system

Abstract

We provide the system. [Solution] A means for users to register their interests upon initial registration, A means of obtaining the user's current location, A means for generating relevant information based on acquired location information and interest information, A means of providing the generated related information as voice guidance, A means of updating interest information based on user responses, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] When visiting a specific place or tourist destination, there is a lack of means to know about the history and background of that place during the journey to reach the destination without prior detailed investigation. Also, due to the difficulty of providing customized information based on the interests and concerns of individual users, there is a need to provide in real-time the information that users really want to know, rather than just guide information. Furthermore, a system is required to improve the enjoyment of going out or taking a walk through these information provisions and to address the lack of exercise.

Means for Solving the Problems

[0005] This invention provides a system comprising means for initially registering user interest information, means for acquiring the user's current location, means for generating relevant information based on the acquired location information and interest information, means for providing the generated relevant information as voice guidance, and means for updating interest information based on the user's response. This system provides the user with optimized relevant information using a generative model, records the user's response, and performs reinforcement learning to constantly update the user's interest information to the latest state. As a result, the user can acquire necessary information in real time simply by walking, enabling a new experience that could not be obtained with conventional guidance methods.

[0006] "User interest information" refers to information that indicates the subjects and themes that the user is interested in, and is used to customize the guide content.

[0007] "Current location" refers to information indicating the user's geographical location, as detected by a location information acquisition device.

[0008] "Related information" refers to detailed information related to a location, extracted based on the user's interests and current location.

[0009] "Voice guidance" is a method for providing users with relevant information acquired through voice.

[0010] A "generative model" is a computational algorithm or method for optimizing and generating relevant information based on a user's interests and current location.

[0011] "Reinforcement learning" is a technology in which a system continuously learns the optimal actions based on user responses and uses this information to update its user interests. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, a tagged communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a guide system that provides customized voice guidance based on the user's interest information and location information. Embodiments of the invention are described below.

[0034] System Configuration

[0035] 1. Terminal

[0036] The user installs the app on their device and registers their interests upon first launch. The device then saves this information locally.

[0037] The device periodically obtains its current location using GPS while in motion and sends this information to the server.

[0038] 2. Server

[0039] When the server receives location information sent from the terminal, it searches the database for information related to that location based on the user's interests.

[0040] Related information is optimized using a generative model and generated as valuable guidance text for the user.

[0041] Finally, the generated voice guidance message is sent to the device.

[0042] 3. Voice guidance

[0043] The terminal converts the guidance text from the server into audio and provides it to the user. This audio guidance can be used even while walking, allowing users to obtain information without using their hands.

[0044] Specific example

[0045] For example, suppose a user is interested in history and is walking around a particular city. The device obtains its current location using GPS and sends that information to a server. The server, taking into account the user's interest in "history," searches its database for information about historical buildings located at that location. Unlike general information provided to the general public, this guidance is tailored to the user's interests. For example, it generates a guide message such as, "This location has a building constructed during the XX era, and it clearly reflects the characteristics of that era..." The device then plays this information as audio, providing it to the user in real time. This allows the user to obtain interesting information without having to do any research beforehand, resulting in a more enriching experience.

[0046] Furthermore, user responses (such as the playback time of voice guidance and preferred update information) are recorded on the device, and the server learns from this data to improve the quality of information provided in the future. In this way, the system deepens the user's interests and helps them discover new interests.

[0047] The following describes the processing flow.

[0048] Step 1:

[0049] When a user first launches the app on their device, the device displays a screen for entering their interests. The user selects or enters their areas of interest, and the device saves this information to a local database.

[0050] Step 2:

[0051] The device uses its GPS function in the background to periodically obtain its current location. When the location information meets certain conditions, it prepares to send the current location to the server.

[0052] Step 3:

[0053] The device uses a secure communication protocol to send the latest location information to the server. This transmission occurs at pre-specified intervals.

[0054] Step 4:

[0055] The server analyzes the received location information, compares it with the user's interest information, and searches the database for relevant information. It selects the most relevant data and proceeds to the next step.

[0056] Step 5:

[0057] The server uses a generative model to create guidance text to provide to the user. Based on relevant information, it optimizes the audio to deliver useful content to the user in a natural way.

[0058] Step 6:

[0059] The server sends the generated message to the terminal. This communication is also conducted through a secure protocol to ensure the protection of user data.

[0060] Step 7:

[0061] The terminal converts the received guidance text into audio data using a speech synthesis engine and plays it back to the user. The user can listen to this information in real time.

[0062] Step 8:

[0063] The device records user behavior data (playback time, whether it was paused, whether it was repeated, etc.) when the user listens to the guidance message. This data is later used to update the user's interest information.

[0064] Step 9:

[0065] When user behavior data is sent from the terminal to the server, the server updates the user's interest model using a reinforcement learning algorithm. This prepares the server to generate more relevant content for future information delivery.

[0066] (Example 1)

[0067] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0068] Despite users wanting to receive timely, interest-based information while on the go, current systems struggle to provide individually customized information, resulting in a limited user experience. There is a need to address this issue and deliver a richer experience by providing real-time information based on user interests.

[0069] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0070] In this invention, the server includes means for registering information about the user's interests, means for measuring the user's location, and means for generating relevant information based on the measured location information and the information about the user's interests. This enables the immediate provision of customized information tailored to the user's interests, creating a valuable experience for the user.

[0071] A "user" refers to an individual or organization that uses the system to obtain information.

[0072] "Interest-related information" refers to data about the user's preferences and areas of interest that they have registered in advance.

[0073] "Location information" refers to data that indicates a user's current geographical location, obtained using GPS or other location measurement technologies.

[0074] "Related information" refers to data that is useful to the user, generated based on information about the user's interests and location.

[0075] "Providing information via audio" means converting the generated relevant information into speech using text-to-speech technology and presenting it to the user.

[0076] A "generative model" is an information generation algorithm that utilizes artificial intelligence technology to optimize related information.

[0077] "Machine learning" is an algorithm that analyzes user reactions and usage history, and updates or improves the system based on the results of that analysis.

[0078] This invention is a guide system that provides real-time voice guidance with customized information based on the user's interests. This system combines the user's interest information and location information, generates relevant information using a generative AI model, and provides it to the user.

[0079] When a user installs an application on their device and uses the app for the first time, they register information about their interests. This information is stored in the device's local storage. The device uses GPS technology to obtain location information and periodically sends its current location to the server.

[0080] The server searches its database for relevant data based on the received location information and the user's interests. At this stage, it utilizes a generative AI model to generate highly relevant guidance text for the user. This generation process uses prompts such as, "The user's interest is history, and their current location is Kyoto. Please provide historical background information about this place."

[0081] The generated guidance text is sent from the server to the terminal, where it is converted into speech using text-to-speech (TTS) technology. This technology utilizes commonly available speech synthesis software, such as a speech synthesis API. The converted guidance is then delivered to the user through the terminal, allowing them to receive information hands-free.

[0082] As a concrete example, consider a user who is interested in history and is walking around Kyoto. The device acquires its current location information and sends it to the server. Based on the location information and the user's interests, the server searches for information about historical buildings and cultural backgrounds in Kyoto and generates voice guidance using a generative AI model. The device converts the generated guidance into voice and provides it to the user, allowing the user to obtain interesting information in real time. In this way, it is possible to enrich the user's experience and stimulate new interests.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user installs the application on their device and enters information about their interests upon first launch. This information is selected from categories such as travel, history, and food. The device saves the entered information to local storage. The input data is the user's interest information, and the output is the interest information stored locally.

[0086] Step 2:

[0087] The device periodically obtains the user's current location using its GPS function. The obtained location information includes latitude and longitude. The device encrypts this location information using a security protocol and sends it to the server. The input is the obtained location information, and the output is the transmission of the encrypted location information to the server.

[0088] Step 3:

[0089] The server receives location information from the terminal and accesses the database. Using the user's interest information as a key, it searches for information related to that location. The server extracts the relevant data and inputs it into the generating AI model. The input is the user's interest information and location information, and the output is the relevant data ready to be input into the generating AI model.

[0090] Step 4:

[0091] The server uses a generative AI model to generate customized guidance text based on relevant data. Specifically, it utilizes natural language processing techniques to output guidance text that reflects the user's interests. Prompt text is used in this process. The input is relevant data and prompt text, and the output is the generated guidance text.

[0092] Step 5:

[0093] The generated guidance text is sent from the server to the terminal. The terminal uses Text-to-Speech (TTS) technology to convert the guidance text into audio data. A third-party TTS API is used for the conversion. The input is the guidance text from the server, and the output is the audio data played back to the user.

[0094] Step 6:

[0095] The user continues moving while listening to the generated voice guidance. The device records the user's reactions while the guidance is playing. The user's playback time, preference updates, and playback frequency are recorded. The input is the user's reactions, and the output is information stored in local storage as data for the next learning session.

[0096] Step 7:

[0097] Subsequently, the terminal synchronizes the recorded user response data with the server. The server then feeds this data into a machine learning algorithm to update the user's interest information and use it to generate future guidance. The input is the user's response data, and the output is the updated interest information.

[0098] (Application Example 1)

[0099] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0100] Because it was not possible to provide users traveling in automated vehicles with personalized voice guidance that leveraged their individual interests in real time, users had limited means of maintaining their interest during their journey to their destination. As a result, it was difficult to enrich the travel experience and discover new interests in users.

[0101] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0102] In this invention, the server includes means for initially registering the user's interest information, means for acquiring the user's current location, means for generating relevant information based on the acquired location information and interest information, means for providing the generated relevant information as voice guidance, and means for updating the interest information based on the user's response. This makes it possible to provide users with personalized interest points along the driving route in an automatically controlled vehicle.

[0103] "User interest information" refers to information based on the interests and preferences of individual users.

[0104] "Initial registration method" refers to a function that allows users to register their interests and preferences.

[0105] "Means of obtaining current location" refers to a function that uses location information services such as GPS to determine where the user is currently located.

[0106] "Means for generating relevant information" refers to a function that creates useful information for the user based on the user's interests and location information.

[0107] "Means of providing information as voice guidance" refers to a function that transmits generated information to the user via voice.

[0108] "A means of updating interest information based on user responses" refers to a function that reviews and improves interest information based on user feedback.

[0109] An "automatically controlled vehicle" is a car that drives autonomously under computer control.

[0110] "Points of interest along the travel route" refer to locations or pieces of information that exist along the travel route and are likely to attract the user's attention.

[0111] Modes for carrying out the invention

[0112] In order to implement this invention, the following system configuration and processing are necessary.

[0113] First, when boarding the automated vehicle, the user operates a dedicated terminal to register their interests. This terminal has a built-in function to store the user's interests and can periodically acquire the user's current location via a GPS module.

[0114] Location information transmitted from the device is sent to a server, which then searches a database for relevant information based on the user's interests and this location information. This relevant information is optimized using generative models such as OpenAI's® generative AI model and generated as personalized guidance information for the user.

[0115] The generated voice guidance information is related to points along the route that are likely to be of interest to the user, and this information is received by a computer installed in the automatically controlled vehicle. This computer processes the information and provides real-time voice guidance to the user.

[0116] The user's response to the voice guidance is recorded on the device. This response data is processed on a server, and through reinforcement learning, the user's interest information is dynamically updated to improve the accuracy of future guidance.

[0117] For example, if a user has a particular interest in history, nearby historical sites may be explained during the ride. An example of a prompt for the generative AI model might be, "Generate information guiding me to interesting historical landmarks within 10 kilometers of my current location." In this way, the system is designed to make travel an intellectual adventure for the user, rather than just a means of transportation.

[0118] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0119] Step 1:

[0120] Users register their interests by operating the device. They select their interests from categories such as travel, history, and nature, and this information is stored locally on the device. This information is crucial because it is used for subsequent processing.

[0121] Step 2:

[0122] The device periodically uses a GPS module to acquire its current location information. This acquired location information is sent to a server and used as basic data to track the user's dynamic location.

[0123] Step 3:

[0124] The server receives location information sent from the terminal and pre-registered interest information. Based on this, it extracts information related to the user's interests from the database. This process generates customized data that associates location information with interest information.

[0125] Step 4:

[0126] The server optimizes the extracted information using a generative AI model, such as an OpenAI model. The generative AI model receives prompts such as "Generate information guiding the user to points of interest from their current location," and a user-specific guidance message is output.

[0127] Step 5:

[0128] The generated guidance text is converted into audio data and sent to the terminal. The terminal receives this data and provides it to the user as audio guidance in real time. This allows the user to instantly obtain information relevant to their interests while on the move.

[0129] Step 6:

[0130] The device records user responses. For example, feedback data such as the number of times voice instructions for guidance are played and changes in user interest is sent to the server to customize information for future use.

