system

Abstract

We provide the system. [Solution] Means for acquiring user behavior data, preference data, and environmental data, A means of predicting user behavior patterns and interests based on the acquired data, A means for presenting customized information and services to the user based on the aforementioned prediction results, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, due to information overload and the complexity of schedule management, it is difficult for users to efficiently manage their lives. Also, by using various digital services individually, the user experience becomes fragmented, and as a result, the overall productivity and quality of life decline, which is an issue.

Means for Solving the Problems

[0005] This invention provides a system that collects user behavior data, preference data, and environmental data, and uses them to predict user behavior patterns and interests. Furthermore, it presents customized information and services to the user based on these prediction results, and enables natural dialogue with the user using speech recognition technology and natural language processing technology. This allows the system to utilize user feedback in future predictions and continuously optimize the user experience.

[0006] "User behavior data" refers to information about various behavioral histories generated by users in their daily lives and while using digital devices.

[0007] "Preference data" refers to information that reflects a user's interests, concerns, and preferences, and is composed of the user's choices and usage history.

[0008] "Environmental data" refers to information about the physical and digital environment in which a user is located, including location information, weather, and information about surrounding devices.

[0009] "Behavioral patterns" refer to the regularity or tendencies of behaviors that users exhibit over a certain period of time, and they form the basis for prediction and analysis.

[0010] "Prediction results" refer to information that predicts the user's future behavior and needs based on collected data and analytical algorithms.

[0011] "Customized information and services" refers to forms of information and service delivery that are tailored to the characteristics and needs of individual users.

[0012] "Speech recognition technology" is a technology that converts speech into text data so that digital devices can understand its content.

[0013] "Natural language processing technology" is a technology that interprets human language and enables computers to understand, generate, and respond to it.

[0014] "Feedback" refers to evaluations, suggestions for improvement, or opinions based on user experience that users provide regarding a system. [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.

Mode 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 numbered 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 numbered 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 numbered 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, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[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] This invention is a system that efficiently collects and analyzes user behavior data, preference data, and environmental data to provide users with optimal information and services. This system is primarily composed of server, terminal, and user interaction.

[0037] The server aggregates data acquired from individual users and analyzes it using the latest machine learning algorithms. Based on the data analysis, the server recognizes user behavior patterns and preferences and continuously updates a model that predicts future needs. The predictive model is generated based on the user's past behavior history and environmental information.

[0038] The terminal is responsible for displaying information and services tailored to the user as a holographic interface, based on prediction results sent from the server. The terminal can provide information to the user and engage in natural dialogue through visual and auditory means. Speech recognition technology can accurately understand the user's voice instructions, and natural language processing technology can be used to generate responses.

[0039] Users interact with the presented information and services. When users input feedback into their devices, this feedback information is sent to the server and stored in a database. This information is used to make future predictions, improving the overall accuracy and personalization capabilities of the system.

[0040] As a concrete example, if a user uses this system for daily task management, the server analyzes the user's past calendar events and task management data and automatically generates a schedule that takes into account meetings and events scheduled for the next day. The terminal displays this schedule as a hologram, and the user can check and modify its contents using voice and gestures.

[0041] Thus, the present invention aims to enable efficient and intuitive service provision tailored to the individual needs of each user, and to improve the user experience by continuously learning the user's lifestyle and preferences.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server automatically acquires user behavior data, preference data, and environmental data from various digital devices. This includes location information, calendar events, voice input history, and application usage history.

[0045] Step 2:

[0046] The server analyzes the collected data in real time and identifies user behavior patterns. It uses machine learning algorithms to update models that predict future needs based on users' past behavior and preferences.

[0047] Step 3:

[0048] The server generates recommendations and services based on predicted needs. This information is designed to optimize the user's life and includes, for example, schedule suggestions and recommendations for using specific services.

[0049] Step 4:

[0050] The terminal visually presents the user with recommended information received from the server in hologram format. The interface used here is intuitive and also supports voice commands through the use of speech recognition technology.

[0051] Step 5:

[0052] Users provide feedback on the presented information and services using voice or gestures. This feedback includes, for example, approving, rejecting, or requesting changes to proposals.

[0053] Step 6:

[0054] The device sends user feedback to the server. This information is stored in a database and used to improve the predictive model for future use.

[0055] Step 7:

[0056] The server adjusts its analysis algorithms based on user feedback, preparing to make more accurate predictions. This continuously improves overall system performance and user experience.

[0057] (Example 1)

[0058] 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."

[0059] In today's information society, users are exposed to vast amounts of information, making it difficult to efficiently find information and services that match their interests and behaviors. Furthermore, providing information tailored to individual user needs requires accurate predictions based on various data, but there is a lack of systems that can effectively achieve this. Additionally, there is a need to continuously improve systems by incorporating user feedback.

[0060] 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.

[0061] In this invention, the server includes means for acquiring information about user behavior, preferences, and environment; means for generating a computational model for predicting user behavior patterns and interests based on the acquired information; and means for providing personalized information and services to the user based on the prediction results. This makes it possible to efficiently provide information that meets the user's needs and improve the user experience.

[0062] "Information about user behavior" refers to data that includes users' daily activities, travel history, and usage history.

[0063] "Information about preferences" refers to data that shows the areas and tastes that a user is interested in.

[0064] "Environmental information" refers to data about the physical or social environment in which the user resides.

[0065] A "computational model" is a mathematical or statistical framework used to make predictions or classifications based on acquired data.

[0066] "Personalized information and services" refer to specialized information and services provided to meet the specific needs of a user.

[0067] "Speech recognition technology" is a technology that converts the voice spoken by a user into text and understands it.

[0068] "Natural language processing technology" is a technology for analyzing user interactions using natural language and generating responses.

[0069] A "generative AI model" is an algorithm that uses artificial intelligence to create content and information for users.

[0070] This invention is a system that efficiently provides personalized information and services using diverse user data. This system mainly consists of a server, terminals, and users.

[0071] The server collects information about user behavior, preferences, and environment. This includes data from smartphones and wearable devices. The collected data is then processed using a database management system (e.g., a typical relational database), followed by data cleaning and integration.

[0072] Next, the server uses the collected data to build a computational model based on a generative AI model. This process utilizes machine learning frameworks such as Python's Scikit-learn and TENSORFLOW®. This model is used to predict user behavior patterns and interests, thereby enabling the generation of information and services tailored to user needs.

[0073] On the other hand, the terminal receives information provided by the server and presents it to the user. The terminal is equipped with a visual display device using a holographic interface, allowing for intuitive visualization of information. Furthermore, it can engage in natural conversations with the user using speech recognition and natural language processing technologies. This allows the user to interact with the system through voice commands and easily obtain the necessary information.

[0074] After receiving the information or service provided, users send feedback to their devices. This feedback information is then sent back to the server and stored in a database. The stored feedback is used to improve the overall accuracy and personalization capabilities of the system by being utilized in subsequent predictions.

[0075] For example, if a user uses this system for daily task management, the server analyzes the user's past schedules and task management data, and automatically generates an optimal schedule considering the tasks scheduled for the following day. The terminal displays this schedule as a hologram, and the user can check and modify its contents using voice and gestures. Such features contribute to improving user convenience.

[0076] A possible input prompt for the generating AI model could be, "Based on the user's behavioral and preference data, predict the next day's schedule and display it as a hologram." This prompt allows the system to provide information tailored to the user's specific needs.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server collects information about user behavior, preferences, and environment. Sensor data from smartphones and wearable devices is taken as input. Specifically, it collects GPS data, app usage history, and sensor activity data in real time. This data is integrated, stored in a database, and then processed with noise reduction and missing value imputation before outputting the refined data.

[0080] Step 2:

[0081] The server constructs a predictive algorithm using a generative AI model based on organized data stored in the database. The input consists of the user's past behavior and preference data. Specifically, it uses Python's Scikit-learn and TensorFlow to apply machine learning algorithms and learn behavioral patterns. The resulting model predicts the user's future behavior and needs.

