the location from a database and generates a description in natural language. The generated description is converted into audio data using Text-to-Speech (TTS) technology and sent to the device. Finally, the user can play this audio data on their device and hear detailed information about the tourist spot aloud.

[0037] Program processing

[0038] User Behavior

[0039] Users take photos of tourist spots, launch the app, and upload the photos. The uploading process is very simple, and the user interface is designed to be intuitive.

[0040] Terminal handling

[0041] 1. The device sends the photo data uploaded by the user to the server.

[0042] 2. The terminal notifies the user of successful transmission.

[0043] Server Processing

[0044] 1. The server receives the image data sent from the terminal.

[0045] 2. The server uses CNN to analyze the image data and identify tourist spots.

[0046] 3. The server retrieves information related to the identified tourist destination from the database.

[0047] 4. Based on the information obtained by the server, a description is generated in natural language.

[0048] 5. The server uses TTS technology to convert the generated description into audio data.

[0049] 6. The server sends the generated voice data to the device.

[0050] Terminal handling

[0051] 1. The device receives the voice data sent from the server.

[0052] 2. The device plays the audio data in the app for playback to the user.

[0053] Specific examples

[0054] For example, consider the case where a user takes a photo of a famous tourist spot, such as a temple, and uploads it to an app. The device sends this image data to a server. The server uses CNN to analyze the image and identify the temple. The server retrieves information about the temple's history and attractions from a database and generates a natural language description, such as "This temple was built in the 14th century and is designated as an Important Cultural Property." This description is then converted into audio data by a TTS engine and sent to the device. The user can play this audio data through their device and listen to the history and attractions of the temple.

[0055] This tourism application system allows users to obtain detailed information about tourist spots without the need for a guide, enabling free movement and efficient information gathering. The system is also multilingual, helping to eliminate language barriers. This will further enhance the tourist experience and contribute greatly to local industry and regional development.

[0056] The processing flow will be explained below.

[0057] Step 1:

[0058] The user takes a photo of a tourist spot. The user takes a photo of the tourist spot using a device such as a smartphone or tablet.

[0059] Step 2:

[0060] The user launches the app and uploads the photos they have taken. The app has a simple interface and is designed to make it easy for users to perform the necessary operations.

[0061] Step 3:

[0062] The device reads the uploaded photo data. The device application temporarily stores the uploaded photo data and prepares it to be sent to the server.

[0063] Step 4:

[0064] The device sends the photo data to the server, which then transmits the photo data to the server via the Internet, and the user is informed of the transmission status.

[0065] Step 5:

[0066] The server receives the photo data from the device. The server's receiving function stores the photo data and proceeds to the next analysis step.

[0067] Step 6:

[0068] The server analyzes the photo data using a Convolutional Neural Network (CNN), which extracts features from the image and begins the process of identifying tourist destination names.

[0069] Step 7:

[0070] The server searches a database based on the name of the identified tourist attraction, which stores detailed information about multiple tourist attractions.

[0071] Step 8:

[0072] The server retrieves historical and tourist attraction information from a database, such as "This temple was built in the 14th century..."

[0073] Step 9:

[0074] The server generates a description in natural language based on the information it has acquired. The natural language generation engine creates a description such as, "This temple has been designated as an Important Cultural Property."

[0075] Step 10:

[0076] The server-generated description is converted into audio data using a Text-to-Speech (TTS) engine, resulting in high-quality audio data.

[0077] Step 11:

[0078] The server sends the generated voice data to the terminal, which then returns it to the user's terminal via the Internet.

[0079] Step 12:

[0080] The device prepares to play the audio data received from the server. The playback function within the app is launched, and the device is ready to play the audio data.

[0081] Step 13:

[0082] The device plays back audio data, and the user hears detailed information about the tourist spot.The device plays back audio, and the user can learn about the attractions of the tourist spot without the need for a guide.

[0083] Example 1

[0084] 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."

[0085] In conventional tourist information systems, when users use their mobile devices to obtain information about tourist spots, they often have to manually input text, which makes them difficult to use. Furthermore, systems that identify tourist spots from images and provide detailed information via voice are limited, and multilingual support is lacking. This often results in an incomplete tourist experience.

[0086] 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.

[0087] In this invention, the server includes means for receiving captured image data, means for analyzing the received image data and identifying the location where the image was taken using a convolutional neural network, means for retrieving information related to the identified location from a database, means for generating a description in natural language using a generative AI model, and means for converting the generated description into audio data using speech synthesis technology. This allows users to easily receive detailed information about tourist spots by voice, enabling efficient information gathering without the need for a guide.

[0088] The "means for receiving captured image data" refers to a function for transferring image data captured by a user using a terminal to a server and receiving the data.

[0089] A "convolutional neural network" is a deep learning model for automatically extracting and analyzing features in images and videos.

[0090] "Means for obtaining from a database" refers to the function of the server searching for and obtaining the necessary information from a database that stores information related to the identified tourist destination.

[0091] A "generative AI model" refers to an artificial intelligence model that has the ability to generate natural language explanations based on input data.

[0092] "Speech synthesis technology" refers to technology that converts text data into voice data, allowing users to hear information as voice.

[0093] "Means for receiving and playing audio data" refers to a function that allows a terminal to receive audio data sent from a server and play the audio so that the user can listen to it.

[0094] MODE FOR CARRYING OUT THE INVENTION

[0095] The present invention relates to a tourism application that analyzes image data of tourist spots taken by users and provides detailed information about the locations by voice. This system functions as follows between a server, a terminal, and a user.

[0096] System Overview

[0097] Users upload photos they take at tourist spots to the app. This image data is sent from the device to the server. The server uses a convolutional neural network (CNN) to analyze the image and identify the location where it was taken. The server then retrieves relevant information about the location from a database and uses a generative AI model to generate a description in natural language. The generated description is converted into audio data using speech synthesis technology and sent to the device. Finally, the user can play this audio data on their device and hear detailed information about the tourist spot aloud.

[0098] Hardware and software used

[0099] This system is implemented using the following hardware and software.

[0100] Hardware: User's mobile device (smartphone, tablet, etc.), internet connection, server

[0101] software:

[0102] Image analysis: Convolutional Neural Network (CNN) implemented using TENSORFLOW® on a connected server

[0103] Information acquisition: Database system stored on the server (e.g., MySQL (registered trademark))

[0104] Narrative generation: Generative AI models such as OpenAI's GPT-3 model

[0105] Speech conversion: Speech synthesis technologies such as Google® Cloud Text-to-Speech API and Amazon Polly

[0106] Specific examples

[0107] For example, consider a case where a user takes a photo of a famous tourist spot, such as a temple, and uploads it to an app. The user launches the tourism app on their device and uploads the photo. The device then sends the image data to the server as an HTTP POST request. The server stores the received image data in temporary storage and performs image analysis using a convolutional neural network (CNN) to identify the temple. The server then retrieves information about the temple from a database and uses a generative AI model (e.g., OpenAI GPT-3) to generate a natural language description, such as "This temple was built in the 14th century and is designated as an Important Cultural Property." This description is then converted into audio data using speech synthesis technology such as Google Cloud Text-to-Speech API and sent to the device. The user can then play the audio data on their device and hear a detailed description of the temple's history and highlights.

[0108] Prompt Sentence Examples

[0109] "Please tell me the process flow of a tourism app that generates detailed information about a tourist spot when you upload a photo of the spot and provides it via audio."

[0110] This tourism application system allows users to obtain detailed information about tourist spots without the need for a guide, and allows them to efficiently gather information while moving freely. It also supports multiple languages, helping to eliminate language barriers. This will further enhance the tourist experience and make a significant contribution to local industries and regional development.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Specific flow of program processing

[0113] User Behavior

[0114] Step 1:

[0115] A user takes a photo using a smartphone or tablet camera at a tourist spot, for example, taking a photo of the facade of a famous temple or an important monument.

[0116] Step 2:

[0117] The user launches the dedicated tourism app and taps the "Upload Photos" button within the app. The photo selection screen appears, and the user selects the photos they have just taken and uploads them.

[0118] Terminal handling

[0119] Step 3:

[0120] The device receives the photo data selected by the user and sends it to the server via an HTTP POST request. At this time, the photo data is encoded in binary format and included in the HTTP body. The input is the photo data selected by the user, and the output is the photo data sent to the server. Specifically, the device encodes the photo data, constructs an HTTP request, and sends it to the specified URL on the server.

[0121] Step 4:

[0122] If the photo data is successfully sent, the device notifies the user with a pop-up message saying "Photo sent." Here, the input is the transmission result and the output is the notification to the user. Specifically, it analyzes the HTTP response from the server and displays a success message to the user.

[0123] Server Processing

[0124] Step 5:

[0125] The server receives an HTTP POST request sent from the terminal, extracts the image data, and saves it in temporary storage on the server. It also records the request log. The input is the sent HTTP request, and the output is the saved image data. Specifically, it analyzes and extracts the image data from the request and saves it in a specified directory.

[0126] Step 6:

[0127] The server analyzes the stored image data using a convolutional neural network (CNN). Specifically, it uses a TensorFlow model to extract image features and identify tourist destinations. The input is the stored image data, and the output is the labels of the identified tourist destinations. Specifically, the CNN model inputs the image data and compares the resulting feature vector with a list of known tourist destinations to identify the best match.

[0128] Step 7:

[0129] The server retrieves information related to the identified tourist destination from a database. The database contains information such as the name, history, attractions, and access methods of the tourist destination. The input is the label of the identified tourist destination, and the output is detailed information about the tourist destination. Specifically, the server executes an SQL query based on the retrieved label to retrieve information from the database.

[0130] Step 8:

[0131] Based on the information acquired by the server, a generative AI model (e.g., OpenAI GPT-3) is used to generate a description in natural language. The input is detailed information about the acquired tourist spot, and the output is the generated description in natural language. Specifically, the tourist spot information is passed to the API as a prompt, and the generated text is received.

[0132] Step 9:

[0133] The server converts the generated description into audio data using Text-to-Speech (TTS) technology. For example, it uses the Google Cloud Text-to-Speech API to convert the description into an MP3 audio file. The input is the generated natural language description, and the output is the generated audio data. Specifically, the description text is sent to the TTS API and the generated audio data is received.

[0134] Step 10:

[0135] The generated audio data is sent to the terminal as an HTTP response. The audio file is encoded in binary format and sent. The input is the generated audio data, and the output is the audio data sent to the terminal. Specifically, the audio data is included in the body of the HTTP response and sent to the terminal.

[0136] Terminal handling

[0137] Step 11:

[0138] The device receives the HTTP response from the server, extracts the audio data, and saves it in temporary storage. Once saving is complete, it notifies the user that it is ready to play. The input is the received HTTP response, and the output is the saved audio data. Specifically, it analyzes and extracts the audio data from the response, saves it in temporary storage, and displays a notification that it is ready.

[0139] Step 12:

[0140] When the user taps the play button, the device plays the saved audio data. The user listens to the audio played within the app to obtain detailed information about tourist spots. The input is the playback request, and the output is the audio that is played. Specifically, the audio data is passed to the media player and playback begins.

[0141] (Application example 1)

[0142] 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."

[0143] Conventional tourist information systems require users to manually search for information about tourist destinations, which requires time and effort to identify tourist destinations. In particular, it is difficult to obtain detailed information about tourist destinations while traveling, which hinders the quality of the tourist experience. Furthermore, with the spread of autonomous vehicles, there is a growing need to obtain real-time tourist information from inside the vehicle. Given this background, there is a demand for a system that allows users to identify tourist destinations and obtain that information quickly and efficiently.

[0144] 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.

[0145] In this invention, the server includes a means for receiving captured image data, a means for analyzing the received image data to identify the location where the image was taken, and a means for retrieving information related to the identified location from a database. This makes it possible to quickly analyze images taken by an in-vehicle camera and provide detailed tourist information to users in real time in the infotainment system of an autonomous vehicle.

[0146] The "means for receiving captured image data" is a function for importing image data captured by a user or an on-board camera into the system.

[0147] The "means for analyzing received image data to identify the location where the image was taken" is a function for analyzing the captured image data and determining the specific location where the image was taken.

[0148] The "means for obtaining information related to the identified location from a database" is a function for retrieving detailed information about the identified location from a referenceable database.

[0149] "Means for generating explanatory text in natural language based on acquired information" is a function that creates sentences in natural language that are easy for users to understand based on information acquired from a database.

[0150] The "means for converting the generated explanatory text into audio data" is a function for converting the generated explanatory text in natural language into digital audio data for output as audio.

[0151] "Means for analyzing image data captured by an on-board camera" refers to a function for analyzing images captured by a camera equipped on an autonomous vehicle.

[0152] "Means integrated into the infotainment system of an autonomous vehicle" means a function integrated into the information and entertainment system on board an autonomous vehicle.

[0153] The "means for reproducing the generated voice data" is a function for actually outputting the generated voice data as voice and letting the user hear it.

[0154] The present invention relates to a system that analyzes captured image data and provides detailed information about the location by voice. The present invention includes the following configurations as an embodiment.

[0155] The system works between a user, a terminal, and a server. The user takes images of tourist spots using a camera mounted on an autonomous vehicle. The image data taken by the camera is received by the terminal and then sent to the server.

[0156] The server analyzes the image data using a convolutional neural network (CNN) to identify the location where the image was taken. This analysis uses the ResNet model, a common image recognition architecture. Information related to the identified location is retrieved from the server's database, including the location's history, points of interest, cultural background, etc.

[0157] Next, a description is generated based on the acquired information using natural language processing technology. The generated description is converted into audio data using Text-to-Speech (TTS) technology and sent to the device. The device then plays the received audio data through the autonomous vehicle's infotainment system and provides it to the user.

[0158] The system has the following features:

[0159] A method for analyzing image data captured by an onboard camera: Using a camera mounted on an autonomous vehicle, the captured image is analyzed.

[0160] Integrated into the infotainment system of an autonomous vehicle: The analysis results are played back in the information and entertainment system within the autonomous vehicle.

[0161] A means for playing the generated audio data: The generated audio data is actually played and listened to by the user.

[0162] As a concrete example, consider the case where a user visits a famous temple in an autonomous vehicle. The onboard camera takes a photo of the temple and sends the image to a server. The server uses CNN to identify the temple and obtain information about its history and attractions. An explanatory text, such as "This temple was built in the 14th century and is designated as an Important Cultural Property," is generated and converted into speech by a TTS engine. This audio data is sent to the terminal, and the user can listen to the explanation in the autonomous vehicle.

[0163] An example of a prompt for a generative AI model is:

[0164] "Please upload any images taken by your in-car camera and provide details about the location of the image."

[0165] As described above, the embodiments of the present invention provide a system that allows users to obtain tourist information in real time while in an autonomous vehicle, thereby improving the quality of the tourist experience.

[0166] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0167] Step 1:

[0168] A user takes images of tourist spots using a camera mounted on an autonomous vehicle.

[0169] Input: Images of tourist attractions.

[0170] Output: Captured image data.

[0171] Specific operation: The user takes photos of scenery and buildings at tourist spots with the camera and saves them on the device.

[0172] Step 2:

[0173] The terminal receives the captured image data and transmits it to the server.

[0174] Input: Image data captured by the user.

[0175] Output: Image data sent to the server.

[0176] Specific operation: An application on the device uploads image data to the server in response to user operation.

[0177] Step 3:

[0178] The server analyzes the received image data and identifies the location where the photo was taken.

[0179] Input: The submitted image data.

[0180] Output: Identified location information.

[0181] Specific operation: The server analyzes the received image data using a convolutional neural network (CNN) algorithm and identifies tourist spots in the image.

[0182] Step 4:

[0183] The server retrieves information related to the identified location from a database.

[0184] Input: Identified location information.

[0185] Output: Detailed information retrieved from the database.

[0186] What happens: The server accesses an internal database to retrieve historical and tourist information about the identified tourist destination.

[0187] Step 5:

[0188] The server generates a natural language description based on the information it has acquired.

[0189] Input: The details retrieved from the database.

[0190] Output: Natural language description.

[0191] Specific operation: The server uses natural language processing technology to convert the acquired information into sentences that are easy for the user to understand.

[0192] Step 6:

[0193] The server converts the generated description into audio data.

[0194] Input: A natural language description.

[0195] Output: Audio data.

[0196] Specific operation: The server uses Text-to-Speech (TTS) technology to convert the generated text into audio data.

[0197] Step 7:

[0198] The server transmits the generated voice data to the terminal.

[0199] Input: Audio data.

[0200] Output: The audio data sent to the device.

[0201] Specific operation: After the server completes processing, it sends the generated voice data to the device.

[0202] Step 8:

[0203] The device receives the audio data sent from the server and plays it through the infotainment system of the autonomous vehicle.

[0204] Input: Audio data sent from the server.

[0205] Output: Audio data played in the car.

[0206] Specific operation: The terminal provides the received voice data to the infotainment system, plays the voice through the car speakers, and provides the user with information about tourist spots.

[0207] The above are the specific processing steps of the program for the system that realizes the application example.

[0208] 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.

[0209] This invention relates to a tourism application that analyzes image data of tourist spots taken by the user and provides detailed information about the location via voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the information provided and the content of the voice guidance can be adjusted. This system functions as follows between the server, the terminal, and the user.

[0210] System Overview

[0211] Users upload photos they take at tourist spots to the app. The image data is sent from the device to the server, which uses a convolutional neural network (CNN) to analyze the image and identify the location where it was taken. The server then retrieves relevant information about the location from a database and generates a description in natural language. The generated description is converted into audio data using Text-to-Speech (TTS) technology and sent to the device. Finally, the user can play this audio data on their device and hear detailed information about the tourist spot. Furthermore, an emotion engine recognizes the user's emotions in real time and adjusts the information and audio guidance based on the results.

[0212] Program processing

[0213] User Behavior

[0214] Users take photos of tourist spots, launch the app, and upload the photos. The uploading process is very simple, and the user interface is designed to be intuitive.

[0215] Terminal handling

[0216] 1. The device sends the photo data uploaded by the user to the server.

[0217] 2. The device notifies the user of the successful transmission and collects data to recognize the user's emotions and sends it to the server.

[0218] Server Processing

[0219] 1. The server receives the image data and emotion data sent from the device.

[0220] 2. The server uses CNN to analyze the image data and identify tourist spots.

[0221] 3. The server searches the database based on the name of the identified tourist attraction.

[0222] 4. The server retrieves historical and sightseeing information about the tourist destination from the database.

[0223] 5. The server generates a description in natural language based on the information obtained.

[0224] 6. The server uses TTS technology to convert the generated description into audio data.

[0225] 7. At the same time, the server analyzes the emotional data using an emotion engine and adjusts the description and voice.

[0226] 8. The server sends the optimized audio data to the device.

[0227] Terminal handling

[0228] 1. The device receives the voice data sent from the server.

[0229] 2. The device plays the audio data to the user.

[0230] Specific examples

[0231] For example, suppose a user takes a photo of a tourist spot that is a historical building and uploads it to an app. The device sends this image data to a server. The server uses CNN to analyze the image and identify the building. The server retrieves information about the building from a database and generates a description such as, "This building was built in the 18th century..." This description is then converted into voice data by a TTS engine. At the same time, the emotion engine analyzes the user's emotions, such as excitement or attention, and adjusts the description and voice tone. The adjusted voice data is then sent to the device, which plays the audio, allowing the user to hear about the building's history and highlights, with the audio adjusted according to their emotions.