[0131] Step 7:

[0132] The server uses the received feedback data to update interest information using a reinforcement learning model. Through this process, the system continuously improves itself so that the next information provided is more tailored to the user's preferences.

[0133] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0134] This invention combines a voice guidance system based on user interest information and location information with an emotion recognition engine. Specific embodiments of the system are described below.

[0135] System Configuration

[0136] 1. Terminal

[0137] Through an app installed on the device, the user initially registers their interests. The device then saves this information locally.

[0138] The device obtains its current location using GPS sensors while moving and periodically sends this information to the server.

[0139] Furthermore, the device has a function to sense the user's voice and facial expressions, which the emotion engine uses to recognize the user's emotions in real time.

[0140] 2. Server

[0141] The server receives location information and sentiment data transmitted from the terminal. Based on this, it searches the database for relevant information that matches the user's interests.

[0142] The retrieved information is optimized by a generative model to suit the user's emotional state. For example, if the user is perceived as excited, the information can be provided in more detail and include more explanations.

[0143] The generated message is sent from the server to the terminal.

[0144] 3. Voice guidance

[0145] The terminal converts the guidance text received from the server into speech and provides it to the user. A speech synthesis engine is used in this process.

[0146] Specific example

[0147] For example, consider a scenario where a user is interested in nature and is walking through a forest. The device acquires its current location information and sends it to the server. The server searches for nature information related to this location and generates information from the accumulated data, such as "This forest is home to a rare species of bird."

[0148] The emotion engine analyzes the user's response, and if it recognizes a positive reaction, the server can include additional information or quizzes in the message that are relevant to that emotion. For example, it might add a question like, "What color do you think this bird is?"

[0149] The generated guidance text is then sent to the device and played back as audio. Users can obtain information that makes their individual experiences more interactive and enjoyable, and leads to new discoveries in the moment. Furthermore, by having the server learn from the user's emotional data, subsequent guidance can be further optimized, resulting in information that is more closely matched to each individual's interests.

[0150] The following describes the processing flow.

[0151] Step 1:

[0152] When a user first launches the app, the device displays an interface for registering their interests. The user selects their interests from the displayed categories and saves them to a local database.

[0153] Step 2:

[0154] The device uses GPS in the background to obtain the user's current location at regular intervals. This location information is then formatted for transmission from the device to the server.

[0155] Step 3:

[0156] The device activates its emotion engine and uses the microphone and camera to acquire emotion data in real time from the user's voice tone and facial expressions. If the emotion data meets certain criteria, it is prepared for transmission, including that data.

[0157] Step 4:

[0158] The device transmits the latest location and sentiment data to the server via a secure communication protocol.

[0159] Step 5:

[0160] The server analyzes the received location information and searches the database for information related to that region. It then refines the relevant information based on the user's interests and sentiment data.

[0161] Step 6:

[0162] The server generates optimized guidance text using a generative model. The tone and content of the guidance text are customized to match the user's emotions, as determined by the emotion engine.

[0163] Step 7:

[0164] The generated guidance text is sent from the server to the terminal. The terminal receives it and prepares to output it as voice guidance through speech synthesis.

[0165] Step 8:

[0166] The device plays voice guidance optimized for the user. The user's reactions during playback are also continuously analyzed by the emotion engine, and the guidance may be adjusted based on the results.

[0167] Step 9:

[0168] User reactions and emotional data are recorded again and sent to the server to help improve future guidance. The server learns from this data and updates the user's interests and emotional profile to provide more personalized guidance.

[0169] (Example 2)

[0170] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0171] Conventional audio guide systems only provide guidance based on the user's interests and location, without considering the user's emotions. As a result, the information received by the user is one-sided and lacks the appeal of a personalized experience. Furthermore, the user's interests are not properly updated, leading to problems with long-term satisfaction.

[0172] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0173] In this invention, the server includes means for recognizing the user's emotions, means for generating relevant information based on location information, interest information, and emotion data, and means for using a generative model to optimize the generated relevant information based on the user's emotional state. This makes it possible to provide personalized guidance that responds to the user's emotions, enrich individual experiences, and accurately update interest information to improve long-term satisfaction.

[0174] "User interest information" refers to information indicating a user's preferences and interests, which they register with the audio guide system.

[0175] "Current location" refers to data that indicates the user's geographical location, obtained using GPS or other location information systems.

[0176] "User emotions" refer to data that indicates the user's emotional state, recognized in real time through voice and facial expression sensors.

[0177] "Related information" refers to content generated based on the user's interests, current location, and emotions, and is provided to the user as audio guidance.

[0178] A "device that presents information as audio" refers to equipment or software that converts generated related information into audio and allows the user to hear it.

[0179] A "generative model" is an algorithm or software used to optimize relevant information according to the user's emotional state.

[0180] "Machine learning" is a technology that uses data analysis and predictive model building to update user interest information based on user responses.

[0181] One embodiment of this invention is a system that provides audio guidance based on user interest information, location information, and emotion data. This system consists of a terminal, a server, and network communication.

[0182] The device has an application installed for users to register their interests. Users register their interests through this application, and the device saves this information to local storage. The device also has a built-in GPS function to acquire the user's current location while they are moving. Furthermore, the device is equipped with an emotion engine that uses a microphone and camera to recognize the user's emotions in real time from their voice and facial expressions.

[0183] The server is equipped with the ability to receive location and sentiment data transmitted from the user's device. Based on the received information, the server searches its database for relevant information. This search uses interest information, location information, and sentiment data. The server uses a generative AI model to optimize the acquired relevant information to suit the user's current sentiment state. The optimized guidance information is sent to the device and provided as voice by a speech synthesis engine.

[0184] As a concrete example, consider a scenario where a user is interested in nature exploration and is strolling through a forest. The device obtains the user's current location and sends it to the server. The server searches its database for nature information related to that location and uses a generative AI model to generate a message such as, "This forest is home to a rare XX bird." If the user shows positive emotions, additional information such as a quiz might be provided, such as, "What color do you think this bird is?"

[0185] An example of a prompt might be, "If the user appears happy in the forest, create a guide that includes interesting nature information and a simple question." In this way, the system can provide the user with a more personalized and interactive experience.

[0186] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0187] Step 1:

[0188] On the device, users register their interests through a dedicated app. The input for this process is the user's chosen categories of interest (e.g., "Nature," "History," "Art"), and the output is interest data stored in local storage. This clarifies the user's interests.

[0189] Step 2:

[0190] The device obtains its current location using its built-in GPS sensor. The input is geographical location data obtained from the device's sensor, and the output is the user's current location information. The device updates its location information at regular intervals to prepare for subsequent data transmission.

[0191] Step 3:

[0192] The device acquires the user's voice and facial expressions and sends the data to the emotion engine. The input is real-time audio and video data acquired from the microphone and camera, and the output is emotion data obtained through analysis. Based on this data, the emotion engine recognizes the user's emotions and classifies them into states such as "happy" or "excited."

[0193] Step 4:

[0194] The device transmits the collected location information and sentiment data to the server. The input is the location information and sentiment data obtained in steps 2 and 3, and the output is the integrated data received by the server. The device transmits this information to the server at an appropriate frequency.

[0195] Step 5:

[0196] The server analyzes received location and sentiment data and searches for relevant information in its database. Input is data sent from the terminal, and output is content related to the user's interests (e.g., "This forest is home to a rare species of bird"). The server uses an efficient search algorithm to effectively find relevant information.

[0197] Step 6:

[0198] The server uses a generative AI model to adapt relevant information to the user's emotional state. The input is the relevant information and emotional data obtained in step 5, and the output is a guide optimized for the emotional state. The generative AI model generates information based on a prompt (e.g., "If the user is happy in the forest, create a guide that includes interesting nature information and a simple question.").

[0199] Step 7:

[0200] The server sends an optimized message to the terminal. The input is the message generated in step 6, and the output is what the terminal receives.

[0201] Step 8:

[0202] The terminal converts received guidance text into speech using a speech synthesis engine and provides it to the user. The input is guidance text sent from the server, and the output is an audio message for the user to hear. This allows the user to obtain information in audio format that is tailored to their interests and the situation at hand.

[0203] (Application Example 2)

[0204] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0205] Existing user guidance systems lack the ability to provide appropriate information tailored to users' interests and emotional states, resulting in a failure to deliver individually optimized experiences. This is especially true in physical stores, where a diverse range of products and services must be presented concisely and effectively to meet user interests.

[0206] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0207] In this invention, the server includes means for initially registering user interest data, means for acquiring the user's current location, means for recognizing the user's emotional state through a smart wearable device, means for optimizing relevant information using a generative AI model, and means for recording user responses and performing reinforcement learning based on that data. This makes it possible to provide information tailored to the user's interests and emotions, and to individually optimize the in-store experience.

[0208] "User interest data" refers to information about topics that users are particularly interested in, and is initially registered for the purpose of providing individual services and suggesting information.

[0209] "Current location" refers to information indicating the geographical location where a user is located at a specific point in time, and is the underlying data for location-based services.

[0210] "Related information" refers to specific information generated based on the user's interests and current location, and is considered to be interesting and useful to the user.

[0211] "Audio presentation" is a method of conveying information through sound without relying on visual information, enabling users to receive information through audio.

[0212] A "smart wearable device" is an electronic device worn on the body that has the function of detecting the user's movements and emotional state.

[0213] "Emotional state" refers to the user's psychological and mental state, and is information that is recognized in real time using indicators such as facial expressions and tone of voice.

[0214] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to create new information and suggestions from data, and is intended for optimizing and personalizing information.

[0215] Reinforcement learning is a machine learning technique that uses trial and error to obtain the optimal result in action selection, and is a technology used to improve the system's behavior based on user responses.

[0216] This invention is an information delivery system that takes into account the user's interests and emotional state, enabling a more personalized experience in physical stores. The system's program is executed by a smart wearable device carried by the user, a server, and the network infrastructure connecting them.

[0217] The smart wearable device is equipped with a GPS sensor and a camera and microphone for emotion recognition, acquiring the user's current location and emotional state in real time. Based on this, it is transmitted to a server along with the user's initial interest data.

[0218] The server begins processing information based on the received data. It searches the database for product information suitable for the user's current location and emotions, and optimizes it using a generative AI model. This generative AI model is used to generate expressions that resonate most with the user's current emotions. For example, if the user expresses surprise, the server will generate information that evokes surprise and matches that emotion.

[0219] The optimized information is transmitted to the device as audio presentations by a speech synthesis engine and provided to the user. This allows the user to receive high-quality information in real time via audio. The user's responses are recorded again on the device and sent to the server, where a reinforcement learning algorithm further optimizes the interest data, which can then be used to provide information during subsequent visits.

[0220] As a concrete example, consider a scenario where a user is in an electronics store and is in the display area for a new smartphone. In this case, when the emotion recognition software detects the user's excitement, the server uses a generated AI model to create a message such as, "This smartphone is equipped with the latest camera technology. Do you have any questions?" and plays it back using a speech synthesis engine.

[0221] An example of a prompt message is as follows:

[0222] "When the user's emotional state is heightened, generate a detailed description and special information about the product. The product category should be smartphones, and the features should include camera performance, cutting-edge technology, and brand innovation, incorporating relevant knowledge into the description."

[0223] In this way, the system can significantly improve the user's experience in physical stores.

[0224] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0225] Step 1:

[0226] The device obtains the user's current location using a GPS sensor. The input used is GPS sensor data. The device sends this location data, along with the user's interest data, to the server. The output is a set of location data and interest data.

[0227] Step 2:

[0228] The device detects the user's voice and facial expressions, and uses emotion recognition software to analyze their emotional state in real time. The input consists of data from the device's microphone and camera. The output is data indicating the user's emotional state, which is also sent to the server.

[0229] Step 3:

[0230] The server receives location data, interest data, and sentiment data transmitted from the terminal. It searches and extracts information related to the user's location and interests from the database. A database search engine is used in this process. The output is a set of relevant information.

[0231] Step 4:

[0232] The server uses a generative AI model to optimize the extracted relevant information according to the user's emotional state. The input consists of relevant information and emotional data. The generative AI model uses these as prompts to generate the guidance text to be output.

[0233] Step 5:

[0234] The server passes the generated guidance text to the speech synthesis engine, which converts it into audio data. The input is the text data of the guidance text. The output is audio data, which is sent to the terminal.

[0235] Step 6:

[0236] The terminal plays audio data received from the server, providing voice guidance to the user. This allows the user to receive engaging audio information in real time. The output is the user's auditory reception of the information.

[0237] Step 7:

[0238] The system collects user responses (e.g., voice responses or additional gestures) again and sends them from the terminal to the server. The input is user response data. The server uses this data to update interest data using a reinforcement learning algorithm. The output is the updated interest data, which is used to optimize guidance for future visits.