[0082] Step 3:

[0083] The server uses a constructed predictive model to generate personalized information and services for the user. The predictive model and the user's current environment information are used as input. Specifically, the generative model automatically generates the user's schedule and suggests optimal tasks for the next day. It also prepares information in text format and converts it into a format suitable for the user. This result is then output and sent to the terminal.

[0084] Step 4:

[0085] The terminal presents information to the user visually and audibly, based on information sent from the server. Input includes personalized information and service details from the server. Specifically, it visualizes schedules using a holographic interface and uses speech recognition technology to understand user voice commands and generate responses. The user can receive the information, review its contents, and make changes as needed.

[0086] Step 5:

[0087] Users input feedback on the presented information and services into the terminal. This input can be via voice commands or text. Specifically, users express their opinions via voice or text and evaluate convenience and satisfaction. This feedback is sent to the server by the terminal.

[0088] Step 6:

[0089] The server receives feedback sent from the terminal and stores it in a database. Input includes user feedback and change history. Specifically, the stored feedback is used to improve future predictions and to refine new generative AI models. This process continuously improves the system's accuracy and personalization capabilities, enabling the delivery of better services.

[0090] (Application Example 1)

[0091] 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."

[0092] In today's world, there is a demand for information and services that accurately meet the diverse preferences and needs of consumers. However, general product information systems struggle to personalize information by fully considering the preferences and behavioral data of each individual consumer, hindering improvements in the customer experience. Therefore, there is a need for new methods that provide personalized information and products in real time based on consumer behavioral data and preferences.

[0093] 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.

[0094] In this invention, the server includes means for acquiring user behavior data, preference data, and environmental data; means for predicting the user's behavior patterns and interests based on the acquired data; means for presenting personalized information and services to the user based on the prediction results; and means for visually displaying product information to the user using an augmented reality interface. This makes it possible to provide personalized information that meets the individual needs of each consumer.

[0095] "User behavior data" refers to information collected based on consumers' location, movements, and activity logs.

[0096] "Data representing preferences" refers to data that includes consumers' interests and preferences, purchase history, browsing history, and so on.

[0097] "Environmental data" refers to information about the physical or social environment surrounding consumers, including time, weather, and congestion levels.

[0098] "Behavioral patterns" refer to consistent consumer activity and behavioral tendencies that can be inferred from past data.

[0099] "Personalized information and services" refer to information and services that are selected and provided based on the individual consumer's behavioral data and preferences.

[0100] An "augmented reality interface" is a technology that overlays digital information onto the real world and displays it visually.

[0101] "Means of visual display" refers to methods that allow information to be viewed directly with the eyes through devices such as liquid crystal displays and smart glasses.

[0102] The system of the present invention functions through a server, a user's terminal, and user interaction. First, the server collects user behavior data, preference data, and environmental data in real time. This data is acquired through the user's smart glasses or mobile terminal. The server analyzes this data using machine learning algorithms to generate a model that predicts the user's behavior patterns and interests. Google® Cloud ML Engine is used for this purpose.

[0103] Based on this, the server personalizes recommended products and information and creates a set of information to display via an augmented reality interface. This visual information is displayed on smart glasses and other digital devices using Unity. The terminal visually presents product information and service details as holograms, and the user interacts with them naturally using voice and gestures. This interaction is realized through speech recognition and natural language processing technologies via Dialogflow.

[0104] When a user provides feedback on products or information through the interface, the device collects that feedback and sends it to a server. This feedback is stored in the user's data set and used to make predictions for the future. For example, when a user visits a clothing store, new products are recommended based on items they have purchased in the past. In this case, the prompt might be in the format of, "How can we select recommended products based on the user's past purchase history and preference data and display them as holograms on smart glasses?"

[0105] With the above configuration, users can receive individually customized product information in real time, regardless of time or location. This invention can improve the quality of the consumer experience and increase the efficiency of services for businesses.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server collects behavioral data, preference data, and environmental data received from the user's smart glasses or mobile device. This data input is real-time data from when the user uses the device. The server stores this input data in a database and prepares it for subsequent analysis.

[0109] Step 2:

[0110] The server uses machine learning algorithms to predict user behavior patterns and interests based on the collected data. The input is the data collected in step 1, and the output is a predictive model of user interests and preferences. In this step, Google Cloud ML Engine is used to analyze the data and continuously update the predictive model.

[0111] Step 3:

[0112] The server selects personalized information and recommended products for the user based on the results of the predictive model. The input is the output of the predictive model obtained in step 2, and this is used to select specific information. This information is personalized taking into account the user's preferences, and the output is recommended information and a list of products.

[0113] Step 4:

[0114] The device receives recommendation information sent from the server and prepares to display it visually to the user. This display preparation process takes the output information from step 3 as input and generates data for the augmented reality interface as output. This data is processed using Unity and displayed as a hologram on the smart glasses.

[0115] Step 5:

[0116] Users visually view information and products and interact with them using voice or gestures. User input consists of voice commands and hand movements used during the interaction, which transmit the user's intentions to the device. Using Dialogflow, user instructions are understood through speech recognition and natural language processing technologies.

[0117] Step 6:

[0118] User feedback is sent from the device to the server and stored as data for future use. The input here is the feedback provided by the user to the device, which can be used to improve the accuracy of future predictions and recommendations. The output is an updated user database, and the feedback is also used as prompt text for the generative AI model.

[0119] 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.

[0120] This invention provides a user-adaptive information delivery system that utilizes emotional data in addition to user behavioral data, preference data, and environmental data. The system includes a server, terminals, and advanced analytical functions including an emotional engine to optimize the user experience.

[0121] The server centrally manages diverse data sent by the user and uses it to learn the user's behavior patterns and emotional states. The emotion engine analyzes the user's voice tone and facial expression data to recognize the user's emotions in real time. The recognized emotion data, along with behavioral data, is sent to the server and used as material for analysis. The server uses machine learning algorithms to accumulate and analyze the user's emotional history and updates models that predict the user's expectations in future situations.

[0122] The terminal plays a role in presenting information sent from the server to the user in an intuitive and effective manner. It utilizes holograms, audio, and visual interfaces to present the most suitable information and services to the user. For example, if the terminal determines that the user is experiencing stress, it might recommend relaxation music or videos to help them relax.

[0123] Users make choices based on the information and services provided through their devices, and in some cases, provide feedback on their preferences and emotions to the device. This feedback information is then sent to a server, stored in a database, and used to improve the predictive model for future use.

[0124] For example, if an emotion is detected through a terminal while a user is working, the server considers the user's past stress levels and work performance and sends a notification to the terminal prompting them to take a break at an appropriate time. This improves the user's work-life balance and enables more efficient work. In this way, the present invention is designed to comprehensively manage user emotions and behavior and improve the user experience.

[0125] The following describes the processing flow.

[0126] Step 1:

[0127] When users use their devices in their daily lives, they provide data that includes emotions to the device through voice and camera. The device collects this data in real time and sends it to the emotion engine.

[0128] Step 2:

[0129] The emotion engine analyzes the user's voice tone and facial expression data to recognize their current emotional state. This allows it to identify emotions such as "happy" or "stressed."

[0130] Step 3:

[0131] The device transmits user behavior data, preference data, and environmental data to the server along with recognized emotion data. This allows for centralized data management and comprehensive analysis.

[0132] Step 4:

[0133] The server analyzes all received data using the latest machine learning algorithms to predict future needs based on user behavior patterns and emotional history. This prediction takes into account user preferences and emotional patterns.

[0134] Step 5:

[0135] The server generates user-optimized information and services based on the prediction results. In doing so, consideration is given to providing suggestions that align with the user's emotions.

[0136] Step 6:

[0137] The device presents information obtained from the server to the user through holograms and voice interfaces. It delivers information intuitively and effectively, tailored to the user's emotional state.

[0138] Step 7:

[0139] Users react to the information presented and provide feedback to their devices as needed. This feedback may include comments such as "This suggestion was helpful" or "I would like to see other options."

[0140] Step 8:

[0141] The device sends user feedback to the server, where it is stored in a database. The server analyzes this feedback to improve the accuracy of future predictive models.

[0142] (Example 2)

[0143] 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".

