system

Abstract

We provide the system. [Solution] A means for obtaining user input and converting that input from audio data to text data, A means of referring to user profile information and analyzing user preferences based on that profile, A means of transmitting user input data and preference data to the cloud, Within the cloud, a means of selecting an appropriate external information provision method based on user input data and acquiring that information, A means of generating the acquired information in a form optimized for the user, A means of presenting the generated information to the user, 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, and includes 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] In modern information services, it is important to provide personalized information for each user in real time. However, in conventional systems, it is necessary to aggregate huge amounts of data into a single AI, which poses problems of high cost and high energy consumption. In addition, there is also a lack of flexibility to quickly respond to different needs of each user. Against such a background, there is a demand to provide services optimized for users while suppressing costs.

Means for Solving the Problems

[0005] This invention provides means for acquiring user input and converting voice data into text data. Furthermore, it provides means for analyzing user preferences based on user profile information and transmitting user data to the cloud. Within the cloud, appropriate external information provision means are selected based on user input to acquire information, and this information is generated in a form optimized for the user. The generated information is presented visually or audibly, realizing optimal information provision for the user.

[0006] A "user" is an individual or group that uses a system to obtain information.

[0007] "Means of acquiring input" refers to the process of sensing the user's voice or text utterances and actions and capturing them as digital data.

[0008] "Methods for converting to text data" refers to the process of analyzing acquired audio data and converting it into textual information.

[0009] "Profile information" refers to a collection of data that includes a user's past behavioral history, preferences, and settings.

[0010] "Methods for analyzing preferences" refer to the process of inferring a user's preferences and tendencies based on their profile information.

[0011] "Cloud" is a concept that refers to remote servers and services that can be accessed via the internet.

[0012] "External information provision means" refers to external APIs and services that are linked to obtain information in response to user requests.

[0013] "Methods for generating in an optimized state" refers to the process of processing information to suit the user's preferences and context, and preparing the output in the most useful form.

[0014] "Means of presentation" refers to devices or methods for conveying generated information to the user visually or audibly. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a labeled 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.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a labeled 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.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The system of this invention aims to optimize and provide information based on user input. This system is primarily implemented through the coordination of terminals, servers, and the cloud.

[0037] First, the user uses the terminal to input information via voice or text. For example, a user might ask a ticket vending machine in a ramen shop, "How do I order soy sauce ramen?" using voice. The terminal receives this input and, if it is voice data, begins the process of converting it into text data.

[0038] Next, the device references the user's profile information and analyzes their preferences. This profile includes data on past order history and preferences. Based on this data, the device understands the user's preferences and prepares to provide appropriate information.

[0039] Subsequently, the terminal sends the input information along with the user's profile data to the cloud server. The server in the cloud receives this data and selects the most suitable external information provision method for the user, such as a traffic information API or a store information service, and retrieves the data.

[0040] The cloud server analyzes the acquired data and uses generative AI to generate personalized information for the user. This makes it possible to provide value-added information that takes into account the user's preferences and past behavior history.

[0041] The generated information is sent back to the terminal, which then presents this information to the user in an easy-to-understand format. This could involve providing information via voice through a voice assistant or displaying it visually on the screen. For example, it might say, "To purchase soy sauce ramen, press the A button."

[0042] This system allows users to quickly obtain the information they need and provides them with an optimal experience tailored to their preferences.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The user uses the device to input information via voice or text. For example, the user might say to the device, "How many more stops until I get home?"

[0046] Step 2:

[0047] The device acquires voice input and converts the voice data into text data using its built-in speech recognition technology. If the input is text, the data is acquired directly.

[0048] Step 3:

[0049] The device references the user's profile information and analyzes the user's preferences based on the behavioral history and preference data stored there. This allows the device to understand what choices the user has made in the past.

[0050] Step 4:

[0051] The device combines text data and user preference data and creates a request to send this information to the cloud server. This request is sent to the cloud using a secure communication protocol.

[0052] Step 5:

[0053] The server analyzes the data received on the cloud and selects the appropriate external service API. For example, it might select a traffic information API and configure the parameters for using it.

[0054] Step 6:

[0055] The server executes the selected API and retrieves information corresponding to the user's request. This information is then organized on the server in a user-specific format.

[0056] Step 7:

[0057] The server generates user-optimized information and sends it back to the terminal. This data is then converted into a format that is easy for the user to understand.

[0058] Step 8:

[0059] The terminal displays the received information to the user through screen display or audio output. For example, information such as "You will arrive home in two more stations" may be displayed on the screen or announced by voice.

[0060] (Example 1)

[0061] 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."

[0062] In today's information environment, users are surrounded by a vast amount of information, making it difficult to find information optimized to their preferences. In this situation, there is a need to provide information that efficiently and effectively meets user needs. However, conventional systems simply process user input, making it difficult to provide personalized information that takes into account individual preferences and past behavioral history.

[0063] 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.

[0064] In this invention, the server includes means for acquiring user input and converting voice information into text information, means for referring to user attribute information and analyzing preferences, and means for transmitting user input information and preference information to a remote server. This makes it possible to provide value-added information based on the individual preferences of each user.

[0065] A "user" refers to an individual or group that uses a system to obtain information or utilize services.

[0066] "Input" refers to the information that users provide to the system as voice or text information.

[0067] "Audio information" refers to auditory data generated by a user's speech and processed by the system.

[0068] "Textual information" refers to data in which audio information is represented as text.

[0069] "Attribute information" refers to data that includes individual information such as user preferences and past behavioral history.

[0070] "Preferences" refers to information that indicates a user's tastes and preferences.

[0071] A "remote server" refers to computing resources located in the cloud that are used to process and store user input information and preference data.

[0072] "Information delivery methods" refer to the functions and processes for selecting and delivering information optimized for the user.

[0073] "Generation means" refers to the function of creating newly personalized data based on acquired information.

[0074] A "generative artificial intelligence model" refers to an algorithm or system that uses machine learning to generate information that is optimal for the user.

[0075] A "prompt statement" refers to a text statement used to instruct a generative artificial intelligence model to generate information.

[0076] This system is primarily implemented through the coordination of terminals, servers, and cloud technologies. A specific implementation is described below.

[0077] The user first provides input via voice or text through the terminal. The terminal uses speech recognition technology for voice input, for example, by using common speech recognition software to convert the voice into text. This conversion process forms the basis for understanding the user's intent.

[0078] The terminal then retrieves user attribute information from a local or remote database. This attribute information includes the user's past behavioral history and preferences. The terminal uses this information to analyze the user's preferences and prepare to provide the user with the most relevant information.

[0079] Attribute information and user input information are sent to a remote server in the cloud. The remote server analyzes the received data and selects external information provision methods to provide the maximum value to the user. In this process, open data and specific service APIs can be used.

[0080] The server uses a generative AI model to generate user-specific information. The generative AI model receives instructions via prompts and generates information tailored to each individual user. For example, it might use a prompt like, "The user wants to order soy sauce ramen again at a ramen shop. According to past preference data, the user likes a boiled egg as an additional topping. Please create an order guide that includes this."

[0081] Finally, the generated information is sent back to the device, which then presents the information to the user visually or audibly. This allows the user to intuitively receive individually optimized information. Specific implementations include displaying navigation on the screen or providing information through a voice assistant.

[0082] In this way, the system enables users to obtain information efficiently and in a personalized manner.

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

[0084] Step 1:

[0085] The user inputs information using a terminal via voice or text. For example, they can ask "How do I order soy sauce ramen?" by voice. The terminal receives this input and, if it is voice data, converts it into text information using speech recognition technology. In this way, the system obtains the user's request in a form that can be processed as text data. Specifically, speech recognition software performs phoneme analysis on the voice waveform data and converts it into a string of characters.

[0086] Step 2:

[0087] The terminal accesses the user's attribute information. This attribute information includes data on past order history and user preferences. Based on this, the terminal analyzes the user's preferences and prepares to provide personalized service based on text data. Specifically, it accesses the attribute information database and performs profile analysis using preference analysis algorithms to verify whether it matches the user's preferences and past behavior.

[0088] Step 3:

[0089] The terminal sends the processed input information and analyzed attribute information to a remote server. The server uses this received data as input and performs analysis to select the most suitable external information provision method in response to the user's request. The server attempts to access various external data services and obtain the necessary information. Specifically, data collection and query processing are performed via APIs.

[0090] Step 4:

[0091] The server uses a generative AI model to generate information tailored to the user's requests based on the acquired data. Instructions are given to the AI ​​model through prompts, and the generated data takes into account the user's preferences and past behavioral history. Specifically, the generative AI model uses natural language processing techniques to generate output data based on the prompts, and then forms it as a direct response to the user's requests.

[0092] Step 5:

[0093] The generated information is sent back to the terminal, which then selects the best way to present this information to the user. This can be done by providing voice guidance using a voice assistant or by visually displaying the information on a screen. Specific actions include rendering a visual interface and outputting voice using speech synthesis technology, allowing the user to instantly obtain the information and take action.

[0094] (Application Example 1)

[0095] 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."

[0096] A problem exists in that customers cannot quickly and effectively obtain information that meets their needs in physical stores. This situation leads to decreased customer satisfaction and, consequently, a decline in purchasing intent. Furthermore, it is difficult for store staff to provide personalized recommendations to each customer, which hinders efficient service delivery. To solve this problem, a system is needed that provides information in real time based on customer preferences and past behavior history.

[0097] 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.

[0098] In this invention, the server includes means for acquiring user input and converting that input from voice data to text data; means for referring to user profile information and analyzing user preferences based on that profile; means for transmitting user input data and preference data to the cloud; means for selecting appropriate external information provision means based on user input data within the cloud and acquiring information; means for generating the acquired information in a state optimized for the user; and means for displaying the generated information to the user through a visual presentation device. This makes it possible for customers to instantly acquire individually customized and valuable information in physical stores, thereby improving their purchasing experience.

[0099] "User input" refers to information provided by the user through their device, either via voice or text.

[0100] "Means for converting audio data to text data" refers to a process or apparatus for converting audio signals into textual information.

[0101] "User profile information" refers to a collection of information that includes an individual user's preferences, past behavioral history, and other personal data.

[0102] "Means for analyzing preferences" refers to methods or devices for analyzing user preferences and tendencies based on user profile information.

[0103] "Means of sending to the cloud" refers to the technology or process for transferring data to a remote server.

[0104] "External information provision means" refers to information sources and service APIs that are accessible via the cloud.

[0105] "Means of acquiring information" refers to the processes and devices used to obtain necessary information from external information sources.

[0106] "Means of generating in an optimized state" refers to methods or techniques for personalizing information based on user preferences and context.

[0107] A "visual presentation device" is a device used to visually display generated information to a user.

[0108] This invention relates to a system for providing customized information to individual customers in physical stores. Specifically, it begins with a terminal acquiring voice commands uttered by the user (customer) and converting them into text data. The terminal uses a voice recognition API to quickly convert the voice data into text.

[0109] Once text data is generated, the device sends data to the server based on the user's profile information. This profile information includes preferences and past purchase history stored in Google Cloud. This allows the server to analyze the user's preferences with high accuracy.

[0110] When the server receives user data, it uses external information provision services connected to the cloud to obtain the necessary information. This includes services such as traffic information APIs and store information services. Specifically, it uses the OpenAI® GPT model to generate information optimized for the user's past behavior history and current context in real time.

[0111] The generated information is provided to the user through a visual presentation device, such as the display of smart glasses. This allows the user to quickly obtain the appropriate information when needed, enabling efficient purchasing and decision-making.

[0112] For example, when a customer asks, "What products do you recommend?", the smart glasses might display, "Today's special offer is XX. We have a trial coupon available."

[0113] Example of a prompt

[0114] The customer likes matcha. Please recommend some products that he / she might be interested in.

[0115] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0116] Step 1:

[0117] The user provides voice input. The device receives this voice and converts it into text data using a speech recognition API. The input is voice data, and the output is the converted text data. The speech recognition API analyzes the voice signal and generates the corresponding text.