[0232] This tourism application system allows users to obtain detailed information about tourist spots without the need for a guide, enabling free movement and efficient information gathering. Furthermore, the emotion engine allows for flexible responses based on the user's emotions, providing an even more appealing tourism experience. This is expected to improve the quality of tourism and contribute greatly to local industries and regional development.

[0233] The processing flow will be explained below.

[0234] Step 1:

[0235] The user takes a photo of a tourist spot. The user takes a photo of a specific tourist spot using a device such as a smartphone or tablet.

[0236] Step 2:

[0237] The user launches the app and uploads the photos they have taken. The app has a simple interface and is designed to be intuitive for users to use.

[0238] Step 3:

[0239] The device reads the uploaded photo data and sends it to the server. The device application temporarily stores the photo data and prepares it for transfer to the server.

[0240] Step 4:

[0241] The device collects additional data (such as facial expressions and voice) to recognize the user's emotions, which are then analyzed in real time using a camera and microphone and sent to a server.

[0242] Step 5:

[0243] The device sends the photo data and emotion data to the server, which then sends the data to the server via the Internet.

[0244] Step 6:

[0245] The server receives the photo data and emotion data sent from the device. The received data is stored on the server and then goes to the next analysis step.

[0246] Step 7:

[0247] The server analyzes the photo data using a Convolutional Neural Network (CNN), which extracts features within the image and identifies specific tourist destination names.

[0248] Step 8:

[0249] The server searches a database based on the name of the identified tourist spot, which stores detailed information about the tourist spot (such as its history and attractions).

[0250] Step 9:

[0251] The server retrieves information about the tourist destination from a database, such as "This building was built in the 18th century."

[0252] Step 10:

[0253] The server generates a description in natural language based on the information it has acquired. The natural language generation engine creates a description such as, "This building was built in the 18th century, and its history is..."

[0254] Step 11:

[0255] The server uses an emotion engine to analyze the received emotion data and adjusts the content of the explanatory text and voice guidance based on the emotion data.

[0256] Step 12:

[0257] The server converts the tailored description into audio data using a Text-to-Speech (TTS) engine, generating high-quality audio data.

[0258] Step 13:

[0259] The server sends the generated voice data to the terminal, which then returns it to the user's terminal via the Internet.

[0260] Step 14:

[0261] The device receives the audio data sent from the server and prepares to play it. The playback function within the app is activated.

[0262] Step 15:

[0263] The device plays the audio data, and the user hears detailed information about the tourist spot. The audio explanation is provided with flexible adjustments based on the user's emotions.

[0264] This allows users to listen to detailed information about tourist spots in audio format without the need for a guide, enabling free movement and efficient information gathering. Furthermore, the emotion engine enables flexible responses based on the user's emotions, making the tourist experience even more appealing.

[0265] Example 2

[0266] 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."

[0267] Conventional tourism applications have the problem of providing only static information and not being able to flexibly respond to the user's emotions. As a result, the user experience is uniform, making it difficult to provide an attractive tourism experience that responds to each individual user's interests and emotions. Furthermore, existing systems often require complex operations, which can be a burden to users.

[0268] 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.

[0269] In this invention, the server includes means for receiving captured image data, means for analyzing the received image data to identify the location where the image was taken, means for retrieving information related to the identified location from a database, means for generating a natural language description based on the retrieved information, means for converting the generated description into audio data, means for collecting user emotion data, means for analyzing the collected emotion data, means for adjusting the description and audio data based on the emotion data, and means for playing the audio data. This enables flexible information provision based on the user's emotions, allowing for a personalized and attractive tourist experience. Furthermore, intuitive operation reduces the burden on users and improves ease of use.

[0270] "Means for receiving captured image data" refers to the function by which the server receives image data uploaded by the user from the terminal.

[0271] "Means for analyzing image data to identify the location where the image was taken" refers to technology for analyzing received image data and identifying and specifying the location where the image was taken.

[0272] "Means for retrieving information from a database" refers to a function for searching and retrieving information related to a specified location from a pre-stored database.

[0273] "Means for generating explanatory text in natural language" refers to technology that automatically generates explanatory text in natural language format that is easy for users to understand based on acquired information.

[0274] "Means for converting explanatory text into audio data" refers to a speech synthesis technology for converting the generated natural language explanatory text into audio data.

[0275] "Means for collecting user emotional data" refers to the function of collecting emotions in real time from the user's facial expressions, tone of voice, etc. using sensors on the device.

[0276] "Means for analyzing emotional data" refers to a technology for analyzing collected emotional data and evaluating the user's psychological state based on the data.

[0277] "Means for adjusting explanatory text or audio data" refers to technology for adjusting the content or tone of explanatory text or audio data based on the results of analyzed emotional data.

[0278] "Means for playing audio data" refers to a function for allowing a user to listen to the generated audio data using a terminal or other playback device.

[0279] This invention relates to a tourism application that analyzes image data of tourist spots taken by the user and provides detailed information about the location by voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the information provided and the content of the voice guidance can be adjusted.

[0280] System Overview

[0281] The system functions as follows between the user, the terminal, and the server.

[0282] User operations

[0283] Users take photos at tourist spots, launch the app, and upload them. The user interface is intuitive and designed to allow users to upload photos with simple steps.

[0284] Terminal handling

[0285] The device sends the photo data uploaded by the user to the server. It notifies the user that the transfer was successful, and also analyzes the user's facial expressions in real time using the front camera to collect emotional data. The device then sends the collected emotional data to the server.

[0286] Server Processing

[0287] The server receives the image data and emotion data sent from the device. It analyzes the image data using a Convolutional Neural Network (CNN) to identify tourist spots. It searches a database based on the names of the identified tourist spots to obtain information about their history and attractions. It then generates explanatory text using a natural language processing engine based on the obtained information.

[0288] The generated description is converted into voice data using Text-to-Speech (TTS) technology. An emotion engine analyzes the emotional data and adjusts the tone of the description and voice data based on the results. The optimized voice data is then sent to the device.

[0289] Finalizing the device

[0290] The terminal receives the audio data sent from the server and plays the audio data for the user, who can listen to the audio data through the terminal's speaker or headphones.

[0291] Hardware and software used

[0292] Device: A mobile device such as a smartphone or tablet.

[0293] Server: A high-performance computer (including cloud servers).

[0294] CNN (Convolutional Neural Network): A deep learning model for image analysis.

[0295] Database: A database that stores information about tourist destinations.

[0296] Natural language processing engine: Software for generating explanatory text.

[0297] Text-to-Speech (TTS) technology: Software for synthesizing speech.

[0298] Emotion engine: Software for analyzing emotion data.

[0299] Specific examples

[0300] For example, if a user takes a photo of Tokyo Tower, a historical building, and uploads it to the app, the process will be as follows:

[0301] 1. The user launches the app and uploads a photo of Tokyo Tower that they have taken.

[0302] 2. The device sends this image data to the server, notifies the user of the success of the transmission, and analyzes facial expressions to collect emotional data, which is then sent to the server.

[0303] 3. The server analyzes the received image data using CNN and identifies it as Tokyo Tower.

[0304] 4. The server retrieves information about Tokyo Tower from the database and generates a description such as "Tokyo Tower was built in 1958..."

[0305] 5. The description is converted into voice data using TTS technology. The emotion engine detects the user's excitement and adjusts the tone of the voice.

[0306] 6. The adjusted audio data is sent to the device, which then plays it back to the user. The user can hear about Tokyo Tower's history and highlights, adjusted according to their emotions.

[0307] Prompt Sentence Examples

[0308] "Analyze photos of tourist attractions and generate natural language descriptions that provide detailed information about the place."

[0309] "Please explain how you can tailor audio guide content for tourist attractions based on user sentiment data."

[0310] This system allows users to obtain detailed information about tourist spots through voice without the need for a guide. The emotion engine allows for flexible responses based on the user's emotions, providing a more engaging sightseeing experience.

[0311] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0312] Step 1:

[0313] Users take photos of tourist spots and upload them to the app.

[0314] Input: A photo of a tourist spot taken by the user

[0315] How it works: The user launches the app and takes a photo and uploads it with a few simple steps.

[0316] Output: Uploaded photo data is saved on the device.

[0317] Step 2:

[0318] The terminal transmits the photo data uploaded by the user to the server.

[0319] Input: Saved photo data

[0320] How it works: Your device sends photo data over the network to a server, optimizing the image files to speed up the transfer.

[0321] Output: Photo data sent to the server

[0322] Step 3:

[0323] The terminal notifies the user of the successful transmission and collects the user's emotion data.

[0324] Input: Status of successful photo data transmission

[0325] How it works: The device notifies the user on the screen that "Image upload complete" and uses the front camera to analyze the user's facial expressions in real time to collect emotional data.

[0326] Output: Emotion data sent to the server

[0327] Step 4:

[0328] The server receives the image data and emotion data sent from the terminal.

[0329] Input: Photo data and emotion data sent from the device

[0330] Operation: The server temporarily stores the received image data and emotion data.

[0331] Output: Saved image data and emotion data

[0332] Step 5:

[0333] The server uses CNN to analyze the image data and identify tourist spots.

[0334] Input: Saved photo data

[0335] How it works: The server extracts image features using a Convolutional Neural Network (CNN) and identifies tourist spots. This is done at high speed using parallel computing.

[0336] Output: Name and ID of identified tourist spot

[0337] Step 6:

[0338] The server retrieves information from a database based on the name of the identified tourist attraction.

[0339] Input: Name or ID of tourist spot

[0340] How it works: The server queries the database to retrieve historical and tourist attraction information.

[0341] Output: Information about the tourist spots obtained

[0342] Step 7:

[0343] The server generates a description based on the information it has obtained.

[0344] Input: Information retrieved from a database

[0345] How it works: The server uses a natural language processing engine to create a description based on the information it retrieves, summarizing the information and removing unnecessary parts.

[0346] Output: Generated natural language description

[0347] Step 8:

[0348] The server converts the generated description into audio data.

[0349] Input: Generated description

[0350] How it works: The server uses Text-to-Speech (TTS) technology to convert the description into audio data, for example, using Google's Cloud Text-to-Speech API.

[0351] Output: Generated audio data

[0352] Step 9:

[0353] The server uses an emotion engine to analyze the emotion data and adjust the tone and speed of the description and audio data.

[0354] Input: Collected emotion data and generated speech data

[0355] How it works: The server analyzes the emotional data using the emotion engine and adjusts the tone and speed of the voice data based on the analysis results.

[0356] Output: Modified audio data

[0357] Step 10:

[0358] The server sends the optimized audio data to the device.

[0359] Input: Modified audio data

[0360] Operation: The server sends voice data to the terminal using data compression technology while taking into consideration the stability of data communication.

[0361] Output: Audio data sent to the device

[0362] Step 11:

[0363] The terminal receives the audio data sent from the server and plays it back to the user.

[0364] Input: Audio data sent from the server

[0365] How it works: The device temporarily stores the audio data and uses the audio playback feature to play it back to the user, who can listen to it through headphones or the built-in speaker.

[0366] Output: Played audio data

[0367] This allows users to obtain detailed information about tourist spots through audio without the need for a guide, enabling them to enjoy a personalized and fascinating sightseeing experience.

[0368] (Application example 2)

[0369] 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."

[0370] Conventional tourism applications do not take into account use in autonomous vehicles and lack the ability to dynamically adjust tourist information based on passenger emotions. This results in one-sided provision of tourist information, making it difficult to provide passengers with an optimal tourist experience. Furthermore, providing information that ignores passengers' emotional state poses a challenge, as it fails to stimulate their enjoyment and interest in sightseeing.

[0371] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving captured image data, means for analyzing the received image data to identify the location where the image was taken, means for acquiring information related to the identified location from a database, means for generating a description in natural language based on the acquired information, means for converting the generated description into audio data, means for playing the audio data, means for recognizing passenger emotions, means for adjusting information to be provided and audio guidance based on the recognized emotions, and means for playing the audio data in the vehicle's on-board system. This allows passengers to obtain detailed information about tourist spots through images taken inside the autonomous vehicle, and the information is dynamically adjusted according to the passenger's emotions, making it possible to provide an optimal sightseeing experience.

[0372] "Photographed image data" refers to photographic data taken by passengers at tourist spots.

[0373] "Means for receiving" refers to the mechanism by which the terminal or in-vehicle system transmits image data to the server via the network, and the server receives it.

[0374] "Means for analyzing image data to identify the location where the image was taken" is a process that uses a convolutional neural network (CNN) to analyze the image and identify the tourist destination.

[0375] The "means for obtaining information related to the identified place from a database" refers to a method by which the server searches and obtains information related to the identified tourist destination from an external or internal database.

[0376] "Means for generating explanatory text in natural language" refers to a method in which a computer uses human language to create easy-to-understand explanatory text based on acquired information.

[0377] "Means for converting narrative text into audio data" means the process of converting generated natural language text into audio data using text-to-speech (TTS) technology.

[0378] The "means for playing back audio data" refers to a mechanism for playing back the generated audio data through the speakers of the terminal or the in-vehicle system so that passengers can hear it.

[0379] The "means for recognizing passenger emotions" is a system that uses cameras and sensors to analyze passengers' facial expressions and vocal tones to determine their emotions in real time.

[0380] The "means for adjusting information provided and audio announcements based on emotions" refers to a process for changing the content of explanatory text and the tone and pace of audio announcements depending on the passenger's recognized emotions.

[0381] "Means for playing audio data in an in-vehicle system" refers to a method for outputting audio data using speakers or monitors installed inside an autonomous vehicle.

[0382] This invention relates to a tourist guide application used in autonomous vehicles. This system allows passengers to take and upload photos of tourist spots visible from the vehicle window and provides detailed information about those locations via voice. Furthermore, it recognizes passengers' emotions in real time and adjusts the information and voice guidance provided to provide an optimal tourist experience. The specific configuration and operation of this system are described below.

[0383] Server Roles and Operations

[0384] The server plays a central role in analyzing image data and emotion data received from the device. First, the server receives the captured image data. Next, it analyzes the image using a Convolutional Neural Network (CNN) to identify tourist attractions. Information related to the identified tourist attractions is retrieved from an internal or external database on the server. Based on the retrieved information, a description is generated in natural language. Then, Text-to-Speech (TTS) technology is used to convert the description into audio data. The emotion engine is also used to analyze the emotion data and dynamically adjust the content and tone of the audio guidance. The hardware and software used in this process include the CNN, TTS engine, emotion engine, and databases (SQL, NoSQL).

[0385] Terminal roles and processing

[0386] The device is primarily responsible for capturing and uploading image data and playing audio data. Passengers take photos of tourist spots and upload them to the device. The uploaded data is sent to the server for processing. Audio data sent from the server is played back by the device's in-vehicle system. For emotion recognition, the device also uses a camera and microphone to analyze passengers' facial expressions and voices. This data is also sent to the server.

[0387] Specific examples

[0388] For example, consider the case where a passenger takes a photo of a historical building visible from the window of an autonomous vehicle and uploads it to the app. The server analyzes this image data and identifies the building. It retrieves information about the building from a database and generates a description such as, "This building was built in the 18th century..." This description is then converted into voice data by a TTS engine. At the same time, an emotion engine analyzes the passenger's emotions, such as excitement or attention, and adjusts the description and voice tone accordingly. The adjusted voice data is then sent to the device, which plays the audio, allowing the passenger to hear about the building's history and highlights, with the audio adjusted according to their emotion.

[0389] Prompt Sentence Examples

[0390] "Take a photo of a tourist attraction that you can see from your location, and the app will provide you with audio guidance about the location's history and highlights."

[0391] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0392] Step 1:

[0393] A user takes a photo of a tourist spot and uploads it to the app on their device. The input here is the image data taken by the user, and the output is the image data being sent from the device to the server. The device then sends this image data to the server via the network, and notifies the server of the success or failure of the transmission.

[0394] Step 2:

[0395] The server receives image data sent from the terminal. The input here is the image data sent from the terminal, and the output is the image data stored on the server. The server prepares to analyze the received image data.

[0396] Step 3:

[0397] The server uses a Convolutional Neural Network (CNN) to analyze the received image data and identify the locations where the photos were taken. The input is the received image data, and the output is the names and identification information of the identified tourist spots. Specifically, the CNN model extracts image features and matches them with an existing database to identify tourist spots.

[0398] Step 4:

[0399] The server retrieves information related to the identified tourist destination from the database. The input is the identification information of the identified tourist destination, and the output is detailed information about the tourist destination, such as its history and attractions. The server searches the database and retrieves the related information.

[0400] Step 5:

[0401] The server generates a natural language description based on the acquired information. The input is detailed information about tourist attractions, and the output is a description written in natural language. The server uses a natural language generation (NLG) model to generate the description from the acquired information.

[0402] Step 6:

[0403] The server converts the generated description into audio data using Text-to-Speech (TTS) technology. The input is a natural language description, and the output is audio data. The TTS engine analyzes the input text and generates the corresponding audio.

[0404] Step 7:

[0405] The server simultaneously analyzes the emotional data using an emotion engine. The input is the passenger's emotional data, and the output is the analyzed emotional information. The emotion engine uses technologies such as face detection and voice analysis to evaluate the passenger's emotional state in real time.

[0406] Step 8:

[0407] The server adjusts the content of the explanatory text and voice guidance provided based on the recognized emotion. The input is the analyzed emotion information and explanatory text data, and the output is the adjusted voice data. The server regenerates the voice with a tone and pace that corresponds to the emotional state.

[0408] Step 9:

[0409] The server sends the optimized audio data to the terminal. The input is the adjusted audio data, and the output is the audio data sent to the terminal. The server sends the audio data to the terminal through the network.

[0410] Step 10:

[0411] The terminal receives the voice data sent from the server and plays it through the in-vehicle system. The input is the voice data from the server, and the output is voice guidance for passengers. The in-vehicle system can play the voice through the speaker and simultaneously display related information on the monitor.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] [Second embodiment]

[0416] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0417] 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.

[0418] 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).

[0419] 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.

[0420] 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.

[0421] 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).

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] 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."

[0428] The present invention relates to a tourism application that analyzes image data of tourist spots taken by users and provides detailed information about the locations by voice. This system functions as follows between a server, a terminal, and a user.

[0429] System Overview

[0430] Users upload photos they take at tourist spots to the app. This image data is sent from the device to the server. The server uses a Convolutional Neural Network (CNN) to analyze the image and identify the location where the photo was taken. The server then retrieves relevant information about the location from a database and generates a description in natural language. The generated description is converted into audio data using Text-to-Speech (TTS) technology and sent to the device. Finally, the user can play this audio data on their device and hear detailed information about the tourist spot aloud.

[0431] Program processing

[0432] User Behavior

[0433] Users take photos of tourist spots, launch the app, and upload the photos. The uploading process is very simple, and the user interface is designed to be intuitive.

[0434] Terminal handling

[0435] 1. The device sends the photo data uploaded by the user to the server.

[0436] 2. The terminal notifies the user of successful transmission.

[0437] Server Processing

[0438] 1. The server receives the image data sent from the terminal.

[0439] 2. The server uses CNN to analyze the image data and identify tourist spots.

[0440] 3. The server retrieves information related to the identified tourist destination from the database.

[0441] 4. Based on the information obtained by the server, a description is generated in natural language.

[0442] 5. The server uses TTS technology to convert the generated description into audio data.

[0443] 6. The server sends the generated voice data to the device.

[0444] Terminal handling

[0445] 1. The device receives the voice data sent from the server.

[0446] 2. The device plays the audio data in the app for playback to the user.