[0239] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0240] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0241] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0242] [Second Embodiment]

[0243] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0244] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0245] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0246] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0247] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0248] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0249] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0250] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0251] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0252] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0253] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0254] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0255] This invention is a guide system that provides customized voice guidance based on the user's interest information and location information. Embodiments of the invention are described below.

[0256] System Configuration

[0257] 1. Terminal

[0258] The user installs the app on their device and registers their interests upon first launch. The device then saves this information locally.

[0259] The device periodically obtains its current location using GPS while in motion and sends this information to the server.

[0260] 2. Server

[0261] When the server receives location information sent from the terminal, it searches the database for information related to that location based on the user's interests.

[0262] Related information is optimized using a generative model and generated as valuable guidance text for the user.

[0263] Finally, the generated voice guidance message is sent to the device.

[0264] 3. Voice guidance

[0265] The terminal converts the guidance text from the server into audio and provides it to the user. This audio guidance can be used even while walking, allowing users to obtain information without using their hands.

[0266] Specific example

[0267] For example, suppose a user is interested in history and is walking around a particular city. The device obtains its current location using GPS and sends that information to a server. The server, taking into account the user's interest in "history," searches its database for information about historical buildings located at that location. Unlike general information provided to the general public, this guidance is tailored to the user's interests. For example, it generates a guide message such as, "This location has a building constructed during the XX era, and it clearly reflects the characteristics of that era..." The device then plays this information as audio, providing it to the user in real time. This allows the user to obtain interesting information without having to do any research beforehand, resulting in a more enriching experience.

[0268] Furthermore, user responses (such as the playback time of voice guidance and preferred update information) are recorded on the device, and the server learns from this data to improve the quality of information provided in the future. In this way, the system deepens the user's interests and helps them discover new interests.

[0269] The following describes the processing flow.

[0270] Step 1:

[0271] When a user first launches the app on their device, the device displays a screen for entering their interests. The user selects or enters their areas of interest, and the device saves this information to a local database.

[0272] Step 2:

[0273] The device uses its GPS function in the background to periodically obtain its current location. When the location information meets certain conditions, it prepares to send the current location to the server.

[0274] Step 3:

[0275] The device uses a secure communication protocol to send the latest location information to the server. This transmission occurs at pre-specified intervals.

[0276] Step 4:

[0277] The server analyzes the received location information, compares it with the user's interest information, and searches the database for relevant information. It selects the most relevant data and proceeds to the next step.

[0278] Step 5:

[0279] The server uses a generative model to create guidance text to provide to the user. Based on relevant information, it optimizes the audio to deliver useful content to the user in a natural way.

[0280] Step 6:

[0281] The server sends the generated guidance text to the terminal. This communication is also carried out through a secure protocol to ensure the protection of user data.

[0282] Step 7:

[0283] The terminal converts the received guidance text into voice data using a speech synthesis engine and plays it for the user. The user can listen to this information in real time.

[0284] Step 8:

[0285] The terminal records the user's behavior data (such as play time, pause during playback, and whether there is a repeat) when the user listens to the guidance text. This data is later used to update the user's interest information.

[0286] Step 9:

[0287] When the user's behavior data is sent from the terminal to the server, the server updates the user's interest model using a reinforcement learning algorithm. This prepares to generate more suitable content for the next information provision.

[0288] (Example 1)

[0289] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] Although the user wants to immediately receive information according to their interests while moving, in the current system, it is difficult to provide individually customized information, and there is a problem that the user experience is limited. A method is required to solve this problem and realize a richer experience by providing information based on the user's interests in real time. <00009​​The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0292] In this invention, the server includes means for registering information about the user's interests, means for measuring the user's location, and means for generating relevant information based on the measured location information and the information about the user's interests. This enables the immediate provision of customized information tailored to the user's interests, creating a valuable experience for the user.

[0293] A "user" refers to an individual or organization that uses the system to obtain information.

[0294] "Interest-related information" refers to data about the user's preferences and areas of interest that they have registered in advance.

[0295] "Location information" refers to data that indicates a user's current geographical location, obtained using GPS or other location measurement technologies.

[0296] "Related information" refers to data that is useful to the user, generated based on information about the user's interests and location.

[0297] "Providing information via audio" means converting the generated relevant information into speech using text-to-speech technology and presenting it to the user.

[0298] A "generative model" is an information generation algorithm that utilizes artificial intelligence technology to optimize related information.

[0299] "Machine learning" is an algorithm that analyzes user reactions and usage history, and updates or improves the system based on the results of that analysis.

[0300] This invention is a guide system that provides real-time voice guidance with customized information based on the user's interests. This system combines the user's interest information and location information, generates relevant information using a generative AI model, and provides it to the user.

[0301] When a user installs an application on their device and uses the app for the first time, they register information about their interests. This information is stored in the device's local storage. The device uses GPS technology to obtain location information and periodically sends its current location to the server.

[0302] The server searches its database for relevant data based on the received location information and the user's interests. At this stage, it utilizes a generative AI model to generate highly relevant guidance text for the user. This generation process uses prompts such as, "The user's interest is history, and their current location is Kyoto. Please provide historical background information about this place."

[0303] The generated guidance text is sent from the server to the terminal, where it is converted into speech using text-to-speech (TTS) technology. This technology utilizes commonly available speech synthesis software, such as a speech synthesis API. The converted guidance is then delivered to the user through the terminal, allowing them to receive information hands-free.

[0304] As a concrete example, consider a user who is interested in history and is walking around Kyoto. The device acquires its current location information and sends it to the server. Based on the location information and the user's interests, the server searches for information about historical buildings and cultural backgrounds in Kyoto and generates voice guidance using a generative AI model. The device converts the generated guidance into voice and provides it to the user, allowing the user to obtain interesting information in real time. In this way, it is possible to enrich the user's experience and stimulate new interests.

[0305] The flow of the specific process in Example 1 will be described using FIG. 11.

[0306] Step 1:

[0307] The user installs an application on the terminal and enters information related to their interests at the first startup. This information is selected from categories such as travel, history, food, etc. The terminal saves the entered information in the local storage. As input, the user's interest information is obtained, and the output is the interest information saved locally.

[0308] Step 2:

[0309] The terminal periodically obtains the user's current location using the GPS function. The obtained location information includes latitude and longitude. The terminal encrypts and transmits this location information to the server using a security protocol. The input is the obtained location information, and the output is the transmission of the encrypted location information to the server.

[0310] Step 3:

[0311] The server receives the location information from the terminal and accesses the database. Using the user's interest information as a key, it searches for information related to that location. The server extracts the relevant data and inputs that data into the generative AI model. The input is the user's interest information and location information, and the output is the relevant data prepared for input into the generative AI model.

[0312] Step 4:

[0313] The server uses the generative AI model to generate customized guidance text based on the relevant data. Specifically, it utilizes natural language processing technology to output guidance text that reflects the user's interests. A prompt text is used in this process. The input is the relevant data and the prompt text, and the output is the generated guidance text.

[0314] Step 5:

[0315] The generated guidance text is sent from the server to the terminal. The terminal uses Text-to-Speech (TTS) technology to convert the guidance text into audio data. A third-party TTS API is used for the conversion. The input is the guidance text from the server, and the output is the audio data played back to the user.

[0316] Step 6:

[0317] The user continues moving while listening to the generated voice guidance. The device records the user's reactions while the guidance is playing. The user's playback time, preference updates, and playback frequency are recorded. The input is the user's reactions, and the output is information stored in local storage as data for the next learning session.

[0318] Step 7:

[0319] Subsequently, the terminal synchronizes the recorded user response data with the server. The server then feeds this data into a machine learning algorithm to update the user's interest information and use it to generate future guidance. The input is the user's response data, and the output is the updated interest information.

[0320] (Application Example 1)

[0321] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0322] Because it was not possible to provide users traveling in automated vehicles with personalized voice guidance that leveraged their individual interests in real time, users had limited means of maintaining their interest during their journey to their destination. As a result, it was difficult to enrich the travel experience and discover new interests in users.

[0323] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0324] In this invention, the server includes means for initially registering the user's interest information, means for acquiring the user's current location, means for generating relevant information based on the acquired location information and interest information, means for providing the generated relevant information as voice guidance, and means for updating the interest information based on the user's response. This makes it possible to provide users with personalized interest points along the driving route in an automatically controlled vehicle.

[0325] "User interest information" refers to information based on the interests and preferences of individual users.

[0326] "Initial registration method" refers to a function that allows users to register their interests and preferences.

[0327] "Means of obtaining current location" refers to a function that uses location information services such as GPS to determine where the user is currently located.

[0328] "Means for generating relevant information" refers to a function that creates useful information for the user based on the user's interests and location information.

[0329] "Means of providing information as voice guidance" refers to a function that transmits generated information to the user via voice.

[0330] "A means of updating interest information based on user responses" refers to a function that reviews and improves interest information based on user feedback.

[0331] An "automatically controlled vehicle" is a car that drives autonomously under computer control.

[0332] "Points of interest along the travel route" refer to locations or pieces of information that exist along the travel route and are likely to attract the user's attention.

[0333] Modes for carrying out the invention

[0334] In order to implement this invention, the following system configuration and processing are necessary.

[0335] First, when boarding the automated vehicle, the user operates a dedicated terminal to register their interests. This terminal has a built-in function to store the user's interests and can periodically acquire the user's current location via a GPS module.

[0336] Location information transmitted from the device is sent to a server, which then searches its database for relevant information based on the user's interests and this location information. This relevant information is optimized using generative models such as OpenAI's generative AI model and generated as personalized guidance information for the user.

[0337] The generated voice guidance information is related to points along the route that are likely to be of interest to the user, and this information is received by a computer installed in the automatically controlled vehicle. This computer processes the information and provides real-time voice guidance to the user.

[0338] The user's response to the voice guidance is recorded on the device. This response data is processed on a server, and through reinforcement learning, the user's interest information is dynamically updated to improve the accuracy of future guidance.

[0339] For example, if a user has a particular interest in history, nearby historical sites may be explained during the ride. An example of a prompt for the generative AI model might be, "Generate information guiding me to interesting historical landmarks within 10 kilometers of my current location." In this way, the system is designed to make travel an intellectual adventure for the user, rather than just a means of transportation.

[0340] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0341] Step 1:

[0342] Users register their interests by operating the device. They select their interests from categories such as travel, history, and nature, and this information is stored locally on the device. This information is crucial because it is used for subsequent processing.

[0343] Step 2:

[0344] The device periodically uses a GPS module to acquire its current location information. This acquired location information is sent to a server and used as basic data to track the user's dynamic location.

[0345] Step 3:

[0346] The server receives location information sent from the terminal and pre-registered interest information. Based on this, it extracts information related to the user's interests from the database. This process generates customized data that associates location information with interest information.

[0347] Step 4:

[0348] The server optimizes the extracted information using a generative AI model, such as an OpenAI model. The generative AI model receives prompts such as "Generate information guiding the user to points of interest from their current location," and a user-specific guidance message is output.

[0349] Step 5:

[0350] The generated guidance text is converted into audio data and sent to the terminal. The terminal receives this data and provides it to the user as audio guidance in real time. This allows the user to instantly obtain information relevant to their interests while on the move.

[0351] Step 6:

[0352] The device records user responses. For example, feedback data such as the number of times voice instructions for guidance are played and changes in user interest is sent to the server to customize information for future use.

[0353] Step 7:

[0354] The server uses the received feedback data to update interest information using a reinforcement learning model. Through this process, the system continuously improves itself so that the next information provided is more tailored to the user's preferences.

[0355] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0356] This invention combines a voice guidance system based on user interest information and location information with an emotion recognition engine. Specific embodiments of the system are described below.

[0357] System Configuration

[0358] 1. Terminal

[0359] Through an app installed on the device, the user initially registers their interests. The device then saves this information locally.

[0360] The device obtains its current location using GPS sensors while moving and periodically sends this information to the server.

[0361] Furthermore, the device has a function to sense the user's voice and facial expressions, which the emotion engine uses to recognize the user's emotions in real time.

[0362] 2. Server

[0363] The server receives location information and sentiment data transmitted from the terminal. Based on this, it searches the database for relevant information that matches the user's interests.

[0364] The retrieved information is optimized by a generative model to suit the user's emotional state. For example, if the user is perceived as excited, the information can be provided in more detail and include more explanations.

[0365] The generated message is sent from the server to the terminal.

[0366] 3. Voice guidance

[0367] The terminal converts the guidance text received from the server into speech and provides it to the user. A speech synthesis engine is used in this process.

[0368] Specific example

[0369] For example, consider a scenario where a user is interested in nature and is walking through a forest. The device acquires its current location information and sends it to the server. The server searches for nature information related to this location and generates information from the accumulated data, such as "This forest is home to a rare species of bird."