[0118] Step 2:

[0119] The terminal uses the converted text data to prepare to connect to the cloud server. Here, the terminal references the user's profile information and packages the input text and profile information for transmission to the cloud. The input consists of text data and profile information, while the output is an integrated data package sent to the cloud. The profile information includes individual preferences and past behavioral history.

[0120] Step 3:

[0121] The cloud server analyzes the received integrated data package. Using a generative AI model, the cloud server performs the analysis and selects the most appropriate external information source to provide the best information. The input is the integrated data package, and the output is the selected information source and the request for the necessary data. The generative AI model constructs information tailored to the user's needs.

[0122] Step 4:

[0123] The cloud server retrieves data from selected external sources and generates information optimized for the user. Data processing and calculations are performed here, and the information is customized according to the user's preferences. The input is data from external sources, and the output is information personalized for the user.

[0124] Step 5:

[0125] The device receives personalized information sent from a cloud server. This information is displayed on a visual display device and presented to the user visually. The input is the personalized information, and the output is the displayed information. The user can confirm the optimized information through the visual display device.

[0126] 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.

[0127] The present invention aims to further enhance the user experience by combining a system that provides personalized information based on user input with an emotion engine. The system is primarily implemented through the coordination of terminals, servers, and the cloud.

[0128] The user provides input to the device via voice or text. For example, a user might say, "The train is delayed and I'm having trouble," near a ticket machine at a train station. The device receives this input and converts it to text if it's voice. Furthermore, an emotion engine analyzes the user's voice to determine their emotions.

[0129] The device analyzes the user's preferences by referring to the user's profile information and acquired sentiment data. This profile includes data on past behavioral history and preferences, while the sentiment data indicates the user's state of mind at that moment.

[0130] Next, the device sends text data, preference data, and sentiment data to a cloud server. Based on the user's input and sentiment, the server in the cloud selects the appropriate means of providing external information. For example, it might use a traffic information API to obtain real-time delay information.

[0131] The acquired information is adjusted by the emotion engine to match the user's emotional state, and generated in the most appropriate format for the user. The server then sends this information back to the terminal.

[0132] The device presents received information to the user visually or audibly. The content and tone of the information are adjusted to take into account the user's emotional state. For example, the user may be provided with a message such as, "Train delays have been resolved and trains will soon be running on schedule. Please rest assured."

[0133] As described above, the system of the present invention enables the provision of optimal information that reflects the user's current emotional state, dramatically improving the user experience.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user inputs information into the device via voice or text. For example, the user might say to the device, "I'm in a bad mood today."

[0137] Step 2:

[0138] The device acquires the user's voice input and converts the voice data into text data using speech recognition technology.

[0139] Step 3:

[0140] The device uses an emotion engine to recognize the user's emotions from the acquired audio and analyzes their state of joy, anger, sadness, and other feelings.

[0141] Step 4:

[0142] The device references the user's profile information and analyzes recognized emotional data along with behavioral history and preference data.

[0143] Step 5:

[0144] The device sends text data, preference data, and sentiment data to a cloud server. A secure communication protocol is used for this transmission.

[0145] Step 6:

[0146] The server analyzes this data in the cloud and selects the most suitable external information delivery method (e.g., news information API or music recommendation service) according to the user's emotional state.

[0147] Step 7:

[0148] The server retrieves data from selected external information sources and organizes the retrieved data in a way that aligns with the user's emotions.

[0149] Step 8:

[0150] The server sends the generated information back to the terminal and shapes the message with a tone that matches the user's emotions.

[0151] Step 9:

[0152] The device will present the returned information to the user visually or audibly. For example, a message such as "We recommend some relaxing music for today" might be displayed.

[0153] (Example 2)

[0154] 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".

[0155] Traditional personalized information delivery systems have only provided information based on user preferences and past behavioral history, and have not been able to optimize information to reflect the user's emotional state. Therefore, there is a need for a mechanism that allows users to quickly and accurately obtain information that matches their emotional state at that moment.

[0156] 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.

[0157] In this invention, the server includes means for converting user input from speech to text, means for analyzing the user's emotions, and means for providing information optimized based on the user's preferences and emotional data. This makes it possible to provide information that is tailored to the user's emotional state.

[0158] "Audio data" refers to information that represents audio signals in digital or analog format.

[0159] "Text data" refers to digital information recorded as a string of characters, in a format that can be processed by a computer.

[0160] "Emotional data" refers to indicators that show a user's emotional state, and is data obtained from the user's voice, facial expressions, and other sources.

[0161] "Profile information" refers to information about individual users, including their past behavioral history, preferences, and basic personal attributes.

[0162] "Preferences" refer to data that indicates a user's personal likes and preferences, including tendencies towards specific situations and choices.

[0163] A "cloud server" is a remote server accessible via the internet, which serves as a computing resource for storing and processing data.

[0164] "External information" refers to data obtained from outside the system, including real-time information such as traffic and weather data.

[0165] "Optimized state" refers to the form or condition that best suits a particular purpose or condition.

[0166] "Presentation means" refers to technical means for displaying or transmitting information generated by a system to a user.

[0167] To implement this invention, a system is needed to efficiently receive user input, analyze, process, and optimize it for providing information. This is primarily achieved through the coordination of terminals, servers, and the cloud.

[0168] The terminal is a computer device equipped with a microphone and touchscreen that receives voice or text input from the user. This input is converted from voice data to text data using speech recognition software (e.g., Google Speech-to-Text API).

[0169] The device further analyzes the user's emotions from their voice data using an emotion engine (e.g., IBM Watson® Tone Analyzer). This emotion data, along with the user's profile information, is sent to a cloud server. This profile information includes data on the user's past behavior and preferences, which is referenced to provide appropriate information.

[0170] The server selects the appropriate external information provision method based on user text data, preference data, and sentiment data received in the cloud environment. Specifically, it uses an external database (e.g., a traffic API) to obtain real-time information such as traffic and weather information.

[0171] The acquired external information is optimized for the user's emotional state by a generative AI model and processed by the server. An example of a message generated for the user might be the prompt, "I want to know the new train arrival and departure times."

[0172] Ultimately, the device presents the received information to the user visually or audibly. This presentation utilizes displays, speakers, and other means, and the information is delivered in a way that takes the user's emotional state into consideration. This method allows the user to quickly and effectively receive information appropriate to the situation.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The device receives voice input from the user. Specifically, it collects voice data using a microphone. The device then converts the voice data into text data using speech recognition software. The output is text data.

[0176] Step 2:

[0177] The device processes the converted text data and uses an emotion engine to analyze the user's emotions. Specifically, it inputs the text data into an API for emotion analysis to determine the emotional state. The input is text data, and the output is emotion data.

[0178] Step 3:

[0179] The device retrieves the user's profile information. This profile information includes past behavioral history and preferences. This allows for the analysis of emotional data in a way that takes the user's preferences into account. The input is the user's identification information, and the output is profile data.

[0180] Step 4:

[0181] The device sends text data, profile data, and sentiment data to a cloud server. Specifically, it converts the data into packets using a secure protocol and transfers them to the server over the network. The input is a set of data, and the output is the completion of the data transfer to the server.

[0182] Step 5:

[0183] The server selects the appropriate external information provision method based on the data received on the cloud. Specifically, it analyzes the received data using an algorithm and makes a request to the external database. The input is the data received by the server, and the output is the identification of the external information.

[0184] Step 6:

[0185] The server uses a generation AI model to optimize acquired external information according to the user's emotional state. This process processes the information and generates messages in the most appropriate format for the user. The input is external information and emotional data, and the output is the optimized information.

[0186] Step 7:

[0187] The terminal presents optimized information received from the server to the user. Specifically, it provides information visually or audibly using a display and speakers. The input is the optimized information, and the output is the presentation of that information to the user.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0190] Conventional information delivery systems have a problem in that information is provided without considering the user's emotional state, resulting in insufficient improvement of the user experience. In particular, in vehicles, there is a need to provide information and content that responds to passengers' emotions, but there was no optimal means of providing information based on emotional information.

[0191] 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.

[0192] In this invention, the server includes means for converting user input from voice data to text data, means for analyzing user preferences by referring to user profile information, and means for transmitting input data and preference data to the cloud. This enables the provision of information based on user input and emotional data.

[0193] "User input" refers to information that a user provides to the system via voice or text.

[0194] "Means of converting audio data to text data" refers to a function that uses speech recognition technology to convert audio information into text information.

[0195] "User profile information" is a general term for data related to a user's past behavioral history and preferences.

[0196] "Methods for analyzing preferences" refer to functions that analyze a user's preferences based on their profile information.

[0197] "Means of sending input data and preference data to the cloud" refers to the function of sending data to a server via the internet.

[0198] "Emotional data" refers to data extracted from a user's voice or text that indicates their emotional state at that time.

[0199] "Means of selecting external information provision methods and acquiring information" refers to a function that selects the optimal information source according to the user's state and acquires the necessary information.

[0200] "Means of generating and presenting information to users" refers to a function that arranges acquired information to match the user's emotions and provides it through visual or auditory means.

[0201] "A means of selecting and providing music that matches the user's emotions" refers to a function that selects and plays the most suitable music based on the user's emotional state.

[0202] "Means of improving user experience" refer to functions that adjust the information and content provided so that they are more useful and comfortable for the user.

[0203] The system for carrying out this invention is configured as follows: First, the terminal obtains voice or text input from the user. The obtained voice data is converted into text data using a speech recognition API. This conversion process utilizes a speech recognition service such as Google Speech-to-Text.

[0204] Next, we analyze the user's profile information and preferences. This profile information includes past behavioral history and sentiment data, and we analyze the user's state using sentiment analysis APIs such as Microsoft® Azure® Text Analytics.

[0205] The analyzed data is sent to a cloud server. The cloud server utilizes AWS® Lambda and other technologies to search for external information based on user input data and emotional data, and selects the most appropriate means of providing information. For example, if a user is feeling anxious in a vehicle, relaxing music and operational information are integrated and provided.

[0206] The data processing and calculations used during transmission incorporate algorithms that take into account the user's emotional state, generating information in a format optimized for the user. As a result, users receive reassuringly tailored messages.

[0207] For example, if a passenger says, "I want to relax today," a particularly calming piece of classical music will be selected, and a message such as, "Traffic is currently smooth. Please enjoy your favorite music," will be delivered.

[0208] An example of a specific prompt for the generative AI model would be: "Assess the passenger's current mood and select the most appropriate content. For example, if the passenger is seeking relaxation, select soothing music."

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The device receives voice input from the user. When the user speaks into the device, the device captures the voice data with its microphone and sends it to a speech recognition API. The input is voice data, and the output is text data. The speech recognition API (e.g., Google Speech-to-Text) converts the voice to text and passes this text data to the next processing step.

[0212] Step 2:

[0213] The device analyzes preferences using converted text data and user profile information. Profile information includes past behavioral history and past selections. Input is text data and profile information, and output is preference data. This allows for analysis of what kind of information and content the user prefers.

[0214] Step 3:

[0215] The device sends text data and preference data to the server. During this process, a sentiment analysis API (e.g., Microsoft Azure Text Analytics) is used to analyze the user's text data to determine their sentiment. The input is text data, and the output is sentiment data. This data is sent to the cloud server and used for subsequent processing.

[0216] Step 4:

[0217] The server selects the appropriate external information provision method based on input data, preference data, and sentiment data acquired within the cloud. For example, it may obtain real-time traffic information or music information from external sources using APIs. The input is user data, and the output is the acquired external information. This ensures that information optimized for the user's emotions and preferences is collected.

[0218] Step 5:

[0219] The server generates and optimizes information based on the user's emotional state, using acquired external information. The output is information tailored to the user. Specifically, it generates music that matches the user's emotions and messages that provide a sense of security. This generated information is adjusted considering the user's emotions and preferences.

[0220] Step 6:

[0221] The terminal presents the user with optimized information received from the server. Specifically, it delivers operational information and music in a tone that matches the user's emotions through a visual display and speakers. This provides a customized experience that allows the user to spend time comfortably in the vehicle. The input is optimized information, and the output is an improvement to the user experience.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] [Second Embodiment]

[0226] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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).