[0447] Specific examples

[0448] For example, consider the case where a user takes a photo of a famous tourist spot, such as a temple, and uploads it to an app. The device sends this image data to a server. The server uses CNN to analyze the image and identify the temple. The server retrieves information about the temple's history and attractions from a database and generates a natural language description, such as "This temple was built in the 14th century and is designated as an Important Cultural Property." This description is then converted into audio data by a TTS engine and sent to the device. The user can play this audio data through their device and listen to the history and attractions of the temple.

[0449] This tourism application system allows users to obtain detailed information about tourist spots without the need for a guide, enabling free movement and efficient information gathering. The system is also multilingual, helping to eliminate language barriers. This will further enhance the tourist experience and contribute greatly to local industry and regional development.

[0450] The processing flow will be explained below.

[0451] Step 1:

[0452] The user takes a photo of a tourist spot. The user takes a photo of the tourist spot using a device such as a smartphone or tablet.

[0453] Step 2:

[0454] The user launches the app and uploads the photos they have taken. The app has a simple interface and is designed to make it easy for users to perform the necessary operations.

[0455] Step 3:

[0456] The device reads the uploaded photo data. The device application temporarily stores the uploaded photo data and prepares it to be sent to the server.

[0457] Step 4:

[0458] The device sends the photo data to the server, which then transmits the photo data to the server via the Internet, and the user is informed of the transmission status.

[0459] Step 5:

[0460] The server receives the photo data from the device. The server's receiving function stores the photo data and proceeds to the next analysis step.

[0461] Step 6:

[0462] The server analyzes the photo data using a Convolutional Neural Network (CNN), which extracts features from the image and begins the process of identifying tourist destination names.

[0463] Step 7:

[0464] The server searches a database based on the name of the identified tourist attraction, which stores detailed information about multiple tourist attractions.

[0465] Step 8:

[0466] The server retrieves historical and tourist attraction information from a database, such as "This temple was built in the 14th century..."

[0467] Step 9:

[0468] The server generates a description in natural language based on the information it has acquired. The natural language generation engine creates a description such as, "This temple has been designated as an Important Cultural Property."

[0469] Step 10:

[0470] The server-generated description is converted into audio data using a Text-to-Speech (TTS) engine, resulting in high-quality audio data.

[0471] Step 11:

[0472] The server sends the generated voice data to the terminal, which then returns it to the user's terminal via the Internet.

[0473] Step 12:

[0474] The device prepares to play the audio data received from the server. The playback function within the app is launched, and the device is ready to play the audio data.

[0475] Step 13:

[0476] The device plays back audio data, and the user hears detailed information about the tourist spot.The device plays back audio, and the user can learn about the attractions of the tourist spot without the need for a guide.

[0477] Example 1

[0478] 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."

[0479] In conventional tourist information systems, when users use their mobile devices to obtain information about tourist spots, they often have to manually input text, which makes them difficult to use. Furthermore, systems that identify tourist spots from images and provide detailed information via voice are limited, and multilingual support is lacking. This often results in an incomplete tourist experience.

[0480] 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.

[0481] In this invention, the server includes means for receiving captured image data, means for analyzing the received image data and identifying the location where the image was taken using a convolutional neural network, means for retrieving information related to the identified location from a database, means for generating a description in natural language using a generative AI model, and means for converting the generated description into audio data using speech synthesis technology. This allows users to easily receive detailed information about tourist spots by voice, enabling efficient information gathering without the need for a guide.

[0482] The "means for receiving captured image data" refers to a function for transferring image data captured by a user using a terminal to a server and receiving the data.

[0483] A "convolutional neural network" is a deep learning model for automatically extracting and analyzing features in images and videos.

[0484] "Means for obtaining from a database" refers to the function of the server searching for and obtaining the necessary information from a database that stores information related to the identified tourist destination.

[0485] A "generative AI model" refers to an artificial intelligence model that has the ability to generate natural language explanations based on input data.

[0486] "Speech synthesis technology" refers to technology that converts text data into voice data, allowing users to hear information as voice.

[0487] "Means for receiving and playing audio data" refers to a function that allows a terminal to receive audio data sent from a server and play the audio so that the user can listen to it.

[0488] MODE FOR CARRYING OUT THE INVENTION

[0489] The present invention relates to a tourism application that analyzes image data of tourist spots taken by users and provides detailed information about the locations by voice. This system functions as follows between a server, a terminal, and a user.

[0490] System Overview

[0491] Users upload photos they take at tourist spots to the app. This image data is sent from the device to the server. The server uses a convolutional neural network (CNN) to analyze the image and identify the location where it was taken. The server then retrieves relevant information about the location from a database and uses a generative AI model to generate a description in natural language. The generated description is converted into audio data using speech synthesis technology and sent to the device. Finally, the user can play this audio data on their device and hear detailed information about the tourist spot aloud.

[0492] Hardware and software used

[0493] This system is implemented using the following hardware and software.

[0494] Hardware: User's mobile device (smartphone, tablet, etc.), internet connection, server

[0495] software:

[0496] Image analysis: Convolutional Neural Networks (CNNs) implemented using TensorFlow on a connected server

[0497] Information acquisition: Database system stored on the server (e.g. MySQL)

[0498] Narrative generation: Generative AI models such as OpenAI's GPT-3 model

[0499] Text-to-speech: Text-to-speech technologies such as Google Cloud Text-to-Speech API and Amazon Polly

[0500] Specific examples

[0501] For example, consider a case where a user takes a photo of a famous tourist spot, such as a temple, and uploads it to an app. The user launches the tourism app on their device and uploads the photo. The device then sends the image data to the server as an HTTP POST request. The server stores the received image data in temporary storage and performs image analysis using a convolutional neural network (CNN) to identify the temple. The server then retrieves information about the temple from a database and uses a generative AI model (e.g., OpenAI GPT-3) to generate a natural language description, such as "This temple was built in the 14th century and is designated as an Important Cultural Property." This description is then converted into audio data using speech synthesis technology such as Google Cloud Text-to-Speech API and sent to the device. The user can then play the audio data on their device and hear a detailed description of the temple's history and highlights.

[0502] Prompt Sentence Examples

[0503] "Please tell me the process flow of a tourism app that generates detailed information about a tourist spot when you upload a photo of the spot and provides it via audio."

[0504] This tourism application system allows users to obtain detailed information about tourist spots without the need for a guide, and allows them to efficiently gather information while moving freely. It also supports multiple languages, helping to eliminate language barriers. This will further enhance the tourist experience and make a significant contribution to local industries and regional development.

[0505] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0506] Specific flow of program processing

[0507] User Behavior

[0508] Step 1:

[0509] A user takes a photo using a smartphone or tablet camera at a tourist spot, for example, taking a photo of the facade of a famous temple or an important monument.

[0510] Step 2:

[0511] The user launches the dedicated tourism app and taps the "Upload Photos" button within the app. The photo selection screen appears, and the user selects the photos they have just taken and uploads them.

[0512] Terminal handling

[0513] Step 3:

[0514] The device receives the photo data selected by the user and sends it to the server via an HTTP POST request. At this time, the photo data is encoded in binary format and included in the HTTP body. The input is the photo data selected by the user, and the output is the photo data sent to the server. Specifically, the device encodes the photo data, constructs an HTTP request, and sends it to the specified URL on the server.

[0515] Step 4:

[0516] If the photo data is successfully sent, the device notifies the user with a pop-up message saying "Photo sent." Here, the input is the transmission result and the output is the notification to the user. Specifically, it analyzes the HTTP response from the server and displays a success message to the user.

[0517] Server Processing

[0518] Step 5:

[0519] The server receives an HTTP POST request sent from the terminal, extracts the image data, and saves it in temporary storage on the server. It also records the request log. The input is the sent HTTP request, and the output is the saved image data. Specifically, it analyzes and extracts the image data from the request and saves it in a specified directory.

[0520] Step 6:

[0521] The server analyzes the stored image data using a convolutional neural network (CNN). Specifically, it uses a TensorFlow model to extract image features and identify tourist destinations. The input is the stored image data, and the output is the labels of the identified tourist destinations. Specifically, the CNN model inputs the image data and compares the resulting feature vector with a list of known tourist destinations to identify the best match.

[0522] Step 7:

[0523] The server retrieves information related to the identified tourist destination from a database. The database contains information such as the name, history, attractions, and access methods of the tourist destination. The input is the label of the identified tourist destination, and the output is detailed information about the tourist destination. Specifically, the server executes an SQL query based on the retrieved label to retrieve information from the database.

[0524] Step 8:

[0525] Based on the information acquired by the server, a generative AI model (e.g., OpenAI GPT-3) is used to generate a description in natural language. The input is detailed information about the acquired tourist spot, and the output is the generated description in natural language. Specifically, the tourist spot information is passed to the API as a prompt, and the generated text is received.

[0526] Step 9:

[0527] The server converts the generated description into audio data using Text-to-Speech (TTS) technology. For example, it uses the Google Cloud Text-to-Speech API to convert the description into an MP3 audio file. The input is the generated natural language description, and the output is the generated audio data. Specifically, the description text is sent to the TTS API and the generated audio data is received.

[0528] Step 10:

[0529] The generated audio data is sent to the terminal as an HTTP response. The audio file is encoded in binary format and sent. The input is the generated audio data, and the output is the audio data sent to the terminal. Specifically, the audio data is included in the body of the HTTP response and sent to the terminal.

[0530] Terminal handling

[0531] Step 11:

[0532] The device receives the HTTP response from the server, extracts the audio data, and saves it in temporary storage. Once saving is complete, it notifies the user that it is ready to play. The input is the received HTTP response, and the output is the saved audio data. Specifically, it analyzes and extracts the audio data from the response, saves it in temporary storage, and displays a notification that it is ready.

[0533] Step 12:

[0534] When the user taps the play button, the device plays the saved audio data. The user listens to the audio played within the app to obtain detailed information about tourist spots. The input is the playback request, and the output is the audio that is played. Specifically, the audio data is passed to the media player and playback begins.

[0535] (Application example 1)

[0536] 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."

[0537] Conventional tourist information systems require users to manually search for information about tourist destinations, which requires time and effort to identify tourist destinations. In particular, it is difficult to obtain detailed information about tourist destinations while traveling, which hinders the quality of the tourist experience. Furthermore, with the spread of autonomous vehicles, there is a growing need to obtain real-time tourist information from inside the vehicle. Given this background, there is a demand for a system that allows users to identify tourist destinations and obtain that information quickly and efficiently.

[0538] 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.

[0539] In this invention, the server includes a means for receiving captured image data, a means for analyzing the received image data to identify the location where the image was taken, and a means for retrieving information related to the identified location from a database. This makes it possible to quickly analyze images taken by an in-vehicle camera and provide detailed tourist information to users in real time in the infotainment system of an autonomous vehicle.

[0540] The "means for receiving captured image data" is a function for importing image data captured by a user or an on-board camera into the system.

[0541] The "means for analyzing received image data to identify the location where the image was taken" is a function for analyzing the captured image data and determining the specific location where the image was taken.

[0542] The "means for obtaining information related to the identified location from a database" is a function for retrieving detailed information about the identified location from a referenceable database.

[0543] "Means for generating explanatory text in natural language based on acquired information" is a function that creates sentences in natural language that are easy for users to understand based on information acquired from a database.

[0544] The "means for converting the generated explanatory text into audio data" is a function for converting the generated explanatory text in natural language into digital audio data for output as audio.

[0545] "Means for analyzing image data captured by an on-board camera" refers to a function for analyzing images captured by a camera equipped on an autonomous vehicle.

[0546] "Means integrated into the infotainment system of an autonomous vehicle" means a function integrated into the information and entertainment system on board an autonomous vehicle.

[0547] The "means for reproducing the generated voice data" is a function for actually outputting the generated voice data as voice and letting the user hear it.

[0548] The present invention relates to a system that analyzes captured image data and provides detailed information about the location by voice. The present invention includes the following configurations as an embodiment.

[0549] The system works between a user, a terminal, and a server. The user takes images of tourist spots using a camera mounted on an autonomous vehicle. The image data taken by the camera is received by the terminal and then sent to the server.

[0550] The server analyzes the image data using a convolutional neural network (CNN) to identify the location where the image was taken. This analysis uses the ResNet model, a common image recognition architecture. Information related to the identified location is retrieved from the server's database, including the location's history, points of interest, cultural background, etc.

[0551] Next, a description is generated based on the acquired information using natural language processing technology. The generated description is converted into audio data using Text-to-Speech (TTS) technology and sent to the device. The device then plays the received audio data through the autonomous vehicle's infotainment system and provides it to the user.

[0552] The system has the following features:

[0553] A method for analyzing image data captured by an onboard camera: Using a camera mounted on an autonomous vehicle, the captured image is analyzed.

[0554] Integrated into the infotainment system of an autonomous vehicle: The analysis results are played back in the information and entertainment system within the autonomous vehicle.

[0555] A means for playing the generated audio data: The generated audio data is actually played and listened to by the user.

[0556] As a concrete example, consider the case where a user visits a famous temple in an autonomous vehicle. The onboard camera takes a photo of the temple and sends the image to a server. The server uses CNN to identify the temple and obtain information about its history and attractions. An explanatory text, such as "This temple was built in the 14th century and is designated as an Important Cultural Property," is generated and converted into speech by a TTS engine. This audio data is sent to the terminal, and the user can listen to the explanation in the autonomous vehicle.

[0557] An example of a prompt for a generative AI model is:

[0558] "Please upload any images taken by your in-car camera and provide details about the location of the image."

[0559] As described above, the embodiments of the present invention provide a system that allows users to obtain tourist information in real time while in an autonomous vehicle, thereby improving the quality of the tourist experience.

[0560] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0561] Step 1:

[0562] A user takes images of tourist spots using a camera mounted on an autonomous vehicle.

[0563] Input: Images of tourist attractions.

[0564] Output: Captured image data.

[0565] Specific operation: The user takes photos of scenery and buildings at tourist spots with the camera and saves them on the device.

[0566] Step 2:

[0567] The terminal receives the captured image data and transmits it to the server.

[0568] Input: Image data captured by the user.

[0569] Output: Image data sent to the server.

[0570] Specific operation: An application on the device uploads image data to the server in response to user operation.

[0571] Step 3:

[0572] The server analyzes the received image data and identifies the location where the photo was taken.

[0573] Input: The submitted image data.

[0574] Output: Identified location information.

[0575] Specific operation: The server analyzes the received image data using a convolutional neural network (CNN) algorithm and identifies tourist spots in the image.

[0576] Step 4:

[0577] The server retrieves information related to the identified location from a database.

[0578] Input: Identified location information.

[0579] Output: Detailed information retrieved from the database.

[0580] What happens: The server accesses an internal database to retrieve historical and tourist information about the identified tourist destination.

[0581] Step 5:

[0582] The server generates a natural language description based on the information it has acquired.

[0583] Input: The details retrieved from the database.

[0584] Output: Natural language description.

[0585] Specific operation: The server uses natural language processing technology to convert the acquired information into sentences that are easy for the user to understand.

[0586] Step 6:

[0587] The server converts the generated description into audio data.

[0588] Input: A natural language description.

[0589] Output: Audio data.

[0590] Specific operation: The server uses Text-to-Speech (TTS) technology to convert the generated text into audio data.

[0591] Step 7:

[0592] The server transmits the generated voice data to the terminal.

[0593] Input: Audio data.

[0594] Output: The audio data sent to the device.

[0595] Specific operation: After the server completes processing, it sends the generated voice data to the device.

[0596] Step 8:

[0597] The device receives the audio data sent from the server and plays it through the infotainment system of the autonomous vehicle.

[0598] Input: Audio data sent from the server.

[0599] Output: Audio data played in the car.

[0600] Specific operation: The terminal provides the received voice data to the infotainment system, plays the voice through the car speakers, and provides the user with information about tourist spots.

[0601] The above are the specific processing steps of the program for the system that realizes the application example.

[0602] 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.

[0603] This invention relates to a tourism application that analyzes image data of tourist spots taken by the user and provides detailed information about the location via voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the information provided and the content of the voice guidance can be adjusted. This system functions as follows between the server, the terminal, and the user.

[0604] System Overview

[0605] Users upload photos they take at tourist spots to the app. The image data is sent from the device to the server, which uses a convolutional neural network (CNN) to analyze the image and identify the location where it was taken. The server then retrieves relevant information about the location from a database and generates a description in natural language. The generated description is converted into audio data using Text-to-Speech (TTS) technology and sent to the device. Finally, the user can play this audio data on their device and hear detailed information about the tourist spot. Furthermore, an emotion engine recognizes the user's emotions in real time and adjusts the information and audio guidance based on the results.

[0606] Program processing

[0607] User Behavior

[0608] Users take photos of tourist spots, launch the app, and upload the photos. The uploading process is very simple, and the user interface is designed to be intuitive.

[0609] Terminal handling

[0610] 1. The device sends the photo data uploaded by the user to the server.

[0611] 2. The device notifies the user of the successful transmission and collects data to recognize the user's emotions and sends it to the server.

[0612] Server Processing

[0613] 1. The server receives the image data and emotion data sent from the device.

[0614] 2. The server uses CNN to analyze the image data and identify tourist spots.

[0615] 3. The server searches the database based on the name of the identified tourist attraction.

[0616] 4. The server retrieves historical and sightseeing information about the tourist destination from the database.

[0617] 5. The server generates a description in natural language based on the information obtained.

[0618] 6. The server uses TTS technology to convert the generated description into audio data.

[0619] 7. At the same time, the server analyzes the emotional data using an emotion engine and adjusts the description and voice.

[0620] 8. The server sends the optimized audio data to the device.

[0621] Terminal handling

[0622] 1. The device receives the voice data sent from the server.

[0623] 2. The device plays the audio data to the user.

[0624] Specific examples

[0625] For example, suppose a user takes a photo of a tourist spot that is a historical building and uploads it to an app. The device sends this image data to a server. The server uses CNN to analyze the image and identify the building. The server retrieves information about the building from a database and generates a description such as, "This building was built in the 18th century..." This description is then converted into voice data by a TTS engine. At the same time, the emotion engine analyzes the user's emotions, such as excitement or attention, and adjusts the description and voice tone. The adjusted voice data is then sent to the device, which plays the audio, allowing the user to hear about the building's history and highlights, with the audio adjusted according to their emotions.

[0626] This tourism application system allows users to obtain detailed information about tourist spots without the need for a guide, enabling free movement and efficient information gathering. Furthermore, the emotion engine allows for flexible responses based on the user's emotions, providing an even more appealing tourism experience. This is expected to improve the quality of tourism and contribute greatly to local industries and regional development.

[0627] The processing flow will be explained below.

[0628] Step 1:

[0629] The user takes a photo of a tourist spot. The user takes a photo of a specific tourist spot using a device such as a smartphone or tablet.

[0630] Step 2:

[0631] The user launches the app and uploads the photos they have taken. The app has a simple interface and is designed to be intuitive for users to use.

[0632] Step 3:

[0633] The device reads the uploaded photo data and sends it to the server. The device application temporarily stores the photo data and prepares it for transfer to the server.

[0634] Step 4:

[0635] The device collects additional data (such as facial expressions and voice) to recognize the user's emotions, which are then analyzed in real time using a camera and microphone and sent to a server.

[0636] Step 5:

[0637] The device sends the photo data and emotion data to the server, which then sends the data to the server via the Internet.