[0144] Conventional information delivery systems could only provide limited information based on user behavior and preference data, and could not adequately consider the user's emotional state. As a result, it was difficult to provide appropriate information and services that met user expectations in a timely manner, limiting the improvement of the user experience. Furthermore, the insufficient utilization of user feedback made it difficult to improve the system's predictive accuracy.

[0145] 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.

[0146] In this invention, the server includes means for acquiring user behavior data, preference data, environmental data, and emotional data; means for learning the user's behavior patterns and emotional state based on the acquired data and predicting the user's expectations; and means for presenting the user with customized information and services in an intuitive and effective manner based on the prediction results. This enables the provision of appropriate information that takes into account the user's emotional state, and by providing optimal services to individual users, the user experience can be improved.

[0147] "User behavior data" refers to information about a user's activity history, location information, and services used, and is used to analyze user behavior patterns.

[0148] "Preference data" refers to information about a user's past interests, preferences, and selection history, and is data used to understand the individual needs of each user.

[0149] "Environmental data" refers to information about the user's surroundings and conditions, including time of day, weather, and location information.

[0150] "Emotional data" refers to information about a user's psychological state extracted from their voice tone, facial expressions, and other biosignals, and is used to identify the user's emotions.

[0151] An "emotion engine" is a collection of algorithms and technologies that analyze a user's voice and facial expression data to recognize their emotions in real time.

[0152] A "generative AI model" is an artificial intelligence system that automatically generates knowledge from data and proposes optimal information and services based on the user's behavior and emotions.

[0153] A "prompt" is an instruction or question used as input to a generative AI model, and it forms the basis for the model's responses and information generation.

[0154] This invention is a user-adaptive information delivery system that optimizes the user experience by utilizing user behavior data, preference data, environmental data, and emotional data. This system consists of a server, terminals, and advanced analytical functions including an emotional engine.

[0155] The server centrally manages diverse data sent from users and learns user behavior patterns and emotional states based on this data. Specifically, a system equipped with an emotion engine analyzes the user's voice tone and facial expression data to recognize the user's emotions in real time. This recognized emotional data is sent to the server along with behavioral data. The server uses machine learning algorithms to analyze this data, accumulates the user's emotional history, and updates models that predict the user's expectations in future situations.

[0156] The terminal functions as a device that presents information transmitted from the server to the user. It uses holograms, voice interfaces, and visual interfaces to intuitively and effectively provide the user with the most suitable information and services. For example, if analysis indicates that the user is experiencing stress, the terminal can recommend relaxation music or videos.

[0157] Users can make choices regarding the information and services provided through their devices and provide feedback on their preferences and feelings. This feedback information is sent via the device to a server and stored in a database. This process helps improve future predictive models.

[0158] For example, if the system detects an emotion in a user while they are working, the server will consider their past stress levels and work performance and send a notification through their device to encourage them to take a break at an appropriate time. In this way, it is possible to improve the user's work-life balance and support efficient work progress.

[0159] Furthermore, the system uses a generative AI model to create prompts and suggest the most suitable information and services for the user. Examples of prompts include, "Please provide ways to relax, taking into account the user's current emotional state," and "Please suggest ways to streamline the next task based on the user's past data."

[0160] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0161] Step 1:

[0162] Users perform daily activities through their devices, generating behavioral data, preference data, environmental data, and emotional data. This data is collected through the device's sensors and application APIs. For example, a smartphone's microphone and camera capture voice tone and facial expressions, which are then compiled together with the user's location information and activity logs.

[0163] Step 2:

[0164] The terminal sends the collected data to the server. This transmission occurs in real time, continuously providing data to the server. The input is the user's collected data, and the output is data packets that are forwarded to the server. For example, each data point is encrypted and sent over a secure channel.

[0165] Step 3:

[0166] The server begins data analysis based on the received data. Using an emotion engine, it extracts emotional data from voice tone and facial expressions, and analyzes behavioral patterns using machine learning algorithms. The input is data from the terminal, and the output is a model of the user's emotional state and behavioral prediction. Specifically, the server stores the emotional history in a database and updates the prediction model with new data.

[0167] Step 4:

[0168] The server generates prompt messages using a generative AI model based on the analysis results. These prompt messages are designed to provide users with appropriate information and services. The input is the analyzed data and the predictive model, and the output is the prompt message. For example, a prompt such as "Please provide ways to relax, taking into account the user's current emotional state" might be generated.

[0169] Step 5:

[0170] The terminal provides information and services to the user based on generated prompt messages. These are presented in an intuitively understandable format through holograms and voice interfaces. Input is the prompt message from the server, and output is the interface content the user sees. For example, relaxing music is streamed, and relaxing images are displayed visually.

[0171] Step 6:

[0172] Users can react to the information and services presented and provide feedback. The device sends this feedback to the server, where it is stored in a database. The input is the user's feedback data, and the output is the data sent to the server. Specifically, the user evaluates their satisfaction with the displayed content, and this is used to predict improvements for the next time.

[0173] (Application Example 2)

[0174] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0175] Online shopping and virtual stores face the challenge of providing information tailored to individual user interests and emotional states, often resulting in a uniform user experience. Traditional systems recommend products based solely on explicit user preference data, making it difficult to provide dynamic information that responds to emotional changes or fleeting interests.

[0176] 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.

[0177] In this invention, the server includes means for acquiring data on user behavior, preferences, environment, and emotions; means for predicting user behavior patterns, emotional states, and interests based on the acquired data; and means for presenting the most suitable information and services to the user using holograms, voice, and visual interfaces based on the prediction results. This enables a personalized and superior user experience by providing appropriate products and services in response to the user's real-time emotional state.

[0178] "User behavior data" refers to data that shows specific actions taken by users, such as product browsing history, purchase history, and the flow of operations.

[0179] "Preference data" refers to information about user preferences and preferred attributes, such as data on favorite product categories and brands.

[0180] "Environmental data" refers to information related to the user's surroundings, including data such as the type of device being used, the time of day, and location information.

[0181] "Emotional data" refers to data that indicates the user's emotional state, representing the nature of emotions recognized in real time through information obtained from voice and facial expressions.

[0182] A "hologram" refers to a technology that generates three-dimensional images, or the resulting images themselves, and is used to present visual information to users.

[0183] A "voice interface" is a method for users and information systems to communicate using voice-based input and output.

[0184] A "visual interface" is a method of presenting information visually through graphics or displays, and is used to provide information to users.

[0185] "Means of presenting the most appropriate information and services to the user" refers to system components that include methods for presenting personalized information and services based on the user's behavioral patterns and emotional state.

[0186] To realize this application, the system consists of a server, a terminal, and an emotion engine. The server is responsible for collecting data on user behavior, preferences, environment, and emotions. The server comprehensively analyzes this diverse data and uses machine learning algorithms to predict user behavior patterns and emotional states with high accuracy. This involves real-time emotion recognition using the user's voice tone and facial expression data analyzed by the emotion engine.

[0187] The terminal uses holograms, audio, and visual interfaces to provide users with the most relevant information and services based on analysis results sent from the server. This means that when a user browses products in a virtual store, they are presented with product and promotional information tailored to their current interests and emotions.

[0188] A concrete example is a scenario where a user is using smart glasses while virtual shopping. If the user smiles while browsing fashion items, the emotion engine recognizes this as a positive emotion and sends that data to the server. The server then selects highly relevant new products and special offers and presents them to the user visually as holograms through the device. This allows the user to gain inspiration for new purchases.

[0189] An example of a prompt message might be, "Design a system that analyzes user facial expression data and recommends fashion items based on the user's interests." In this way, the present invention aims to improve the personalized user experience by comprehensively utilizing user behavior and emotional information.

[0190] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0191] Step 1:

[0192] The server collects data on user behavior, preferences, environment, and emotions. This includes user operation history, voice tone, and facial expression data. Based on this input data, the server begins to understand the user's behavior patterns and emotional state. The data is centrally managed and organized for the next analysis step.

[0193] Step 2:

[0194] The server applies machine learning algorithms to the collected data. This process analyzes behavioral patterns and emotional states to build a model that predicts the user's next interests and emotions. Inputs include historical user data and real-time data, and the predictive model is updated as output. This step, performed by the server, enables highly accurate predictions, forming the foundation for future information provision.