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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".

[0238] The system of this invention aims to optimize and provide information based on user input. This system is primarily implemented through the coordination of terminals, servers, and the cloud.

[0239] First, the user uses the terminal to input information via voice or text. For example, a user might ask a ticket vending machine in a ramen shop, "How do I order soy sauce ramen?" using voice. The terminal receives this input and, if it is voice data, begins the process of converting it into text data.

[0240] Next, the device references the user's profile information and analyzes their preferences. This profile includes data on past order history and preferences. Based on this data, the device understands the user's preferences and prepares to provide appropriate information.

[0241] Subsequently, the terminal sends the input information along with the user's profile data to the cloud server. The server in the cloud receives this data and selects the most suitable external information provision method for the user, such as a traffic information API or a store information service, and retrieves the data.

[0242] The cloud server analyzes the acquired data and uses generative AI to generate personalized information for the user. This makes it possible to provide value-added information that takes into account the user's preferences and past behavior history.

[0243] The generated information is sent back to the terminal, which then presents this information to the user in an easy-to-understand format. This could involve providing information via voice through a voice assistant or displaying it visually on the screen. For example, it might say, "To purchase soy sauce ramen, press the A button."

[0244] This system allows users to quickly obtain the information they need and provides them with an optimal experience tailored to their preferences.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user uses the device to input information via voice or text. For example, the user might say to the device, "How many more stops until I get home?"

[0248] Step 2:

[0249] The device acquires voice input and converts the voice data into text data using its built-in speech recognition technology. If the input is text, the data is acquired directly.

[0250] Step 3:

[0251] The device references the user's profile information and analyzes the user's preferences based on the behavioral history and preference data stored there. This allows the device to understand what choices the user has made in the past.

[0252] Step 4:

[0253] The device combines text data and user preference data and creates a request to send this information to the cloud server. This request is sent to the cloud using a secure communication protocol.

[0254] Step 5:

[0255] The server analyzes the data received on the cloud and selects the appropriate external service API. For example, it might select a traffic information API and configure the parameters for using it.

[0256] Step 6:

[0257] The server executes the selected API and retrieves information corresponding to the user's request. This information is then organized on the server in a user-specific format.

[0258] Step 7:

[0259] The server generates user-optimized information and sends it back to the terminal. This data is then converted into a format that is easy for the user to understand.

[0260] Step 8:

[0261] The terminal displays the received information to the user through screen display or audio output. For example, information such as "You will arrive home in two more stations" may be displayed on the screen or announced by voice.

[0262] (Example 1)

[0263] 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 glasses 214 will be referred to as the "terminal."

[0264] In today's information environment, users are surrounded by a vast amount of information, making it difficult to find information optimized to their preferences. In this situation, there is a need to provide information that efficiently and effectively meets user needs. However, conventional systems simply process user input, making it difficult to provide personalized information that takes into account individual preferences and past behavioral history.

[0265] 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.

[0266] In this invention, the server includes means for acquiring user input and converting voice information into text information, means for referring to user attribute information and analyzing preferences, and means for transmitting user input information and preference information to a remote server. This makes it possible to provide value-added information based on the individual preferences of each user.

[0267] A "user" refers to an individual or group that uses a system to obtain information or utilize services.

[0268] "Input" refers to the information that users provide to the system as voice or text information.

[0269] "Audio information" refers to auditory data generated by a user's speech and processed by the system.

[0270] "Textual information" refers to data in which audio information is represented as text.

[0271] "Attribute information" refers to data that includes individual information such as user preferences and past behavioral history.

[0272] "Preferences" refers to information that indicates a user's tastes and preferences.

[0273] A "remote server" refers to computing resources located in the cloud that are used to process and store user input information and preference data.

[0274] "Information delivery methods" refer to the functions and processes for selecting and delivering information optimized for the user.

[0275] "Generation means" refers to the function of creating newly personalized data based on acquired information.

[0276] A "generative artificial intelligence model" refers to an algorithm or system that uses machine learning to generate information that is optimal for the user.

[0277] A "prompt statement" refers to a text statement used to instruct a generative artificial intelligence model to generate information.

[0278] This system is primarily implemented through the coordination of terminals, servers, and cloud technologies. A specific implementation is described below.

[0279] The user first provides input via voice or text through the terminal. The terminal uses speech recognition technology for voice input, for example, by using common speech recognition software to convert the voice into text. This conversion process forms the basis for understanding the user's intent.

[0280] The terminal then refers to the user's attribute information from a local or remote database. This attribute information includes the user's past behavior history and preference information. The terminal uses this information to analyze the user's preferences and prepare to provide the user with optimal information.

[0281] The attribute information and the user's input information are sent to a remote server on the cloud. The remote server analyzes the received data and selects an external information providing means to provide the maximum value to the user. In this process, open data, specific service APIs, etc. can be used.

[0282] The server uses a generative AI model to generate information specialized for the user. The generative AI model receives instructions via a prompt sentence and generates information tailored to individual users. For example, a prompt sentence like "The user wants to order soy sauce ramen again at a ramen shop. According to past preference data, the user likes boiled eggs as an additional topping. Please create an order description including this." is used.

[0283] Finally, the generated information is returned to the terminal, and the terminal presents the information to the user visually or audibly. In this way, the user can intuitively receive information optimized individually. As a specific implementation, there are methods such as displaying navigation on a display or providing information through a voice assistant.

[0284] In this way, this system enables efficient and personalized information acquisition for the user.

[0285] The flow of the specific process in Example 1 will be described using FIG. 11.

[0286] Step 1:

[0287] The user inputs information using a terminal via voice or text. For example, they can ask "How do I order soy sauce ramen?" by voice. The terminal receives this input and, if it is voice data, converts it into text information using speech recognition technology. In this way, the system obtains the user's request in a form that can be processed as text data. Specifically, speech recognition software performs phoneme analysis on the voice waveform data and converts it into a string of characters.

[0288] Step 2:

[0289] The terminal accesses the user's attribute information. This attribute information includes data on past order history and user preferences. Based on this, the terminal analyzes the user's preferences and prepares to provide personalized service based on text data. Specifically, it accesses the attribute information database and performs profile analysis using preference analysis algorithms to verify whether it matches the user's preferences and past behavior.

[0290] Step 3:

[0291] The terminal sends the processed input information and analyzed attribute information to a remote server. The server uses this received data as input and performs analysis to select the most suitable external information provision method in response to the user's request. The server attempts to access various external data services and obtain the necessary information. Specifically, data collection and query processing are performed via APIs.

[0292] Step 4:

[0293] The server uses a generative AI model to generate information tailored to the user's requests based on the acquired data. Instructions are given to the AI ​​model through prompts, and the generated data takes into account the user's preferences and past behavioral history. Specifically, the generative AI model uses natural language processing techniques to generate output data based on the prompts, and then forms it as a direct response to the user's requests.

[0294] Step 5:

[0295] The generated information is sent back to the terminal, which then selects the best way to present this information to the user. This can be done by providing voice guidance using a voice assistant or by visually displaying the information on a screen. Specific actions include rendering a visual interface and outputting voice using speech synthesis technology, allowing the user to instantly obtain the information and take action.

[0296] (Application Example 1)

[0297] 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."

[0298] A problem exists in that customers cannot quickly and effectively obtain information that meets their needs in physical stores. This situation leads to decreased customer satisfaction and, consequently, a decline in purchasing intent. Furthermore, it is difficult for store staff to provide personalized recommendations to each customer, which hinders efficient service delivery. To solve this problem, a system is needed that provides information in real time based on customer preferences and past behavior history.

[0299] 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.

[0300] In this invention, the server includes means for acquiring a user's input and converting the input from voice data into text data, means for referring to the user's profile information and analyzing the user's preferences based on the profile, means for transmitting the user's input data and preference data to the cloud, means for selecting an appropriate external information providing means based on the user's input data within the cloud and acquiring information, means for generating the acquired information in a state optimized for the user, and means for displaying the generated information to the user through a visual presentation device. As a result, it becomes possible for customers to instantaneously obtain individually customized valuable information at a physical store and improve the purchasing experience.

[0301] The "user input" is information provided by the user through a terminal in the form of voice or text.

[0302] The "means for converting from voice data to text data" is a process or device for converting a voice signal into character information.

[0303] The "user profile information" is a collection of information including the preferences of individual users, their past behavior history, and other personal data.

[0304] The "means for analyzing preferences" is a method or device for analyzing the user's preferences and tendencies based on the user's profile information.

[0305] The "means for transmitting to the cloud" is a technology or process for transferring data to a remote server.

[0306] The "external information providing means" refers to information sources accessible in the cloud, service APIs, etc.

[0307] The "means for acquiring information" is a process or device for taking in necessary information from the external information providing means.

[0308] "Means of generating in an optimized state" refers to methods or techniques for personalizing information based on user preferences and context.

[0309] A "visual presentation device" is a device used to visually display generated information to a user.

[0310] This invention relates to a system for providing customized information to individual customers in physical stores. Specifically, it begins with a terminal acquiring voice commands uttered by the user (customer) and converting them into text data. The terminal uses a voice recognition API to quickly convert the voice data into text.

[0311] Once text data is generated, the device sends data to the server based on the user's profile information. This profile information includes preferences and past purchase history stored in Google Cloud. This allows the server to analyze the user's preferences with high accuracy.

[0312] When the server receives user data, it uses external information provision services connected to the cloud to obtain the necessary information. This includes services such as traffic information APIs and store information services. Specifically, it uses the OpenAI GPT model to generate information optimized for the user's past behavior history and current context in real time.

[0313] The generated information is provided to the user through a visual presentation device, such as the display of smart glasses. This allows the user to quickly obtain the appropriate information when needed, enabling efficient purchasing and decision-making.

[0314] For example, when a customer asks, "What products do you recommend?", the smart glasses might display, "Today's special offer is XX. We have a trial coupon available."

[0315] Example of a prompt

[0316] The customer likes matcha. Please recommend some products that he / she might be interested in.

[0317] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0318] Step 1:

[0319] The user provides voice input. The device receives this voice and converts it into text data using a speech recognition API. The input is voice data, and the output is the converted text data. The speech recognition API analyzes the voice signal and generates the corresponding text.

[0320] Step 2:

[0321] The terminal uses the converted text data to prepare to connect to the cloud server. Here, the terminal references the user's profile information and packages the input text and profile information for transmission to the cloud. The input consists of text data and profile information, while the output is an integrated data package sent to the cloud. The profile information includes individual preferences and past behavioral history.

[0322] Step 3:

[0323] The cloud server analyzes the received integrated data package. Using a generative AI model, the cloud server performs the analysis and selects the most appropriate external information source to provide the best information. The input is the integrated data package, and the output is the selected information source and the request for the necessary data. The generative AI model constructs information tailored to the user's needs.

[0324] Step 4:

[0325] The cloud server retrieves data from selected external sources and generates information optimized for the user. Data processing and calculations are performed here, and the information is customized according to the user's preferences. The input is data from external sources, and the output is information personalized for the user.

[0326] Step 5:

[0327] The device receives personalized information sent from a cloud server. This information is displayed on a visual display device and presented to the user visually. The input is the personalized information, and the output is the displayed information. The user can confirm the optimized information through the visual display device.

[0328] 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.

[0329] The present invention aims to further enhance the user experience by combining a system that provides personalized information based on user input with an emotion engine. The system is primarily implemented through the coordination of terminals, servers, and the cloud.

[0330] The user provides input to the device via voice or text. For example, a user might say, "The train is delayed and I'm having trouble," near a ticket machine at a train station. The device receives this input and converts it to text if it's voice. Furthermore, an emotion engine analyzes the user's voice to determine their emotions.

[0331] The device analyzes the user's preferences by referring to the user's profile information and acquired sentiment data. This profile includes data on past behavioral history and preferences, while the sentiment data indicates the user's state of mind at that moment.