[0638] Step 6:

[0639] The server receives the photo data and emotion data sent from the device. The received data is stored on the server and then goes to the next analysis step.

[0640] Step 7:

[0641] The server analyzes the photo data using a Convolutional Neural Network (CNN), which extracts features within the image and identifies specific tourist destination names.

[0642] Step 8:

[0643] The server searches a database based on the name of the identified tourist spot, which stores detailed information about the tourist spot (such as its history and attractions).

[0644] Step 9:

[0645] The server retrieves information about the tourist destination from a database, such as "This building was built in the 18th century."

[0646] Step 10:

[0647] The server generates a description in natural language based on the information it has acquired. The natural language generation engine creates a description such as, "This building was built in the 18th century, and its history is..."

[0648] Step 11:

[0649] The server uses an emotion engine to analyze the received emotion data and adjusts the content of the explanatory text and voice guidance based on the emotion data.

[0650] Step 12:

[0651] The server converts the tailored description into audio data using a Text-to-Speech (TTS) engine, generating high-quality audio data.

[0652] Step 13:

[0653] The server sends the generated voice data to the terminal, which then returns it to the user's terminal via the Internet.

[0654] Step 14:

[0655] The device receives the audio data sent from the server and prepares to play it. The playback function within the app is activated.

[0656] Step 15:

[0657] The device plays the audio data, and the user hears detailed information about the tourist spot. The audio explanation is provided with flexible adjustments based on the user's emotions.

[0658] This allows users to listen to detailed information about tourist spots in audio format without the need for a guide, enabling free movement and efficient information gathering. Furthermore, the emotion engine enables flexible responses based on the user's emotions, making the tourist experience even more appealing.

[0659] Example 2

[0660] 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."

[0661] Conventional tourism applications have the problem of providing only static information and not being able to flexibly respond to the user's emotions. As a result, the user experience is uniform, making it difficult to provide an attractive tourism experience that responds to each individual user's interests and emotions. Furthermore, existing systems often require complex operations, which can be a burden to users.

[0662] 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.

[0663] In this invention, the server includes means for receiving captured image data, means for analyzing the received image data to identify the location where the image was taken, means for retrieving information related to the identified location from a database, means for generating a natural language description based on the retrieved information, means for converting the generated description into audio data, means for collecting user emotion data, means for analyzing the collected emotion data, means for adjusting the description and audio data based on the emotion data, and means for playing the audio data. This enables flexible information provision based on the user's emotions, allowing for a personalized and attractive tourist experience. Furthermore, intuitive operation reduces the burden on users and improves ease of use.

[0664] "Means for receiving captured image data" refers to the function by which the server receives image data uploaded by the user from the terminal.

[0665] "Means for analyzing image data to identify the location where the image was taken" refers to technology for analyzing received image data and identifying and specifying the location where the image was taken.

[0666] "Means for retrieving information from a database" refers to a function for searching and retrieving information related to a specified location from a pre-stored database.

[0667] "Means for generating explanatory text in natural language" refers to technology that automatically generates explanatory text in natural language format that is easy for users to understand based on acquired information.

[0668] "Means for converting explanatory text into audio data" refers to a speech synthesis technology for converting the generated natural language explanatory text into audio data.

[0669] "Means for collecting user emotional data" refers to the function of collecting emotions in real time from the user's facial expressions, tone of voice, etc. using sensors on the device.

[0670] "Means for analyzing emotional data" refers to a technology for analyzing collected emotional data and evaluating the user's psychological state based on the data.

[0671] "Means for adjusting explanatory text or audio data" refers to technology for adjusting the content or tone of explanatory text or audio data based on the results of analyzed emotional data.

[0672] "Means for playing audio data" refers to a function for allowing a user to listen to the generated audio data using a terminal or other playback device.

[0673] This invention relates to a tourism application that analyzes image data of tourist spots taken by the user and provides detailed information about the location by voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the information provided and the content of the voice guidance can be adjusted.

[0674] System Overview

[0675] The system functions as follows between the user, the terminal, and the server.

[0676] User operations

[0677] Users take photos at tourist spots, launch the app, and upload them. The user interface is intuitive and designed to allow users to upload photos with simple steps.

[0678] Terminal handling

[0679] The device sends the photo data uploaded by the user to the server. It notifies the user that the transfer was successful, and also analyzes the user's facial expressions in real time using the front camera to collect emotional data. The device then sends the collected emotional data to the server.

[0680] Server Processing

[0681] The server receives the image data and emotion data sent from the device. It analyzes the image data using a Convolutional Neural Network (CNN) to identify tourist spots. It searches a database based on the names of the identified tourist spots to obtain information about their history and attractions. It then generates explanatory text using a natural language processing engine based on the obtained information.

[0682] The generated description is converted into voice data using Text-to-Speech (TTS) technology. An emotion engine analyzes the emotional data and adjusts the tone of the description and voice data based on the results. The optimized voice data is then sent to the device.

[0683] Finalizing the device

[0684] The terminal receives the audio data sent from the server and plays the audio data for the user, who can listen to the audio data through the terminal's speaker or headphones.

[0685] Hardware and software used

[0686] Device: A mobile device such as a smartphone or tablet.

[0687] Server: A high-performance computer (including cloud servers).

[0688] CNN (Convolutional Neural Network): A deep learning model for image analysis.

[0689] Database: A database that stores information about tourist destinations.

[0690] Natural language processing engine: Software for generating explanatory text.

[0691] Text-to-Speech (TTS) technology: Software for synthesizing speech.

[0692] Emotion engine: Software for analyzing emotion data.

[0693] Specific examples

[0694] For example, if a user takes a photo of Tokyo Tower, a historical building, and uploads it to the app, the process will be as follows:

[0695] 1. The user launches the app and uploads a photo of Tokyo Tower that they have taken.

[0696] 2. The device sends this image data to the server, notifies the user of the success of the transmission, and analyzes facial expressions to collect emotional data, which is then sent to the server.

[0697] 3. The server analyzes the received image data using CNN and identifies it as Tokyo Tower.

[0698] 4. The server retrieves information about Tokyo Tower from the database and generates a description such as "Tokyo Tower was built in 1958..."

[0699] 5. The description is converted into voice data using TTS technology. The emotion engine detects the user's excitement and adjusts the tone of the voice.

[0700] 6. The adjusted audio data is sent to the device, which then plays it back to the user. The user can hear about Tokyo Tower's history and highlights, adjusted according to their emotions.

[0701] Prompt Sentence Examples

[0702] "Analyze photos of tourist attractions and generate natural language descriptions that provide detailed information about the place."

[0703] "Please explain how you can tailor audio guide content for tourist attractions based on user sentiment data."

[0704] This system allows users to obtain detailed information about tourist spots through voice without the need for a guide. The emotion engine allows for flexible responses based on the user's emotions, providing a more engaging sightseeing experience.

[0705] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0706] Step 1:

[0707] Users take photos of tourist spots and upload them to the app.

[0708] Input: A photo of a tourist spot taken by the user

[0709] How it works: The user launches the app and takes a photo and uploads it with a few simple steps.

[0710] Output: Uploaded photo data is saved on the device.

[0711] Step 2:

[0712] The terminal transmits the photo data uploaded by the user to the server.

[0713] Input: Saved photo data

[0714] How it works: Your device sends photo data over the network to a server, optimizing the image files to speed up the transfer.

[0715] Output: Photo data sent to the server

[0716] Step 3:

[0717] The terminal notifies the user of the successful transmission and collects the user's emotion data.

[0718] Input: Status of successful photo data transmission

[0719] How it works: The device notifies the user on the screen that "Image upload complete" and uses the front camera to analyze the user's facial expressions in real time to collect emotional data.

[0720] Output: Emotion data sent to the server

[0721] Step 4:

[0722] The server receives the image data and emotion data sent from the terminal.

[0723] Input: Photo data and emotion data sent from the device

[0724] Operation: The server temporarily stores the received image data and emotion data.

[0725] Output: Saved image data and emotion data

[0726] Step 5:

[0727] The server uses CNN to analyze the image data and identify tourist spots.

[0728] Input: Saved photo data

[0729] How it works: The server extracts image features using a Convolutional Neural Network (CNN) and identifies tourist spots. This is done at high speed using parallel computing.

[0730] Output: Name and ID of identified tourist spot

[0731] Step 6:

[0732] The server retrieves information from a database based on the name of the identified tourist attraction.

[0733] Input: Name or ID of tourist spot

[0734] How it works: The server queries the database to retrieve historical and tourist attraction information.

[0735] Output: Information about the tourist spots obtained

[0736] Step 7:

[0737] The server generates a description based on the information it has obtained.

[0738] Input: Information retrieved from a database

[0739] How it works: The server uses a natural language processing engine to create a description based on the information it retrieves, summarizing the information and removing unnecessary parts.

[0740] Output: Generated natural language description

[0741] Step 8:

[0742] The server converts the generated description into audio data.

[0743] Input: Generated description

[0744] How it works: The server uses Text-to-Speech (TTS) technology to convert the description into audio data, for example, using Google's Cloud Text-to-Speech API.

[0745] Output: Generated audio data

[0746] Step 9:

[0747] The server uses an emotion engine to analyze the emotion data and adjust the tone and speed of the description and audio data.

[0748] Input: Collected emotion data and generated speech data

[0749] How it works: The server analyzes the emotional data using the emotion engine and adjusts the tone and speed of the voice data based on the analysis results.

[0750] Output: Modified audio data

[0751] Step 10:

[0752] The server sends the optimized audio data to the device.

[0753] Input: Modified audio data

[0754] Operation: The server sends voice data to the terminal using data compression technology while taking into consideration the stability of data communication.

[0755] Output: Audio data sent to the device

[0756] Step 11:

[0757] The terminal receives the audio data sent from the server and plays it back to the user.

[0758] Input: Audio data sent from the server

[0759] How it works: The device temporarily stores the audio data and uses the audio playback feature to play it back to the user, who can listen to it through headphones or the built-in speaker.

[0760] Output: Played audio data

[0761] This allows users to obtain detailed information about tourist spots through audio without the need for a guide, enabling them to enjoy a personalized and fascinating sightseeing experience.

[0762] (Application example 2)

[0763] 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."

[0764] Conventional tourism applications do not take into account use in autonomous vehicles and lack the ability to dynamically adjust tourist information based on passenger emotions. This results in one-sided provision of tourist information, making it difficult to provide passengers with an optimal tourist experience. Furthermore, providing information that ignores passengers' emotional state poses a challenge, as it fails to stimulate their enjoyment and interest in sightseeing.

[0765] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving captured image data, means for analyzing the received image data to identify the location where the image was taken, means for acquiring information related to the identified location from a database, means for generating a description in natural language based on the acquired information, means for converting the generated description into audio data, means for playing the audio data, means for recognizing passenger emotions, means for adjusting information to be provided and audio guidance based on the recognized emotions, and means for playing the audio data in the vehicle's on-board system. This allows passengers to obtain detailed information about tourist spots through images taken inside the autonomous vehicle, and the information is dynamically adjusted according to the passenger's emotions, making it possible to provide an optimal sightseeing experience.

[0766] "Photographed image data" refers to photographic data taken by passengers at tourist spots.

[0767] "Means for receiving" refers to the mechanism by which the terminal or in-vehicle system transmits image data to the server via the network, and the server receives it.

[0768] "Means for analyzing image data to identify the location where the image was taken" is a process that uses a convolutional neural network (CNN) to analyze the image and identify the tourist destination.

[0769] The "means for obtaining information related to the identified place from a database" refers to a method by which the server searches and obtains information related to the identified tourist destination from an external or internal database.

[0770] "Means for generating explanatory text in natural language" refers to a method in which a computer uses human language to create easy-to-understand explanatory text based on acquired information.

[0771] "Means for converting narrative text into audio data" means the process of converting generated natural language text into audio data using text-to-speech (TTS) technology.

[0772] The "means for playing back audio data" refers to a mechanism for playing back the generated audio data through the speakers of the terminal or the in-vehicle system so that passengers can hear it.

[0773] The "means for recognizing passenger emotions" is a system that uses cameras and sensors to analyze passengers' facial expressions and vocal tones to determine their emotions in real time.

[0774] The "means for adjusting information provided and audio announcements based on emotions" refers to a process for changing the content of explanatory text and the tone and pace of audio announcements depending on the passenger's recognized emotions.

[0775] "Means for playing audio data in an in-vehicle system" refers to a method for outputting audio data using speakers or monitors installed inside an autonomous vehicle.

[0776] This invention relates to a tourist guide application used in autonomous vehicles. This system allows passengers to take and upload photos of tourist spots visible from the vehicle window and provides detailed information about those locations via voice. Furthermore, it recognizes passengers' emotions in real time and adjusts the information and voice guidance provided to provide an optimal tourist experience. The specific configuration and operation of this system are described below.

[0777] Server Roles and Operations

[0778] The server plays a central role in analyzing image data and emotion data received from the device. First, the server receives the captured image data. Next, it analyzes the image using a Convolutional Neural Network (CNN) to identify tourist attractions. Information related to the identified tourist attractions is retrieved from an internal or external database on the server. Based on the retrieved information, a description is generated in natural language. Then, Text-to-Speech (TTS) technology is used to convert the description into audio data. The emotion engine is also used to analyze the emotion data and dynamically adjust the content and tone of the audio guidance. The hardware and software used in this process include the CNN, TTS engine, emotion engine, and databases (SQL, NoSQL).

[0779] Terminal roles and processing

[0780] The device is primarily responsible for capturing and uploading image data and playing audio data. Passengers take photos of tourist spots and upload them to the device. The uploaded data is sent to the server for processing. Audio data sent from the server is played back by the device's in-vehicle system. For emotion recognition, the device also uses a camera and microphone to analyze passengers' facial expressions and voices. This data is also sent to the server.

[0781] Specific examples

[0782] For example, consider the case where a passenger takes a photo of a historical building visible from the window of an autonomous vehicle and uploads it to the app. The server analyzes this image data and identifies the building. It retrieves information about the building from a database and generates a description such as, "This building was built in the 18th century..." This description is then converted into voice data by a TTS engine. At the same time, an emotion engine analyzes the passenger's emotions, such as excitement or attention, and adjusts the description and voice tone accordingly. The adjusted voice data is then sent to the device, which plays the audio, allowing the passenger to hear about the building's history and highlights, with the audio adjusted according to their emotion.

[0783] Prompt Sentence Examples

[0784] "Take a photo of a tourist attraction that you can see from your location, and the app will provide you with audio guidance about the location's history and highlights."

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

[0786] Step 1:

[0787] A user takes a photo of a tourist spot and uploads it to the app on their device. The input here is the image data taken by the user, and the output is the image data being sent from the device to the server. The device then sends this image data to the server via the network, and notifies the server of the success or failure of the transmission.

[0788] Step 2:

[0789] The server receives image data sent from the terminal. The input here is the image data sent from the terminal, and the output is the image data stored on the server. The server prepares to analyze the received image data.

[0790] Step 3:

[0791] The server uses a Convolutional Neural Network (CNN) to analyze the received image data and identify the locations where the photos were taken. The input is the received image data, and the output is the names and identification information of the identified tourist spots. Specifically, the CNN model extracts image features and matches them with an existing database to identify tourist spots.

[0792] Step 4:

[0793] The server retrieves information related to the identified tourist destination from the database. The input is the identification information of the identified tourist destination, and the output is detailed information about the tourist destination, such as its history and attractions. The server searches the database and retrieves the related information.

[0794] Step 5:

[0795] The server generates a natural language description based on the acquired information. The input is detailed information about tourist attractions, and the output is a description written in natural language. The server uses a natural language generation (NLG) model to generate the description from the acquired information.

[0796] Step 6:

[0797] The server converts the generated description into audio data using Text-to-Speech (TTS) technology. The input is a natural language description, and the output is audio data. The TTS engine analyzes the input text and generates the corresponding audio.

[0798] Step 7:

[0799] The server simultaneously analyzes the emotional data using an emotion engine. The input is the passenger's emotional data, and the output is the analyzed emotional information. The emotion engine uses technologies such as face detection and voice analysis to evaluate the passenger's emotional state in real time.

[0800] Step 8:

[0801] The server adjusts the content of the explanatory text and voice guidance provided based on the recognized emotion. The input is the analyzed emotion information and explanatory text data, and the output is the adjusted voice data. The server regenerates the voice with a tone and pace that corresponds to the emotional state.

[0802] Step 9:

[0803] The server sends the optimized audio data to the terminal. The input is the adjusted audio data, and the output is the audio data sent to the terminal. The server sends the audio data to the terminal through the network.

[0804] Step 10:

[0805] The terminal receives the voice data sent from the server and plays it through the in-vehicle system. The input is the voice data from the server, and the output is voice guidance for passengers. The in-vehicle system can play the voice through the speaker and simultaneously display related information on the monitor.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] [Third embodiment]

[0810] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0811] 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.

[0812] 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).

[0813] 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.

[0814] 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.

[0815] 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).

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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."

[0822] The present invention relates to a tourism application that analyzes image data of tourist spots taken by users and provides detailed information about the locations by voice. This system functions as follows between a server, a terminal, and a user.

[0823] System Overview

[0824] Users upload photos they take at tourist spots to the app. This image data is sent from the device to the server. The server uses a Convolutional Neural Network (CNN) to analyze the image and identify the location where the photo was taken. The server then retrieves relevant information about the location from a database and generates a description in natural language. The generated description is converted into audio data using Text-to-Speech (TTS) technology and sent to the device. Finally, the user can play this audio data on their device and hear detailed information about the tourist spot aloud.

[0825] Program processing

[0826] User Behavior

[0827] Users take photos of tourist spots, launch the app, and upload the photos. The uploading process is very simple, and the user interface is designed to be intuitive.

[0828] Terminal handling

[0829] 1. The device sends the photo data uploaded by the user to the server.

[0830] 2. The terminal notifies the user of successful transmission.

[0831] Server Processing

[0832] 1. The server receives the image data sent from the terminal.

[0833] 2. The server uses CNN to analyze the image data and identify tourist spots.

[0834] 3. The server retrieves information related to the identified tourist destination from the database.

[0835] 4. Based on the information obtained by the server, a description is generated in natural language.

[0836] 5. The server uses TTS technology to convert the generated description into audio data.

[0837] 6. The server sends the generated voice data to the device.

[0838] Terminal handling

[0839] 1. The device receives the voice data sent from the server.

[0840] 2. The device plays the audio data in the app for playback to the user.

[0841] Specific examples

[0842] For example, consider the case where a user takes a photo of a famous tourist spot, such as a temple, and uploads it to an app. The device sends this image data to a server. The server uses CNN to analyze the image and identify the temple. The server retrieves information about the temple's history and attractions from a database and generates a natural language description, such as "This temple was built in the 14th century and is designated as an Important Cultural Property." This description is then converted into audio data by a TTS engine and sent to the device. The user can play this audio data through their device and listen to the history and attractions of the temple.

[0843] This tourism application system allows users to obtain detailed information about tourist spots without the need for a guide, enabling free movement and efficient information gathering. The system is also multilingual, helping to eliminate language barriers. This will further enhance the tourist experience and contribute greatly to local industry and regional development.

[0844] The processing flow will be explained below.

[0845] Step 1:

[0846] The user takes a photo of a tourist spot. The user takes a photo of the tourist spot using a device such as a smartphone or tablet.

[0847] Step 2:

[0848] The user launches the app and uploads the photos they have taken. The app has a simple interface and is designed to make it easy for users to perform the necessary operations.