[0195] Step 3:

[0196] The server generates data to present the user with the most relevant information and services based on an updated predictive model. This output data includes suggestions for products and services related to the user's interests. Specifically, the server selects products based on sentiment data and sends that information to the terminal.

[0197] Step 4:

[0198] The terminal receives suggestion information sent from the server and presents it to the user through holograms, audio, and visual interfaces. This process displays information in a way that easily captures the user's attention and facilitates interaction. Specifically, products are positioned so that the hologram display is within the user's line of sight, and audio guidance is also provided.

[0199] Step 5:

[0200] Users review the information presented by the device and provide feedback on products and services that interest them. This feedback, including the user's choices and reactions, is used for future data analysis. In this step, the feedback is collected, sent back to the server, and stored in the database. The user's specific choices and requests are treated as information that contributes to future service improvements.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] [Second Embodiment]

[0205] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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".

[0217] This invention is a system that efficiently collects and analyzes user behavior data, preference data, and environmental data to provide users with optimal information and services. This system is primarily composed of server, terminal, and user interaction.

[0218] The server aggregates data acquired from individual users and analyzes it using the latest machine learning algorithms. Based on the data analysis, the server recognizes user behavior patterns and preferences and continuously updates a model that predicts future needs. The predictive model is generated based on the user's past behavior history and environmental information.

[0219] The terminal is responsible for displaying information and services tailored to the user as a holographic interface, based on prediction results sent from the server. The terminal can provide information to the user and engage in natural dialogue through visual and auditory means. Speech recognition technology can accurately understand the user's voice instructions, and natural language processing technology can be used to generate responses.

[0220] Users interact with the presented information and services. When users input feedback into their devices, this feedback information is sent to the server and stored in a database. This information is used to make future predictions, improving the overall accuracy and personalization capabilities of the system.

[0221] As a concrete example, if a user uses this system for daily task management, the server analyzes the user's past calendar events and task management data and automatically generates a schedule that takes into account meetings and events scheduled for the next day. The terminal displays this schedule as a hologram, and the user can check and modify its contents using voice and gestures.

[0222] Thus, the present invention aims to enable efficient and intuitive service provision tailored to the individual needs of each user, and to improve the user experience by continuously learning the user's lifestyle and preferences.

[0223] The following describes the processing flow.

[0224] Step 1:

[0225] The server automatically acquires user behavior data, preference data, and environmental data from various digital devices. This includes location information, calendar events, voice input history, and application usage history.

[0226] Step 2:

[0227] The server analyzes the collected data in real time and identifies user behavior patterns. It uses machine learning algorithms to update models that predict future needs based on users' past behavior and preferences.

[0228] Step 3:

[0229] The server generates recommendations and services based on predicted needs. This information is designed to optimize the user's life and includes, for example, schedule suggestions and recommendations for using specific services.

[0230] Step 4:

[0231] The terminal visually presents the user with recommended information received from the server in hologram format. The interface used here is intuitive and also supports voice commands through the use of speech recognition technology.

[0232] Step 5:

[0233] Users provide feedback on the presented information and services using voice or gestures. This feedback includes, for example, approving, rejecting, or requesting changes to proposals.

[0234] Step 6:

[0235] The device sends user feedback to the server. This information is stored in a database and used to improve the predictive model for future use.

[0236] Step 7:

[0237] The server adjusts its analysis algorithms based on user feedback, preparing to make more accurate predictions. This continuously improves overall system performance and user experience.

[0238] (Example 1)

[0239] 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."

[0240] In today's information society, users are exposed to vast amounts of information, making it difficult to efficiently find information and services that match their interests and behaviors. Furthermore, providing information tailored to individual user needs requires accurate predictions based on various data, but there is a lack of systems that can effectively achieve this. Additionally, there is a need to continuously improve systems by incorporating user feedback.

[0241] 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.

[0242] In this invention, the server includes means for acquiring information about user behavior, preferences, and environment; means for generating a computational model for predicting user behavior patterns and interests based on the acquired information; and means for providing personalized information and services to the user based on the prediction results. This makes it possible to efficiently provide information that meets the user's needs and improve the user experience.

[0243] "Information about user behavior" refers to data that includes users' daily activities, travel history, and usage history.

[0244] "Information about preferences" refers to data that shows the areas and tastes that a user is interested in.

[0245] "Environmental information" refers to data about the physical or social environment in which the user resides.

[0246] A "computational model" is a mathematical or statistical framework used to make predictions or classifications based on acquired data.

[0247] "Personalized information and services" refer to specialized information and services provided to meet the specific needs of a user.

[0248] "Speech recognition technology" is a technology that converts the voice spoken by a user into text and understands it.

[0249] "Natural language processing technology" is a technology for analyzing user interactions using natural language and generating responses.

[0250] A "generative AI model" is an algorithm that uses artificial intelligence to create content and information for users.

[0251] This invention is a system that efficiently provides personalized information and services using diverse user data. This system mainly consists of a server, terminals, and users.

[0252] The server collects information about user behavior, preferences, and environment. This includes data from smartphones and wearable devices. The collected data is then processed using a database management system (e.g., a typical relational database), followed by data cleaning and integration.

[0253] Next, the server uses the collected data to build a computational model based on a generative AI model. This process utilizes machine learning frameworks such as Python's Scikit-learn and TensorFlow. This model is used to predict user behavior patterns and interests, thereby enabling the generation of information and services tailored to user needs.

[0254] On the other hand, the terminal receives information provided by the server and presents it to the user. The terminal is equipped with a visual display device using a holographic interface, allowing for intuitive visualization of information. Furthermore, it can engage in natural conversations with the user using speech recognition and natural language processing technologies. This allows the user to interact with the system through voice commands and easily obtain the necessary information.

[0255] After receiving the information or service provided, users send feedback to their devices. This feedback information is then sent back to the server and stored in a database. The stored feedback is used to improve the overall accuracy and personalization capabilities of the system by being utilized in subsequent predictions.

[0256] For example, if a user uses this system for daily task management, the server analyzes the user's past schedules and task management data, and automatically generates an optimal schedule considering the tasks scheduled for the following day. The terminal displays this schedule as a hologram, and the user can check and modify its contents using voice and gestures. Such features contribute to improving user convenience.

[0257] A possible input prompt for the generating AI model could be, "Based on the user's behavioral and preference data, predict the next day's schedule and display it as a hologram." This prompt allows the system to provide information tailored to the user's specific needs.

[0258] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0259] Step 1:

[0260] The server collects information about user behavior, preferences, and environment. Sensor data from smartphones and wearable devices is taken as input. Specifically, it collects GPS data, app usage history, and sensor activity data in real time. This data is integrated, stored in a database, and then processed with noise reduction and missing value imputation before outputting the refined data.

[0261] Step 2:

[0262] The server constructs a predictive algorithm using a generative AI model based on organized data stored in the database. The input consists of the user's past behavior and preference data. Specifically, it uses Python's Scikit-learn and TensorFlow to apply machine learning algorithms and learn behavioral patterns. The resulting model predicts the user's future behavior and needs.

[0263] Step 3:

[0264] The server uses a constructed predictive model to generate personalized information and services for the user. The predictive model and the user's current environment information are used as input. Specifically, the generative model automatically generates the user's schedule and suggests optimal tasks for the next day. It also prepares information in text format and converts it into a format suitable for the user. This result is then output and sent to the terminal.

[0265] Step 4:

[0266] The terminal presents information to the user visually and audibly, based on information sent from the server. Input includes personalized information and service details from the server. Specifically, it visualizes schedules using a holographic interface and uses speech recognition technology to understand user voice commands and generate responses. The user can receive the information, review its contents, and make changes as needed.

[0267] Step 5:

[0268] Users input feedback on the presented information and services into the terminal. This input can be via voice commands or text. Specifically, users express their opinions via voice or text and evaluate convenience and satisfaction. This feedback is sent to the server by the terminal.