[0370] The emotion engine analyzes the user's response, and if it recognizes a positive reaction, the server can include additional information or quizzes in the message that are relevant to that emotion. For example, it might add a question like, "What color do you think this bird is?"

[0371] The generated guidance text is then sent to the device and played back as audio. Users can obtain information that makes their individual experiences more interactive and enjoyable, and leads to new discoveries in the moment. Furthermore, by having the server learn from the user's emotional data, subsequent guidance can be further optimized, resulting in information that is more closely matched to each individual's interests.

[0372] The following describes the processing flow.

[0373] Step 1:

[0374] When a user first launches the app, the device displays an interface for registering their interests. The user selects their interests from the displayed categories and saves them to a local database.

[0375] Step 2:

[0376] The device uses GPS in the background to obtain the user's current location at regular intervals. This location information is then formatted for transmission from the device to the server.

[0377] Step 3:

[0378] The device activates its emotion engine and uses the microphone and camera to acquire emotion data in real time from the user's voice tone and facial expressions. If the emotion data meets certain criteria, it is prepared for transmission, including that data.

[0379] Step 4:

[0380] The device transmits the latest location and sentiment data to the server via a secure communication protocol.

[0381] Step 5:

[0382] The server analyzes the received location information and searches the database for information related to that region. It then refines the relevant information based on the user's interests and sentiment data.

[0383] Step 6:

[0384] The server generates optimized guidance text using a generative model. The tone and content of the guidance text are customized to match the user's emotions, as determined by the emotion engine.

[0385] Step 7:

[0386] The generated guidance text is sent from the server to the terminal. The terminal receives it and prepares to output it as voice guidance through speech synthesis.

[0387] Step 8:

[0388] The device plays voice guidance optimized for the user. The user's reactions during playback are also continuously analyzed by the emotion engine, and the guidance may be adjusted based on the results.

[0389] Step 9:

[0390] User reactions and emotional data are recorded again and sent to the server to help improve future guidance. The server learns from this data and updates the user's interests and emotional profile to provide more personalized guidance.

[0391] (Example 2)

[0392] Next, we will describe Example 2. 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".

[0393] Conventional audio guide systems only provide guidance based on the user's interests and location, without considering the user's emotions. As a result, the information received by the user is one-sided and lacks the appeal of a personalized experience. Furthermore, the user's interests are not properly updated, leading to problems with long-term satisfaction.

[0394] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0395] In this invention, the server includes means for recognizing the user's emotions, means for generating relevant information based on location information, interest information, and emotion data, and means for using a generative model to optimize the generated relevant information based on the user's emotional state. This makes it possible to provide personalized guidance that responds to the user's emotions, enrich individual experiences, and accurately update interest information to improve long-term satisfaction.

[0396] "User interest information" refers to information indicating a user's preferences and interests, which they register with the audio guide system.

[0397] "Current location" refers to data that indicates the user's geographical location, obtained using GPS or other location information systems.

[0398] "User emotions" refer to data that indicates the user's emotional state, recognized in real time through voice and facial expression sensors.

[0399] "Related information" refers to content generated based on the user's interests, current location, and emotions, and is provided to the user as audio guidance.

[0400] A "device that presents information as audio" refers to equipment or software that converts generated related information into audio and allows the user to hear it.

[0401] A "generative model" is an algorithm or software used to optimize relevant information according to the user's emotional state.

[0402] "Machine learning" is a technology that uses data analysis and predictive model building to update user interest information based on user responses.

[0403] One embodiment of this invention is a system that provides audio guidance based on user interest information, location information, and emotion data. This system consists of a terminal, a server, and network communication.

[0404] The device has an application installed for users to register their interests. Users register their interests through this application, and the device saves this information to local storage. The device also has a built-in GPS function to acquire the user's current location while they are moving. Furthermore, the device is equipped with an emotion engine that uses a microphone and camera to recognize the user's emotions in real time from their voice and facial expressions.

[0405] The server is equipped with the ability to receive location and sentiment data transmitted from the user's device. Based on the received information, the server searches its database for relevant information. This search uses interest information, location information, and sentiment data. The server uses a generative AI model to optimize the acquired relevant information to suit the user's current sentiment state. The optimized guidance information is sent to the device and provided as voice by a speech synthesis engine.

[0406] As a concrete example, consider a scenario where a user is interested in nature exploration and is strolling through a forest. The device obtains the user's current location and sends it to the server. The server searches its database for nature information related to that location and uses a generative AI model to generate a message such as, "This forest is home to a rare XX bird." If the user shows positive emotions, additional information such as a quiz might be provided, such as, "What color do you think this bird is?"

[0407] An example of a prompt might be, "If the user appears happy in the forest, create a guide that includes interesting nature information and a simple question." In this way, the system can provide the user with a more personalized and interactive experience.

[0408] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0409] Step 1:

[0410] On the device, users register their interests through a dedicated app. The input for this process is the user's chosen categories of interest (e.g., "Nature," "History," "Art"), and the output is interest data stored in local storage. This clarifies the user's interests.

[0411] Step 2:

[0412] The device obtains its current location using its built-in GPS sensor. The input is geographical location data obtained from the device's sensor, and the output is the user's current location information. The device updates its location information at regular intervals to prepare for subsequent data transmission.

[0413] Step 3:

[0414] The device acquires the user's voice and facial expressions and sends the data to the emotion engine. The input is real-time audio and video data acquired from the microphone and camera, and the output is emotion data obtained through analysis. Based on this data, the emotion engine recognizes the user's emotions and classifies them into states such as "happy" or "excited."

[0415] Step 4:

[0416] The device transmits the collected location information and sentiment data to the server. The input is the location information and sentiment data obtained in steps 2 and 3, and the output is the integrated data received by the server. The device transmits this information to the server at an appropriate frequency.

[0417] Step 5:

[0418] The server analyzes received location and sentiment data and searches for relevant information in its database. Input is data sent from the terminal, and output is content related to the user's interests (e.g., "This forest is home to a rare species of bird"). The server uses an efficient search algorithm to effectively find relevant information.

[0419] Step 6:

[0420] The server uses a generative AI model to adapt relevant information to the user's emotional state. The input is the relevant information and emotional data obtained in step 5, and the output is a guide optimized for the emotional state. The generative AI model generates information based on a prompt (e.g., "If the user is happy in the forest, create a guide that includes interesting nature information and a simple question.").

[0421] Step 7:

[0422] The server sends an optimized message to the terminal. The input is the message generated in step 6, and the output is what the terminal receives.

[0423] Step 8:

[0424] The terminal converts received guidance text into speech using a speech synthesis engine and provides it to the user. The input is guidance text sent from the server, and the output is an audio message for the user to hear. This allows the user to obtain information in audio format that is tailored to their interests and the situation at hand.

[0425] (Application Example 2)

[0426] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0427] Existing user guidance systems lack the ability to provide appropriate information tailored to users' interests and emotional states, resulting in a failure to deliver individually optimized experiences. This is especially true in physical stores, where a diverse range of products and services must be presented concisely and effectively to meet user interests.

[0428] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0429] In this invention, the server includes means for initially registering user interest data, means for acquiring the user's current location, means for recognizing the user's emotional state through a smart wearable device, means for optimizing relevant information using a generative AI model, and means for recording user responses and performing reinforcement learning based on that data. This makes it possible to provide information tailored to the user's interests and emotions, and to individually optimize the in-store experience.

[0430] "User interest data" refers to information about topics that users are particularly interested in, and is initially registered for the purpose of providing individual services and suggesting information.

[0431] "Current location" refers to information indicating the geographical location where a user is located at a specific point in time, and is the underlying data for location-based services.

[0432] "Related information" refers to specific information generated based on the user's interests and current location, and is considered to be interesting and useful to the user.

[0433] "Audio presentation" is a method of conveying information through sound without relying on visual information, enabling users to receive information through audio.

[0434] A "smart wearable device" is an electronic device worn on the body that has the function of detecting the user's movements and emotional state.

[0435] "Emotional state" refers to the user's psychological and mental state, and is information that is recognized in real time using indicators such as facial expressions and tone of voice.

[0436] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to create new information and suggestions from data, and is intended for optimizing and personalizing information.

[0437] Reinforcement learning is a machine learning technique that uses trial and error to obtain the optimal result in action selection, and is a technology used to improve the system's behavior based on user responses.

[0438] This invention is an information delivery system that takes into account the user's interests and emotional state, enabling a more personalized experience in physical stores. The system's program is executed by a smart wearable device carried by the user, a server, and the network infrastructure connecting them.

[0439] The smart wearable device is equipped with a GPS sensor and a camera and microphone for emotion recognition, acquiring the user's current location and emotional state in real time. Based on this, it is transmitted to a server along with the user's initial interest data.

[0440] The server begins processing information based on the received data. It searches the database for product information suitable for the user's current location and emotions, and optimizes it using a generative AI model. This generative AI model is used to generate expressions that resonate most with the user's current emotions. For example, if the user expresses surprise, the server will generate information that evokes surprise and matches that emotion.

[0441] The optimized information is transmitted to the device as audio presentations by a speech synthesis engine and provided to the user. This allows the user to receive high-quality information in real time via audio. The user's responses are recorded again on the device and sent to the server, where a reinforcement learning algorithm further optimizes the interest data, which can then be used to provide information during subsequent visits.

[0442] As a concrete example, consider a scenario where a user is in an electronics store and is in the display area for a new smartphone. In this case, when the emotion recognition software detects the user's excitement, the server uses a generated AI model to create a message such as, "This smartphone is equipped with the latest camera technology. Do you have any questions?" and plays it back using a speech synthesis engine.

[0443] An example of a prompt message is as follows:

[0444] "When the user's emotional state is heightened, generate a detailed description and special information about the product. The product category should be smartphones, and the features should include camera performance, cutting-edge technology, and brand innovation, incorporating relevant knowledge into the description."

[0445] In this way, the system can significantly improve the user's experience in physical stores.

[0446] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0447] Step 1:

[0448] The device obtains the user's current location using a GPS sensor. The input used is GPS sensor data. The device sends this location data, along with the user's interest data, to the server. The output is a set of location data and interest data.

[0449] Step 2:

[0450] The device detects the user's voice and facial expressions, and uses emotion recognition software to analyze their emotional state in real time. The input consists of data from the device's microphone and camera. The output is data indicating the user's emotional state, which is also sent to the server.

[0451] Step 3:

[0452] The server receives location data, interest data, and sentiment data transmitted from the terminal. It searches and extracts information related to the user's location and interests from the database. A database search engine is used in this process. The output is a set of relevant information.

[0453] Step 4:

[0454] The server uses a generative AI model to optimize the extracted relevant information according to the user's emotional state. The input consists of relevant information and emotional data. The generative AI model uses these as prompts to generate the guidance text to be output.

[0455] Step 5:

[0456] The server passes the generated guidance text to the speech synthesis engine, which converts it into audio data. The input is the text data of the guidance text. The output is audio data, which is sent to the terminal.

[0457] Step 6:

[0458] The terminal plays audio data received from the server, providing voice guidance to the user. This allows the user to receive engaging audio information in real time. The output is the user's auditory reception of the information.

[0459] Step 7:

[0460] The system collects user responses (e.g., voice responses or additional gestures) again and sends them from the terminal to the server. The input is user response data. The server uses this data to update interest data using a reinforcement learning algorithm. The output is the updated interest data, which is used to optimize guidance for future visits.

[0461] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0462] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0463] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0464] [Third Embodiment]

[0465] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0466] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0467] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0468] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0469] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0470] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0471] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0472] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0473] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0474] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0475] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0476] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0477] This invention is a guide system that provides customized voice guidance based on the user's interest information and location information. Embodiments of the invention are described below.

[0478] System Configuration

[0479] 1. Terminal

[0480] The user installs the app on their device and registers their interests upon first launch. The device then saves this information locally.

[0481] The device periodically obtains its current location using GPS while in motion and sends this information to the server.

[0482] 2. Server

[0483] When the server receives location information sent from the terminal, it searches the database for information related to that location based on the user's interests.

[0484] Related information is optimized using a generative model and generated as valuable guidance text for the user.

[0485] Finally, the generated voice guidance message is sent to the device.

[0486] 3. Voice guidance

[0487] The terminal converts the guidance text from the server into audio and provides it to the user. This audio guidance can be used even while walking, allowing users to obtain information without using their hands.

[0488] Specific example

[0489] For example, suppose a user is interested in history and is walking around a particular city. The device obtains its current location using GPS and sends that information to a server. The server, taking into account the user's interest in "history," searches its database for information about historical buildings located at that location. Unlike general information provided to the general public, this guidance is tailored to the user's interests. For example, it generates a guide message such as, "This location has a building constructed during the XX era, and it clearly reflects the characteristics of that era..." The device then plays this information as audio, providing it to the user in real time. This allows the user to obtain interesting information without having to do any research beforehand, resulting in a more enriching experience.