[0849] Step 3:

[0850] The device reads the uploaded photo data. The device application temporarily stores the uploaded photo data and prepares it to be sent to the server.

[0851] Step 4:

[0852] The device sends the photo data to the server, which then transmits the photo data to the server via the Internet, and the user is informed of the transmission status.

[0853] Step 5:

[0854] The server receives the photo data from the device. The server's receiving function stores the photo data and proceeds to the next analysis step.

[0855] Step 6:

[0856] The server analyzes the photo data using a Convolutional Neural Network (CNN), which extracts features from the image and begins the process of identifying tourist destination names.

[0857] Step 7:

[0858] The server searches a database based on the name of the identified tourist attraction, which stores detailed information about multiple tourist attractions.

[0859] Step 8:

[0860] The server retrieves historical and tourist attraction information from a database, such as "This temple was built in the 14th century..."

[0861] Step 9:

[0862] The server generates a description in natural language based on the information it has acquired. The natural language generation engine creates a description such as, "This temple has been designated as an Important Cultural Property."

[0863] Step 10:

[0864] The server-generated description is converted into audio data using a Text-to-Speech (TTS) engine, resulting in high-quality audio data.

[0865] Step 11:

[0866] The server sends the generated voice data to the terminal, which then returns it to the user's terminal via the Internet.

[0867] Step 12:

[0868] The device prepares to play the audio data received from the server. The playback function within the app is launched, and the device is ready to play the audio data.

[0869] Step 13:

[0870] The device plays back audio data, and the user hears detailed information about the tourist spot.The device plays back audio, and the user can learn about the attractions of the tourist spot without the need for a guide.

[0871] Example 1

[0872] 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."

[0873] In conventional tourist information systems, when users use their mobile devices to obtain information about tourist spots, they often have to manually input text, which makes them difficult to use. Furthermore, systems that identify tourist spots from images and provide detailed information via voice are limited, and multilingual support is lacking. This often results in an incomplete tourist experience.

[0874] 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.

[0875] In this invention, the server includes means for receiving captured image data, means for analyzing the received image data and identifying the location where the image was taken using a convolutional neural network, means for retrieving information related to the identified location from a database, means for generating a description in natural language using a generative AI model, and means for converting the generated description into audio data using speech synthesis technology. This allows users to easily receive detailed information about tourist spots by voice, enabling efficient information gathering without the need for a guide.

[0876] The "means for receiving captured image data" refers to a function for transferring image data captured by a user using a terminal to a server and receiving the data.

[0877] A "convolutional neural network" is a deep learning model for automatically extracting and analyzing features in images and videos.

[0878] "Means for obtaining from a database" refers to the function of the server searching for and obtaining the necessary information from a database that stores information related to the identified tourist destination.

[0879] A "generative AI model" refers to an artificial intelligence model that has the ability to generate natural language explanations based on input data.

[0880] "Speech synthesis technology" refers to technology that converts text data into voice data, allowing users to hear information as voice.

[0881] "Means for receiving and playing audio data" refers to a function that allows a terminal to receive audio data sent from a server and play the audio so that the user can listen to it.

[0882] MODE FOR CARRYING OUT THE INVENTION

[0883] The present invention relates to a tourism application that analyzes image data of tourist spots taken by users and provides detailed information about the locations by voice. This system functions as follows between a server, a terminal, and a user.

[0884] System Overview

[0885] Users upload photos they take at tourist spots to the app. This image data is sent from the device to the server. The server uses a convolutional neural network (CNN) to analyze the image and identify the location where it was taken. The server then retrieves relevant information about the location from a database and uses a generative AI model to generate a description in natural language. The generated description is converted into audio data using speech synthesis technology and sent to the device. Finally, the user can play this audio data on their device and hear detailed information about the tourist spot aloud.

[0886] Hardware and software used

[0887] This system is implemented using the following hardware and software.

[0888] Hardware: User's mobile device (smartphone, tablet, etc.), internet connection, server

[0889] software:

[0890] Image analysis: Convolutional Neural Networks (CNNs) implemented using TensorFlow on a connected server

[0891] Information acquisition: Database system stored on the server (e.g. MySQL)

[0892] Narrative generation: Generative AI models such as OpenAI's GPT-3 model

[0893] Text-to-speech: Text-to-speech technologies such as Google Cloud Text-to-Speech API and Amazon Polly

[0894] Specific examples

[0895] For example, consider a case where a user takes a photo of a famous tourist spot, such as a temple, and uploads it to an app. The user launches the tourism app on their device and uploads the photo. The device then sends the image data to the server as an HTTP POST request. The server stores the received image data in temporary storage and performs image analysis using a convolutional neural network (CNN) to identify the temple. The server then retrieves information about the temple from a database and uses a generative AI model (e.g., OpenAI GPT-3) to generate a natural language description, such as "This temple was built in the 14th century and is designated as an Important Cultural Property." This description is then converted into audio data using speech synthesis technology such as Google Cloud Text-to-Speech API and sent to the device. The user can then play the audio data on their device and hear a detailed description of the temple's history and highlights.

[0896] Prompt Sentence Examples

[0897] "Please tell me the process flow of a tourism app that generates detailed information about a tourist spot when you upload a photo of the spot and provides it via audio."

[0898] This tourism application system allows users to obtain detailed information about tourist spots without the need for a guide, and allows them to efficiently gather information while moving freely. It also supports multiple languages, helping to eliminate language barriers. This will further enhance the tourist experience and make a significant contribution to local industries and regional development.

[0899] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0900] Specific flow of program processing

[0901] User Behavior

[0902] Step 1:

[0903] A user takes a photo using a smartphone or tablet camera at a tourist spot, for example, taking a photo of the facade of a famous temple or an important monument.

[0904] Step 2:

[0905] The user launches the dedicated tourism app and taps the "Upload Photos" button within the app. The photo selection screen appears, and the user selects the photos they have just taken and uploads them.

[0906] Terminal handling

[0907] Step 3:

[0908] The device receives the photo data selected by the user and sends it to the server via an HTTP POST request. At this time, the photo data is encoded in binary format and included in the HTTP body. The input is the photo data selected by the user, and the output is the photo data sent to the server. Specifically, the device encodes the photo data, constructs an HTTP request, and sends it to the specified URL on the server.

[0909] Step 4:

[0910] If the photo data is successfully sent, the device notifies the user with a pop-up message saying "Photo sent." Here, the input is the transmission result and the output is the notification to the user. Specifically, it analyzes the HTTP response from the server and displays a success message to the user.

[0911] Server Processing

[0912] Step 5:

[0913] The server receives an HTTP POST request sent from the terminal, extracts the image data, and saves it in temporary storage on the server. It also records the request log. The input is the sent HTTP request, and the output is the saved image data. Specifically, it analyzes and extracts the image data from the request and saves it in a specified directory.

[0914] Step 6:

[0915] The server analyzes the stored image data using a convolutional neural network (CNN). Specifically, it uses a TensorFlow model to extract image features and identify tourist destinations. The input is the stored image data, and the output is the labels of the identified tourist destinations. Specifically, the CNN model inputs the image data and compares the resulting feature vector with a list of known tourist destinations to identify the best match.

[0916] Step 7:

[0917] The server retrieves information related to the identified tourist destination from a database. The database contains information such as the name, history, attractions, and access methods of the tourist destination. The input is the label of the identified tourist destination, and the output is detailed information about the tourist destination. Specifically, the server executes an SQL query based on the retrieved label to retrieve information from the database.

[0918] Step 8:

[0919] Based on the information acquired by the server, a generative AI model (e.g., OpenAI GPT-3) is used to generate a description in natural language. The input is detailed information about the acquired tourist spot, and the output is the generated description in natural language. Specifically, the tourist spot information is passed to the API as a prompt, and the generated text is received.

[0920] Step 9:

[0921] The server converts the generated description into audio data using Text-to-Speech (TTS) technology. For example, it uses the Google Cloud Text-to-Speech API to convert the description into an MP3 audio file. The input is the generated natural language description, and the output is the generated audio data. Specifically, the description text is sent to the TTS API and the generated audio data is received.

[0922] Step 10:

[0923] The generated audio data is sent to the terminal as an HTTP response. The audio file is encoded in binary format and sent. The input is the generated audio data, and the output is the audio data sent to the terminal. Specifically, the audio data is included in the body of the HTTP response and sent to the terminal.

[0924] Terminal handling

[0925] Step 11:

[0926] The device receives the HTTP response from the server, extracts the audio data, and saves it in temporary storage. Once saving is complete, it notifies the user that it is ready to play. The input is the received HTTP response, and the output is the saved audio data. Specifically, it analyzes and extracts the audio data from the response, saves it in temporary storage, and displays a notification that it is ready.

[0927] Step 12:

[0928] When the user taps the play button, the device plays the saved audio data. The user listens to the audio played within the app to obtain detailed information about tourist spots. The input is the playback request, and the output is the audio that is played. Specifically, the audio data is passed to the media player and playback begins.

[0929] (Application example 1)

[0930] 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."

[0931] Conventional tourist information systems require users to manually search for information about tourist destinations, which requires time and effort to identify tourist destinations. In particular, it is difficult to obtain detailed information about tourist destinations while traveling, which hinders the quality of the tourist experience. Furthermore, with the spread of autonomous vehicles, there is a growing need to obtain real-time tourist information from inside the vehicle. Given this background, there is a demand for a system that allows users to identify tourist destinations and obtain that information quickly and efficiently.

[0932] 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.

[0933] In this invention, the server includes a means for receiving captured image data, a means for analyzing the received image data to identify the location where the image was taken, and a means for retrieving information related to the identified location from a database. This makes it possible to quickly analyze images taken by an in-vehicle camera and provide detailed tourist information to users in real time in the infotainment system of an autonomous vehicle.

[0934] The "means for receiving captured image data" is a function for importing image data captured by a user or an on-board camera into the system.

[0935] The "means for analyzing received image data to identify the location where the image was taken" is a function for analyzing the captured image data and determining the specific location where the image was taken.

[0936] The "means for obtaining information related to the identified location from a database" is a function for retrieving detailed information about the identified location from a referenceable database.

[0937] "Means for generating explanatory text in natural language based on acquired information" is a function that creates sentences in natural language that are easy for users to understand based on information acquired from a database.

[0938] The "means for converting the generated explanatory text into audio data" is a function for converting the generated explanatory text in natural language into digital audio data for output as audio.

[0939] "Means for analyzing image data captured by an on-board camera" refers to a function for analyzing images captured by a camera equipped on an autonomous vehicle.

[0940] "Means integrated into the infotainment system of an autonomous vehicle" means a function integrated into the information and entertainment system on board an autonomous vehicle.

[0941] The "means for reproducing the generated voice data" is a function for actually outputting the generated voice data as voice and letting the user hear it.

[0942] The present invention relates to a system that analyzes captured image data and provides detailed information about the location by voice. The present invention includes the following configurations as an embodiment.

[0943] The system works between a user, a terminal, and a server. The user takes images of tourist spots using a camera mounted on an autonomous vehicle. The image data taken by the camera is received by the terminal and then sent to the server.

[0944] The server analyzes the image data using a convolutional neural network (CNN) to identify the location where the image was taken. This analysis uses the ResNet model, a common image recognition architecture. Information related to the identified location is retrieved from the server's database, including the location's history, points of interest, cultural background, etc.

[0945] Next, a description is generated based on the acquired information using natural language processing technology. The generated description is converted into audio data using Text-to-Speech (TTS) technology and sent to the device. The device then plays the received audio data through the autonomous vehicle's infotainment system and provides it to the user.

[0946] The system has the following features:

[0947] A method for analyzing image data captured by an onboard camera: Using a camera mounted on an autonomous vehicle, the captured image is analyzed.

[0948] Integrated into the infotainment system of an autonomous vehicle: The analysis results are played back in the information and entertainment system within the autonomous vehicle.

[0949] A means for playing the generated audio data: The generated audio data is actually played and listened to by the user.

[0950] As a concrete example, consider the case where a user visits a famous temple in an autonomous vehicle. The onboard camera takes a photo of the temple and sends the image to a server. The server uses CNN to identify the temple and obtain information about its history and attractions. An explanatory text, such as "This temple was built in the 14th century and is designated as an Important Cultural Property," is generated and converted into speech by a TTS engine. This audio data is sent to the terminal, and the user can listen to the explanation in the autonomous vehicle.

[0951] An example of a prompt for a generative AI model is:

[0952] "Please upload any images taken by your in-car camera and provide details about the location of the image."

[0953] As described above, the embodiments of the present invention provide a system that allows users to obtain tourist information in real time while in an autonomous vehicle, thereby improving the quality of the tourist experience.

[0954] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0955] Step 1:

[0956] A user takes images of tourist spots using a camera mounted on an autonomous vehicle.

[0957] Input: Images of tourist attractions.

[0958] Output: Captured image data.

[0959] Specific operation: The user takes photos of scenery and buildings at tourist spots with the camera and saves them on the device.

[0960] Step 2:

[0961] The terminal receives the captured image data and transmits it to the server.

[0962] Input: Image data captured by the user.

[0963] Output: Image data sent to the server.

[0964] Specific operation: An application on the device uploads image data to the server in response to user operation.

[0965] Step 3:

[0966] The server analyzes the received image data and identifies the location where the photo was taken.

[0967] Input: The submitted image data.

[0968] Output: Identified location information.

[0969] Specific operation: The server analyzes the received image data using a convolutional neural network (CNN) algorithm and identifies tourist spots in the image.

[0970] Step 4:

[0971] The server retrieves information related to the identified location from a database.

[0972] Input: Identified location information.

[0973] Output: Detailed information retrieved from the database.

[0974] What happens: The server accesses an internal database to retrieve historical and tourist information about the identified tourist destination.

[0975] Step 5:

[0976] The server generates a natural language description based on the information it has acquired.

[0977] Input: The details retrieved from the database.

[0978] Output: Natural language description.

[0979] Specific operation: The server uses natural language processing technology to convert the acquired information into sentences that are easy for the user to understand.

[0980] Step 6:

[0981] The server converts the generated description into audio data.

[0982] Input: A natural language description.

[0983] Output: Audio data.

[0984] Specific operation: The server uses Text-to-Speech (TTS) technology to convert the generated text into audio data.

[0985] Step 7:

[0986] The server transmits the generated voice data to the terminal.

[0987] Input: Audio data.

[0988] Output: The audio data sent to the device.

[0989] Specific operation: After the server completes processing, it sends the generated voice data to the device.

[0990] Step 8:

[0991] The device receives the audio data sent from the server and plays it through the infotainment system of the autonomous vehicle.

[0992] Input: Audio data sent from the server.

[0993] Output: Audio data played in the car.

[0994] Specific operation: The terminal provides the received voice data to the infotainment system, plays the voice through the car speakers, and provides the user with information about tourist spots.

[0995] The above are the specific processing steps of the program for the system that realizes the application example.

[0996] 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.

[0997] This invention relates to a tourism application that analyzes image data of tourist spots taken by the user and provides detailed information about the location via voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the information provided and the content of the voice guidance can be adjusted. This system functions as follows between the server, the terminal, and the user.

[0998] System Overview

[0999] Users upload photos they take at tourist spots to the app. The image data is sent from the device to the server, which uses a convolutional neural network (CNN) to analyze the image and identify the location where it was taken. The server then retrieves relevant information about the location from a database and generates a description in natural language. The generated description is converted into audio data using Text-to-Speech (TTS) technology and sent to the device. Finally, the user can play this audio data on their device and hear detailed information about the tourist spot. Furthermore, an emotion engine recognizes the user's emotions in real time and adjusts the information and audio guidance based on the results.

[1000] Program processing

[1001] User Behavior

[1002] Users take photos of tourist spots, launch the app, and upload the photos. The uploading process is very simple, and the user interface is designed to be intuitive.

[1003] Terminal handling

[1004] 1. The device sends the photo data uploaded by the user to the server.

[1005] 2. The device notifies the user of the successful transmission and collects data to recognize the user's emotions and sends it to the server.

[1006] Server Processing

[1007] 1. The server receives the image data and emotion data sent from the device.

[1008] 2. The server uses CNN to analyze the image data and identify tourist spots.

[1009] 3. The server searches the database based on the name of the identified tourist attraction.

[1010] 4. The server retrieves historical and sightseeing information about the tourist destination from the database.

[1011] 5. The server generates a description in natural language based on the information obtained.

[1012] 6. The server uses TTS technology to convert the generated description into audio data.

[1013] 7. At the same time, the server analyzes the emotional data using an emotion engine and adjusts the description and voice.

[1014] 8. The server sends the optimized audio data to the device.

[1015] Terminal handling

[1016] 1. The device receives the voice data sent from the server.

[1017] 2. The device plays the audio data to the user.

[1018] Specific examples

[1019] For example, suppose a user takes a photo of a tourist spot that is a historical building and uploads it to an app. The device sends this image data to a server. The server uses CNN to analyze the image and identify the building. The server retrieves information about the building from a database and generates a description such as, "This building was built in the 18th century..." This description is then converted into voice data by a TTS engine. At the same time, the emotion engine analyzes the user's emotions, such as excitement or attention, and adjusts the description and voice tone. The adjusted voice data is then sent to the device, which plays the audio, allowing the user to hear about the building's history and highlights, with the audio adjusted according to their emotions.

[1020] This tourism application system allows users to obtain detailed information about tourist spots without the need for a guide, enabling free movement and efficient information gathering. Furthermore, the emotion engine allows for flexible responses based on the user's emotions, providing an even more appealing tourism experience. This is expected to improve the quality of tourism and contribute greatly to local industries and regional development.

[1021] The processing flow will be explained below.

[1022] Step 1:

[1023] The user takes a photo of a tourist spot. The user takes a photo of a specific tourist spot using a device such as a smartphone or tablet.

[1024] Step 2:

[1025] The user launches the app and uploads the photos they have taken. The app has a simple interface and is designed to be intuitive for users to use.

[1026] Step 3:

[1027] The device reads the uploaded photo data and sends it to the server. The device application temporarily stores the photo data and prepares it for transfer to the server.

[1028] Step 4:

[1029] The device collects additional data (such as facial expressions and voice) to recognize the user's emotions, which are then analyzed in real time using a camera and microphone and sent to a server.

[1030] Step 5:

[1031] The device sends the photo data and emotion data to the server, which then sends the data to the server via the Internet.

[1032] Step 6:

[1033] The server receives the photo data and emotion data sent from the device. The received data is stored on the server and then goes to the next analysis step.

[1034] Step 7:

[1035] The server analyzes the photo data using a Convolutional Neural Network (CNN), which extracts features within the image and identifies specific tourist destination names.

[1036] Step 8:

[1037] The server searches a database based on the name of the identified tourist spot, which stores detailed information about the tourist spot (such as its history and attractions).

[1038] Step 9:

[1039] The server retrieves information about the tourist destination from a database, such as "This building was built in the 18th century."

[1040] Step 10:

[1041] The server generates a description in natural language based on the information it has acquired. The natural language generation engine creates a description such as, "This building was built in the 18th century, and its history is..."

[1042] Step 11:

[1043] The server uses an emotion engine to analyze the received emotion data and adjusts the content of the explanatory text and voice guidance based on the emotion data.

[1044] Step 12:

[1045] The server converts the tailored description into audio data using a Text-to-Speech (TTS) engine, generating high-quality audio data.

[1046] Step 13:

[1047] The server sends the generated voice data to the terminal, which then returns it to the user's terminal via the Internet.