[0269] Step 6:

[0270] The server receives feedback sent from the terminal and stores it in a database. Input includes user feedback and change history. Specifically, the stored feedback is used to improve future predictions and to refine new generative AI models. This process continuously improves the system's accuracy and personalization capabilities, enabling the delivery of better services.

[0271] (Application Example 1)

[0272] 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."

[0273] In today's world, there is a demand for information and services that accurately meet the diverse preferences and needs of consumers. However, general product information systems struggle to personalize information by fully considering the preferences and behavioral data of each individual consumer, hindering improvements in the customer experience. Therefore, there is a need for new methods that provide personalized information and products in real time based on consumer behavioral data and preferences.

[0274] 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.

[0275] In this invention, the server includes means for acquiring user behavior data, preference data, and environmental data; means for predicting the user's behavior patterns and interests based on the acquired data; means for presenting personalized information and services to the user based on the prediction results; and means for visually displaying product information to the user using an augmented reality interface. This makes it possible to provide personalized information that meets the individual needs of each consumer.

[0276] "User behavior data" refers to information collected based on consumers' location, movements, and activity logs.

[0277] "Data representing preferences" refers to data that includes consumers' interests and preferences, purchase history, browsing history, and so on.

[0278] "Environmental data" refers to information about the physical or social environment surrounding consumers, including time, weather, and congestion levels.

[0279] "Behavioral patterns" refer to consistent consumer activity and behavioral tendencies that can be inferred from past data.

[0280] "Personalized information and services" refer to information and services that are selected and provided based on the individual consumer's behavioral data and preferences.

[0281] An "augmented reality interface" is a technology that overlays digital information onto the real world and displays it visually.

[0282] "Means of visual display" refers to methods that allow information to be viewed directly with the eyes through devices such as liquid crystal displays and smart glasses.

[0283] The system of the present invention functions through a server, a terminal used by a user, and user interaction. First, the server collects in real-time the user's behavior data, data representing preferences, and data regarding the environment. This data is obtained through smart glasses or mobile terminals held by the user. The server analyzes this data using machine learning algorithms to generate a model for predicting the user's behavior patterns and interests. At this time, Google Cloud ML Engine is used.

[0284] Based on this, the server personalizes the recommended products and information, and creates a group of information to be displayed via an augmented reality interface. This visual information is displayed on smart glasses or other digital devices using Unity. The terminal visually presents product information and service content as holograms, and the user conducts natural conversations with this using voice or gestures. This conversation is realized by voice recognition technology and natural language processing technology via Dialogflow.

[0285] When the user provides feedback on products or information via the interface, the terminal collects this feedback and sends it to the server. This feedback is stored in the user's data group and utilized for the next prediction. For example, when the user visits a clothing store, new products are recommended based on the products purchased in the past. At this time, a prompt sentence in the form of "How can I select recommended products from the user's past purchase history and preference data and display them as holograms on smart glasses?" is used.

[0286] With the above configuration, the user can receive individually customized product information in real-time regardless of time or location. This invention can improve the quality of the consumer experience and enhance the efficiency of services in enterprises.

[0287] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0288] Step 1:

[0289] The server collects behavioral data, preference data, and environmental data received from the user's smart glasses or mobile device. This data input is real-time data from when the user uses the device. The server stores this input data in a database and prepares it for subsequent analysis.

[0290] Step 2:

[0291] The server uses machine learning algorithms to predict user behavior patterns and interests based on the collected data. The input is the data collected in step 1, and the output is a predictive model of user interests and preferences. In this step, Google Cloud ML Engine is used to analyze the data and continuously update the predictive model.

[0292] Step 3:

[0293] The server selects personalized information and recommended products for the user based on the results of the predictive model. The input is the output of the predictive model obtained in step 2, and this is used to select specific information. This information is personalized taking into account the user's preferences, and the output is recommended information and a list of products.

[0294] Step 4:

[0295] The device receives recommendation information sent from the server and prepares to display it visually to the user. This display preparation process takes the output information from step 3 as input and generates data for the augmented reality interface as output. This data is processed using Unity and displayed as a hologram on the smart glasses.

[0296] Step 5:

[0297] Users visually view information and products and interact with them using voice or gestures. User input consists of voice commands and hand movements used during the interaction, which transmit the user's intentions to the device. Using Dialogflow, user instructions are understood through speech recognition and natural language processing technologies.

[0298] Step 6:

[0299] User feedback is sent from the device to the server and stored as data for future use. The input here is the feedback provided by the user to the device, which can be used to improve the accuracy of future predictions and recommendations. The output is an updated user database, and the feedback is also used as prompt text for the generative AI model.

[0300] 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.

[0301] This invention provides a user-adaptive information delivery system that utilizes emotional data in addition to user behavioral data, preference data, and environmental data. The system includes a server, terminals, and advanced analytical functions including an emotional engine to optimize the user experience.

[0302] The server centrally manages diverse data sent by the user and uses it to learn the user's behavior patterns and emotional states. The emotion engine analyzes the user's voice tone and facial expression data to recognize the user's emotions in real time. The recognized emotion data, along with behavioral data, is sent to the server and used as material for analysis. The server uses machine learning algorithms to accumulate and analyze the user's emotional history and updates models that predict the user's expectations in future situations.

[0303] The terminal plays a role in intuitively and effectively presenting the information sent from the server to the user. By making full use of holograms, voice, and visual interfaces, it presents the information and services most suitable for the user. For example, when it is determined that the user is feeling stressed, the terminal recommends music for relaxation or videos for distraction.

[0304] The user makes selections according to the information and services provided through the terminal, and in some cases provides feedback on their preferences and feelings to the terminal. This feedback information is then sent to the server, stored in the database, and used to improve the prediction model for subsequent times.

[0305] As a specific example, when the user's emotions are recognized through the terminal during work, the server considers the user's past stress levels and work performance and sends a notification to the terminal to take a break at an appropriate time. Thereby, the user's work-life balance is improved and efficient work progress becomes possible. Thus, the present invention is designed to comprehensively manage the user's emotions and actions and aim to improve the user experience.

[0306] The processing flow will be described below.

[0307] Step 1:

[0308] When the user uses the terminal in daily life, the user provides data including emotions to the terminal through voice or camera. The terminal collects this in real time and sends it to the emotion engine.

[0309] Step 2:

[0310] The emotion engine analyzes the user's voice tone and facial expression data to recognize the current emotional state. Thereby, emotions such as "happy" or "feeling stressed" of the user are identified.

[0311] Step 3:

[0312] The device transmits user behavior data, preference data, and environmental data to the server along with recognized emotion data. This allows for centralized data management and comprehensive analysis.

[0313] Step 4:

[0314] The server analyzes all received data using the latest machine learning algorithms to predict future needs based on user behavior patterns and emotional history. This prediction takes into account user preferences and emotional patterns.

[0315] Step 5:

[0316] The server generates user-optimized information and services based on the prediction results. In doing so, consideration is given to providing suggestions that align with the user's emotions.

[0317] Step 6:

[0318] The device presents information obtained from the server to the user through holograms and voice interfaces. It delivers information intuitively and effectively, tailored to the user's emotional state.

[0319] Step 7:

[0320] Users react to the information presented and provide feedback to their devices as needed. This feedback may include comments such as "This suggestion was helpful" or "I would like to see other options."

[0321] Step 8:

[0322] The device sends user feedback to the server, where it is stored in a database. The server analyzes this feedback to improve the accuracy of future predictive models.

[0323] (Example 2)

[0324] 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".

[0325] Conventional information delivery systems could only provide limited information based on user behavior and preference data, and could not adequately consider the user's emotional state. As a result, it was difficult to provide appropriate information and services that met user expectations in a timely manner, limiting the improvement of the user experience. Furthermore, the insufficient utilization of user feedback made it difficult to improve the system's predictive accuracy.

[0326] 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.

[0327] In this invention, the server includes means for acquiring user behavior data, preference data, environmental data, and emotional data; means for learning the user's behavior patterns and emotional state based on the acquired data and predicting the user's expectations; and means for presenting the user with customized information and services in an intuitive and effective manner based on the prediction results. This enables the provision of appropriate information that takes into account the user's emotional state, and by providing optimal services to individual users, the user experience can be improved.