[0490] Furthermore, user responses (such as the playback time of voice guidance and preferred update information) are recorded on the device, and the server learns from this data to improve the quality of information provided in the future. In this way, the system deepens the user's interests and helps them discover new interests.

[0491] The following describes the processing flow.

[0492] Step 1:

[0493] When a user first launches the app on their device, the device displays a screen for entering their interests. The user selects or enters their areas of interest, and the device saves this information to a local database.

[0494] Step 2:

[0495] The device uses its GPS function in the background to periodically obtain its current location. When the location information meets certain conditions, it prepares to send the current location to the server.

[0496] Step 3:

[0497] The device uses a secure communication protocol to send the latest location information to the server. This transmission occurs at pre-specified intervals.

[0498] Step 4:

[0499] The server analyzes the received location information, compares it with the user's interest information, and searches the database for relevant information. It selects the most relevant data and proceeds to the next step.

[0500] Step 5:

[0501] The server uses a generative model to create guidance text to provide to the user. Based on relevant information, it optimizes the audio to deliver useful content to the user in a natural way.

[0502] Step 6:

[0503] The server sends the generated message to the terminal. This communication is also conducted through a secure protocol to ensure the protection of user data.

[0504] Step 7:

[0505] The terminal converts the received guidance text into audio data using a speech synthesis engine and plays it back to the user. The user can listen to this information in real time.

[0506] Step 8:

[0507] The device records user behavior data (playback time, whether it was paused, whether it was repeated, etc.) when the user listens to the guidance message. This data is later used to update the user's interest information.

[0508] Step 9:

[0509] When user behavior data is sent from the terminal to the server, the server updates the user's interest model using a reinforcement learning algorithm. This prepares the server to generate more relevant content for future information delivery.

[0510] (Example 1)

[0511] Next, we will describe Example 1. 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."

[0512] Despite users wanting to receive timely, interest-based information while on the go, current systems struggle to provide individually customized information, resulting in a limited user experience. There is a need to address this issue and deliver a richer experience by providing real-time information based on user interests.

[0513] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0514] In this invention, the server includes means for registering information about the user's interests, means for measuring the user's location, and means for generating relevant information based on the measured location information and the information about the user's interests. This enables the immediate provision of customized information tailored to the user's interests, creating a valuable experience for the user.

[0515] A "user" refers to an individual or organization that uses the system to obtain information.

[0516] "Interest-related information" refers to data about the user's preferences and areas of interest that they have registered in advance.

[0517] "Location information" refers to data that indicates a user's current geographical location, obtained using GPS or other location measurement technologies.

[0518] "Related information" refers to data that is useful to the user, generated based on information about the user's interests and location.

[0519] "Providing information via audio" means converting the generated relevant information into speech using text-to-speech technology and presenting it to the user.

[0520] A "generative model" is an information generation algorithm that utilizes artificial intelligence technology to optimize related information.

[0521] "Machine learning" is an algorithm that analyzes user reactions and usage history, and updates or improves the system based on the results of that analysis.

[0522] This invention is a guide system that provides real-time voice guidance with customized information based on the user's interests. This system combines the user's interest information and location information, generates relevant information using a generative AI model, and provides it to the user.

[0523] When a user installs an application on their device and uses the app for the first time, they register information about their interests. This information is stored in the device's local storage. The device uses GPS technology to obtain location information and periodically sends its current location to the server.

[0524] The server searches its database for relevant data based on the received location information and the user's interests. At this stage, it utilizes a generative AI model to generate highly relevant guidance text for the user. This generation process uses prompts such as, "The user's interest is history, and their current location is Kyoto. Please provide historical background information about this place."

[0525] The generated guidance text is sent from the server to the terminal, where it is converted into speech using text-to-speech (TTS) technology. This technology utilizes commonly available speech synthesis software, such as a speech synthesis API. The converted guidance is then delivered to the user through the terminal, allowing them to receive information hands-free.

[0526] As a concrete example, consider a user who is interested in history and is walking around Kyoto. The device acquires its current location information and sends it to the server. Based on the location information and the user's interests, the server searches for information about historical buildings and cultural backgrounds in Kyoto and generates voice guidance using a generative AI model. The device converts the generated guidance into voice and provides it to the user, allowing the user to obtain interesting information in real time. In this way, it is possible to enrich the user's experience and stimulate new interests.

[0527] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0528] Step 1:

[0529] The user installs the application on their device and enters information about their interests upon first launch. This information is selected from categories such as travel, history, and food. The device saves the entered information to local storage. The input data is the user's interest information, and the output is the interest information stored locally.

[0530] Step 2:

[0531] The device periodically obtains the user's current location using its GPS function. The obtained location information includes latitude and longitude. The device encrypts this location information using a security protocol and sends it to the server. The input is the obtained location information, and the output is the transmission of the encrypted location information to the server.

[0532] Step 3:

[0533] The server receives location information from the terminal and accesses the database. Using the user's interest information as a key, it searches for information related to that location. The server extracts the relevant data and inputs it into the generating AI model. The input is the user's interest information and location information, and the output is the relevant data ready to be input into the generating AI model.

[0534] Step 4:

[0535] The server uses a generative AI model to generate customized guidance text based on relevant data. Specifically, it utilizes natural language processing techniques to output guidance text that reflects the user's interests. Prompt text is used in this process. The input is relevant data and prompt text, and the output is the generated guidance text.

[0536] Step 5:

[0537] The generated guidance text is sent from the server to the terminal. The terminal uses Text-to-Speech (TTS) technology to convert the guidance text into audio data. A third-party TTS API is used for the conversion. The input is the guidance text from the server, and the output is the audio data played back to the user.

[0538] Step 6:

[0539] The user continues moving while listening to the generated voice guidance. The device records the user's reactions while the guidance is playing. The user's playback time, preference updates, and playback frequency are recorded. The input is the user's reactions, and the output is information stored in local storage as data for the next learning session.

[0540] Step 7:

[0541] Subsequently, the terminal synchronizes the recorded user response data with the server. The server then feeds this data into a machine learning algorithm to update the user's interest information and use it to generate future guidance. The input is the user's response data, and the output is the updated interest information.

[0542] (Application Example 1)

[0543] Next, we will explain Application Example 1. In the following explanation, 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."

[0544] Because it was not possible to provide users traveling in automated vehicles with personalized voice guidance that leveraged their individual interests in real time, users had limited means of maintaining their interest during their journey to their destination. As a result, it was difficult to enrich the travel experience and discover new interests in users.

[0545] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0546] In this invention, the server includes means for initially registering the user's interest information, means for acquiring the user's current location, means for generating relevant information based on the acquired location information and interest information, means for providing the generated relevant information as voice guidance, and means for updating the interest information based on the user's response. This makes it possible to provide users with personalized interest points along the driving route in an automatically controlled vehicle.

[0547] "User interest information" refers to information based on the interests and preferences of individual users.

[0548] "Initial registration method" refers to a function that allows users to register their interests and preferences.

[0549] "Means of obtaining current location" refers to a function that uses location information services such as GPS to determine where the user is currently located.

[0550] "Means for generating relevant information" refers to a function that creates useful information for the user based on the user's interests and location information.

[0551] "Means of providing information as voice guidance" refers to a function that transmits generated information to the user via voice.

[0552] "A means of updating interest information based on user responses" refers to a function that reviews and improves interest information based on user feedback.

[0553] An "automatically controlled vehicle" is a car that drives autonomously under computer control.

[0554] "Points of interest along the travel route" refer to locations or pieces of information that exist along the travel route and are likely to attract the user's attention.

[0555] Modes for carrying out the invention

[0556] In order to implement this invention, the following system configuration and processing are necessary.

[0557] First, when boarding the automated vehicle, the user operates a dedicated terminal to register their interests. This terminal has a built-in function to store the user's interests and can periodically acquire the user's current location via a GPS module.

[0558] Location information transmitted from the device is sent to a server, which then searches its database for relevant information based on the user's interests and this location information. This relevant information is optimized using generative models such as OpenAI's generative AI model and generated as personalized guidance information for the user.

[0559] The generated voice guidance information is related to points along the route that are likely to be of interest to the user, and this information is received by a computer installed in the automatically controlled vehicle. This computer processes the information and provides real-time voice guidance to the user.

[0560] The user's response to the voice guidance is recorded on the device. This response data is processed on a server, and through reinforcement learning, the user's interest information is dynamically updated to improve the accuracy of future guidance.

[0561] For example, if a user has a particular interest in history, nearby historical sites may be explained during the ride. An example of a prompt for the generative AI model might be, "Generate information guiding me to interesting historical landmarks within 10 kilometers of my current location." In this way, the system is designed to make travel an intellectual adventure for the user, rather than just a means of transportation.

[0562] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0563] Step 1:

[0564] Users register their interests by operating the device. They select their interests from categories such as travel, history, and nature, and this information is stored locally on the device. This information is crucial because it is used for subsequent processing.

[0565] Step 2:

[0566] The device periodically uses a GPS module to acquire its current location information. This acquired location information is sent to a server and used as basic data to track the user's dynamic location.

[0567] Step 3:

[0568] The server receives location information sent from the terminal and pre-registered interest information. Based on this, it extracts information related to the user's interests from the database. This process generates customized data that associates location information with interest information.

[0569] Step 4:

[0570] The server optimizes the extracted information using a generative AI model, such as an OpenAI model. The generative AI model receives prompts such as "Generate information guiding the user to points of interest from their current location," and a user-specific guidance message is output.

[0571] Step 5:

[0572] The generated guidance text is converted into audio data and sent to the terminal. The terminal receives this data and provides it to the user as audio guidance in real time. This allows the user to instantly obtain information relevant to their interests while on the move.

[0573] Step 6:

[0574] The device records user responses. For example, feedback data such as the number of times voice instructions for guidance are played and changes in user interest is sent to the server to customize information for future use.

[0575] Step 7:

[0576] The server uses the received feedback data to update interest information using a reinforcement learning model. Through this process, the system continuously improves itself so that the next information provided is more tailored to the user's preferences.

[0577] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0578] This invention combines a voice guidance system based on user interest information and location information with an emotion recognition engine. Specific embodiments of the system are described below.

[0579] System Configuration

[0580] 1. Terminal

[0581] Through an app installed on the device, the user initially registers their interests. The device then saves this information locally.

[0582] The device obtains its current location using GPS sensors while moving and periodically sends this information to the server.

[0583] Furthermore, the device has a function to sense the user's voice and facial expressions, which the emotion engine uses to recognize the user's emotions in real time.

[0584] 2. Server

[0585] The server receives location information and sentiment data transmitted from the terminal. Based on this, it searches the database for relevant information that matches the user's interests.

[0586] The retrieved information is optimized by a generative model to suit the user's emotional state. For example, if the user is perceived as excited, the information can be provided in more detail and include more explanations.

[0587] The generated message is sent from the server to the terminal.

[0588] 3. Voice guidance

[0589] The terminal converts the guidance text received from the server into speech and provides it to the user. A speech synthesis engine is used in this process.

[0590] Specific example

[0591] For example, consider a scenario where a user is interested in nature and is walking through a forest. The device acquires its current location information and sends it to the server. The server searches for nature information related to this location and generates information from the accumulated data, such as "This forest is home to a rare species of bird."

[0592] The emotion engine analyzes the user's response, and if it recognizes a positive reaction, the server can include additional information or quizzes in the message that are relevant to that emotion. For example, it might add a question like, "What color do you think this bird is?"

[0593] The generated guidance text is then sent to the device and played back as audio. Users can obtain information that makes their individual experiences more interactive and enjoyable, and leads to new discoveries in the moment. Furthermore, by having the server learn from the user's emotional data, subsequent guidance can be further optimized, resulting in information that is more closely matched to each individual's interests.

[0594] The following describes the processing flow.

[0595] Step 1:

[0596] When a user first launches the app, the device displays an interface for registering their interests. The user selects their interests from the displayed categories and saves them to a local database.

[0597] Step 2:

[0598] The device uses GPS in the background to obtain the user's current location at regular intervals. This location information is then formatted for transmission from the device to the server.

[0599] Step 3:

[0600] The device activates its emotion engine and uses the microphone and camera to acquire emotion data in real time from the user's voice tone and facial expressions. If the emotion data meets certain criteria, it is prepared for transmission, including that data.

[0601] Step 4:

[0602] The device transmits the latest location and sentiment data to the server via a secure communication protocol.

[0603] Step 5:

[0604] The server analyzes the received location information and searches the database for information related to that region. It then refines the relevant information based on the user's interests and sentiment data.