[0332] Next, the device sends text data, preference data, and sentiment data to a cloud server. Based on the user's input and sentiment, the server in the cloud selects the appropriate means of providing external information. For example, it might use a traffic information API to obtain real-time delay information.

[0333] The acquired information is adjusted by the emotion engine to match the user's emotional state, and generated in the most appropriate format for the user. The server then sends this information back to the terminal.

[0334] The device presents received information to the user visually or audibly. The content and tone of the information are adjusted to take into account the user's emotional state. For example, the user may be provided with a message such as, "Train delays have been resolved and trains will soon be running on schedule. Please rest assured."

[0335] As described above, the system of the present invention enables the provision of optimal information that reflects the user's current emotional state, dramatically improving the user experience.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user inputs information into the device via voice or text. For example, the user might say to the device, "I'm in a bad mood today."

[0339] Step 2:

[0340] The device acquires the user's voice input and converts the voice data into text data using speech recognition technology.

[0341] Step 3:

[0342] The device uses an emotion engine to recognize the user's emotions from the acquired audio and analyzes their state of joy, anger, sadness, and other feelings.

[0343] Step 4:

[0344] The device references the user's profile information and analyzes recognized emotional data along with behavioral history and preference data.

[0345] Step 5:

[0346] The device sends text data, preference data, and sentiment data to a cloud server. A secure communication protocol is used for this transmission.

[0347] Step 6:

[0348] The server analyzes this data in the cloud and selects the most suitable external information delivery method (e.g., news information API or music recommendation service) according to the user's emotional state.

[0349] Step 7:

[0350] The server retrieves data from selected external information sources and organizes the retrieved data in a way that aligns with the user's emotions.

[0351] Step 8:

[0352] The server sends the generated information back to the terminal and shapes the message with a tone that matches the user's emotions.

[0353] Step 9:

[0354] The device will present the returned information to the user visually or audibly. For example, a message such as "We recommend some relaxing music for today" might be displayed.

[0355] (Example 2)

[0356] 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".

[0357] Traditional personalized information delivery systems have only provided information based on user preferences and past behavioral history, and have not been able to optimize information to reflect the user's emotional state. Therefore, there is a need for a mechanism that allows users to quickly and accurately obtain information that matches their emotional state at that moment.

[0358] 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.

[0359] In this invention, the server includes means for converting user input from speech to text, means for analyzing the user's emotions, and means for providing information optimized based on the user's preferences and emotional data. This makes it possible to provide information that is tailored to the user's emotional state.

[0360] "Audio data" refers to information that represents audio signals in digital or analog format.

[0361] "Text data" refers to digital information recorded as a string of characters, in a format that can be processed by a computer.

[0362] "Emotional data" refers to indicators that show a user's emotional state, and is data obtained from the user's voice, facial expressions, and other sources.

[0363] "Profile information" refers to information about individual users, including their past behavioral history, preferences, and basic personal attributes.

[0364] "Preferences" refer to data that indicates a user's personal likes and preferences, including tendencies towards specific situations and choices.

[0365] A "cloud server" is a remote server accessible via the internet, which serves as a computing resource for storing and processing data.

[0366] "External information" refers to data obtained from outside the system, including real-time information such as traffic and weather data.

[0367] "Optimized state" refers to the form or condition that best suits a particular purpose or condition.

[0368] "Presentation means" refers to technical means for displaying or transmitting information generated by a system to a user.

[0369] To implement this invention, a system is needed to efficiently receive user input, analyze, process, and optimize it for providing information. This is primarily achieved through the coordination of terminals, servers, and the cloud.

[0370] The terminal is a computer device equipped with a microphone and touchscreen that receives voice or text input from the user. This input is converted from voice data to text data using speech recognition software (e.g., Google Speech-to-Text API).

[0371] The device further analyzes the user's emotions from their voice data using an emotion engine (e.g., IBM Watson Tone Analyzer). This emotion data, along with the user's profile information, is sent to a cloud server. This profile information includes data on the user's past behavior and preferences, which is referenced to provide appropriate information.

[0372] The server selects the appropriate external information provision method based on user text data, preference data, and sentiment data received in the cloud environment. Specifically, it uses an external database (e.g., a traffic API) to obtain real-time information such as traffic and weather information.

[0373] The acquired external information is optimized for the user's emotional state by a generative AI model and processed by the server. An example of a message generated for the user might be the prompt, "I want to know the new train arrival and departure times."

[0374] Ultimately, the device presents the received information to the user visually or audibly. This presentation utilizes displays, speakers, and other means, and the information is delivered in a way that takes the user's emotional state into consideration. This method allows the user to quickly and effectively receive information appropriate to the situation.

[0375] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0376] Step 1:

[0377] The device receives voice input from the user. Specifically, it collects voice data using a microphone. The device then converts the voice data into text data using speech recognition software. The output is text data.

[0378] Step 2:

[0379] The device processes the converted text data and uses an emotion engine to analyze the user's emotions. Specifically, it inputs the text data into an API for emotion analysis to determine the emotional state. The input is text data, and the output is emotion data.

[0380] Step 3:

[0381] The device retrieves the user's profile information. This profile information includes past behavioral history and preferences. This allows for the analysis of emotional data in a way that takes the user's preferences into account. The input is the user's identification information, and the output is profile data.

[0382] Step 4:

[0383] The device sends text data, profile data, and sentiment data to a cloud server. Specifically, it converts the data into packets using a secure protocol and transfers them to the server over the network. The input is a set of data, and the output is the completion of the data transfer to the server.

[0384] Step 5:

[0385] The server selects the appropriate external information provision method based on the data received on the cloud. Specifically, it analyzes the received data using an algorithm and makes a request to the external database. The input is the data received by the server, and the output is the identification of the external information.

[0386] Step 6:

[0387] The server uses a generation AI model to optimize acquired external information according to the user's emotional state. This process processes the information and generates messages in the most appropriate format for the user. The input is external information and emotional data, and the output is the optimized information.

[0388] Step 7:

[0389] The terminal presents optimized information received from the server to the user. Specifically, it provides information visually or audibly using a display and speakers. The input is the optimized information, and the output is the presentation of that information to the user.

[0390] (Application Example 2)

[0391] 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."

[0392] Conventional information delivery systems have a problem in that information is provided without considering the user's emotional state, resulting in insufficient improvement of the user experience. In particular, in vehicles, there is a need to provide information and content that responds to passengers' emotions, but there was no optimal means of providing information based on emotional information.

[0393] 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.

[0394] In this invention, the server includes means for converting user input from voice data to text data, means for analyzing user preferences by referring to user profile information, and means for transmitting input data and preference data to the cloud. This enables the provision of information based on user input and emotional data.

[0395] "User input" refers to information that a user provides to the system via voice or text.

[0396] "Means of converting audio data to text data" refers to a function that uses speech recognition technology to convert audio information into text information.

[0397] "User profile information" is a general term for data related to a user's past behavioral history and preferences.

[0398] "Methods for analyzing preferences" refer to functions that analyze a user's preferences based on their profile information.

[0399] "Means of sending input data and preference data to the cloud" refers to the function of sending data to a server via the internet.

[0400] "Emotional data" refers to data extracted from a user's voice or text that indicates their emotional state at that time.

[0401] "Means of selecting external information provision methods and acquiring information" refers to a function that selects the optimal information source according to the user's state and acquires the necessary information.

[0402] "Means of generating and presenting information to users" refers to a function that arranges acquired information to match the user's emotions and provides it through visual or auditory means.

[0403] "A means of selecting and providing music that matches the user's emotions" refers to a function that selects and plays the most suitable music based on the user's emotional state.

[0404] "Means of improving user experience" refer to functions that adjust the information and content provided so that they are more useful and comfortable for the user.

[0405] The system for carrying out this invention is configured as follows: First, the terminal obtains voice or text input from the user. The obtained voice data is converted into text data using a speech recognition API. This conversion process utilizes a speech recognition service such as Google Speech-to-Text.

[0406] Next, we analyze the user's profile information and preferences. This profile information includes past behavioral history and sentiment data, and we use sentiment analysis APIs such as Microsoft Azure Text Analytics to analyze the user's state.

[0407] The analyzed data is sent to a cloud server. The cloud server utilizes AWS Lambda and other tools to search for external information based on user input data and emotional data, and selects the most appropriate method of information delivery. For example, if a user is feeling anxious in a vehicle, relaxing music and operational information are integrated and provided.

[0408] The data processing and calculations used during transmission incorporate algorithms that take into account the user's emotional state, generating information in a format optimized for the user. As a result, users receive reassuringly tailored messages.

[0409] For example, if a passenger says, "I want to relax today," a particularly calming piece of classical music will be selected, and a message such as, "Traffic is currently smooth. Please enjoy your favorite music," will be delivered.

[0410] An example of a specific prompt for the generative AI model would be: "Assess the passenger's current mood and select the most appropriate content. For example, if the passenger is seeking relaxation, select soothing music."

[0411] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0412] Step 1:

[0413] The device receives voice input from the user. When the user speaks into the device, the device captures the voice data with its microphone and sends it to a speech recognition API. The input is voice data, and the output is text data. The speech recognition API (e.g., Google Speech-to-Text) converts the voice to text and passes this text data to the next processing step.

[0414] Step 2:

[0415] The device analyzes preferences using converted text data and user profile information. Profile information includes past behavioral history and past selections. Input is text data and profile information, and output is preference data. This allows for analysis of what kind of information and content the user prefers.

[0416] Step 3:

[0417] The device sends text data and preference data to the server. During this process, a sentiment analysis API (e.g., Microsoft Azure Text Analytics) is used to analyze the user's text data to determine their sentiment. The input is text data, and the output is sentiment data. This data is sent to the cloud server and used for subsequent processing.

[0418] Step 4:

[0419] The server selects the appropriate external information provision method based on input data, preference data, and sentiment data acquired within the cloud. For example, it may obtain real-time traffic information or music information from external sources using APIs. The input is user data, and the output is the acquired external information. This ensures that information optimized for the user's emotions and preferences is collected.

[0420] Step 5:

[0421] The server generates and optimizes information based on the user's emotional state, using acquired external information. The output is information tailored to the user. Specifically, it generates music that matches the user's emotions and messages that provide a sense of security. This generated information is adjusted considering the user's emotions and preferences.

[0422] Step 6:

[0423] The terminal presents the user with optimized information received from the server. Specifically, it delivers operational information and music in a tone that matches the user's emotions through a visual display and speakers. This provides a customized experience that allows the user to spend time comfortably in the vehicle. The input is optimized information, and the output is an improvement to the user experience.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Third Embodiment]

[0428] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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".

[0440] The system of this invention aims to optimize and provide information based on user input. This system is primarily implemented through the coordination of terminals, servers, and the cloud.

[0441] First, the user uses the terminal to input information via voice or text. For example, a user might ask a ticket vending machine in a ramen shop, "How do I order soy sauce ramen?" using voice. The terminal receives this input and, if it is voice data, begins the process of converting it into text data.

[0442] Next, the device references the user's profile information and analyzes their preferences. This profile includes data on past order history and preferences. Based on this data, the device understands the user's preferences and prepares to provide appropriate information.

[0443] Subsequently, the terminal sends the input information along with the user's profile data to the cloud server. The server in the cloud receives this data and selects the most suitable external information provision method for the user, such as a traffic information API or a store information service, and retrieves the data.

[0444] The cloud server analyzes the acquired data and uses generative AI to generate personalized information for the user. This makes it possible to provide value-added information that takes into account the user's preferences and past behavior history.

[0445] The generated information is sent back to the terminal, which then presents this information to the user in an easy-to-understand format. This could involve providing information via voice through a voice assistant or displaying it visually on the screen. For example, it might say, "To purchase soy sauce ramen, press the A button."

[0446] This system allows users to quickly obtain the information they need and provides them with an optimal experience tailored to their preferences.

[0447] The following describes the processing flow.

[0448] Step 1:

[0449] The user uses the device to input information via voice or text. For example, the user might say to the device, "How many more stops until I get home?"

[0450] Step 2:

[0451] The device acquires voice input and converts the voice data into text data using its built-in speech recognition technology. If the input is text, the data is acquired directly.