[0328] "User behavior data" refers to information about a user's activity history, location information, and services used, and is used to analyze user behavior patterns.

[0329] "Preference data" refers to information about a user's past interests, preferences, and selection history, and is data used to understand the individual needs of each user.

[0330] "Environmental data" refers to information about the user's surroundings and conditions, including time of day, weather, and location information.

[0331] "Emotional data" refers to information about a user's psychological state extracted from their voice tone, facial expressions, and other biosignals, and is used to identify the user's emotions.

[0332] An "emotion engine" is a collection of algorithms and technologies that analyze a user's voice and facial expression data to recognize their emotions in real time.

[0333] A "generative AI model" is an artificial intelligence system that automatically generates knowledge from data and proposes optimal information and services based on the user's behavior and emotions.

[0334] A "prompt" is an instruction or question used as input to a generative AI model, and it forms the basis for the model's responses and information generation.

[0335] This invention is a user-adaptive information delivery system that optimizes the user experience by utilizing user behavior data, preference data, environmental data, and emotional data. This system consists of a server, terminals, and advanced analytical functions including an emotional engine.

[0336] The server centrally manages diverse data sent from users and learns user behavior patterns and emotional states based on this data. Specifically, a system equipped with an emotion engine analyzes the user's voice tone and facial expression data to recognize the user's emotions in real time. This recognized emotional data is sent to the server along with behavioral data. The server uses machine learning algorithms to analyze this data, accumulates the user's emotional history, and updates models that predict the user's expectations in future situations.

[0337] The terminal functions as a device that presents information transmitted from the server to the user. It uses holograms, voice interfaces, and visual interfaces to intuitively and effectively provide the user with the most suitable information and services. For example, if analysis indicates that the user is experiencing stress, the terminal can recommend relaxation music or videos.

[0338] Users can make choices regarding the information and services provided through their devices and provide feedback on their preferences and feelings. This feedback information is sent via the device to a server and stored in a database. This process helps improve future predictive models.

[0339] For example, if the system detects an emotion in a user while they are working, the server will consider their past stress levels and work performance and send a notification through their device to encourage them to take a break at an appropriate time. In this way, it is possible to improve the user's work-life balance and support efficient work progress.

[0340] Furthermore, the system uses a generative AI model to create prompts and suggest the most suitable information and services for the user. Examples of prompts include, "Please provide ways to relax, taking into account the user's current emotional state," and "Please suggest ways to streamline the next task based on the user's past data."

[0341] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0342] Step 1:

[0343] Users perform daily activities through their devices, generating behavioral data, preference data, environmental data, and emotional data. This data is collected through the device's sensors and application APIs. For example, a smartphone's microphone and camera capture voice tone and facial expressions, which are then compiled together with the user's location information and activity logs.

[0344] Step 2:

[0345] The terminal sends the collected data to the server. This transmission occurs in real time, continuously providing data to the server. The input is the user's collected data, and the output is data packets that are forwarded to the server. For example, each data point is encrypted and sent over a secure channel.

[0346] Step 3:

[0347] The server begins data analysis based on the received data. Using an emotion engine, it extracts emotional data from voice tone and facial expressions, and analyzes behavioral patterns using machine learning algorithms. The input is data from the terminal, and the output is a model of the user's emotional state and behavioral prediction. Specifically, the server stores the emotional history in a database and updates the prediction model with new data.

[0348] Step 4:

[0349] The server generates prompt messages using a generative AI model based on the analysis results. These prompt messages are designed to provide users with appropriate information and services. The input is the analyzed data and the predictive model, and the output is the prompt message. For example, a prompt such as "Please provide ways to relax, taking into account the user's current emotional state" might be generated.

[0350] Step 5:

[0351] The terminal provides information and services to the user based on generated prompt messages. These are presented in an intuitively understandable format through holograms and voice interfaces. Input is the prompt message from the server, and output is the interface content the user sees. For example, relaxing music is streamed, and relaxing images are displayed visually.

[0352] Step 6:

[0353] Users can react to the information and services presented and provide feedback. The device sends this feedback to the server, where it is stored in a database. The input is the user's feedback data, and the output is the data sent to the server. Specifically, the user evaluates their satisfaction with the displayed content, and this is used to predict improvements for the next time.

[0354] (Application Example 2)

[0355] 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."

[0356] Online shopping and virtual stores face the challenge of providing information tailored to individual user interests and emotional states, often resulting in a uniform user experience. Traditional systems recommend products based solely on explicit user preference data, making it difficult to provide dynamic information that responds to emotional changes or fleeting interests.

[0357] 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.

[0358] In this invention, the server includes means for acquiring data on user behavior, preferences, environment, and emotions; means for predicting user behavior patterns, emotional states, and interests based on the acquired data; and means for presenting the most suitable information and services to the user using holograms, voice, and visual interfaces based on the prediction results. This enables a personalized and superior user experience by providing appropriate products and services in response to the user's real-time emotional state.

[0359] "User behavior data" refers to data that shows specific actions taken by users, such as product browsing history, purchase history, and the flow of operations.

[0360] "Preference data" refers to information about user preferences and preferred attributes, such as data on favorite product categories and brands.

[0361] "Environmental data" refers to information related to the user's surroundings, including data such as the type of device being used, the time of day, and location information.

[0362] "Emotional data" refers to data that indicates the user's emotional state, representing the nature of emotions recognized in real time through information obtained from voice and facial expressions.

[0363] A "hologram" refers to a technology that generates three-dimensional images, or the resulting images themselves, and is used to present visual information to users.

[0364] A "voice interface" is a method for users and information systems to communicate using voice-based input and output.

[0365] A "visual interface" is a method of presenting information visually through graphics or displays, and is used to provide information to users.

[0366] "Means of presenting the most appropriate information and services to the user" refers to system components that include methods for presenting personalized information and services based on the user's behavioral patterns and emotional state.

[0367] To realize this application, the system consists of a server, a terminal, and an emotion engine. The server is responsible for collecting data on user behavior, preferences, environment, and emotions. The server comprehensively analyzes this diverse data and uses machine learning algorithms to predict user behavior patterns and emotional states with high accuracy. This involves real-time emotion recognition using the user's voice tone and facial expression data analyzed by the emotion engine.

[0368] The terminal uses holograms, audio, and visual interfaces to provide users with the most relevant information and services based on analysis results sent from the server. This means that when a user browses products in a virtual store, they are presented with product and promotional information tailored to their current interests and emotions.

[0369] A concrete example is a scenario where a user is using smart glasses while virtual shopping. If the user smiles while browsing fashion items, the emotion engine recognizes this as a positive emotion and sends that data to the server. The server then selects highly relevant new products and special offers and presents them to the user visually as holograms through the device. This allows the user to gain inspiration for new purchases.

[0370] An example of a prompt message might be, "Design a system that analyzes user facial expression data and recommends fashion items based on the user's interests." In this way, the present invention aims to improve the personalized user experience by comprehensively utilizing user behavior and emotional information.

[0371] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0372] Step 1:

[0373] The server collects data on user behavior, preferences, environment, and emotions. This includes user operation history, voice tone, and facial expression data. Based on this input data, the server begins to understand the user's behavior patterns and emotional state. The data is centrally managed and organized for the next analysis step.

[0374] Step 2:

[0375] The server applies machine learning algorithms to the collected data. This process analyzes behavioral patterns and emotional states to build a model that predicts the user's next interests and emotions. Inputs include historical user data and real-time data, and the predictive model is updated as output. This step, performed by the server, enables highly accurate predictions, forming the foundation for future information provision.

[0376] Step 3:

[0377] The server generates data to present the user with the most relevant information and services based on an updated predictive model. This output data includes suggestions for products and services related to the user's interests. Specifically, the server selects products based on sentiment data and sends that information to the terminal.

[0378] Step 4:

[0379] The terminal receives suggestion information sent from the server and presents it to the user through holograms, audio, and visual interfaces. This process displays information in a way that easily captures the user's attention and facilitates interaction. Specifically, products are positioned so that the hologram display is within the user's line of sight, and audio guidance is also provided.