[0605] Step 6:

[0606] The server generates optimized guidance text using a generative model. The tone and content of the guidance text are customized to match the user's emotions, as determined by the emotion engine.

[0607] Step 7:

[0608] The generated guidance text is sent from the server to the terminal. The terminal receives it and prepares to output it as voice guidance through speech synthesis.

[0609] Step 8:

[0610] The device plays voice guidance optimized for the user. The user's reactions during playback are also continuously analyzed by the emotion engine, and the guidance may be adjusted based on the results.

[0611] Step 9:

[0612] User reactions and emotional data are recorded again and sent to the server to help improve future guidance. The server learns from this data and updates the user's interests and emotional profile to provide more personalized guidance.

[0613] (Example 2)

[0614] Next, we will describe Example 2. 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."

[0615] Conventional audio guide systems only provide guidance based on the user's interests and location, without considering the user's emotions. As a result, the information received by the user is one-sided and lacks the appeal of a personalized experience. Furthermore, the user's interests are not properly updated, leading to problems with long-term satisfaction.

[0616] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0617] In this invention, the server includes means for recognizing the user's emotions, means for generating relevant information based on location information, interest information, and emotion data, and means for using a generative model to optimize the generated relevant information based on the user's emotional state. This makes it possible to provide personalized guidance that responds to the user's emotions, enrich individual experiences, and accurately update interest information to improve long-term satisfaction.

[0618] "User interest information" refers to information indicating a user's preferences and interests, which they register with the audio guide system.

[0619] "Current location" refers to data that indicates the user's geographical location, obtained using GPS or other location information systems.

[0620] "User emotions" refer to data that indicates the user's emotional state, recognized in real time through voice and facial expression sensors.

[0621] "Related information" refers to content generated based on the user's interests, current location, and emotions, and is provided to the user as audio guidance.

[0622] A "device that presents information as audio" refers to equipment or software that converts generated related information into audio and allows the user to hear it.

[0623] A "generative model" is an algorithm or software used to optimize relevant information according to the user's emotional state.

[0624] "Machine learning" is a technology that uses data analysis and predictive model building to update user interest information based on user responses.

[0625] One embodiment of this invention is a system that provides audio guidance based on user interest information, location information, and emotion data. This system consists of a terminal, a server, and network communication.

[0626] The device has an application installed for users to register their interests. Users register their interests through this application, and the device saves this information to local storage. The device also has a built-in GPS function to acquire the user's current location while they are moving. Furthermore, the device is equipped with an emotion engine that uses a microphone and camera to recognize the user's emotions in real time from their voice and facial expressions.

[0627] The server is equipped with the ability to receive location and sentiment data transmitted from the user's device. Based on the received information, the server searches its database for relevant information. This search uses interest information, location information, and sentiment data. The server uses a generative AI model to optimize the acquired relevant information to suit the user's current sentiment state. The optimized guidance information is sent to the device and provided as voice by a speech synthesis engine.

[0628] As a concrete example, consider a scenario where a user is interested in nature exploration and is strolling through a forest. The device obtains the user's current location and sends it to the server. The server searches its database for nature information related to that location and uses a generative AI model to generate a message such as, "This forest is home to a rare XX bird." If the user shows positive emotions, additional information such as a quiz might be provided, such as, "What color do you think this bird is?"

[0629] An example of a prompt might be, "If the user appears happy in the forest, create a guide that includes interesting nature information and a simple question." In this way, the system can provide the user with a more personalized and interactive experience.

[0630] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0631] Step 1:

[0632] On the device, users register their interests through a dedicated app. The input for this process is the user's chosen categories of interest (e.g., "Nature," "History," "Art"), and the output is interest data stored in local storage. This clarifies the user's interests.

[0633] Step 2:

[0634] The device obtains its current location using its built-in GPS sensor. The input is geographical location data obtained from the device's sensor, and the output is the user's current location information. The device updates its location information at regular intervals to prepare for subsequent data transmission.

[0635] Step 3:

[0636] The device acquires the user's voice and facial expressions and sends the data to the emotion engine. The input is real-time audio and video data acquired from the microphone and camera, and the output is emotion data obtained through analysis. Based on this data, the emotion engine recognizes the user's emotions and classifies them into states such as "happy" or "excited."

[0637] Step 4:

[0638] The device transmits the collected location information and sentiment data to the server. The input is the location information and sentiment data obtained in steps 2 and 3, and the output is the integrated data received by the server. The device transmits this information to the server at an appropriate frequency.

[0639] Step 5:

[0640] The server analyzes received location and sentiment data and searches for relevant information in its database. Input is data sent from the terminal, and output is content related to the user's interests (e.g., "This forest is home to a rare species of bird"). The server uses an efficient search algorithm to effectively find relevant information.

[0641] Step 6:

[0642] The server uses a generative AI model to adapt relevant information to the user's emotional state. The input is the relevant information and emotional data obtained in step 5, and the output is a guide optimized for the emotional state. The generative AI model generates information based on a prompt (e.g., "If the user is happy in the forest, create a guide that includes interesting nature information and a simple question.").

[0643] Step 7:

[0644] The server sends an optimized message to the terminal. The input is the message generated in step 6, and the output is what the terminal receives.

[0645] Step 8:

[0646] The terminal converts received guidance text into speech using a speech synthesis engine and provides it to the user. The input is guidance text sent from the server, and the output is an audio message for the user to hear. This allows the user to obtain information in audio format that is tailored to their interests and the situation at hand.

[0647] (Application Example 2)

[0648] Next, we will explain application example 2. In the following explanation, 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."

[0649] Existing user guidance systems lack the ability to provide appropriate information tailored to users' interests and emotional states, resulting in a failure to deliver individually optimized experiences. This is especially true in physical stores, where a diverse range of products and services must be presented concisely and effectively to meet user interests.

[0650] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0651] In this invention, the server includes means for initially registering user interest data, means for acquiring the user's current location, means for recognizing the user's emotional state through a smart wearable device, means for optimizing relevant information using a generative AI model, and means for recording user responses and performing reinforcement learning based on that data. This makes it possible to provide information tailored to the user's interests and emotions, and to individually optimize the in-store experience.

[0652] "User interest data" refers to information about topics that users are particularly interested in, and is initially registered for the purpose of providing individual services and suggesting information.

[0653] "Current location" refers to information indicating the geographical location where a user is located at a specific point in time, and is the underlying data for location-based services.

[0654] "Related information" refers to specific information generated based on the user's interests and current location, and is considered to be interesting and useful to the user.

[0655] "Audio presentation" is a method of conveying information through sound without relying on visual information, enabling users to receive information through audio.

[0656] A "smart wearable device" is an electronic device worn on the body that has the function of detecting the user's movements and emotional state.

[0657] "Emotional state" refers to the user's psychological and mental state, and is information that is recognized in real time using indicators such as facial expressions and tone of voice.

[0658] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to create new information and suggestions from data, and is intended for optimizing and personalizing information.

[0659] Reinforcement learning is a machine learning technique that uses trial and error to obtain the optimal result in action selection, and is a technology used to improve the system's behavior based on user responses.

[0660] This invention is an information delivery system that takes into account the user's interests and emotional state, enabling a more personalized experience in physical stores. The system's program is executed by a smart wearable device carried by the user, a server, and the network infrastructure connecting them.

[0661] The smart wearable device is equipped with a GPS sensor and a camera and microphone for emotion recognition, acquiring the user's current location and emotional state in real time. Based on this, it is transmitted to a server along with the user's initial interest data.

[0662] The server begins processing information based on the received data. It searches the database for product information suitable for the user's current location and emotions, and optimizes it using a generative AI model. This generative AI model is used to generate expressions that resonate most with the user's current emotions. For example, if the user expresses surprise, the server will generate information that evokes surprise and matches that emotion.

[0663] The optimized information is transmitted to the device as audio presentations by a speech synthesis engine and provided to the user. This allows the user to receive high-quality information in real time via audio. The user's responses are recorded again on the device and sent to the server, where a reinforcement learning algorithm further optimizes the interest data, which can then be used to provide information during subsequent visits.

[0664] As a concrete example, consider a scenario where a user is in an electronics store and is in the display area for a new smartphone. In this case, when the emotion recognition software detects the user's excitement, the server uses a generated AI model to create a message such as, "This smartphone is equipped with the latest camera technology. Do you have any questions?" and plays it back using a speech synthesis engine.

[0665] An example of a prompt message is as follows:

[0666] "When the user's emotional state is heightened, generate a detailed description and special information about the product. The product category should be smartphones, and the features should include camera performance, cutting-edge technology, and brand innovation, incorporating relevant knowledge into the description."

[0667] In this way, the system can significantly improve the user's experience in physical stores.

[0668] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0669] Step 1:

[0670] The device obtains the user's current location using a GPS sensor. The input used is GPS sensor data. The device sends this location data, along with the user's interest data, to the server. The output is a set of location data and interest data.

[0671] Step 2:

[0672] The device detects the user's voice and facial expressions, and uses emotion recognition software to analyze their emotional state in real time. The input consists of data from the device's microphone and camera. The output is data indicating the user's emotional state, which is also sent to the server.

[0673] Step 3:

[0674] The server receives location data, interest data, and sentiment data transmitted from the terminal. It searches and extracts information related to the user's location and interests from the database. A database search engine is used in this process. The output is a set of relevant information.

[0675] Step 4:

[0676] The server uses a generative AI model to optimize the extracted relevant information according to the user's emotional state. The input consists of relevant information and emotional data. The generative AI model uses these as prompts to generate the guidance text to be output.

[0677] Step 5:

[0678] The server passes the generated guidance text to the speech synthesis engine, which converts it into audio data. The input is the text data of the guidance text. The output is audio data, which is sent to the terminal.

[0679] Step 6:

[0680] The terminal plays audio data received from the server, providing voice guidance to the user. This allows the user to receive engaging audio information in real time. The output is the user's auditory reception of the information.

[0681] Step 7:

[0682] The system collects user responses (e.g., voice responses or additional gestures) again and sends them from the terminal to the server. The input is user response data. The server uses this data to update interest data using a reinforcement learning algorithm. The output is the updated interest data, which is used to optimize guidance for future visits.

[0683] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0684] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0685] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0686] [Fourth Embodiment]

[0687] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0688] As shown in Figure 7, the 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.

[0689] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0690] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0691] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0692] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0693] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0694] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0695] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0696] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0697] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0698] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0699] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0700] This invention is a guide system that provides customized voice guidance based on the user's interest information and location information. Embodiments of the invention are described below.

[0701] System Configuration

[0702] 1. Terminal

[0703] The user installs the app on their device and registers their interests upon first launch. The device then saves this information locally.

[0704] The device periodically obtains its current location using GPS while in motion and sends this information to the server.

[0705] 2. Server

[0706] When the server receives location information sent from the terminal, it searches the database for information related to that location based on the user's interests.

[0707] Related information is optimized using a generative model and generated as valuable guidance text for the user.

[0708] Finally, the generated voice guidance message is sent to the device.

[0709] 3. Voice guidance

[0710] The terminal converts the guidance text from the server into audio and provides it to the user. This audio guidance can be used even while walking, allowing users to obtain information without using their hands.

[0711] Specific example

[0712] For example, suppose a user is interested in history and is walking around a particular city. The device obtains its current location using GPS and sends that information to a server. The server, taking into account the user's interest in "history," searches its database for information about historical buildings located at that location. Unlike general information provided to the general public, this guidance is tailored to the user's interests. For example, it generates a guide message such as, "This location has a building constructed during the XX era, and it clearly reflects the characteristics of that era..." The device then plays this information as audio, providing it to the user in real time. This allows the user to obtain interesting information without having to do any research beforehand, resulting in a more enriching experience.

[0713] Furthermore, user responses (such as the playback time of voice guidance and preferred update information) are recorded on the device, and the server learns from this data to improve the quality of information provided in the future. In this way, the system deepens the user's interests and helps them discover new interests.

[0714] The following describes the processing flow.

[0715] Step 1:

[0716] When a user first launches the app on their device, the device displays a screen for entering their interests. The user selects or enters their areas of interest, and the device saves this information to a local database.

[0717] Step 2:

[0718] The device uses its GPS function in the background to periodically obtain its current location. When the location information meets certain conditions, it prepares to send the current location to the server.

[0719] Step 3:

[0720] The device uses a secure communication protocol to send the latest location information to the server. This transmission occurs at pre-specified intervals.

[0721] Step 4:

[0722] The server analyzes the received location information, compares it with the user's interest information, and searches the database for relevant information. It selects the most relevant data and proceeds to the next step.

[0723] Step 5:

[0724] The server uses a generative model to create guidance text to provide to the user. Based on relevant information, it optimizes the audio to deliver useful content to the user in a natural way.