[1048] Step 14:

[1049] The device receives the audio data sent from the server and prepares to play it. The playback function within the app is activated.

[1050] Step 15:

[1051] The device plays the audio data, and the user hears detailed information about the tourist spot. The audio explanation is provided with flexible adjustments based on the user's emotions.

[1052] This allows users to listen to detailed information about tourist spots in audio format without the need for a guide, enabling free movement and efficient information gathering. Furthermore, the emotion engine enables flexible responses based on the user's emotions, making the tourist experience even more appealing.

[1053] Example 2

[1054] 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."

[1055] Conventional tourism applications have the problem of providing only static information and not being able to flexibly respond to the user's emotions. As a result, the user experience is uniform, making it difficult to provide an attractive tourism experience that responds to each individual user's interests and emotions. Furthermore, existing systems often require complex operations, which can be a burden to users.

[1056] 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.

[1057] In this invention, the server includes means for receiving captured image data, means for analyzing the received image data to identify the location where the image was taken, means for retrieving information related to the identified location from a database, means for generating a natural language description based on the retrieved information, means for converting the generated description into audio data, means for collecting user emotion data, means for analyzing the collected emotion data, means for adjusting the description and audio data based on the emotion data, and means for playing the audio data. This enables flexible information provision based on the user's emotions, allowing for a personalized and attractive tourist experience. Furthermore, intuitive operation reduces the burden on users and improves ease of use.

[1058] "Means for receiving captured image data" refers to the function by which the server receives image data uploaded by the user from the terminal.

[1059] "Means for analyzing image data to identify the location where the image was taken" refers to technology for analyzing received image data and identifying and specifying the location where the image was taken.

[1060] "Means for retrieving information from a database" refers to a function for searching and retrieving information related to a specified location from a pre-stored database.

[1061] "Means for generating explanatory text in natural language" refers to technology that automatically generates explanatory text in natural language format that is easy for users to understand based on acquired information.

[1062] "Means for converting explanatory text into audio data" refers to a speech synthesis technology for converting the generated natural language explanatory text into audio data.

[1063] "Means for collecting user emotional data" refers to the function of collecting emotions in real time from the user's facial expressions, tone of voice, etc. using sensors on the device.

[1064] "Means for analyzing emotional data" refers to a technology for analyzing collected emotional data and evaluating the user's psychological state based on the data.

[1065] "Means for adjusting explanatory text or audio data" refers to technology for adjusting the content or tone of explanatory text or audio data based on the results of analyzed emotional data.

[1066] "Means for playing audio data" refers to a function for allowing a user to listen to the generated audio data using a terminal or other playback device.

[1067] This invention relates to a tourism application that analyzes image data of tourist spots taken by the user and provides detailed information about the location by voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the information provided and the content of the voice guidance can be adjusted.

[1068] System Overview

[1069] The system functions as follows between the user, the terminal, and the server.

[1070] User operations

[1071] Users take photos at tourist spots, launch the app, and upload them. The user interface is intuitive and designed to allow users to upload photos with simple steps.

[1072] Terminal handling

[1073] The device sends the photo data uploaded by the user to the server. It notifies the user that the transfer was successful, and also analyzes the user's facial expressions in real time using the front camera to collect emotional data. The device then sends the collected emotional data to the server.

[1074] Server Processing

[1075] The server receives the image data and emotion data sent from the device. It analyzes the image data using a Convolutional Neural Network (CNN) to identify tourist spots. It searches a database based on the names of the identified tourist spots to obtain information about their history and attractions. It then generates explanatory text using a natural language processing engine based on the obtained information.

[1076] The generated description is converted into voice data using Text-to-Speech (TTS) technology. An emotion engine analyzes the emotional data and adjusts the tone of the description and voice data based on the results. The optimized voice data is then sent to the device.

[1077] Finalizing the device

[1078] The terminal receives the audio data sent from the server and plays the audio data for the user, who can listen to the audio data through the terminal's speaker or headphones.

[1079] Hardware and software used

[1080] Device: A mobile device such as a smartphone or tablet.

[1081] Server: A high-performance computer (including cloud servers).

[1082] CNN (Convolutional Neural Network): A deep learning model for image analysis.

[1083] Database: A database that stores information about tourist destinations.

[1084] Natural language processing engine: Software for generating explanatory text.

[1085] Text-to-Speech (TTS) technology: Software for synthesizing speech.

[1086] Emotion engine: Software for analyzing emotion data.

[1087] Specific examples

[1088] For example, if a user takes a photo of Tokyo Tower, a historical building, and uploads it to the app, the process will be as follows:

[1089] 1. The user launches the app and uploads a photo of Tokyo Tower that they have taken.

[1090] 2. The device sends this image data to the server, notifies the user of the success of the transmission, and analyzes facial expressions to collect emotional data, which is then sent to the server.

[1091] 3. The server analyzes the received image data using CNN and identifies it as Tokyo Tower.

[1092] 4. The server retrieves information about Tokyo Tower from the database and generates a description such as "Tokyo Tower was built in 1958..."

[1093] 5. The description is converted into voice data using TTS technology. The emotion engine detects the user's excitement and adjusts the tone of the voice.

[1094] 6. The adjusted audio data is sent to the device, which then plays it back to the user. The user can hear about Tokyo Tower's history and highlights, adjusted according to their emotions.

[1095] Prompt Sentence Examples

[1096] "Analyze photos of tourist attractions and generate natural language descriptions that provide detailed information about the place."

[1097] "Please explain how you can tailor audio guide content for tourist attractions based on user sentiment data."

[1098] This system allows users to obtain detailed information about tourist spots through voice without the need for a guide. The emotion engine allows for flexible responses based on the user's emotions, providing a more engaging sightseeing experience.

[1099] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1100] Step 1:

[1101] Users take photos of tourist spots and upload them to the app.

[1102] Input: A photo of a tourist spot taken by the user

[1103] How it works: The user launches the app and takes a photo and uploads it with a few simple steps.

[1104] Output: Uploaded photo data is saved on the device.

[1105] Step 2:

[1106] The terminal transmits the photo data uploaded by the user to the server.

[1107] Input: Saved photo data

[1108] How it works: Your device sends photo data over the network to a server, optimizing the image files to speed up the transfer.

[1109] Output: Photo data sent to the server

[1110] Step 3:

[1111] The terminal notifies the user of the successful transmission and collects the user's emotion data.

[1112] Input: Status of successful photo data transmission

[1113] How it works: The device notifies the user on the screen that "Image upload complete" and uses the front camera to analyze the user's facial expressions in real time to collect emotional data.

[1114] Output: Emotion data sent to the server

[1115] Step 4:

[1116] The server receives the image data and emotion data sent from the terminal.

[1117] Input: Photo data and emotion data sent from the device

[1118] Operation: The server temporarily stores the received image data and emotion data.

[1119] Output: Saved image data and emotion data

[1120] Step 5:

[1121] The server uses CNN to analyze the image data and identify tourist spots.

[1122] Input: Saved photo data

[1123] How it works: The server extracts image features using a Convolutional Neural Network (CNN) and identifies tourist spots. This is done at high speed using parallel computing.

[1124] Output: Name and ID of identified tourist spot

[1125] Step 6:

[1126] The server retrieves information from a database based on the name of the identified tourist attraction.

[1127] Input: Name or ID of tourist spot

[1128] How it works: The server queries the database to retrieve historical and tourist attraction information.

[1129] Output: Information about the tourist spots obtained

[1130] Step 7:

[1131] The server generates a description based on the information it has obtained.

[1132] Input: Information retrieved from a database

[1133] How it works: The server uses a natural language processing engine to create a description based on the information it retrieves, summarizing the information and removing unnecessary parts.

[1134] Output: Generated natural language description

[1135] Step 8:

[1136] The server converts the generated description into audio data.

[1137] Input: Generated description

[1138] How it works: The server uses Text-to-Speech (TTS) technology to convert the description into audio data, for example, using Google's Cloud Text-to-Speech API.

[1139] Output: Generated audio data

[1140] Step 9:

[1141] The server uses an emotion engine to analyze the emotion data and adjust the tone and speed of the description and audio data.

[1142] Input: Collected emotion data and generated speech data

[1143] How it works: The server analyzes the emotional data using the emotion engine and adjusts the tone and speed of the voice data based on the analysis results.

[1144] Output: Modified audio data

[1145] Step 10:

[1146] The server sends the optimized audio data to the device.

[1147] Input: Modified audio data

[1148] Operation: The server sends voice data to the terminal using data compression technology while taking into consideration the stability of data communication.

[1149] Output: Audio data sent to the device

[1150] Step 11:

[1151] The terminal receives the audio data sent from the server and plays it back to the user.

[1152] Input: Audio data sent from the server

[1153] How it works: The device temporarily stores the audio data and uses the audio playback feature to play it back to the user, who can listen to it through headphones or the built-in speaker.

[1154] Output: Played audio data

[1155] This allows users to obtain detailed information about tourist spots through audio without the need for a guide, enabling them to enjoy a personalized and fascinating sightseeing experience.

[1156] (Application example 2)

[1157] 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."

[1158] Conventional tourism applications do not take into account use in autonomous vehicles and lack the ability to dynamically adjust tourist information based on passenger emotions. This results in one-sided provision of tourist information, making it difficult to provide passengers with an optimal tourist experience. Furthermore, providing information that ignores passengers' emotional state poses a challenge, as it fails to stimulate their enjoyment and interest in sightseeing.

[1159] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving captured image data, means for analyzing the received image data to identify the location where the image was taken, means for acquiring information related to the identified location from a database, means for generating a description in natural language based on the acquired information, means for converting the generated description into audio data, means for playing the audio data, means for recognizing passenger emotions, means for adjusting information to be provided and audio guidance based on the recognized emotions, and means for playing the audio data in the vehicle's on-board system. This allows passengers to obtain detailed information about tourist spots through images taken inside the autonomous vehicle, and the information is dynamically adjusted according to the passenger's emotions, making it possible to provide an optimal sightseeing experience.

[1160] "Photographed image data" refers to photographic data taken by passengers at tourist spots.

[1161] "Means for receiving" refers to the mechanism by which the terminal or in-vehicle system transmits image data to the server via the network, and the server receives it.

[1162] "Means for analyzing image data to identify the location where the image was taken" is a process that uses a convolutional neural network (CNN) to analyze the image and identify the tourist destination.

[1163] The "means for obtaining information related to the identified place from a database" refers to a method by which the server searches and obtains information related to the identified tourist destination from an external or internal database.

[1164] "Means for generating explanatory text in natural language" refers to a method in which a computer uses human language to create easy-to-understand explanatory text based on acquired information.

[1165] "Means for converting narrative text into audio data" means the process of converting generated natural language text into audio data using text-to-speech (TTS) technology.

[1166] The "means for playing back audio data" refers to a mechanism for playing back the generated audio data through the speakers of the terminal or the in-vehicle system so that passengers can hear it.

[1167] The "means for recognizing passenger emotions" is a system that uses cameras and sensors to analyze passengers' facial expressions and vocal tones to determine their emotions in real time.

[1168] The "means for adjusting information provided and audio announcements based on emotions" refers to a process for changing the content of explanatory text and the tone and pace of audio announcements depending on the passenger's recognized emotions.

[1169] "Means for playing audio data in an in-vehicle system" refers to a method for outputting audio data using speakers or monitors installed inside an autonomous vehicle.

[1170] This invention relates to a tourist guide application used in autonomous vehicles. This system allows passengers to take and upload photos of tourist spots visible from the vehicle window and provides detailed information about those locations via voice. Furthermore, it recognizes passengers' emotions in real time and adjusts the information and voice guidance provided to provide an optimal tourist experience. The specific configuration and operation of this system are described below.

[1171] Server Roles and Operations

[1172] The server plays a central role in analyzing image data and emotion data received from the device. First, the server receives the captured image data. Next, it analyzes the image using a Convolutional Neural Network (CNN) to identify tourist attractions. Information related to the identified tourist attractions is retrieved from an internal or external database on the server. Based on the retrieved information, a description is generated in natural language. Then, Text-to-Speech (TTS) technology is used to convert the description into audio data. The emotion engine is also used to analyze the emotion data and dynamically adjust the content and tone of the audio guidance. The hardware and software used in this process include the CNN, TTS engine, emotion engine, and databases (SQL, NoSQL).

[1173] Terminal roles and processing

[1174] The device is primarily responsible for capturing and uploading image data and playing audio data. Passengers take photos of tourist spots and upload them to the device. The uploaded data is sent to the server for processing. Audio data sent from the server is played back by the device's in-vehicle system. For emotion recognition, the device also uses a camera and microphone to analyze passengers' facial expressions and voices. This data is also sent to the server.

[1175] Specific examples

[1176] For example, consider the case where a passenger takes a photo of a historical building visible from the window of an autonomous vehicle and uploads it to the app. The server analyzes this image data and identifies the building. It retrieves information about the building from a database and generates a description such as, "This building was built in the 18th century..." This description is then converted into voice data by a TTS engine. At the same time, an emotion engine analyzes the passenger's emotions, such as excitement or attention, and adjusts the description and voice tone accordingly. The adjusted voice data is then sent to the device, which plays the audio, allowing the passenger to hear about the building's history and highlights, with the audio adjusted according to their emotion.

[1177] Prompt Sentence Examples

[1178] "Take a photo of a tourist attraction that you can see from your location, and the app will provide you with audio guidance about the location's history and highlights."

[1179] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1180] Step 1:

[1181] A user takes a photo of a tourist spot and uploads it to the app on their device. The input here is the image data taken by the user, and the output is the image data being sent from the device to the server. The device then sends this image data to the server via the network, and notifies the server of the success or failure of the transmission.

[1182] Step 2:

[1183] The server receives image data sent from the terminal. The input here is the image data sent from the terminal, and the output is the image data stored on the server. The server prepares to analyze the received image data.

[1184] Step 3:

[1185] The server uses a Convolutional Neural Network (CNN) to analyze the received image data and identify the locations where the photos were taken. The input is the received image data, and the output is the names and identification information of the identified tourist spots. Specifically, the CNN model extracts image features and matches them with an existing database to identify tourist spots.

[1186] Step 4:

[1187] The server retrieves information related to the identified tourist destination from the database. The input is the identification information of the identified tourist destination, and the output is detailed information about the tourist destination, such as its history and attractions. The server searches the database and retrieves the related information.

[1188] Step 5:

[1189] The server generates a natural language description based on the acquired information. The input is detailed information about tourist attractions, and the output is a description written in natural language. The server uses a natural language generation (NLG) model to generate the description from the acquired information.

[1190] Step 6:

[1191] The server converts the generated description into audio data using Text-to-Speech (TTS) technology. The input is a natural language description, and the output is audio data. The TTS engine analyzes the input text and generates the corresponding audio.

[1192] Step 7:

[1193] The server simultaneously analyzes the emotional data using an emotion engine. The input is the passenger's emotional data, and the output is the analyzed emotional information. The emotion engine uses technologies such as face detection and voice analysis to evaluate the passenger's emotional state in real time.

[1194] Step 8:

[1195] The server adjusts the content of the explanatory text and voice guidance provided based on the recognized emotion. The input is the analyzed emotion information and explanatory text data, and the output is the adjusted voice data. The server regenerates the voice with a tone and pace that corresponds to the emotional state.

[1196] Step 9:

[1197] The server sends the optimized audio data to the terminal. The input is the adjusted audio data, and the output is the audio data sent to the terminal. The server sends the audio data to the terminal through the network.

[1198] Step 10:

[1199] The terminal receives the voice data sent from the server and plays it through the in-vehicle system. The input is the voice data from the server, and the output is voice guidance for passengers. The in-vehicle system can play the voice through the speaker and simultaneously display related information on the monitor.

[1200] 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.

[1201] 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.

[1202] 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.

[1203] [Fourth embodiment]

[1204] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1205] 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.

[1206] 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).

[1207] 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.

[1208] 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.

[1209] 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).

[1210] 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.

[1211] 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.

[1212] 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.

[1213] 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.

[1214] 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.

[1215] 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.

[1216] 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."

[1217] The present invention relates to a tourism application that analyzes image data of tourist spots taken by users and provides detailed information about the locations by voice. This system functions as follows between a server, a terminal, and a user.

[1218] System Overview

[1219] Users upload photos they take at tourist spots to the app. This image data is sent from the device to the server. The server uses a Convolutional Neural Network (CNN) to analyze the image and identify the location where the photo was taken. The server then retrieves relevant information about the location from a database and generates a description in natural language. The generated description is converted into audio data using Text-to-Speech (TTS) technology and sent to the device. Finally, the user can play this audio data on their device and hear detailed information about the tourist spot aloud.

[1220] Program processing

[1221] User Behavior

[1222] Users take photos of tourist spots, launch the app, and upload the photos. The uploading process is very simple, and the user interface is designed to be intuitive.

[1223] Terminal handling

[1224] 1. The device sends the photo data uploaded by the user to the server.

[1225] 2. The terminal notifies the user of successful transmission.

[1226] Server Processing

[1227] 1. The server receives the image data sent from the terminal.

[1228] 2. The server uses CNN to analyze the image data and identify tourist spots.

[1229] 3. The server retrieves information related to the identified tourist destination from the database.

[1230] 4. Based on the information obtained by the server, a description is generated in natural language.

[1231] 5. The server uses TTS technology to convert the generated description into audio data.

[1232] 6. The server sends the generated voice data to the device.

[1233] Terminal handling

[1234] 1. The device receives the voice data sent from the server.

[1235] 2. The device plays the audio data in the app for playback to the user.

[1236] Specific examples

[1237] For example, consider the case where a user takes a photo of a famous tourist spot, such as a temple, and uploads it to an app. The device sends this image data to a server. The server uses CNN to analyze the image and identify the temple. The server retrieves information about the temple's history and attractions from a database and generates a natural language description, such as "This temple was built in the 14th century and is designated as an Important Cultural Property." This description is then converted into audio data by a TTS engine and sent to the device. The user can play this audio data through their device and listen to the history and attractions of the temple.

[1238] This tourism application system allows users to obtain detailed information about tourist spots without the need for a guide, enabling free movement and efficient information gathering. The system is also multilingual, helping to eliminate language barriers. This will further enhance the tourist experience and contribute greatly to local industry and regional development.

[1239] The processing flow will be explained below.

[1240] Step 1:

[1241] The user takes a photo of a tourist spot. The user takes a photo of the tourist spot using a device such as a smartphone or tablet.

[1242] Step 2:

[1243] The user launches the app and uploads the photos they have taken. The app has a simple interface and is designed to make it easy for users to perform the necessary operations.

[1244] Step 3:

[1245] The device reads the uploaded photo data. The device application temporarily stores the uploaded photo data and prepares it to be sent to the server.

[1246] Step 4:

[1247] The device sends the photo data to the server, which then transmits the photo data to the server via the Internet, and the user is informed of the transmission status.

[1248] Step 5:

[1249] The server receives the photo data from the device. The server's receiving function stores the photo data and proceeds to the next analysis step.

[1250] Step 6:

[1251] The server analyzes the photo data using a Convolutional Neural Network (CNN), which extracts features from the image and begins the process of identifying tourist destination names.

[1252] Step 7:

[1253] The server searches a database based on the name of the identified tourist attraction, which stores detailed information about multiple tourist attractions.

[1254] Step 8:

[1255] The server retrieves historical and tourist attraction information from a database, such as "This temple was built in the 14th century..."

[1256] Step 9:

[1257] The server generates a description in natural language based on the information it has acquired. The natural language generation engine creates a description such as, "This temple has been designated as an Important Cultural Property."