[0452] Step 3:

[0453] The device references the user's profile information and analyzes the user's preferences based on the behavioral history and preference data stored there. This allows the device to understand what choices the user has made in the past.

[0454] Step 4:

[0455] The device combines text data and user preference data and creates a request to send this information to the cloud server. This request is sent to the cloud using a secure communication protocol.

[0456] Step 5:

[0457] The server analyzes the data received on the cloud and selects the appropriate external service API. For example, it might select a traffic information API and configure the parameters for using it.

[0458] Step 6:

[0459] The server executes the selected API and retrieves information corresponding to the user's request. This information is then organized on the server in a user-specific format.

[0460] Step 7:

[0461] The server generates user-optimized information and sends it back to the terminal. This data is then converted into a format that is easy for the user to understand.

[0462] Step 8:

[0463] The terminal displays the received information to the user through screen display or audio output. For example, information such as "You will arrive home in two more stations" may be displayed on the screen or announced by voice.

[0464] (Example 1)

[0465] 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."

[0466] In today's information environment, users are surrounded by a vast amount of information, making it difficult to find information optimized to their preferences. In this situation, there is a need to provide information that efficiently and effectively meets user needs. However, conventional systems simply process user input, making it difficult to provide personalized information that takes into account individual preferences and past behavioral history.

[0467] 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.

[0468] In this invention, the server includes means for acquiring user input and converting voice information into text information, means for referring to user attribute information and analyzing preferences, and means for transmitting user input information and preference information to a remote server. This makes it possible to provide value-added information based on the individual preferences of each user.

[0469] A "user" refers to an individual or group that uses a system to obtain information or utilize services.

[0470] "Input" refers to the information that users provide to the system as voice or text information.

[0471] "Audio information" refers to auditory data generated by a user's speech and processed by the system.

[0472] "Textual information" refers to data in which audio information is represented as text.

[0473] "Attribute information" refers to data that includes individual information such as user preferences and past behavioral history.

[0474] "Preferences" refers to information that indicates a user's tastes and preferences.

[0475] A "remote server" refers to computing resources located in the cloud that are used to process and store user input information and preference data.

[0476] "Information delivery methods" refer to the functions and processes for selecting and delivering information optimized for the user.

[0477] "Generation means" refers to the function of creating newly personalized data based on acquired information.

[0478] A "generative artificial intelligence model" refers to an algorithm or system that uses machine learning to generate information that is optimal for the user.

[0479] A "prompt statement" refers to a text statement used to instruct a generative artificial intelligence model to generate information.

[0480] This system is primarily implemented through the coordination of terminals, servers, and cloud technologies. A specific implementation is described below.

[0481] The user first provides input via voice or text through the terminal. The terminal uses speech recognition technology for voice input, for example, by using common speech recognition software to convert the voice into text. This conversion process forms the basis for understanding the user's intent.

[0482] The terminal then retrieves user attribute information from a local or remote database. This attribute information includes the user's past behavioral history and preferences. The terminal uses this information to analyze the user's preferences and prepare to provide the user with the most relevant information.

[0483] Attribute information and user input information are sent to a remote server in the cloud. The remote server analyzes the received data and selects external information provision methods to provide the maximum value to the user. In this process, open data and specific service APIs can be used.

[0484] The server uses a generative AI model to generate user-specific information. The generative AI model receives instructions via prompts and generates information tailored to each individual user. For example, it might use a prompt like, "The user wants to order soy sauce ramen again at a ramen shop. According to past preference data, the user likes a boiled egg as an additional topping. Please create an order guide that includes this."

[0485] Finally, the generated information is sent back to the device, which then presents the information to the user visually or audibly. This allows the user to intuitively receive individually optimized information. Specific implementations include displaying navigation on the screen or providing information through a voice assistant.

[0486] In this way, the system enables users to obtain information efficiently and in a personalized manner.

[0487] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0488] Step 1:

[0489] The user inputs information using a terminal via voice or text. For example, they can ask "How do I order soy sauce ramen?" by voice. The terminal receives this input and, if it is voice data, converts it into text information using speech recognition technology. In this way, the system obtains the user's request in a form that can be processed as text data. Specifically, speech recognition software performs phoneme analysis on the voice waveform data and converts it into a string of characters.

[0490] Step 2:

[0491] The terminal accesses the user's attribute information. This attribute information includes data on past order history and user preferences. Based on this, the terminal analyzes the user's preferences and prepares to provide personalized service based on text data. Specifically, it accesses the attribute information database and performs profile analysis using preference analysis algorithms to verify whether it matches the user's preferences and past behavior.

[0492] Step 3:

[0493] The terminal sends the processed input information and analyzed attribute information to a remote server. The server uses this received data as input and performs analysis to select the most suitable external information provision method in response to the user's request. The server attempts to access various external data services and obtain the necessary information. Specifically, data collection and query processing are performed via APIs.

[0494] Step 4:

[0495] The server uses a generative AI model to generate information tailored to the user's requests based on the acquired data. Instructions are given to the AI ​​model through prompts, and the generated data takes into account the user's preferences and past behavioral history. Specifically, the generative AI model uses natural language processing techniques to generate output data based on the prompts, and then forms it as a direct response to the user's requests.

[0496] Step 5:

[0497] The generated information is sent back to the terminal, which then selects the best way to present this information to the user. This can be done by providing voice guidance using a voice assistant or by visually displaying the information on a screen. Specific actions include rendering a visual interface and outputting voice using speech synthesis technology, allowing the user to instantly obtain the information and take action.

[0498] (Application Example 1)

[0499] 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."

[0500] A problem exists in that customers cannot quickly and effectively obtain information that meets their needs in physical stores. This situation leads to decreased customer satisfaction and, consequently, a decline in purchasing intent. Furthermore, it is difficult for store staff to provide personalized recommendations to each customer, which hinders efficient service delivery. To solve this problem, a system is needed that provides information in real time based on customer preferences and past behavior history.

[0501] 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.

[0502] In this invention, the server includes means for acquiring user input and converting that input from voice data to text data; means for referring to user profile information and analyzing user preferences based on that profile; means for transmitting user input data and preference data to the cloud; means for selecting appropriate external information provision means based on user input data within the cloud and acquiring information; means for generating the acquired information in a state optimized for the user; and means for displaying the generated information to the user through a visual presentation device. This makes it possible for customers to instantly acquire individually customized and valuable information in physical stores, thereby improving their purchasing experience.

[0503] "User input" refers to information provided by the user through their device, either via voice or text.

[0504] "Means for converting audio data to text data" refers to a process or apparatus for converting audio signals into textual information.

[0505] "User profile information" refers to a collection of information that includes an individual user's preferences, past behavioral history, and other personal data.

[0506] "Means for analyzing preferences" refers to methods or devices for analyzing user preferences and tendencies based on user profile information.

[0507] "Means of sending to the cloud" refers to the technology or process for transferring data to a remote server.

[0508] "External information provision means" refers to information sources and service APIs that are accessible via the cloud.

[0509] "Means of acquiring information" refers to the processes and devices used to obtain necessary information from external information sources.

[0510] "Means of generating in an optimized state" refers to methods or techniques for personalizing information based on user preferences and context.

[0511] A "visual presentation device" is a device used to visually display generated information to a user.

[0512] This invention relates to a system for providing customized information to individual customers in physical stores. Specifically, it begins with a terminal acquiring voice commands uttered by the user (customer) and converting them into text data. The terminal uses a voice recognition API to quickly convert the voice data into text.

[0513] Once text data is generated, the device sends data to the server based on the user's profile information. This profile information includes preferences and past purchase history stored in Google Cloud. This allows the server to analyze the user's preferences with high accuracy.

[0514] When the server receives user data, it uses external information provision services connected to the cloud to obtain the necessary information. This includes services such as traffic information APIs and store information services. Specifically, it uses the OpenAI GPT model to generate information optimized for the user's past behavior history and current context in real time.

[0515] The generated information is provided to the user through a visual presentation device, such as the display of smart glasses. This allows the user to quickly obtain the appropriate information when needed, enabling efficient purchasing and decision-making.

[0516] For example, when a customer asks, "What products do you recommend?", the smart glasses might display, "Today's special offer is XX. We have a trial coupon available."

[0517] Example of a prompt

[0518] The customer likes matcha. Please recommend some products that he / she might be interested in.

[0519] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0520] Step 1:

[0521] The user provides voice input. The device receives this voice and converts it into text data using a speech recognition API. The input is voice data, and the output is the converted text data. The speech recognition API analyzes the voice signal and generates the corresponding text.

[0522] Step 2:

[0523] The terminal uses the converted text data to prepare to connect to the cloud server. Here, the terminal references the user's profile information and packages the input text and profile information for transmission to the cloud. The input consists of text data and profile information, while the output is an integrated data package sent to the cloud. The profile information includes individual preferences and past behavioral history.

[0524] Step 3:

[0525] The cloud server analyzes the received integrated data package. Using a generative AI model, the cloud server performs the analysis and selects the most appropriate external information source to provide the best information. The input is the integrated data package, and the output is the selected information source and the request for the necessary data. The generative AI model constructs information tailored to the user's needs.

[0526] Step 4:

[0527] The cloud server retrieves data from selected external sources and generates information optimized for the user. Data processing and calculations are performed here, and the information is customized according to the user's preferences. The input is data from external sources, and the output is information personalized for the user.

[0528] Step 5:

[0529] The device receives personalized information sent from a cloud server. This information is displayed on a visual display device and presented to the user visually. The input is the personalized information, and the output is the displayed information. The user can confirm the optimized information through the visual display device.

[0530] 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.

[0531] The present invention aims to further enhance the user experience by combining a system that provides personalized information based on user input with an emotion engine. The system is primarily implemented through the coordination of terminals, servers, and the cloud.

[0532] The user provides input to the device via voice or text. For example, a user might say, "The train is delayed and I'm having trouble," near a ticket machine at a train station. The device receives this input and converts it to text if it's voice. Furthermore, an emotion engine analyzes the user's voice to determine their emotions.

[0533] The device analyzes the user's preferences by referring to the user's profile information and acquired sentiment data. This profile includes data on past behavioral history and preferences, while the sentiment data indicates the user's state of mind at that moment.

[0534] Next, the device sends text data, preference data, and sentiment data to a cloud server. Based on the user's input and sentiment, the server in the cloud selects the appropriate means of providing external information. For example, it might use a traffic information API to obtain real-time delay information.

[0535] The acquired information is adjusted by the emotion engine to match the user's emotional state, and generated in the most appropriate format for the user. The server then sends this information back to the terminal.

[0536] The device presents received information to the user visually or audibly. The content and tone of the information are adjusted to take into account the user's emotional state. For example, the user may be provided with a message such as, "Train delays have been resolved and trains will soon be running on schedule. Please rest assured."

[0537] As described above, the system of the present invention enables the provision of optimal information that reflects the user's current emotional state, dramatically improving the user experience.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] The user inputs information into the device via voice or text. For example, the user might say to the device, "I'm in a bad mood today."

[0541] Step 2:

[0542] The device acquires the user's voice input and converts the voice data into text data using speech recognition technology.

[0543] Step 3:

[0544] The device uses an emotion engine to recognize the user's emotions from the acquired audio and analyzes their state of joy, anger, sadness, and other feelings.

[0545] Step 4:

[0546] The device references the user's profile information and analyzes recognized emotional data along with behavioral history and preference data.

[0547] Step 5:

[0548] The device sends text data, preference data, and sentiment data to a cloud server. A secure communication protocol is used for this transmission.

[0549] Step 6:

[0550] The server analyzes this data in the cloud and selects the most suitable external information delivery method (e.g., news information API or music recommendation service) according to the user's emotional state.

[0551] Step 7:

[0552] The server retrieves data from selected external information sources and organizes the retrieved data in a way that aligns with the user's emotions.

[0553] Step 8:

[0554] The server sends the generated information back to the terminal and shapes the message with a tone that matches the user's emotions.