[0725] Step 6:

[0726] The server sends the generated message to the terminal. This communication is also conducted through a secure protocol to ensure the protection of user data.

[0727] Step 7:

[0728] The terminal converts the received guidance text into audio data using a speech synthesis engine and plays it back to the user. The user can listen to this information in real time.

[0729] Step 8:

[0730] The device records user behavior data (playback time, whether it was paused, whether it was repeated, etc.) when the user listens to the guidance message. This data is later used to update the user's interest information.

[0731] Step 9:

[0732] When user behavior data is sent from the terminal to the server, the server updates the user's interest model using a reinforcement learning algorithm. This prepares the server to generate more relevant content for future information delivery.

[0733] (Example 1)

[0734] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0735] Despite users wanting to receive timely, interest-based information while on the go, current systems struggle to provide individually customized information, resulting in a limited user experience. There is a need to address this issue and deliver a richer experience by providing real-time information based on user interests.

[0736] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0737] In this invention, the server includes means for registering information about the user's interests, means for measuring the user's location, and means for generating relevant information based on the measured location information and the information about the user's interests. This enables the immediate provision of customized information tailored to the user's interests, creating a valuable experience for the user.

[0738] A "user" refers to an individual or organization that uses the system to obtain information.

[0739] "Interest-related information" refers to data about the user's preferences and areas of interest that they have registered in advance.

[0740] "Location information" refers to data that indicates a user's current geographical location, obtained using GPS or other location measurement technologies.

[0741] "Related information" refers to data that is useful to the user, generated based on information about the user's interests and location.

[0742] "Providing information via audio" means converting the generated relevant information into speech using text-to-speech technology and presenting it to the user.

[0743] A "generative model" is an information generation algorithm that utilizes artificial intelligence technology to optimize related information.

[0744] "Machine learning" is an algorithm that analyzes user reactions and usage history, and updates or improves the system based on the results of that analysis.

[0745] This invention is a guide system that provides real-time voice guidance with customized information based on the user's interests. This system combines the user's interest information and location information, generates relevant information using a generative AI model, and provides it to the user.

[0746] When a user installs an application on their device and uses the app for the first time, they register information about their interests. This information is stored in the device's local storage. The device uses GPS technology to obtain location information and periodically sends its current location to the server.

[0747] The server searches its database for relevant data based on the received location information and the user's interests. At this stage, it utilizes a generative AI model to generate highly relevant guidance text for the user. This generation process uses prompts such as, "The user's interest is history, and their current location is Kyoto. Please provide historical background information about this place."

[0748] The generated guidance text is sent from the server to the terminal, where it is converted into speech using text-to-speech (TTS) technology. This technology utilizes commonly available speech synthesis software, such as a speech synthesis API. The converted guidance is then delivered to the user through the terminal, allowing them to receive information hands-free.

[0749] As a concrete example, consider a user who is interested in history and is walking around Kyoto. The device acquires its current location information and sends it to the server. Based on the location information and the user's interests, the server searches for information about historical buildings and cultural backgrounds in Kyoto and generates voice guidance using a generative AI model. The device converts the generated guidance into voice and provides it to the user, allowing the user to obtain interesting information in real time. In this way, it is possible to enrich the user's experience and stimulate new interests.

[0750] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0751] Step 1:

[0752] The user installs the application on their device and enters information about their interests upon first launch. This information is selected from categories such as travel, history, and food. The device saves the entered information to local storage. The input data is the user's interest information, and the output is the interest information stored locally.

[0753] Step 2:

[0754] The device periodically obtains the user's current location using its GPS function. The obtained location information includes latitude and longitude. The device encrypts this location information using a security protocol and sends it to the server. The input is the obtained location information, and the output is the transmission of the encrypted location information to the server.

[0755] Step 3:

[0756] The server receives location information from the terminal and accesses the database. Using the user's interest information as a key, it searches for information related to that location. The server extracts the relevant data and inputs it into the generating AI model. The input is the user's interest information and location information, and the output is the relevant data ready to be input into the generating AI model.

[0757] Step 4:

[0758] The server uses a generative AI model to generate customized guidance text based on relevant data. Specifically, it utilizes natural language processing techniques to output guidance text that reflects the user's interests. Prompt text is used in this process. The input is relevant data and prompt text, and the output is the generated guidance text.

[0759] Step 5:

[0760] The generated guidance text is sent from the server to the terminal. The terminal uses Text-to-Speech (TTS) technology to convert the guidance text into audio data. A third-party TTS API is used for the conversion. The input is the guidance text from the server, and the output is the audio data played back to the user.

[0761] Step 6:

[0762] The user continues moving while listening to the generated voice guidance. The device records the user's reactions while the guidance is playing. The user's playback time, preference updates, and playback frequency are recorded. The input is the user's reactions, and the output is information stored in local storage as data for the next learning session.

[0763] Step 7:

[0764] Subsequently, the terminal synchronizes the recorded user response data with the server. The server then feeds this data into a machine learning algorithm to update the user's interest information and use it to generate future guidance. The input is the user's response data, and the output is the updated interest information.

[0765] (Application Example 1)

[0766] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0767] Because it was not possible to provide users traveling in automated vehicles with personalized voice guidance that leveraged their individual interests in real time, users had limited means of maintaining their interest during their journey to their destination. As a result, it was difficult to enrich the travel experience and discover new interests in users.

[0768] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0769] In this invention, the server includes means for initially registering the user's interest information, means for acquiring the user's current location, means for generating relevant information based on the acquired location information and interest information, means for providing the generated relevant information as voice guidance, and means for updating the interest information based on the user's response. This makes it possible to provide users with personalized interest points along the driving route in an automatically controlled vehicle.

[0770] "User interest information" refers to information based on the interests and preferences of individual users.

[0771] "Initial registration method" refers to a function that allows users to register their interests and preferences.

[0772] "Means of obtaining current location" refers to a function that uses location information services such as GPS to determine where the user is currently located.

[0773] "Means for generating relevant information" refers to a function that creates useful information for the user based on the user's interests and location information.

[0774] "Means of providing information as voice guidance" refers to a function that transmits generated information to the user via voice.

[0775] "A means of updating interest information based on user responses" refers to a function that reviews and improves interest information based on user feedback.

[0776] An "automatically controlled vehicle" is a car that drives autonomously under computer control.

[0777] "Points of interest along the travel route" refer to locations or pieces of information that exist along the travel route and are likely to attract the user's attention.

[0778] Modes for carrying out the invention

[0779] In order to implement this invention, the following system configuration and processing are necessary.

[0780] First, when boarding the automated vehicle, the user operates a dedicated terminal to register their interests. This terminal has a built-in function to store the user's interests and can periodically acquire the user's current location via a GPS module.

[0781] Location information transmitted from the device is sent to a server, which then searches its database for relevant information based on the user's interests and this location information. This relevant information is optimized using generative models such as OpenAI's generative AI model and generated as personalized guidance information for the user.

[0782] The generated voice guidance information is related to points along the route that are likely to be of interest to the user, and this information is received by a computer installed in the automatically controlled vehicle. This computer processes the information and provides real-time voice guidance to the user.

[0783] The user's response to the voice guidance is recorded on the device. This response data is processed on a server, and through reinforcement learning, the user's interest information is dynamically updated to improve the accuracy of future guidance.

[0784] For example, if a user has a particular interest in history, nearby historical sites may be explained during the ride. An example of a prompt for the generative AI model might be, "Generate information guiding me to interesting historical landmarks within 10 kilometers of my current location." In this way, the system is designed to make travel an intellectual adventure for the user, rather than just a means of transportation.

[0785] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0786] Step 1:

[0787] Users register their interests by operating the device. They select their interests from categories such as travel, history, and nature, and this information is stored locally on the device. This information is crucial because it is used for subsequent processing.

[0788] Step 2:

[0789] The device periodically uses a GPS module to acquire its current location information. This acquired location information is sent to a server and used as basic data to track the user's dynamic location.

[0790] Step 3:

[0791] The server receives location information sent from the terminal and pre-registered interest information. Based on this, it extracts information related to the user's interests from the database. This process generates customized data that associates location information with interest information.

[0792] Step 4:

[0793] The server optimizes the extracted information using a generative AI model, such as an OpenAI model. The generative AI model receives prompts such as "Generate information guiding the user to points of interest from their current location," and a user-specific guidance message is output.

[0794] Step 5:

[0795] The generated guidance text is converted into audio data and sent to the terminal. The terminal receives this data and provides it to the user as audio guidance in real time. This allows the user to instantly obtain information relevant to their interests while on the move.

[0796] Step 6:

[0797] The device records user responses. For example, feedback data such as the number of times voice instructions for guidance are played and changes in user interest is sent to the server to customize information for future use.

[0798] Step 7:

[0799] The server uses the received feedback data to update interest information using a reinforcement learning model. Through this process, the system continuously improves itself so that the next information provided is more tailored to the user's preferences.

[0800] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0801] This invention combines a voice guidance system based on user interest information and location information with an emotion recognition engine. Specific embodiments of the system are described below.

[0802] System Configuration

[0803] 1. Terminal

[0804] Through an app installed on the device, the user initially registers their interests. The device then saves this information locally.

[0805] The device obtains its current location using GPS sensors while moving and periodically sends this information to the server.

[0806] Furthermore, the device has a function to sense the user's voice and facial expressions, which the emotion engine uses to recognize the user's emotions in real time.

[0807] 2. Server

[0808] The server receives location information and sentiment data transmitted from the terminal. Based on this, it searches the database for relevant information that matches the user's interests.

[0809] The retrieved information is optimized by a generative model to suit the user's emotional state. For example, if the user is perceived as excited, the information can be provided in more detail and include more explanations.

[0810] The generated message is sent from the server to the terminal.

[0811] 3. Voice guidance

[0812] The terminal converts the guidance text received from the server into speech and provides it to the user. A speech synthesis engine is used in this process.

[0813] Specific example

[0814] For example, consider a scenario where a user is interested in nature and is walking through a forest. The device acquires its current location information and sends it to the server. The server searches for nature information related to this location and generates information from the accumulated data, such as "This forest is home to a rare species of bird."

[0815] The emotion engine analyzes the user's response, and if it recognizes a positive reaction, the server can include additional information or quizzes in the message that are relevant to that emotion. For example, it might add a question like, "What color do you think this bird is?"

[0816] The generated guidance text is then sent to the device and played back as audio. Users can obtain information that makes their individual experiences more interactive and enjoyable, and leads to new discoveries in the moment. Furthermore, by having the server learn from the user's emotional data, subsequent guidance can be further optimized, resulting in information that is more closely matched to each individual's interests.

[0817] The following describes the processing flow.

[0818] Step 1:

[0819] When a user first launches the app, the device displays an interface for registering their interests. The user selects their interests from the displayed categories and saves them to a local database.

[0820] Step 2:

[0821] The device uses GPS in the background to obtain the user's current location at regular intervals. This location information is then formatted for transmission from the device to the server.

[0822] Step 3:

[0823] The device activates its emotion engine and uses the microphone and camera to acquire emotion data in real time from the user's voice tone and facial expressions. If the emotion data meets certain criteria, it is prepared for transmission, including that data.

[0824] Step 4:

[0825] The device transmits the latest location and sentiment data to the server via a secure communication protocol.

[0826] Step 5:

[0827] The server analyzes the received location information and searches the database for information related to that region. It then refines the relevant information based on the user's interests and sentiment data.

[0828] Step 6:

[0829] The server generates optimized guidance text using a generative model. The tone and content of the guidance text are customized to match the user's emotions, as determined by the emotion engine.

[0830] Step 7:

[0831] The generated guidance text is sent from the server to the terminal. The terminal receives it and prepares to output it as voice guidance through speech synthesis.

[0832] Step 8:

[0833] The device plays voice guidance optimized for the user. The user's reactions during playback are also continuously analyzed by the emotion engine, and the guidance may be adjusted based on the results.

[0834] Step 9:

[0835] User reactions and emotional data are recorded again and sent to the server to help improve future guidance. The server learns from this data and updates the user's interests and emotional profile to provide more personalized guidance.

[0836] (Example 2)

[0837] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0838] Conventional audio guide systems only provide guidance based on the user's interests and location, without considering the user's emotions. As a result, the information received by the user is one-sided and lacks the appeal of a personalized experience. Furthermore, the user's interests are not properly updated, leading to problems with long-term satisfaction.

[0839] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0840] In this invention, the server includes means for recognizing the user's emotions, means for generating relevant information based on location information, interest information, and emotion data, and means for using a generative model to optimize the generated relevant information based on the user's emotional state. This makes it possible to provide personalized guidance that responds to the user's emotions, enrich individual experiences, and accurately update interest information to improve long-term satisfaction.

[0841] "User interest information" refers to information indicating a user's preferences and interests, which they register with the audio guide system.