[0380] Step 5:

[0381] Users review the information presented by the device and provide feedback on products and services that interest them. This feedback, including the user's choices and reactions, is used for future data analysis. In this step, the feedback is collected, sent back to the server, and stored in the database. The user's specific choices and requests are treated as information that contributes to future service improvements.

[0382] 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.

[0383] 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.

[0384] 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.

[0385] [Third Embodiment]

[0386] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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).

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] 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".

[0398] This invention is a system that efficiently collects and analyzes user behavior data, preference data, and environmental data to provide users with optimal information and services. This system is primarily composed of server, terminal, and user interaction.

[0399] The server aggregates data acquired from individual users and analyzes it using the latest machine learning algorithms. Based on the data analysis, the server recognizes user behavior patterns and preferences and continuously updates a model that predicts future needs. The predictive model is generated based on the user's past behavior history and environmental information.

[0400] The terminal is responsible for displaying information and services tailored to the user as a holographic interface, based on prediction results sent from the server. The terminal can provide information to the user and engage in natural dialogue through visual and auditory means. Speech recognition technology can accurately understand the user's voice instructions, and natural language processing technology can be used to generate responses.

[0401] Users interact with the presented information and services. When users input feedback into their devices, this feedback information is sent to the server and stored in a database. This information is used to make future predictions, improving the overall accuracy and personalization capabilities of the system.

[0402] As a concrete example, if a user uses this system for daily task management, the server analyzes the user's past calendar events and task management data and automatically generates a schedule that takes into account meetings and events scheduled for the next day. The terminal displays this schedule as a hologram, and the user can check and modify its contents using voice and gestures.

[0403] Thus, the present invention aims to enable efficient and intuitive service provision tailored to the individual needs of each user, and to improve the user experience by continuously learning the user's lifestyle and preferences.

[0404] The following describes the processing flow.

[0405] Step 1:

[0406] The server automatically acquires user behavior data, preference data, and environmental data from various digital devices. This includes location information, calendar events, voice input history, and application usage history.

[0407] Step 2:

[0408] The server analyzes the collected data in real time and identifies user behavior patterns. It uses machine learning algorithms to update models that predict future needs based on users' past behavior and preferences.

[0409] Step 3:

[0410] The server generates recommendations and services based on predicted needs. This information is designed to optimize the user's life and includes, for example, schedule suggestions and recommendations for using specific services.

[0411] Step 4:

[0412] The terminal visually presents the user with recommended information received from the server in hologram format. The interface used here is intuitive and also supports voice commands through the use of speech recognition technology.

[0413] Step 5:

[0414] Users provide feedback on the presented information and services using voice or gestures. This feedback includes, for example, approving, rejecting, or requesting changes to proposals.

[0415] Step 6:

[0416] The device sends user feedback to the server. This information is stored in a database and used to improve the predictive model for future use.

[0417] Step 7:

[0418] The server adjusts its analysis algorithms based on user feedback, preparing to make more accurate predictions. This continuously improves overall system performance and user experience.

[0419] (Example 1)

[0420] 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."

[0421] In today's information society, users are exposed to vast amounts of information, making it difficult to efficiently find information and services that match their interests and behaviors. Furthermore, providing information tailored to individual user needs requires accurate predictions based on various data, but there is a lack of systems that can effectively achieve this. Additionally, there is a need to continuously improve systems by incorporating user feedback.

[0422] 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.

[0423] In this invention, the server includes means for acquiring information about user behavior, preferences, and environment; means for generating a computational model for predicting user behavior patterns and interests based on the acquired information; and means for providing personalized information and services to the user based on the prediction results. This makes it possible to efficiently provide information that meets the user's needs and improve the user experience.

[0424] "Information about user behavior" refers to data that includes users' daily activities, travel history, and usage history.

[0425] "Information about preferences" refers to data that shows the areas and tastes that a user is interested in.

[0426] "Environmental information" refers to data about the physical or social environment in which the user resides.

[0427] A "computational model" is a mathematical or statistical framework used to make predictions or classifications based on acquired data.

[0428] "Personalized information and services" refer to specialized information and services provided to meet the specific needs of a user.

[0429] "Speech recognition technology" is a technology that converts the voice spoken by a user into text and understands it.

[0430] "Natural language processing technology" is a technology for analyzing user interactions using natural language and generating responses.

[0431] A "generative AI model" is an algorithm that uses artificial intelligence to create content and information for users.

[0432] This invention is a system that efficiently provides personalized information and services using diverse user data. This system mainly consists of a server, terminals, and users.

[0433] The server collects information about user behavior, preferences, and environment. This includes data from smartphones and wearable devices. The collected data is then processed using a database management system (e.g., a typical relational database), followed by data cleaning and integration.

[0434] Next, the server uses the collected data to build a computational model based on a generative AI model. This process utilizes machine learning frameworks such as Python's Scikit-learn and TensorFlow. This model is used to predict user behavior patterns and interests, thereby enabling the generation of information and services tailored to user needs.

[0435] On the other hand, the terminal receives information provided by the server and presents it to the user. The terminal is equipped with a visual display device using a holographic interface, allowing for intuitive visualization of information. Furthermore, it can engage in natural conversations with the user using speech recognition and natural language processing technologies. This allows the user to interact with the system through voice commands and easily obtain the necessary information.

[0436] After receiving the information or service provided, users send feedback to their devices. This feedback information is then sent back to the server and stored in a database. The stored feedback is used to improve the overall accuracy and personalization capabilities of the system by being utilized in subsequent predictions.

[0437] For example, if a user uses this system for daily task management, the server analyzes the user's past schedules and task management data, and automatically generates an optimal schedule considering the tasks scheduled for the following day. The terminal displays this schedule as a hologram, and the user can check and modify its contents using voice and gestures. Such features contribute to improving user convenience.

[0438] A possible input prompt for the generating AI model could be, "Based on the user's behavioral and preference data, predict the next day's schedule and display it as a hologram." This prompt allows the system to provide information tailored to the user's specific needs.

[0439] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0440] Step 1:

[0441] The server collects information about user behavior, preferences, and environment. Sensor data from smartphones and wearable devices is taken as input. Specifically, it collects GPS data, app usage history, and sensor activity data in real time. This data is integrated, stored in a database, and then processed with noise reduction and missing value imputation before outputting the refined data.

[0442] Step 2:

[0443] The server constructs a predictive algorithm using a generative AI model based on organized data stored in the database. The input consists of the user's past behavior and preference data. Specifically, it uses Python's Scikit-learn and TensorFlow to apply machine learning algorithms and learn behavioral patterns. The resulting model predicts the user's future behavior and needs.

[0444] Step 3:

[0445] The server uses a constructed predictive model to generate personalized information and services for the user. The predictive model and the user's current environment information are used as input. Specifically, the generative model automatically generates the user's schedule and suggests optimal tasks for the next day. It also prepares information in text format and converts it into a format suitable for the user. This result is then output and sent to the terminal.

[0446] Step 4:

[0447] The terminal presents information to the user visually and audibly, based on information sent from the server. Input includes personalized information and service details from the server. Specifically, it visualizes schedules using a holographic interface and uses speech recognition technology to understand user voice commands and generate responses. The user can receive the information, review its contents, and make changes as needed.

[0448] Step 5:

[0449] Users input feedback on the presented information and services into the terminal. This input can be via voice commands or text. Specifically, users express their opinions via voice or text and evaluate convenience and satisfaction. This feedback is sent to the server by the terminal.

[0450] Step 6:

[0451] The server receives feedback sent from the terminal and stores it in a database. Input includes user feedback and change history. Specifically, the stored feedback is used to improve future predictions and to refine new generative AI models. This process continuously improves the system's accuracy and personalization capabilities, enabling the delivery of better services.

[0452] (Application Example 1)

[0453] 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."

[0454] In today's world, there is a demand for information and services that accurately meet the diverse preferences and needs of consumers. However, general product information systems struggle to personalize information by fully considering the preferences and behavioral data of each individual consumer, hindering improvements in the customer experience. Therefore, there is a need for new methods that provide personalized information and products in real time based on consumer behavioral data and preferences.