[1258] Step 10:

[1259] The server-generated description is converted into audio data using a Text-to-Speech (TTS) engine, resulting in high-quality audio data.

[1260] Step 11:

[1261] The server sends the generated voice data to the terminal, which then returns it to the user's terminal via the Internet.

[1262] Step 12:

[1263] The device prepares to play the audio data received from the server. The playback function within the app is launched, and the device is ready to play the audio data.

[1264] Step 13:

[1265] The device plays back audio data, and the user hears detailed information about the tourist spot.The device plays back audio, and the user can learn about the attractions of the tourist spot without the need for a guide.

[1266] Example 1

[1267] 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."

[1268] In conventional tourist information systems, when users use their mobile devices to obtain information about tourist spots, they often have to manually input text, which makes them difficult to use. Furthermore, systems that identify tourist spots from images and provide detailed information via voice are limited, and multilingual support is lacking. This often results in an incomplete tourist experience.

[1269] 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.

[1270] In this invention, the server includes means for receiving captured image data, means for analyzing the received image data and identifying the location where the image was taken using a convolutional neural network, means for retrieving information related to the identified location from a database, means for generating a description in natural language using a generative AI model, and means for converting the generated description into audio data using speech synthesis technology. This allows users to easily receive detailed information about tourist spots by voice, enabling efficient information gathering without the need for a guide.

[1271] The "means for receiving captured image data" refers to a function for transferring image data captured by a user using a terminal to a server and receiving the data.

[1272] A "convolutional neural network" is a deep learning model for automatically extracting and analyzing features in images and videos.

[1273] "Means for obtaining from a database" refers to the function of the server searching for and obtaining the necessary information from a database that stores information related to the identified tourist destination.

[1274] A "generative AI model" refers to an artificial intelligence model that has the ability to generate natural language explanations based on input data.

[1275] "Speech synthesis technology" refers to technology that converts text data into voice data, allowing users to hear information as voice.

[1276] "Means for receiving and playing audio data" refers to a function that allows a terminal to receive audio data sent from a server and play the audio so that the user can listen to it.

[1277] MODE FOR CARRYING OUT THE INVENTION

[1278] The present invention relates to a tourism application that analyzes image data of tourist spots taken by users and provides detailed information about the locations by voice. This system functions as follows between a server, a terminal, and a user.

[1279] System Overview

[1280] Users upload photos they take at tourist spots to the app. This image data is sent from the device to the server. The server uses a convolutional neural network (CNN) to analyze the image and identify the location where it was taken. The server then retrieves relevant information about the location from a database and uses a generative AI model to generate a description in natural language. The generated description is converted into audio data using speech synthesis technology and sent to the device. Finally, the user can play this audio data on their device and hear detailed information about the tourist spot aloud.

[1281] Hardware and software used

[1282] This system is implemented using the following hardware and software.

[1283] Hardware: User's mobile device (smartphone, tablet, etc.), internet connection, server

[1284] software:

[1285] Image analysis: Convolutional Neural Networks (CNNs) implemented using TensorFlow on a connected server

[1286] Information acquisition: Database system stored on the server (e.g. MySQL)

[1287] Narrative generation: Generative AI models such as OpenAI's GPT-3 model

[1288] Text-to-speech: Text-to-speech technologies such as Google Cloud Text-to-Speech API and Amazon Polly

[1289] Specific examples

[1290] For example, consider a case where a user takes a photo of a famous tourist spot, such as a temple, and uploads it to an app. The user launches the tourism app on their device and uploads the photo. The device then sends the image data to the server as an HTTP POST request. The server stores the received image data in temporary storage and performs image analysis using a convolutional neural network (CNN) to identify the temple. The server then retrieves information about the temple from a database and uses a generative AI model (e.g., OpenAI GPT-3) to generate a natural language description, such as "This temple was built in the 14th century and is designated as an Important Cultural Property." This description is then converted into audio data using speech synthesis technology such as Google Cloud Text-to-Speech API and sent to the device. The user can then play the audio data on their device and hear a detailed description of the temple's history and highlights.

[1291] Prompt Sentence Examples

[1292] "Please tell me the process flow of a tourism app that generates detailed information about a tourist spot when you upload a photo of the spot and provides it via audio."

[1293] This tourism application system allows users to obtain detailed information about tourist spots without the need for a guide, and allows them to efficiently gather information while moving freely. It also supports multiple languages, helping to eliminate language barriers. This will further enhance the tourist experience and make a significant contribution to local industries and regional development.

[1294] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1295] Specific flow of program processing

[1296] User Behavior

[1297] Step 1:

[1298] A user takes a photo using a smartphone or tablet camera at a tourist spot, for example, taking a photo of the facade of a famous temple or an important monument.

[1299] Step 2:

[1300] The user launches the dedicated tourism app and taps the "Upload Photos" button within the app. The photo selection screen appears, and the user selects the photos they have just taken and uploads them.

[1301] Terminal handling

[1302] Step 3:

[1303] The device receives the photo data selected by the user and sends it to the server via an HTTP POST request. At this time, the photo data is encoded in binary format and included in the HTTP body. The input is the photo data selected by the user, and the output is the photo data sent to the server. Specifically, the device encodes the photo data, constructs an HTTP request, and sends it to the specified URL on the server.

[1304] Step 4:

[1305] If the photo data is successfully sent, the device notifies the user with a pop-up message saying "Photo sent." Here, the input is the transmission result and the output is the notification to the user. Specifically, it analyzes the HTTP response from the server and displays a success message to the user.

[1306] Server Processing

[1307] Step 5:

[1308] The server receives an HTTP POST request sent from the terminal, extracts the image data, and saves it in temporary storage on the server. It also records the request log. The input is the sent HTTP request, and the output is the saved image data. Specifically, it analyzes and extracts the image data from the request and saves it in a specified directory.

[1309] Step 6:

[1310] The server analyzes the stored image data using a convolutional neural network (CNN). Specifically, it uses a TensorFlow model to extract image features and identify tourist destinations. The input is the stored image data, and the output is the labels of the identified tourist destinations. Specifically, the CNN model inputs the image data and compares the resulting feature vector with a list of known tourist destinations to identify the best match.

[1311] Step 7:

[1312] The server retrieves information related to the identified tourist destination from a database. The database contains information such as the name, history, attractions, and access methods of the tourist destination. The input is the label of the identified tourist destination, and the output is detailed information about the tourist destination. Specifically, the server executes an SQL query based on the retrieved label to retrieve information from the database.

[1313] Step 8:

[1314] Based on the information acquired by the server, a generative AI model (e.g., OpenAI GPT-3) is used to generate a description in natural language. The input is detailed information about the acquired tourist spot, and the output is the generated description in natural language. Specifically, the tourist spot information is passed to the API as a prompt, and the generated text is received.

[1315] Step 9:

[1316] The server converts the generated description into audio data using Text-to-Speech (TTS) technology. For example, it uses the Google Cloud Text-to-Speech API to convert the description into an MP3 audio file. The input is the generated natural language description, and the output is the generated audio data. Specifically, the description text is sent to the TTS API and the generated audio data is received.

[1317] Step 10:

[1318] The generated audio data is sent to the terminal as an HTTP response. The audio file is encoded in binary format and sent. The input is the generated audio data, and the output is the audio data sent to the terminal. Specifically, the audio data is included in the body of the HTTP response and sent to the terminal.

[1319] Terminal handling

[1320] Step 11:

[1321] The device receives the HTTP response from the server, extracts the audio data, and saves it in temporary storage. Once saving is complete, it notifies the user that it is ready to play. The input is the received HTTP response, and the output is the saved audio data. Specifically, it analyzes and extracts the audio data from the response, saves it in temporary storage, and displays a notification that it is ready.

[1322] Step 12:

[1323] When the user taps the play button, the device plays the saved audio data. The user listens to the audio played within the app to obtain detailed information about tourist spots. The input is the playback request, and the output is the audio that is played. Specifically, the audio data is passed to the media player and playback begins.

[1324] (Application example 1)

[1325] 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."

[1326] Conventional tourist information systems require users to manually search for information about tourist destinations, which requires time and effort to identify tourist destinations. In particular, it is difficult to obtain detailed information about tourist destinations while traveling, which hinders the quality of the tourist experience. Furthermore, with the spread of autonomous vehicles, there is a growing need to obtain real-time tourist information from inside the vehicle. Given this background, there is a demand for a system that allows users to identify tourist destinations and obtain that information quickly and efficiently.

[1327] 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.

[1328] In this invention, the server includes a means for receiving captured image data, a means for analyzing the received image data to identify the location where the image was taken, and a means for retrieving information related to the identified location from a database. This makes it possible to quickly analyze images taken by an in-vehicle camera and provide detailed tourist information to users in real time in the infotainment system of an autonomous vehicle.

[1329] The "means for receiving captured image data" is a function for importing image data captured by a user or an on-board camera into the system.

[1330] The "means for analyzing received image data to identify the location where the image was taken" is a function for analyzing the captured image data and determining the specific location where the image was taken.

[1331] The "means for obtaining information related to the identified location from a database" is a function for retrieving detailed information about the identified location from a referenceable database.

[1332] "Means for generating explanatory text in natural language based on acquired information" is a function that creates sentences in natural language that are easy for users to understand based on information acquired from a database.

[1333] The "means for converting the generated explanatory text into audio data" is a function for converting the generated explanatory text in natural language into digital audio data for output as audio.

[1334] "Means for analyzing image data captured by an on-board camera" refers to a function for analyzing images captured by a camera equipped on an autonomous vehicle.

[1335] "Means integrated into the infotainment system of an autonomous vehicle" means a function integrated into the information and entertainment system on board an autonomous vehicle.

[1336] The "means for reproducing the generated voice data" is a function for actually outputting the generated voice data as voice and letting the user hear it.

[1337] The present invention relates to a system that analyzes captured image data and provides detailed information about the location by voice. The present invention includes the following configurations as an embodiment.

[1338] The system works between a user, a terminal, and a server. The user takes images of tourist spots using a camera mounted on an autonomous vehicle. The image data taken by the camera is received by the terminal and then sent to the server.

[1339] The server analyzes the image data using a convolutional neural network (CNN) to identify the location where the image was taken. This analysis uses the ResNet model, a common image recognition architecture. Information related to the identified location is retrieved from the server's database, including the location's history, points of interest, cultural background, etc.

[1340] Next, a description is generated based on the acquired information using natural language processing technology. The generated description is converted into audio data using Text-to-Speech (TTS) technology and sent to the device. The device then plays the received audio data through the autonomous vehicle's infotainment system and provides it to the user.

[1341] The system has the following features:

[1342] A method for analyzing image data captured by an onboard camera: Using a camera mounted on an autonomous vehicle, the captured image is analyzed.

[1343] Integrated into the infotainment system of an autonomous vehicle: The analysis results are played back in the information and entertainment system within the autonomous vehicle.

[1344] A means for playing the generated audio data: The generated audio data is actually played and listened to by the user.

[1345] As a concrete example, consider the case where a user visits a famous temple in an autonomous vehicle. The onboard camera takes a photo of the temple and sends the image to a server. The server uses CNN to identify the temple and obtain information about its history and attractions. An explanatory text, such as "This temple was built in the 14th century and is designated as an Important Cultural Property," is generated and converted into speech by a TTS engine. This audio data is sent to the terminal, and the user can listen to the explanation in the autonomous vehicle.

[1346] An example of a prompt for a generative AI model is:

[1347] "Please upload any images taken by your in-car camera and provide details about the location of the image."

[1348] As described above, the embodiments of the present invention provide a system that allows users to obtain tourist information in real time while in an autonomous vehicle, thereby improving the quality of the tourist experience.

[1349] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1350] Step 1:

[1351] A user takes images of tourist spots using a camera mounted on an autonomous vehicle.

[1352] Input: Images of tourist attractions.

[1353] Output: Captured image data.

[1354] Specific operation: The user takes photos of scenery and buildings at tourist spots with the camera and saves them on the device.

[1355] Step 2:

[1356] The terminal receives the captured image data and transmits it to the server.

[1357] Input: Image data captured by the user.

[1358] Output: Image data sent to the server.

[1359] Specific operation: An application on the device uploads image data to the server in response to user operation.

[1360] Step 3:

[1361] The server analyzes the received image data and identifies the location where the photo was taken.

[1362] Input: The submitted image data.

[1363] Output: Identified location information.

[1364] Specific operation: The server analyzes the received image data using a convolutional neural network (CNN) algorithm and identifies tourist spots in the image.

[1365] Step 4:

[1366] The server retrieves information related to the identified location from a database.

[1367] Input: Identified location information.

[1368] Output: Detailed information retrieved from the database.

[1369] What happens: The server accesses an internal database to retrieve historical and tourist information about the identified tourist destination.

[1370] Step 5:

[1371] The server generates a natural language description based on the information it has acquired.

[1372] Input: The details retrieved from the database.

[1373] Output: Natural language description.

[1374] Specific operation: The server uses natural language processing technology to convert the acquired information into sentences that are easy for the user to understand.

[1375] Step 6:

[1376] The server converts the generated description into audio data.

[1377] Input: A natural language description.

[1378] Output: Audio data.

[1379] Specific operation: The server uses Text-to-Speech (TTS) technology to convert the generated text into audio data.

[1380] Step 7:

[1381] The server transmits the generated voice data to the terminal.

[1382] Input: Audio data.

[1383] Output: The audio data sent to the device.

[1384] Specific operation: After the server completes processing, it sends the generated voice data to the device.

[1385] Step 8:

[1386] The device receives the audio data sent from the server and plays it through the infotainment system of the autonomous vehicle.

[1387] Input: Audio data sent from the server.

[1388] Output: Audio data played in the car.

[1389] Specific operation: The terminal provides the received voice data to the infotainment system, plays the voice through the car speakers, and provides the user with information about tourist spots.

[1390] The above are the specific processing steps of the program for the system that realizes the application example.

[1391] 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.

[1392] This invention relates to a tourism application that analyzes image data of tourist spots taken by the user and provides detailed information about the location via voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the information provided and the content of the voice guidance can be adjusted. This system functions as follows between the server, the terminal, and the user.

[1393] System Overview

[1394] Users upload photos they take at tourist spots to the app. The image data is sent from the device to the server, which uses a convolutional neural network (CNN) to analyze the image and identify the location where it was taken. The server then retrieves relevant information about the location from a database and generates a description in natural language. The generated description is converted into audio data using Text-to-Speech (TTS) technology and sent to the device. Finally, the user can play this audio data on their device and hear detailed information about the tourist spot. Furthermore, an emotion engine recognizes the user's emotions in real time and adjusts the information and audio guidance based on the results.

[1395] Program processing

[1396] User Behavior

[1397] Users take photos of tourist spots, launch the app, and upload the photos. The uploading process is very simple, and the user interface is designed to be intuitive.

[1398] Terminal handling

[1399] 1. The device sends the photo data uploaded by the user to the server.

[1400] 2. The device notifies the user of the successful transmission and collects data to recognize the user's emotions and sends it to the server.

[1401] Server Processing

[1402] 1. The server receives the image data and emotion data sent from the device.

[1403] 2. The server uses CNN to analyze the image data and identify tourist spots.

[1404] 3. The server searches the database based on the name of the identified tourist attraction.

[1405] 4. The server retrieves historical and sightseeing information about the tourist destination from the database.

[1406] 5. The server generates a description in natural language based on the information obtained.

[1407] 6. The server uses TTS technology to convert the generated description into audio data.

[1408] 7. At the same time, the server analyzes the emotional data using an emotion engine and adjusts the description and voice.

[1409] 8. The server sends the optimized audio data to the device.

[1410] Terminal handling

[1411] 1. The device receives the voice data sent from the server.

[1412] 2. The device plays the audio data to the user.

[1413] Specific examples

[1414] For example, suppose a user takes a photo of a tourist spot that is a historical building and uploads it to an app. The device sends this image data to a server. The server uses CNN to analyze the image and identify the building. The server retrieves information about the building from a database and generates a description such as, "This building was built in the 18th century..." This description is then converted into voice data by a TTS engine. At the same time, the emotion engine analyzes the user's emotions, such as excitement or attention, and adjusts the description and voice tone. The adjusted voice data is then sent to the device, which plays the audio, allowing the user to hear about the building's history and highlights, with the audio adjusted according to their emotions.

[1415] This tourism application system allows users to obtain detailed information about tourist spots without the need for a guide, enabling free movement and efficient information gathering. Furthermore, the emotion engine allows for flexible responses based on the user's emotions, providing an even more appealing tourism experience. This is expected to improve the quality of tourism and contribute greatly to local industries and regional development.

[1416] The processing flow will be explained below.

[1417] Step 1:

[1418] The user takes a photo of a tourist spot. The user takes a photo of a specific tourist spot using a device such as a smartphone or tablet.

[1419] Step 2:

[1420] The user launches the app and uploads the photos they have taken. The app has a simple interface and is designed to be intuitive for users to use.

[1421] Step 3:

[1422] The device reads the uploaded photo data and sends it to the server. The device application temporarily stores the photo data and prepares it for transfer to the server.

[1423] Step 4:

[1424] The device collects additional data (such as facial expressions and voice) to recognize the user's emotions, which are then analyzed in real time using a camera and microphone and sent to a server.

[1425] Step 5:

[1426] The device sends the photo data and emotion data to the server, which then sends the data to the server via the Internet.

[1427] Step 6:

[1428] The server receives the photo data and emotion data sent from the device. The received data is stored on the server and then goes to the next analysis step.

[1429] Step 7:

[1430] The server analyzes the photo data using a Convolutional Neural Network (CNN), which extracts features within the image and identifies specific tourist destination names.

[1431] Step 8:

[1432] The server searches a database based on the name of the identified tourist spot, which stores detailed information about the tourist spot (such as its history and attractions).

[1433] Step 9:

[1434] The server retrieves information about the tourist destination from a database, such as "This building was built in the 18th century."

[1435] Step 10:

[1436] The server generates a description in natural language based on the information it has acquired. The natural language generation engine creates a description such as, "This building was built in the 18th century, and its history is..."

[1437] Step 11:

[1438] The server uses an emotion engine to analyze the received emotion data and adjusts the content of the explanatory text and voice guidance based on the emotion data.

[1439] Step 12:

[1440] The server converts the tailored description into audio data using a Text-to-Speech (TTS) engine, generating high-quality audio data.

[1441] Step 13:

[1442] The server sends the generated voice data to the terminal, which then returns it to the user's terminal via the Internet.

[1443] Step 14:

[1444] The device receives the audio data sent from the server and prepares to play it. The playback function within the app is activated.

[1445] Step 15:

[1446] The device plays the audio data, and the user hears detailed information about the tourist spot. The audio explanation is provided with flexible adjustments based on the user's emotions.

[1447] This allows users to listen to detailed information about tourist spots in audio format without the need for a guide, enabling free movement and efficient information gathering. Furthermore, the emotion engine enables flexible responses based on the user's emotions, making the tourist experience even more appealing.

[1448] Example 2

[1449] 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."

[1450] Conventional tourism applications have the problem of providing only static information and not being able to flexibly respond to the user's emotions. As a result, the user experience is uniform, making it difficult to provide an attractive tourism experience that responds to each individual user's interests and emotions. Furthermore, existing systems often require complex operations, which can be a burden to users.

[1451] 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.