[0555] Step 9:

[0556] The device will present the returned information to the user visually or audibly. For example, a message such as "We recommend some relaxing music for today" might be displayed.

[0557] (Example 2)

[0558] 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."

[0559] Traditional personalized information delivery systems have only provided information based on user preferences and past behavioral history, and have not been able to optimize information to reflect the user's emotional state. Therefore, there is a need for a mechanism that allows users to quickly and accurately obtain information that matches their emotional state at that moment.

[0560] 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.

[0561] In this invention, the server includes means for converting user input from speech to text, means for analyzing the user's emotions, and means for providing information optimized based on the user's preferences and emotional data. This makes it possible to provide information that is tailored to the user's emotional state.

[0562] "Audio data" refers to information that represents audio signals in digital or analog format.

[0563] "Text data" refers to digital information recorded as a string of characters, in a format that can be processed by a computer.

[0564] "Emotional data" refers to indicators that show a user's emotional state, and is data obtained from the user's voice, facial expressions, and other sources.

[0565] "Profile information" refers to information about individual users, including their past behavioral history, preferences, and basic personal attributes.

[0566] "Preferences" refer to data that indicates a user's personal likes and preferences, including tendencies towards specific situations and choices.

[0567] A "cloud server" is a remote server accessible via the internet, which serves as a computing resource for storing and processing data.

[0568] "External information" refers to data obtained from outside the system, including real-time information such as traffic and weather data.

[0569] "Optimized state" refers to the form or condition that best suits a particular purpose or condition.

[0570] "Presentation means" refers to technical means for displaying or transmitting information generated by a system to a user.

[0571] To implement this invention, a system is needed to efficiently receive user input, analyze, process, and optimize it for providing information. This is primarily achieved through the coordination of terminals, servers, and the cloud.

[0572] The terminal is a computer device equipped with a microphone and touchscreen that receives voice or text input from the user. This input is converted from voice data to text data using speech recognition software (e.g., Google Speech-to-Text API).

[0573] The device further analyzes the user's emotions from their voice data using an emotion engine (e.g., IBM Watson Tone Analyzer). This emotion data, along with the user's profile information, is sent to a cloud server. This profile information includes data on the user's past behavior and preferences, which is referenced to provide appropriate information.

[0574] The server selects the appropriate external information provision method based on user text data, preference data, and sentiment data received in the cloud environment. Specifically, it uses an external database (e.g., a traffic API) to obtain real-time information such as traffic and weather information.

[0575] The acquired external information is optimized for the user's emotional state by a generative AI model and processed by the server. An example of a message generated for the user might be the prompt, "I want to know the new train arrival and departure times."

[0576] Ultimately, the device presents the received information to the user visually or audibly. This presentation utilizes displays, speakers, and other means, and the information is delivered in a way that takes the user's emotional state into consideration. This method allows the user to quickly and effectively receive information appropriate to the situation.

[0577] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0578] Step 1:

[0579] The device receives voice input from the user. Specifically, it collects voice data using a microphone. The device then converts the voice data into text data using speech recognition software. The output is text data.

[0580] Step 2:

[0581] The device processes the converted text data and uses an emotion engine to analyze the user's emotions. Specifically, it inputs the text data into an API for emotion analysis to determine the emotional state. The input is text data, and the output is emotion data.

[0582] Step 3:

[0583] The device retrieves the user's profile information. This profile information includes past behavioral history and preferences. This allows for the analysis of emotional data in a way that takes the user's preferences into account. The input is the user's identification information, and the output is profile data.

[0584] Step 4:

[0585] The device sends text data, profile data, and sentiment data to a cloud server. Specifically, it converts the data into packets using a secure protocol and transfers them to the server over the network. The input is a set of data, and the output is the completion of the data transfer to the server.

[0586] Step 5:

[0587] The server selects the appropriate external information provision method based on the data received on the cloud. Specifically, it analyzes the received data using an algorithm and makes a request to the external database. The input is the data received by the server, and the output is the identification of the external information.

[0588] Step 6:

[0589] The server uses a generation AI model to optimize acquired external information according to the user's emotional state. This process processes the information and generates messages in the most appropriate format for the user. The input is external information and emotional data, and the output is the optimized information.

[0590] Step 7:

[0591] The terminal presents optimized information received from the server to the user. Specifically, it provides information visually or audibly using a display and speakers. The input is the optimized information, and the output is the presentation of that information to the user.

[0592] (Application Example 2)

[0593] 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."

[0594] Conventional information delivery systems have a problem in that information is provided without considering the user's emotional state, resulting in insufficient improvement of the user experience. In particular, in vehicles, there is a need to provide information and content that responds to passengers' emotions, but there was no optimal means of providing information based on emotional information.

[0595] 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.

[0596] In this invention, the server includes means for converting user input from voice data to text data, means for analyzing user preferences by referring to user profile information, and means for transmitting input data and preference data to the cloud. This enables the provision of information based on user input and emotional data.

[0597] "User input" refers to information that a user provides to the system via voice or text.

[0598] "Means of converting audio data to text data" refers to a function that uses speech recognition technology to convert audio information into text information.

[0599] "User profile information" is a general term for data related to a user's past behavioral history and preferences.

[0600] "Methods for analyzing preferences" refer to functions that analyze a user's preferences based on their profile information.

[0601] "Means of sending input data and preference data to the cloud" refers to the function of sending data to a server via the internet.

[0602] "Emotional data" refers to data extracted from a user's voice or text that indicates their emotional state at that time.

[0603] "Means of selecting external information provision methods and acquiring information" refers to a function that selects the optimal information source according to the user's state and acquires the necessary information.

[0604] "Means of generating and presenting information to users" refers to a function that arranges acquired information to match the user's emotions and provides it through visual or auditory means.

[0605] "A means of selecting and providing music that matches the user's emotions" refers to a function that selects and plays the most suitable music based on the user's emotional state.

[0606] "Means of improving user experience" refer to functions that adjust the information and content provided so that they are more useful and comfortable for the user.

[0607] The system for carrying out this invention is configured as follows: First, the terminal obtains voice or text input from the user. The obtained voice data is converted into text data using a speech recognition API. This conversion process utilizes a speech recognition service such as Google Speech-to-Text.

[0608] Next, we analyze the user's profile information and preferences. This profile information includes past behavioral history and sentiment data, and we use sentiment analysis APIs such as Microsoft Azure Text Analytics to analyze the user's state.

[0609] The analyzed data is sent to a cloud server. The cloud server utilizes AWS Lambda and other tools to search for external information based on user input data and emotional data, and selects the most appropriate method of information delivery. For example, if a user is feeling anxious in a vehicle, relaxing music and operational information are integrated and provided.

[0610] The data processing and calculations used during transmission incorporate algorithms that take into account the user's emotional state, generating information in a format optimized for the user. As a result, users receive reassuringly tailored messages.

[0611] For example, if a passenger says, "I want to relax today," a particularly calming piece of classical music will be selected, and a message such as, "Traffic is currently smooth. Please enjoy your favorite music," will be delivered.

[0612] An example of a specific prompt for the generative AI model would be: "Assess the passenger's current mood and select the most appropriate content. For example, if the passenger is seeking relaxation, select soothing music."

[0613] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0614] Step 1:

[0615] The device receives voice input from the user. When the user speaks into the device, the device captures the voice data with its microphone and sends it to a speech recognition API. The input is voice data, and the output is text data. The speech recognition API (e.g., Google Speech-to-Text) converts the voice to text and passes this text data to the next processing step.

[0616] Step 2:

[0617] The device analyzes preferences using converted text data and user profile information. Profile information includes past behavioral history and past selections. Input is text data and profile information, and output is preference data. This allows for analysis of what kind of information and content the user prefers.

[0618] Step 3:

[0619] The device sends text data and preference data to the server. During this process, a sentiment analysis API (e.g., Microsoft Azure Text Analytics) is used to analyze the user's text data to determine their sentiment. The input is text data, and the output is sentiment data. This data is sent to the cloud server and used for subsequent processing.

[0620] Step 4:

[0621] The server selects the appropriate external information provision method based on input data, preference data, and sentiment data acquired within the cloud. For example, it may obtain real-time traffic information or music information from external sources using APIs. The input is user data, and the output is the acquired external information. This ensures that information optimized for the user's emotions and preferences is collected.

[0622] Step 5:

[0623] The server generates and optimizes information based on the user's emotional state, using acquired external information. The output is information tailored to the user. Specifically, it generates music that matches the user's emotions and messages that provide a sense of security. This generated information is adjusted considering the user's emotions and preferences.

[0624] Step 6:

[0625] The terminal presents the user with optimized information received from the server. Specifically, it delivers operational information and music in a tone that matches the user's emotions through a visual display and speakers. This provides a customized experience that allows the user to spend time comfortably in the vehicle. The input is optimized information, and the output is an improvement to the user experience.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] [Fourth Embodiment]

[0630] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0631] 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.

[0632] 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).

[0633] 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.

[0634] 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.

[0635] 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).

[0636] 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.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] 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.

[0642] 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".

[0643] The system of this invention aims to optimize and provide information based on user input. This system is primarily implemented through the coordination of terminals, servers, and the cloud.

[0644] First, the user uses the terminal to input information via voice or text. For example, a user might ask a ticket vending machine in a ramen shop, "How do I order soy sauce ramen?" using voice. The terminal receives this input and, if it is voice data, begins the process of converting it into text data.

[0645] Next, the device references the user's profile information and analyzes their preferences. This profile includes data on past order history and preferences. Based on this data, the device understands the user's preferences and prepares to provide appropriate information.

[0646] Subsequently, the terminal sends the input information along with the user's profile data to the cloud server. The server in the cloud receives this data and selects the most suitable external information provision method for the user, such as a traffic information API or a store information service, and retrieves the data.

[0647] The cloud server analyzes the acquired data and uses generative AI to generate personalized information for the user. This makes it possible to provide value-added information that takes into account the user's preferences and past behavior history.

[0648] The generated information is sent back to the terminal, which then presents this information to the user in an easy-to-understand format. This could involve providing information via voice through a voice assistant or displaying it visually on the screen. For example, it might say, "To purchase soy sauce ramen, press the A button."

[0649] This system allows users to quickly obtain the information they need and provides them with an optimal experience tailored to their preferences.

[0650] The following describes the processing flow.

[0651] Step 1:

[0652] The user uses the device to input information via voice or text. For example, the user might say to the device, "How many more stops until I get home?"

[0653] Step 2:

[0654] The device acquires voice input and converts the voice data into text data using its built-in speech recognition technology. If the input is text, the data is acquired directly.

[0655] Step 3:

[0656] The device references the user's profile information and analyzes the user's preferences based on the behavioral history and preference data stored there. This allows the device to understand what choices the user has made in the past.

[0657] Step 4:

[0658] The device combines text data and user preference data and creates a request to send this information to the cloud server. This request is sent to the cloud using a secure communication protocol.

[0659] Step 5:

[0660] The server analyzes the data received on the cloud and selects the appropriate external service API. For example, it might select a traffic information API and configure the parameters for using it.

[0661] Step 6:

[0662] The server executes the selected API and retrieves information corresponding to the user's request. This information is then organized on the server in a user-specific format.

[0663] Step 7:

[0664] The server generates user-optimized information and sends it back to the terminal. This data is then converted into a format that is easy for the user to understand.

[0665] Step 8:

[0666] The terminal displays the received information to the user through screen display or audio output. For example, information such as "You will arrive home in two more stations" may be displayed on the screen or announced by voice.

[0667] (Example 1)

[0668] 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".

[0669] In today's information environment, users are surrounded by a vast amount of information, making it difficult to find information optimized to their preferences. In this situation, there is a need to provide information that efficiently and effectively meets user needs. However, conventional systems simply process user input, making it difficult to provide personalized information that takes into account individual preferences and past behavioral history.

[0670] 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.

[0671] In this invention, the server includes means for acquiring user input and converting voice information into text information, means for referring to user attribute information and analyzing preferences, and means for transmitting user input information and preference information to a remote server. This makes it possible to provide value-added information based on the individual preferences of each user.