[0842] "Current location" refers to data that indicates the user's geographical location, obtained using GPS or other location information systems.

[0843] "User emotions" refer to data that indicates the user's emotional state, recognized in real time through voice and facial expression sensors.

[0844] "Related information" refers to content generated based on the user's interests, current location, and emotions, and is provided to the user as audio guidance.

[0845] A "device that presents information as audio" refers to equipment or software that converts generated related information into audio and allows the user to hear it.

[0846] A "generative model" is an algorithm or software used to optimize relevant information according to the user's emotional state.

[0847] "Machine learning" is a technology that uses data analysis and predictive model building to update user interest information based on user responses.

[0848] One embodiment of this invention is a system that provides audio guidance based on user interest information, location information, and emotion data. This system consists of a terminal, a server, and network communication.

[0849] The device has an application installed for users to register their interests. Users register their interests through this application, and the device saves this information to local storage. The device also has a built-in GPS function to acquire the user's current location while they are moving. Furthermore, the device is equipped with an emotion engine that uses a microphone and camera to recognize the user's emotions in real time from their voice and facial expressions.

[0850] The server is equipped with the ability to receive location and sentiment data transmitted from the user's device. Based on the received information, the server searches its database for relevant information. This search uses interest information, location information, and sentiment data. The server uses a generative AI model to optimize the acquired relevant information to suit the user's current sentiment state. The optimized guidance information is sent to the device and provided as voice by a speech synthesis engine.

[0851] As a concrete example, consider a scenario where a user is interested in nature exploration and is strolling through a forest. The device obtains the user's current location and sends it to the server. The server searches its database for nature information related to that location and uses a generative AI model to generate a message such as, "This forest is home to a rare XX bird." If the user shows positive emotions, additional information such as a quiz might be provided, such as, "What color do you think this bird is?"

[0852] An example of a prompt might be, "If the user appears happy in the forest, create a guide that includes interesting nature information and a simple question." In this way, the system can provide the user with a more personalized and interactive experience.

[0853] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0854] Step 1:

[0855] On the device, users register their interests through a dedicated app. The input for this process is the user's chosen categories of interest (e.g., "Nature," "History," "Art"), and the output is interest data stored in local storage. This clarifies the user's interests.

[0856] Step 2:

[0857] The device obtains its current location using its built-in GPS sensor. The input is geographical location data obtained from the device's sensor, and the output is the user's current location information. The device updates its location information at regular intervals to prepare for subsequent data transmission.

[0858] Step 3:

[0859] The device acquires the user's voice and facial expressions and sends the data to the emotion engine. The input is real-time audio and video data acquired from the microphone and camera, and the output is emotion data obtained through analysis. Based on this data, the emotion engine recognizes the user's emotions and classifies them into states such as "happy" or "excited."

[0860] Step 4:

[0861] The device transmits the collected location information and sentiment data to the server. The input is the location information and sentiment data obtained in steps 2 and 3, and the output is the integrated data received by the server. The device transmits this information to the server at an appropriate frequency.

[0862] Step 5:

[0863] The server analyzes received location and sentiment data and searches for relevant information in its database. Input is data sent from the terminal, and output is content related to the user's interests (e.g., "This forest is home to a rare species of bird"). The server uses an efficient search algorithm to effectively find relevant information.

[0864] Step 6:

[0865] The server uses a generative AI model to adapt relevant information to the user's emotional state. The input is the relevant information and emotional data obtained in step 5, and the output is a guide optimized for the emotional state. The generative AI model generates information based on a prompt (e.g., "If the user is happy in the forest, create a guide that includes interesting nature information and a simple question.").

[0866] Step 7:

[0867] The server sends an optimized message to the terminal. The input is the message generated in step 6, and the output is what the terminal receives.

[0868] Step 8:

[0869] The terminal converts received guidance text into speech using a speech synthesis engine and provides it to the user. The input is guidance text sent from the server, and the output is an audio message for the user to hear. This allows the user to obtain information in audio format that is tailored to their interests and the situation at hand.

[0870] (Application Example 2)

[0871] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0872] Existing user guidance systems lack the ability to provide appropriate information tailored to users' interests and emotional states, resulting in a failure to deliver individually optimized experiences. This is especially true in physical stores, where a diverse range of products and services must be presented concisely and effectively to meet user interests.

[0873] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0874] In this invention, the server includes means for initially registering user interest data, means for acquiring the user's current location, means for recognizing the user's emotional state through a smart wearable device, means for optimizing relevant information using a generative AI model, and means for recording user responses and performing reinforcement learning based on that data. This makes it possible to provide information tailored to the user's interests and emotions, and to individually optimize the in-store experience.

[0875] "User interest data" refers to information about topics that users are particularly interested in, and is initially registered for the purpose of providing individual services and suggesting information.

[0876] "Current location" refers to information indicating the geographical location where a user is located at a specific point in time, and is the underlying data for location-based services.

[0877] "Related information" refers to specific information generated based on the user's interests and current location, and is considered to be interesting and useful to the user.

[0878] "Audio presentation" is a method of conveying information through sound without relying on visual information, enabling users to receive information through audio.

[0879] A "smart wearable device" is an electronic device worn on the body that has the function of detecting the user's movements and emotional state.

[0880] "Emotional state" refers to the user's psychological and mental state, and is information that is recognized in real time using indicators such as facial expressions and tone of voice.

[0881] A "generative AI model" is an artificial intelligence technology that uses machine learning algorithms to create new information and suggestions from data, and is intended for optimizing and personalizing information.

[0882] Reinforcement learning is a machine learning technique that uses trial and error to obtain the optimal result in action selection, and is a technology used to improve the system's behavior based on user responses.

[0883] This invention is an information delivery system that takes into account the user's interests and emotional state, enabling a more personalized experience in physical stores. The system's program is executed by a smart wearable device carried by the user, a server, and the network infrastructure connecting them.

[0884] The smart wearable device is equipped with a GPS sensor and a camera and microphone for emotion recognition, acquiring the user's current location and emotional state in real time. Based on this, it is transmitted to a server along with the user's initial interest data.

[0885] The server begins processing information based on the received data. It searches the database for product information suitable for the user's current location and emotions, and optimizes it using a generative AI model. This generative AI model is used to generate expressions that resonate most with the user's current emotions. For example, if the user expresses surprise, the server will generate information that evokes surprise and matches that emotion.

[0886] The optimized information is transmitted to the device as audio presentations by a speech synthesis engine and provided to the user. This allows the user to receive high-quality information in real time via audio. The user's responses are recorded again on the device and sent to the server, where a reinforcement learning algorithm further optimizes the interest data, which can then be used to provide information during subsequent visits.

[0887] As a concrete example, consider a scenario where a user is in an electronics store and is in the display area for a new smartphone. In this case, when the emotion recognition software detects the user's excitement, the server uses a generated AI model to create a message such as, "This smartphone is equipped with the latest camera technology. Do you have any questions?" and plays it back using a speech synthesis engine.

[0888] An example of a prompt message is as follows:

[0889] "When the user's emotional state is heightened, generate a detailed description and special information about the product. The product category should be smartphones, and the features should include camera performance, cutting-edge technology, and brand innovation, incorporating relevant knowledge into the description."

[0890] In this way, the system can significantly improve the user's experience in physical stores.

[0891] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0892] Step 1:

[0893] The device obtains the user's current location using a GPS sensor. The input used is GPS sensor data. The device sends this location data, along with the user's interest data, to the server. The output is a set of location data and interest data.

[0894] Step 2:

[0895] The device detects the user's voice and facial expressions, and uses emotion recognition software to analyze their emotional state in real time. The input consists of data from the device's microphone and camera. The output is data indicating the user's emotional state, which is also sent to the server.

[0896] Step 3:

[0897] The server receives location data, interest data, and sentiment data transmitted from the terminal. It searches and extracts information related to the user's location and interests from the database. A database search engine is used in this process. The output is a set of relevant information.

[0898] Step 4:

[0899] The server uses a generative AI model to optimize the extracted relevant information according to the user's emotional state. The input consists of relevant information and emotional data. The generative AI model uses these as prompts to generate the guidance text to be output.

[0900] Step 5:

[0901] The server passes the generated guidance text to the speech synthesis engine, which converts it into audio data. The input is the text data of the guidance text. The output is audio data, which is sent to the terminal.

[0902] Step 6:

[0903] The terminal plays audio data received from the server, providing voice guidance to the user. This allows the user to receive engaging audio information in real time. The output is the user's auditory reception of the information.

[0904] Step 7:

[0905] The system collects user responses (e.g., voice responses or additional gestures) again and sends them from the terminal to the server. The input is user response data. The server uses this data to update interest data using a reinforcement learning algorithm. The output is the updated interest data, which is used to optimize guidance for future visits.

[0906] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0907] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0908] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0909] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0910] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0911] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0912] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0913] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0914] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0915] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0916] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0917] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0918] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0919] 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.

[0920] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0921] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0922] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0923] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0924] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0925] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0926] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0927] The following is further disclosed regarding the embodiments described above.

[0928] (Claim 1)

[0929] A means for users to register their interests upon initial registration,

[0930] A means of obtaining the user's current location,

[0931] A means for generating relevant information based on acquired location information and interest information,

[0932] A means of providing the generated related information as voice guidance,

[0933] A means of updating interest information based on user responses,

[0934] A system that includes this.

[0935] (Claim 2)

[0936] The system according to claim 1, which uses a generative model to optimize the generated related information.

[0937] (Claim 3)

[0938] The system according to claim 1, which records user responses and updates interest information by performing reinforcement learning based on that data.

[0939] "Example 1"

[0940] (Claim 1)

[0941] A device for registering information about the user's interests,

[0942] A device for measuring the user's location,

[0943] A device that generates relevant information based on measured location information and information on interests,

[0944] A device that provides the generated related information in audio,

[0945] A device that updates information about user interests based on user responses,

[0946] A system that includes this.

[0947] (Claim 2)

[0948] The system according to claim 1, which uses a generative model to optimize the generated related information.

[0949] (Claim 3)

[0950] The system according to claim 1, which records user responses and updates information related to interests by performing machine learning based on that data.

[0951] "Application Example 1"

[0952] (Claim 1)

[0953] A means for users to register their interests upon initial registration,

[0954] A means of obtaining the user's current location,

[0955] A means for generating relevant information based on acquired location information and interest information,

[0956] A means of providing the generated related information as voice guidance,

[0957] A means of updating interest information based on user responses,

[0958] A means of guiding passengers to points of interest along their route, mounted on an automatically controlled vehicle.

[0959] A system that includes this.

[0960] (Claim 2)

[0961] The system according to claim 1, which uses a generative model to optimize the generated related information.

[0962] (Claim 3)

[0963] The system according to claim 1, which records user responses, performs reinforcement learning based on that data to update interest information, and improves the accuracy of guidance for automatically controlled vehicles.

[0964] "Example 2 of combining an emotion engine"

[0965] (Claim 1)

[0966] A device for initial registration of user interest information,

[0967] A device that obtains the user's current location,

[0968] A device that recognizes the user's emotions,

[0969] A device that generates relevant information based on acquired location information, interest information, and emotion data,

[0970] A device that presents the generated related information as audio,

[0971] A device that updates interest information based on user responses,

[0972] A device for storing the generated related information in a storage device,

[0973] A system that includes this.

[0974] (Claim 2)

[0975] The system according to claim 1, which uses a generative model to optimize the generated related information based on the user's emotional state.

[0976] (Claim 3)

[0977] The system according to claim 1, which records user responses and updates interest information by performing machine learning based on that data.

[0978] "Application example 2 when combining with an emotional engine"

[0979] (Claim 1)

[0980] A means of registering user interest data for the first time,

[0981] A means of obtaining the user's current location,

[0982] A means for generating relevant information based on acquired location data and interest data,

[0983] A means of providing the generated related information as an audio presentation,

[0984] A means of recognizing the user's emotional state through a smart wearable device,

[0985] A means of adjusting relevant information based on the user's emotions,

[0986] A means of updating interest data based on user responses,

[0987] An information processing system that includes this.

[0988] (Claim 2)

[0989] The information processing system according to claim 1, which uses a generative AI model to optimize the generated related information.

[0990] (Claim 3)

[0991] The information processing system according to claim 1, which records user responses and updates interest data by performing reinforcement learning based on that data. [Explanation of symbols]

[0992] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to register their interests upon initial registration, A means of obtaining the user's current location, A means for generating relevant information based on acquired location information and interest information, A means of providing the generated related information as voice guidance, A means of updating interest information based on user responses, A system that includes this.

2. The system according to claim 1, which uses a generative model to optimize the generated related information.

3. The system according to claim 1, which records user responses and updates interest information by performing reinforcement learning based on that data.

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