[1452] In this invention, the server includes means for receiving captured image data, means for analyzing the received image data to identify the location where the image was taken, means for retrieving information related to the identified location from a database, means for generating a natural language description based on the retrieved information, means for converting the generated description into audio data, means for collecting user emotion data, means for analyzing the collected emotion data, means for adjusting the description and audio data based on the emotion data, and means for playing the audio data. This enables flexible information provision based on the user's emotions, allowing for a personalized and attractive tourist experience. Furthermore, intuitive operation reduces the burden on users and improves ease of use.

[1453] "Means for receiving captured image data" refers to the function by which the server receives image data uploaded by the user from the terminal.

[1454] "Means for analyzing image data to identify the location where the image was taken" refers to technology for analyzing received image data and identifying and specifying the location where the image was taken.

[1455] "Means for retrieving information from a database" refers to a function for searching and retrieving information related to a specified location from a pre-stored database.

[1456] "Means for generating explanatory text in natural language" refers to technology that automatically generates explanatory text in natural language format that is easy for users to understand based on acquired information.

[1457] "Means for converting explanatory text into audio data" refers to a speech synthesis technology for converting the generated natural language explanatory text into audio data.

[1458] "Means for collecting user emotional data" refers to the function of collecting emotions in real time from the user's facial expressions, tone of voice, etc. using sensors on the device.

[1459] "Means for analyzing emotional data" refers to a technology for analyzing collected emotional data and evaluating the user's psychological state based on the data.

[1460] "Means for adjusting explanatory text or audio data" refers to technology for adjusting the content or tone of explanatory text or audio data based on the results of analyzed emotional data.

[1461] "Means for playing audio data" refers to a function for allowing a user to listen to the generated audio data using a terminal or other playback device.

[1462] This invention relates to a tourism application that analyzes image data of tourist spots taken by the user and provides detailed information about the location by voice. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the information provided and the content of the voice guidance can be adjusted.

[1463] System Overview

[1464] The system functions as follows between the user, the terminal, and the server.

[1465] User operations

[1466] Users take photos at tourist spots, launch the app, and upload them. The user interface is intuitive and designed to allow users to upload photos with simple steps.

[1467] Terminal handling

[1468] The device sends the photo data uploaded by the user to the server. It notifies the user that the transfer was successful, and also analyzes the user's facial expressions in real time using the front camera to collect emotional data. The device then sends the collected emotional data to the server.

[1469] Server Processing

[1470] The server receives the image data and emotion data sent from the device. It analyzes the image data using a Convolutional Neural Network (CNN) to identify tourist spots. It searches a database based on the names of the identified tourist spots to obtain information about their history and attractions. It then generates explanatory text using a natural language processing engine based on the obtained information.

[1471] The generated description is converted into voice data using Text-to-Speech (TTS) technology. An emotion engine analyzes the emotional data and adjusts the tone of the description and voice data based on the results. The optimized voice data is then sent to the device.

[1472] Finalizing the device

[1473] The terminal receives the audio data sent from the server and plays the audio data for the user, who can listen to the audio data through the terminal's speaker or headphones.

[1474] Hardware and software used

[1475] Device: A mobile device such as a smartphone or tablet.

[1476] Server: A high-performance computer (including cloud servers).

[1477] CNN (Convolutional Neural Network): A deep learning model for image analysis.

[1478] Database: A database that stores information about tourist destinations.

[1479] Natural language processing engine: Software for generating explanatory text.

[1480] Text-to-Speech (TTS) technology: Software for synthesizing speech.

[1481] Emotion engine: Software for analyzing emotion data.

[1482] Specific examples

[1483] For example, if a user takes a photo of Tokyo Tower, a historical building, and uploads it to the app, the process will be as follows:

[1484] 1. The user launches the app and uploads a photo of Tokyo Tower that they have taken.

[1485] 2. The device sends this image data to the server, notifies the user of the success of the transmission, and analyzes facial expressions to collect emotional data, which is then sent to the server.

[1486] 3. The server analyzes the received image data using CNN and identifies it as Tokyo Tower.

[1487] 4. The server retrieves information about Tokyo Tower from the database and generates a description such as "Tokyo Tower was built in 1958..."

[1488] 5. The description is converted into voice data using TTS technology. The emotion engine detects the user's excitement and adjusts the tone of the voice.

[1489] 6. The adjusted audio data is sent to the device, which then plays it back to the user. The user can hear about Tokyo Tower's history and highlights, adjusted according to their emotions.

[1490] Prompt Sentence Examples

[1491] "Analyze photos of tourist attractions and generate natural language descriptions that provide detailed information about the place."

[1492] "Please explain how you can tailor audio guide content for tourist attractions based on user sentiment data."

[1493] This system allows users to obtain detailed information about tourist spots through voice without the need for a guide. The emotion engine allows for flexible responses based on the user's emotions, providing a more engaging sightseeing experience.

[1494] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1495] Step 1:

[1496] Users take photos of tourist spots and upload them to the app.

[1497] Input: A photo of a tourist spot taken by the user

[1498] How it works: The user launches the app and takes a photo and uploads it with a few simple steps.

[1499] Output: Uploaded photo data is saved on the device.

[1500] Step 2:

[1501] The terminal transmits the photo data uploaded by the user to the server.

[1502] Input: Saved photo data

[1503] How it works: Your device sends photo data over the network to a server, optimizing the image files to speed up the transfer.

[1504] Output: Photo data sent to the server

[1505] Step 3:

[1506] The terminal notifies the user of the successful transmission and collects the user's emotion data.

[1507] Input: Status of successful photo data transmission

[1508] How it works: The device notifies the user on the screen that "Image upload complete" and uses the front camera to analyze the user's facial expressions in real time to collect emotional data.

[1509] Output: Emotion data sent to the server

[1510] Step 4:

[1511] The server receives the image data and emotion data sent from the terminal.

[1512] Input: Photo data and emotion data sent from the device

[1513] Operation: The server temporarily stores the received image data and emotion data.

[1514] Output: Saved image data and emotion data

[1515] Step 5:

[1516] The server uses CNN to analyze the image data and identify tourist spots.

[1517] Input: Saved photo data

[1518] How it works: The server extracts image features using a Convolutional Neural Network (CNN) and identifies tourist spots. This is done at high speed using parallel computing.

[1519] Output: Name and ID of identified tourist spot

[1520] Step 6:

[1521] The server retrieves information from a database based on the name of the identified tourist attraction.

[1522] Input: Name or ID of tourist spot

[1523] How it works: The server queries the database to retrieve historical and tourist attraction information.

[1524] Output: Information about the tourist spots obtained

[1525] Step 7:

[1526] The server generates a description based on the information it has obtained.

[1527] Input: Information retrieved from a database

[1528] How it works: The server uses a natural language processing engine to create a description based on the information it retrieves, summarizing the information and removing unnecessary parts.

[1529] Output: Generated natural language description

[1530] Step 8:

[1531] The server converts the generated description into audio data.

[1532] Input: Generated description

[1533] How it works: The server uses Text-to-Speech (TTS) technology to convert the description into audio data, for example, using Google's Cloud Text-to-Speech API.

[1534] Output: Generated audio data

[1535] Step 9:

[1536] The server uses an emotion engine to analyze the emotion data and adjust the tone and speed of the description and audio data.

[1537] Input: Collected emotion data and generated speech data

[1538] How it works: The server analyzes the emotional data using the emotion engine and adjusts the tone and speed of the voice data based on the analysis results.

[1539] Output: Modified audio data

[1540] Step 10:

[1541] The server sends the optimized audio data to the device.

[1542] Input: Modified audio data

[1543] Operation: The server sends voice data to the terminal using data compression technology while taking into consideration the stability of data communication.

[1544] Output: Audio data sent to the device

[1545] Step 11:

[1546] The terminal receives the audio data sent from the server and plays it back to the user.

[1547] Input: Audio data sent from the server

[1548] How it works: The device temporarily stores the audio data and uses the audio playback feature to play it back to the user, who can listen to it through headphones or the built-in speaker.

[1549] Output: Played audio data

[1550] This allows users to obtain detailed information about tourist spots through audio without the need for a guide, enabling them to enjoy a personalized and fascinating sightseeing experience.

[1551] (Application example 2)

[1552] 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."

[1553] Conventional tourism applications do not take into account use in autonomous vehicles and lack the ability to dynamically adjust tourist information based on passenger emotions. This results in one-sided provision of tourist information, making it difficult to provide passengers with an optimal tourist experience. Furthermore, providing information that ignores passengers' emotional state poses a challenge, as it fails to stimulate their enjoyment and interest in sightseeing.

[1554] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving captured image data, means for analyzing the received image data to identify the location where the image was taken, means for acquiring information related to the identified location from a database, means for generating a description in natural language based on the acquired information, means for converting the generated description into audio data, means for playing the audio data, means for recognizing passenger emotions, means for adjusting information to be provided and audio guidance based on the recognized emotions, and means for playing the audio data in the vehicle's on-board system. This allows passengers to obtain detailed information about tourist spots through images taken inside the autonomous vehicle, and the information is dynamically adjusted according to the passenger's emotions, making it possible to provide an optimal sightseeing experience.

[1555] "Photographed image data" refers to photographic data taken by passengers at tourist spots.

[1556] "Means for receiving" refers to the mechanism by which the terminal or in-vehicle system transmits image data to the server via the network, and the server receives it.

[1557] "Means for analyzing image data to identify the location where the image was taken" is a process that uses a convolutional neural network (CNN) to analyze the image and identify the tourist destination.

[1558] The "means for obtaining information related to the identified place from a database" refers to a method by which the server searches and obtains information related to the identified tourist destination from an external or internal database.

[1559] "Means for generating explanatory text in natural language" refers to a method in which a computer uses human language to create easy-to-understand explanatory text based on acquired information.

[1560] "Means for converting narrative text into audio data" means the process of converting generated natural language text into audio data using text-to-speech (TTS) technology.

[1561] The "means for playing back audio data" refers to a mechanism for playing back the generated audio data through the speakers of the terminal or the in-vehicle system so that passengers can hear it.

[1562] The "means for recognizing passenger emotions" is a system that uses cameras and sensors to analyze passengers' facial expressions and vocal tones to determine their emotions in real time.

[1563] The "means for adjusting information provided and audio announcements based on emotions" refers to a process for changing the content of explanatory text and the tone and pace of audio announcements depending on the passenger's recognized emotions.

[1564] "Means for playing audio data in an in-vehicle system" refers to a method for outputting audio data using speakers or monitors installed inside an autonomous vehicle.

[1565] This invention relates to a tourist guide application used in autonomous vehicles. This system allows passengers to take and upload photos of tourist spots visible from the vehicle window and provides detailed information about those locations via voice. Furthermore, it recognizes passengers' emotions in real time and adjusts the information and voice guidance provided to provide an optimal tourist experience. The specific configuration and operation of this system are described below.

[1566] Server Roles and Operations

[1567] The server plays a central role in analyzing image data and emotion data received from the device. First, the server receives the captured image data. Next, it analyzes the image using a Convolutional Neural Network (CNN) to identify tourist attractions. Information related to the identified tourist attractions is retrieved from an internal or external database on the server. Based on the retrieved information, a description is generated in natural language. Then, Text-to-Speech (TTS) technology is used to convert the description into audio data. The emotion engine is also used to analyze the emotion data and dynamically adjust the content and tone of the audio guidance. The hardware and software used in this process include the CNN, TTS engine, emotion engine, and databases (SQL, NoSQL).

[1568] Terminal roles and processing

[1569] The device is primarily responsible for capturing and uploading image data and playing audio data. Passengers take photos of tourist spots and upload them to the device. The uploaded data is sent to the server for processing. Audio data sent from the server is played back by the device's in-vehicle system. For emotion recognition, the device also uses a camera and microphone to analyze passengers' facial expressions and voices. This data is also sent to the server.

[1570] Specific examples

[1571] For example, consider the case where a passenger takes a photo of a historical building visible from the window of an autonomous vehicle and uploads it to the app. The server analyzes this image data and identifies the building. It retrieves information about the building from a database and generates a description such as, "This building was built in the 18th century..." This description is then converted into voice data by a TTS engine. At the same time, an emotion engine analyzes the passenger's emotions, such as excitement or attention, and adjusts the description and voice tone accordingly. The adjusted voice data is then sent to the device, which plays the audio, allowing the passenger to hear about the building's history and highlights, with the audio adjusted according to their emotion.

[1572] Prompt Sentence Examples

[1573] "Take a photo of a tourist attraction that you can see from your location, and the app will provide you with audio guidance about the location's history and highlights."

[1574] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1575] Step 1:

[1576] A user takes a photo of a tourist spot and uploads it to the app on their device. The input here is the image data taken by the user, and the output is the image data being sent from the device to the server. The device then sends this image data to the server via the network, and notifies the server of the success or failure of the transmission.

[1577] Step 2:

[1578] The server receives image data sent from the terminal. The input here is the image data sent from the terminal, and the output is the image data stored on the server. The server prepares to analyze the received image data.

[1579] Step 3:

[1580] The server uses a Convolutional Neural Network (CNN) to analyze the received image data and identify the locations where the photos were taken. The input is the received image data, and the output is the names and identification information of the identified tourist spots. Specifically, the CNN model extracts image features and matches them with an existing database to identify tourist spots.

[1581] Step 4:

[1582] The server retrieves information related to the identified tourist destination from the database. The input is the identification information of the identified tourist destination, and the output is detailed information about the tourist destination, such as its history and attractions. The server searches the database and retrieves the related information.

[1583] Step 5:

[1584] The server generates a natural language description based on the acquired information. The input is detailed information about tourist attractions, and the output is a description written in natural language. The server uses a natural language generation (NLG) model to generate the description from the acquired information.

[1585] Step 6:

[1586] The server converts the generated description into audio data using Text-to-Speech (TTS) technology. The input is a natural language description, and the output is audio data. The TTS engine analyzes the input text and generates the corresponding audio.

[1587] Step 7:

[1588] The server simultaneously analyzes the emotional data using an emotion engine. The input is the passenger's emotional data, and the output is the analyzed emotional information. The emotion engine uses technologies such as face detection and voice analysis to evaluate the passenger's emotional state in real time.

[1589] Step 8:

[1590] The server adjusts the content of the explanatory text and voice guidance provided based on the recognized emotion. The input is the analyzed emotion information and explanatory text data, and the output is the adjusted voice data. The server regenerates the voice with a tone and pace that corresponds to the emotional state.

[1591] Step 9:

[1592] The server sends the optimized audio data to the terminal. The input is the adjusted audio data, and the output is the audio data sent to the terminal. The server sends the audio data to the terminal through the network.

[1593] Step 10:

[1594] The terminal receives the voice data sent from the server and plays it through the in-vehicle system. The input is the voice data from the server, and the output is voice guidance for passengers. The in-vehicle system can play the voice through the speaker and simultaneously display related information on the monitor.

[1595] 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.

[1596] 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.

[1597] 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.

[1598] 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.

[1599] 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.

[1600] 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.

[1601] 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).

[1602] 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.

[1603] 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."

[1604] 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.

[1605] 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).

[1606] 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.

[1607] 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.

[1608] 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.

[1609] 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.

[1610] 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.

[1611] 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.

[1612] 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.

[1613] 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.

[1614] 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.

[1615] 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.

[1616] The following is further disclosed regarding the above embodiment.

[1617] (Claim 1)

[1618] A means for receiving captured image data;

[1619] A means for analyzing the received image data to identify the location where the image was taken;

[1620] means for retrieving information relating to the identified location from a database;

[1621] A means for generating a description in natural language based on the acquired information;

[1622] A means for converting the generated description into audio data;

[1623] means for playing audio data;

[1624] A system including:

[1625] (Claim 2)

[1626] 10. The system of claim 1, wherein the system uses a convolutional neural network to identify the location where the image was taken.

[1627] (Claim 3)

[1628] 10. The system of claim 1, wherein the system uses voice synthesis technology to generate the voice data.

[1629] "Example 1"

[1630] (Claim 1)

[1631] A means for receiving captured image data;

[1632] A means for analyzing the received image data and identifying the location where the image was taken using a convolutional neural network;

[1633] means for retrieving information relating to the identified location from a database;

[1634] A means for generating a natural language description using a generative AI model based on the acquired information;

[1635] A means for converting the generated description into voice data using voice synthesis technology;

[1636] means for receiving and playing audio data;

[1637] A system including:

[1638] (Claim 2)

[1639] 10. The system of claim 1, wherein the system uses a convolutional neural network to identify the location where the image was taken.

[1640] (Claim 3)

[1641] 10. The system of claim 1, wherein the system uses voice synthesis technology to generate the voice data.

[1642] "Application Example 1"

[1643] (Claim 1)

[1644] A means for receiving captured image data;

[1645] A means for analyzing the received image data to identify the location where the image was taken;

[1646] means for retrieving information relating to the identified location from a database;

[1647] A means for generating a description in natural language based on the acquired information;

[1648] A means for converting the generated description into audio data;

[1649] A means for analyzing image data captured by an on-board camera;

[1650] a means incorporated into an infotainment system of the autonomous vehicle;

[1651] means for playing the generated audio data;

[1652] A system including:

[1653] (Claim 2)

[1654] 10. The system of claim 1, wherein the system uses a convolutional neural network to identify the location where the image was taken.

[1655] (Claim 3)

[1656] 10. The system of claim 1, wherein the system uses voice synthesis technology to generate the voice data.

[1657] "Example 2: Combining Emotion Engines"

[1658] (Claim 1)

[1659] A means for receiving captured image data;

[1660] A means for analyzing the received image data to identify the location where the image was taken;

[1661] means for retrieving information relating to the identified location from a database;

[1662] A means for generating a description in natural language based on the acquired information;

[1663] A means for converting the generated description into audio data;

[1664] means for collecting user emotion data;

[1665] a means for analyzing the collected emotion data;

[1666] a means for adjusting the description and the audio data based on the emotion data;

[1667] means for playing audio data;

[1668] A system including:

[1669] (Claim 2)

[1670] 10. The system of claim 1, wherein the system uses a convolutional neural network to identify the location where the image was taken.

[1671] (Claim 3)

[1672] 10. The system of claim 1, wherein the system uses voice synthesis technology to generate the voice data.

[1673] "Application example 2 when combining emotion engines"

[1674] (Claim 1)

[1675] A means for receiving captured image data;

[1676] A means for analyzing the received image data to identify the location where the image was taken;

[1677] means for retrieving information relating to the identified location from a database;

[1678] A means for generating a description in natural language based on the acquired information;

[1679] A means for converting the generated description into audio data;

[1680] means for playing audio data;

[1681] a means for recognizing passenger emotions;

[1682] means for adjusting the provided information and / or audio guidance based on the recognized emotion;

[1683] means for playing the audio data in an in-vehicle system of the vehicle;

[1684] A system including:

[1685] (Claim 2)

[1686] 10. The system of claim 1, wherein the system uses a convolutional neural network to identify the location where the image was taken.

[1687] (Claim 3)

[1688] 10. The system of claim 1, wherein the system uses voice synthesis technology to generate the voice data. [Explanation of symbols]

[1689] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving captured image data; A means for analyzing the received image data to identify the location where the image was taken; means for retrieving information relating to the identified location from a database; A means for generating a description in natural language based on the acquired information; A means for converting the generated description into audio data; means for playing audio data; A system including:

2. 10. The system of claim 1, wherein the system uses a convolutional neural network to identify the location of the photograph.

3. 10. The system of claim 1, wherein the system uses voice synthesis techniques to generate the voice data.

Images