[0672] A "user" refers to an individual or group that uses a system to obtain information or utilize services.

[0673] "Input" refers to the information that users provide to the system as voice or text information.

[0674] "Audio information" refers to auditory data generated by a user's speech and processed by the system.

[0675] "Textual information" refers to data in which audio information is represented as text.

[0676] "Attribute information" refers to data that includes individual information such as user preferences and past behavioral history.

[0677] "Preferences" refers to information that indicates a user's tastes and preferences.

[0678] A "remote server" refers to computing resources located in the cloud that are used to process and store user input information and preference data.

[0679] "Information delivery methods" refer to the functions and processes for selecting and delivering information optimized for the user.

[0680] "Generation means" refers to the function of creating newly personalized data based on acquired information.

[0681] A "generative artificial intelligence model" refers to an algorithm or system that uses machine learning to generate information that is optimal for the user.

[0682] A "prompt statement" refers to a text statement used to instruct a generative artificial intelligence model to generate information.

[0683] This system is primarily implemented through the coordination of terminals, servers, and cloud technologies. A specific implementation is described below.

[0684] The user first provides input via voice or text through the terminal. The terminal uses speech recognition technology for voice input, for example, by using common speech recognition software to convert the voice into text. This conversion process forms the basis for understanding the user's intent.

[0685] The terminal then retrieves user attribute information from a local or remote database. This attribute information includes the user's past behavioral history and preferences. The terminal uses this information to analyze the user's preferences and prepare to provide the user with the most relevant information.

[0686] Attribute information and user input information are sent to a remote server in the cloud. The remote server analyzes the received data and selects external information provision methods to provide the maximum value to the user. In this process, open data and specific service APIs can be used.

[0687] The server uses a generative AI model to generate user-specific information. The generative AI model receives instructions via prompts and generates information tailored to each individual user. For example, it might use a prompt like, "The user wants to order soy sauce ramen again at a ramen shop. According to past preference data, the user likes a boiled egg as an additional topping. Please create an order guide that includes this."

[0688] Finally, the generated information is sent back to the device, which then presents the information to the user visually or audibly. This allows the user to intuitively receive individually optimized information. Specific implementations include displaying navigation on the screen or providing information through a voice assistant.

[0689] In this way, the system enables users to obtain information efficiently and in a personalized manner.

[0690] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0691] Step 1:

[0692] The user inputs information using a terminal via voice or text. For example, they can ask "How do I order soy sauce ramen?" by voice. The terminal receives this input and, if it is voice data, converts it into text information using speech recognition technology. In this way, the system obtains the user's request in a form that can be processed as text data. Specifically, speech recognition software performs phoneme analysis on the voice waveform data and converts it into a string of characters.

[0693] Step 2:

[0694] The terminal accesses the user's attribute information. This attribute information includes data on past order history and user preferences. Based on this, the terminal analyzes the user's preferences and prepares to provide personalized service based on text data. Specifically, it accesses the attribute information database and performs profile analysis using preference analysis algorithms to verify whether it matches the user's preferences and past behavior.

[0695] Step 3:

[0696] The terminal sends the processed input information and analyzed attribute information to a remote server. The server uses this received data as input and performs analysis to select the most suitable external information provision method in response to the user's request. The server attempts to access various external data services and obtain the necessary information. Specifically, data collection and query processing are performed via APIs.

[0697] Step 4:

[0698] The server uses a generative AI model to generate information tailored to the user's requests based on the acquired data. Instructions are given to the AI ​​model through prompts, and the generated data takes into account the user's preferences and past behavioral history. Specifically, the generative AI model uses natural language processing techniques to generate output data based on the prompts, and then forms it as a direct response to the user's requests.

[0699] Step 5:

[0700] The generated information is sent back to the terminal, which then selects the best way to present this information to the user. This can be done by providing voice guidance using a voice assistant or by visually displaying the information on a screen. Specific actions include rendering a visual interface and outputting voice using speech synthesis technology, allowing the user to instantly obtain the information and take action.

[0701] (Application Example 1)

[0702] 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".

[0703] A problem exists in that customers cannot quickly and effectively obtain information that meets their needs in physical stores. This situation leads to decreased customer satisfaction and, consequently, a decline in purchasing intent. Furthermore, it is difficult for store staff to provide personalized recommendations to each customer, which hinders efficient service delivery. To solve this problem, a system is needed that provides information in real time based on customer preferences and past behavior history.

[0704] 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.

[0705] In this invention, the server includes means for acquiring user input and converting that input from voice data to text data; means for referring to user profile information and analyzing user preferences based on that profile; means for transmitting user input data and preference data to the cloud; means for selecting appropriate external information provision means based on user input data within the cloud and acquiring information; means for generating the acquired information in a state optimized for the user; and means for displaying the generated information to the user through a visual presentation device. This makes it possible for customers to instantly acquire individually customized and valuable information in physical stores, thereby improving their purchasing experience.

[0706] "User input" refers to information provided by the user through their device, either via voice or text.

[0707] "Means for converting audio data to text data" refers to a process or apparatus for converting audio signals into textual information.

[0708] "User profile information" refers to a collection of information that includes an individual user's preferences, past behavioral history, and other personal data.

[0709] "Means for analyzing preferences" refers to methods or devices for analyzing user preferences and tendencies based on user profile information.

[0710] "Means of sending to the cloud" refers to the technology or process for transferring data to a remote server.

[0711] "External information provision means" refers to information sources and service APIs that are accessible via the cloud.

[0712] "Means of acquiring information" refers to the processes and devices used to obtain necessary information from external information sources.

[0713] "Means of generating in an optimized state" refers to methods or techniques for personalizing information based on user preferences and context.

[0714] A "visual presentation device" is a device used to visually display generated information to a user.

[0715] This invention relates to a system for providing customized information to individual customers in physical stores. Specifically, it begins with a terminal acquiring voice commands uttered by the user (customer) and converting them into text data. The terminal uses a voice recognition API to quickly convert the voice data into text.

[0716] Once text data is generated, the device sends data to the server based on the user's profile information. This profile information includes preferences and past purchase history stored in Google Cloud. This allows the server to analyze the user's preferences with high accuracy.

[0717] When the server receives user data, it uses external information provision services connected to the cloud to obtain the necessary information. This includes services such as traffic information APIs and store information services. Specifically, it uses the OpenAI GPT model to generate information optimized for the user's past behavior history and current context in real time.

[0718] The generated information is provided to the user through a visual presentation device, such as the display of smart glasses. This allows the user to quickly obtain the appropriate information when needed, enabling efficient purchasing and decision-making.

[0719] For example, when a customer asks, "What products do you recommend?", the smart glasses might display, "Today's special offer is XX. We have a trial coupon available."

[0720] Example of a prompt

[0721] The customer likes matcha. Please recommend some products that he / she might be interested in.

[0722] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0723] Step 1:

[0724] The user provides voice input. The device receives this voice and converts it into text data using a speech recognition API. The input is voice data, and the output is the converted text data. The speech recognition API analyzes the voice signal and generates the corresponding text.

[0725] Step 2:

[0726] The terminal uses the converted text data to prepare to connect to the cloud server. Here, the terminal references the user's profile information and packages the input text and profile information for transmission to the cloud. The input consists of text data and profile information, while the output is an integrated data package sent to the cloud. The profile information includes individual preferences and past behavioral history.

[0727] Step 3:

[0728] The cloud server analyzes the received integrated data package. Using a generative AI model, the cloud server performs the analysis and selects the most appropriate external information source to provide the best information. The input is the integrated data package, and the output is the selected information source and the request for the necessary data. The generative AI model constructs information tailored to the user's needs.

[0729] Step 4:

[0730] The cloud server retrieves data from selected external sources and generates information optimized for the user. Data processing and calculations are performed here, and the information is customized according to the user's preferences. The input is data from external sources, and the output is information personalized for the user.

[0731] Step 5:

[0732] The device receives personalized information sent from a cloud server. This information is displayed on a visual display device and presented to the user visually. The input is the personalized information, and the output is the displayed information. The user can confirm the optimized information through the visual display device.

[0733] 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.

[0734] The present invention aims to further enhance the user experience by combining a system that provides personalized information based on user input with an emotion engine. The system is primarily implemented through the coordination of terminals, servers, and the cloud.

[0735] The user provides input to the device via voice or text. For example, a user might say, "The train is delayed and I'm having trouble," near a ticket machine at a train station. The device receives this input and converts it to text if it's voice. Furthermore, an emotion engine analyzes the user's voice to determine their emotions.

[0736] The device analyzes the user's preferences by referring to the user's profile information and acquired sentiment data. This profile includes data on past behavioral history and preferences, while the sentiment data indicates the user's state of mind at that moment.

[0737] Next, the device sends text data, preference data, and sentiment data to a cloud server. Based on the user's input and sentiment, the server in the cloud selects the appropriate means of providing external information. For example, it might use a traffic information API to obtain real-time delay information.

[0738] The acquired information is adjusted by the emotion engine to match the user's emotional state, and generated in the most appropriate format for the user. The server then sends this information back to the terminal.

[0739] The device presents received information to the user visually or audibly. The content and tone of the information are adjusted to take into account the user's emotional state. For example, the user may be provided with a message such as, "Train delays have been resolved and trains will soon be running on schedule. Please rest assured."

[0740] As described above, the system of the present invention enables the provision of optimal information that reflects the user's current emotional state, dramatically improving the user experience.

[0741] The following describes the processing flow.

[0742] Step 1:

[0743] The user inputs information into the device via voice or text. For example, the user might say to the device, "I'm in a bad mood today."

[0744] Step 2:

[0745] The device acquires the user's voice input and converts the voice data into text data using speech recognition technology.

[0746] Step 3:

[0747] The device uses an emotion engine to recognize the user's emotions from the acquired audio and analyzes their state of joy, anger, sadness, and other feelings.

[0748] Step 4:

[0749] The device references the user's profile information and analyzes recognized emotional data along with behavioral history and preference data.

[0750] Step 5:

[0751] The device sends text data, preference data, and sentiment data to a cloud server. A secure communication protocol is used for this transmission.

[0752] Step 6:

[0753] The server analyzes this data in the cloud and selects the most suitable external information delivery method (e.g., news information API or music recommendation service) according to the user's emotional state.

[0754] Step 7:

[0755] The server retrieves data from selected external information sources and organizes the retrieved data in a way that aligns with the user's emotions.

[0756] Step 8:

[0757] The server sends the generated information back to the terminal and shapes the message with a tone that matches the user's emotions.

[0758] Step 9:

[0759] The device will present the returned information to the user visually or audibly. For example, a message such as "We recommend some relaxing music for today" might be displayed.

[0760] (Example 2)

[0761] 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".

[0762] Traditional personalized information delivery systems have only provided information based on user preferences and past behavioral history, and have not been able to optimize information to reflect the user's emotional state. Therefore, there is a need for a mechanism that allows users to quickly and accurately obtain information that matches their emotional state at that moment.

[0763] 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.

[0764] In this invention, the server includes means for converting user input from speech to text, means for analyzing the user's emotions, and means for providing information optimized based on the user's preferences and emotional data. This makes it possible to provide information that is tailored to the user's emotional state.

[0765] "Audio data" refers to information that represents audio signals in digital or analog format.

[0766] "Text data" refers to digital information recorded as a string of characters, in a format that can be processed by a computer.

[0767] "Emotional data" refers to indicators that show a user's emotional state, and is data obtained from the user's voice, facial expressions, and other sources.

[0768] "Profile information" refers to information about individual users, including their past behavioral history, preferences, and basic personal attributes.

[0769] "Preferences" refer to data that indicates a user's personal likes and preferences, including tendencies towards specific situations and choices.

[0770] A "cloud server" is a remote server accessible via the internet, which serves as a computing resource for storing and processing data.

[0771] "External information" refers to data obtained from outside the system, including real-time information such as traffic and weather data.

[0772] "Optimized state" refers to the form or condition that best suits a particular purpose or condition.

[0773] "Presentation means" refers to technical means for displaying or transmitting information generated by a system to a user.

[0774] To implement this invention, a system is needed to efficiently receive user input, analyze, process, and optimize it for providing information. This is primarily achieved through the coordination of terminals, servers, and the cloud.

[0775] The terminal is a computer device equipped with a microphone and touchscreen that receives voice or text input from the user. This input is converted from voice data to text data using speech recognition software (e.g., Google Speech-to-Text API).

[0776] The device further analyzes the user's emotions from their voice data using an emotion engine (e.g., IBM Watson Tone Analyzer). This emotion data, along with the user's profile information, is sent to a cloud server. This profile information includes data on the user's past behavior and preferences, which is referenced to provide appropriate information.

[0777] The server selects the appropriate external information provision method based on user text data, preference data, and sentiment data received in the cloud environment. Specifically, it uses an external database (e.g., a traffic API) to obtain real-time information such as traffic and weather information.

[0778] The acquired external information is optimized for the user's emotional state by a generative AI model and processed by the server. An example of a message generated for the user might be the prompt, "I want to know the new train arrival and departure times."

[0779] Ultimately, the device presents the received information to the user visually or audibly. This presentation utilizes displays, speakers, and other means, and the information is delivered in a way that takes the user's emotional state into consideration. This method allows the user to quickly and effectively receive information appropriate to the situation.

[0780] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0781] Step 1:

[0782] The device receives voice input from the user. Specifically, it collects voice data using a microphone. The device then converts the voice data into text data using speech recognition software. The output is text data.

[0783] Step 2:

[0784] The device processes the converted text data and uses an emotion engine to analyze the user's emotions. Specifically, it inputs the text data into an API for emotion analysis to determine the emotional state. The input is text data, and the output is emotion data.

[0785] Step 3:

[0786] The device retrieves the user's profile information. This profile information includes past behavioral history and preferences. This allows for the analysis of emotional data in a way that takes the user's preferences into account. The input is the user's identification information, and the output is profile data.

[0787] Step 4:

[0788] The device sends text data, profile data, and sentiment data to a cloud server. Specifically, it converts the data into packets using a secure protocol and transfers them to the server over the network. The input is a set of data, and the output is the completion of the data transfer to the server.

[0789] Step 5:

[0790] The server selects the appropriate external information provision method based on the data received on the cloud. Specifically, it analyzes the received data using an algorithm and makes a request to the external database. The input is the data received by the server, and the output is the identification of the external information.

[0791] Step 6:

[0792] The server uses a generation AI model to optimize acquired external information according to the user's emotional state. This process processes the information and generates messages in the most appropriate format for the user. The input is external information and emotional data, and the output is the optimized information.

[0793] Step 7:

[0794] The terminal presents optimized information received from the server to the user. Specifically, it provides information visually or audibly using a display and speakers. The input is the optimized information, and the output is the presentation of that information to the user.

[0795] (Application Example 2)

[0796] 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".

[0797] Conventional information delivery systems have a problem in that information is provided without considering the user's emotional state, resulting in insufficient improvement of the user experience. In particular, in vehicles, there is a need to provide information and content that responds to passengers' emotions, but there was no optimal means of providing information based on emotional information.

[0798] 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.

[0799] In this invention, the server includes means for converting user input from voice data to text data, means for analyzing user preferences by referring to user profile information, and means for transmitting input data and preference data to the cloud. This enables the provision of information based on user input and emotional data.

[0800] "User input" refers to information that a user provides to the system via voice or text.

[0801] "Means of converting audio data to text data" refers to a function that uses speech recognition technology to convert audio information into text information.

[0802] "User profile information" is a general term for data related to a user's past behavioral history and preferences.

[0803] "Methods for analyzing preferences" refer to functions that analyze a user's preferences based on their profile information.

[0804] "Means of sending input data and preference data to the cloud" refers to the function of sending data to a server via the internet.

[0805] "Emotional data" refers to data extracted from a user's voice or text that indicates their emotional state at that time.

[0806] "Means of selecting external information provision methods and acquiring information" refers to a function that selects the optimal information source according to the user's state and acquires the necessary information.

[0807] "Means of generating and presenting information to users" refers to a function that arranges acquired information to match the user's emotions and provides it through visual or auditory means.

[0808] "A means of selecting and providing music that matches the user's emotions" refers to a function that selects and plays the most suitable music based on the user's emotional state.

[0809] "Means of improving user experience" refer to functions that adjust the information and content provided so that they are more useful and comfortable for the user.

[0810] The system for carrying out this invention is configured as follows: First, the terminal obtains voice or text input from the user. The obtained voice data is converted into text data using a speech recognition API. This conversion process utilizes a speech recognition service such as Google Speech-to-Text.

[0811] Next, we analyze the user's profile information and preferences. This profile information includes past behavioral history and sentiment data, and we use sentiment analysis APIs such as Microsoft Azure Text Analytics to analyze the user's state.

[0812] The analyzed data is sent to a cloud server. The cloud server utilizes AWS Lambda and other tools to search for external information based on user input data and emotional data, and selects the most appropriate method of information delivery. For example, if a user is feeling anxious in a vehicle, relaxing music and operational information are integrated and provided.

[0813] The data processing and calculations used during transmission incorporate algorithms that take into account the user's emotional state, generating information in a format optimized for the user. As a result, users receive reassuringly tailored messages.

[0814] For example, if a passenger says, "I want to relax today," a particularly calming piece of classical music will be selected, and a message such as, "Traffic is currently smooth. Please enjoy your favorite music," will be delivered.

[0815] An example of a specific prompt for the generative AI model would be: "Assess the passenger's current mood and select the most appropriate content. For example, if the passenger is seeking relaxation, select soothing music."

[0816] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0817] Step 1:

[0818] The device receives voice input from the user. When the user speaks into the device, the device captures the voice data with its microphone and sends it to a speech recognition API. The input is voice data, and the output is text data. The speech recognition API (e.g., Google Speech-to-Text) converts the voice to text and passes this text data to the next processing step.

[0819] Step 2:

[0820] The device analyzes preferences using converted text data and user profile information. Profile information includes past behavioral history and past selections. Input is text data and profile information, and output is preference data. This allows for analysis of what kind of information and content the user prefers.

[0821] Step 3:

[0822] The device sends text data and preference data to the server. During this process, a sentiment analysis API (e.g., Microsoft Azure Text Analytics) is used to analyze the user's text data to determine their sentiment. The input is text data, and the output is sentiment data. This data is sent to the cloud server and used for subsequent processing.

[0823] Step 4:

[0824] The server selects the appropriate external information provision method based on input data, preference data, and sentiment data acquired within the cloud. For example, it may obtain real-time traffic information or music information from external sources using APIs. The input is user data, and the output is the acquired external information. This ensures that information optimized for the user's emotions and preferences is collected.

[0825] Step 5:

[0826] The server generates and optimizes information based on the user's emotional state, using acquired external information. The output is information tailored to the user. Specifically, it generates music that matches the user's emotions and messages that provide a sense of security. This generated information is adjusted considering the user's emotions and preferences.

[0827] Step 6:

[0828] The terminal presents the user with optimized information received from the server. Specifically, it delivers operational information and music in a tone that matches the user's emotions through a visual display and speakers. This provides a customized experience that allows the user to spend time comfortably in the vehicle. The input is optimized information, and the output is an improvement to the user experience.

[0829] 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.

[0830] 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.

[0831] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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."

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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 as being incorporated by reference.

[0850] The following is further disclosed regarding the embodiments described above.

[0851] (Claim 1)

[0852] A means for obtaining user input and converting that input from audio data to text data,

[0853] A means of referring to user profile information and analyzing user preferences based on that profile,

[0854] A means of transmitting user input data and preference data to the cloud,

[0855] Within the cloud, a means of selecting an appropriate external information provision method based on user input data and acquiring that information,

[0856] A means of generating the acquired information in a form optimized for the user,

[0857] A means of presenting the generated information to the user,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, characterized in that it includes means for further personalizing the acquired information based on the user's past behavioral history.

[0861] (Claim 3)

[0862] The system according to claim 1, characterized by comprising means for providing the generated information to the user through visual or auditory presentation means.

[0863] "Example 1"

[0864] (Claim 1)

[0865] A means for obtaining user input and converting that input from speech information to text information,

[0866] A means of referring to user attribute information and analyzing user preferences based on those attributes,

[0867] A means for transmitting user input information and preference information to a remote server,

[0868] A means for selecting an appropriate external information provision method based on user input information within a remote server and acquiring the information,

[0869] A means of generating the acquired information in a form optimized for the user,

[0870] A means of presenting the generated information to the user,

[0871] A means of generating value-added information based on user preferences using a generative artificial intelligence model,

[0872] A means of instructing information generation using prompt statements,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, characterized in that it includes means for further individualizing the acquired information based on the user's past behavioral history.

[0876] (Claim 3)

[0877] The system according to claim 1, characterized by comprising means for providing the generated information to the user through visual or auditory presentation means.

[0878] "Application Example 1"

[0879] (Claim 1)

[0880] A means for obtaining user input and converting that input from audio data to text data,

[0881] A means of referring to user profile information and analyzing user preferences based on that profile,

[0882] A means of transmitting user input data and preference data to the cloud,

[0883] Within the cloud, a means of selecting an appropriate external information provision method based on user input data and acquiring that information,

[0884] A means of generating the acquired information in a form optimized for the user,

[0885] A means for displaying generated information to the user through a visual presentation device,

[0886] A system that includes this.

[0887] (Claim 2)

[0888] The system according to claim 1, characterized in that it includes means for further personalizing the acquired information based on the user's past behavioral history and real-time physical context.

[0889] (Claim 3)

[0890] The system according to claim 1, characterized in that it includes means for providing the generated information to the user through an augmented reality visual device.

[0891] "Example 2 of combining an emotion engine"

[0892] (Claim 1)

[0893] A means for obtaining user input and converting that input from audio data to text data,

[0894] Methods for analyzing user emotions,

[0895] A means of referring to user profile information and analyzing user preferences based on that profile,

[0896] A means for transmitting user text data, preference data, and sentiment data to the cloud,

[0897] Within the cloud, a means of selecting appropriate external information provision methods based on user input data and sentiment data, and acquiring information,

[0898] A means for generating acquired information in an optimized state based on the user's emotional state,

[0899] A means of presenting the generated information to the user,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, characterized in that it includes means for further personalizing the acquired information based on the user's past behavioral history and emotional state.

[0903] (Claim 3)

[0904] The system according to claim 1, characterized in that it includes means for providing the generated information through visual or auditory presentation means, taking into account the user's emotional state.

[0905] "Application example 2 when combining with an emotional engine"

[0906] (Claim 1)

[0907] A means for obtaining user input and converting that input from audio data to text data,

[0908] A means of referring to user profile information and analyzing user preferences based on that profile,

[0909] A means of transmitting user input data and preference data to the cloud,

[0910] Within the cloud, a means of selecting appropriate external information provision methods based on user input data and sentiment data, and acquiring information,

[0911] A means of generating information obtained in a user-optimized state and adjusting it according to the user's emotional state,

[0912] A means of presenting generated information to the user and selecting and providing music that matches the user's emotions,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, characterized in that it includes means for further personalizing the acquired information based on the user's past behavioral history and emotional state.

[0916] (Claim 3)

[0917] The system according to claim 1, characterized by comprising means for providing the generated information to the user through visual or auditory presentation means to improve the user experience in the vehicle. [Explanation of Symbols]

[0918] 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 obtaining user input and converting that input from audio data to text data, A means of referring to user profile information and analyzing user preferences based on that profile, A means of transmitting user input data and preference data to the cloud, Within the cloud, a means of selecting an appropriate external information provision method based on user input data and acquiring that information, A means of generating the acquired information in a form optimized for the user, A means of presenting the generated information to the user, A system that includes this.

2. The system according to claim 1, characterized in that it includes means for further personalizing the acquired information based on the user's past behavioral history.

3. The system according to claim 1, characterized by comprising means for providing the generated information to the user through visual or auditory presentation means.

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