[0455] 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.

[0456] In this invention, the server includes means for acquiring user behavior data, preference data, and environmental data; means for predicting the user's behavior patterns and interests based on the acquired data; means for presenting personalized information and services to the user based on the prediction results; and means for visually displaying product information to the user using an augmented reality interface. This makes it possible to provide personalized information that meets the individual needs of each consumer.

[0457] "User behavior data" refers to information collected based on consumers' location, movements, and activity logs.

[0458] "Data representing preferences" refers to data that includes consumers' interests and preferences, purchase history, browsing history, and so on.

[0459] "Environmental data" refers to information about the physical or social environment surrounding consumers, including time, weather, and congestion levels.

[0460] "Behavioral patterns" refer to consistent consumer activity and behavioral tendencies that can be inferred from past data.

[0461] "Personalized information and services" refer to information and services that are selected and provided based on the individual consumer's behavioral data and preferences.

[0462] An "augmented reality interface" is a technology that overlays digital information onto the real world and displays it visually.

[0463] "Means of visual display" refers to methods that allow information to be viewed directly with the eyes through devices such as liquid crystal displays and smart glasses.

[0464] The system of this invention functions through a server, a user's terminal, and user interaction. First, the server collects user behavior data, preference data, and environmental data in real time. This data is acquired through the user's smart glasses or mobile terminal. The server analyzes this data using machine learning algorithms to generate a model that predicts the user's behavior patterns and interests. Google Cloud ML Engine is used for this purpose.

[0465] Based on this, the server personalizes recommended products and information and creates a set of information to display via an augmented reality interface. This visual information is displayed on smart glasses and other digital devices using Unity. The terminal visually presents product information and service details as holograms, and the user interacts with them naturally using voice and gestures. This interaction is realized through speech recognition and natural language processing technologies via Dialogflow.

[0466] When a user provides feedback on products or information through the interface, the device collects that feedback and sends it to a server. This feedback is stored in the user's data set and used to make predictions for the future. For example, when a user visits a clothing store, new products are recommended based on items they have purchased in the past. In this case, the prompt might be in the format of, "How can we select recommended products based on the user's past purchase history and preference data and display them as holograms on smart glasses?"

[0467] With the above configuration, users can receive individually customized product information in real time, regardless of time or location. This invention can improve the quality of the consumer experience and increase the efficiency of services for businesses.

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] The server collects behavioral data, preference data, and environmental data received from the user's smart glasses or mobile device. This data input is real-time data from when the user uses the device. The server stores this input data in a database and prepares it for subsequent analysis.

[0471] Step 2:

[0472] The server uses machine learning algorithms to predict user behavior patterns and interests based on the collected data. The input is the data collected in step 1, and the output is a predictive model of user interests and preferences. In this step, Google Cloud ML Engine is used to analyze the data and continuously update the predictive model.

[0473] Step 3:

[0474] The server selects personalized information and recommended products for the user based on the results of the predictive model. The input is the output of the predictive model obtained in step 2, and this is used to select specific information. This information is personalized taking into account the user's preferences, and the output is recommended information and a list of products.

[0475] Step 4:

[0476] The device receives recommendation information sent from the server and prepares to display it visually to the user. This display preparation process takes the output information from step 3 as input and generates data for the augmented reality interface as output. This data is processed using Unity and displayed as a hologram on the smart glasses.

[0477] Step 5:

[0478] Users visually view information and products and interact with them using voice or gestures. User input consists of voice commands and hand movements used during the interaction, which transmit the user's intentions to the device. Using Dialogflow, user instructions are understood through speech recognition and natural language processing technologies.

[0479] Step 6:

[0480] User feedback is sent from the device to the server and stored as data for future use. The input here is the feedback provided by the user to the device, which can be used to improve the accuracy of future predictions and recommendations. The output is an updated user database, and the feedback is also used as prompt text for the generative AI model.

[0481] 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.

[0482] This invention provides a user-adaptive information delivery system that utilizes emotional data in addition to user behavioral data, preference data, and environmental data. The system includes a server, terminals, and advanced analytical functions including an emotional engine to optimize the user experience.

[0483] The server centrally manages diverse data sent by the user and uses it to learn the user's behavior patterns and emotional states. The emotion engine analyzes the user's voice tone and facial expression data to recognize the user's emotions in real time. The recognized emotion data, along with behavioral data, is sent to the server and used as material for analysis. The server uses machine learning algorithms to accumulate and analyze the user's emotional history and updates models that predict the user's expectations in future situations.

[0484] The terminal plays a role in presenting information sent from the server to the user in an intuitive and effective manner. It utilizes holograms, audio, and visual interfaces to present the most suitable information and services to the user. For example, if the terminal determines that the user is experiencing stress, it might recommend relaxation music or videos to help them relax.

[0485] Users make choices based on the information and services provided through their devices, and in some cases, provide feedback on their preferences and emotions to the device. This feedback information is then sent to a server, stored in a database, and used to improve the predictive model for future use.

[0486] For example, if an emotion is detected through a terminal while a user is working, the server considers the user's past stress levels and work performance and sends a notification to the terminal prompting them to take a break at an appropriate time. This improves the user's work-life balance and enables more efficient work. In this way, the present invention is designed to comprehensively manage user emotions and behavior and improve the user experience.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] When users use their devices in their daily lives, they provide data that includes emotions to the device through voice and camera. The device collects this data in real time and sends it to the emotion engine.

[0490] Step 2:

[0491] The emotion engine analyzes the user's voice tone and facial expression data to recognize their current emotional state. This allows it to identify emotions such as "happy" or "stressed."

[0492] Step 3:

[0493] The device transmits user behavior data, preference data, and environmental data to the server along with recognized emotion data. This allows for centralized data management and comprehensive analysis.

[0494] Step 4:

[0495] The server analyzes all received data using the latest machine learning algorithms to predict future needs based on user behavior patterns and emotional history. This prediction takes into account user preferences and emotional patterns.

[0496] Step 5:

[0497] The server generates user-optimized information and services based on the prediction results. In doing so, consideration is given to providing suggestions that align with the user's emotions.

[0498] Step 6:

[0499] The device presents information obtained from the server to the user through holograms and voice interfaces. It delivers information intuitively and effectively, tailored to the user's emotional state.

[0500] Step 7:

[0501] Users react to the information presented and provide feedback to their devices as needed. This feedback may include comments such as "This suggestion was helpful" or "I would like to see other options."

[0502] Step 8:

[0503] The device sends user feedback to the server, where it is stored in a database. The server analyzes this feedback to improve the accuracy of future predictive models.

[0504] (Example 2)

[0505] 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."

[0506] Conventional information delivery systems could only provide limited information based on user behavior and preference data, and could not adequately consider the user's emotional state. As a result, it was difficult to provide appropriate information and services that met user expectations in a timely manner, limiting the improvement of the user experience. Furthermore, the insufficient utilization of user feedback made it difficult to improve the system's predictive accuracy.

[0507] 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.

[0508] In this invention, the server includes means for acquiring user behavior data, preference data, environmental data, and emotional data; means for learning the user's behavior patterns and emotional state based on the acquired data and predicting the user's expectations; and means for presenting the user with customized information and services in an intuitive and effective manner based on the prediction results. This enables the provision of appropriate information that takes into account the user's emotional state, and by providing optimal services to individual users, the user experience can be improved.

[0509] "User behavior data" refers to information about a user's activity history, location information, and services used, and is used to analyze user behavior patterns.

[0510] "Preference data" refers to information about a user's past interests, preferences, and selection history, and is data used to understand the individual needs of each user.

[0511] "Environmental data" refers to information about the user's surroundings and conditions, including time of day, weather, and location information.

[0512] "Emotional data" refers to information about a user's psychological state extracted from their voice tone, facial expressions, and other biosignals, and is used to identify the user's emotions.

[0513] An "emotion engine" is a collection of algorithms and technologies that analyze a user's voice and facial expression data to recognize their emotions in real time.

[0514] A "generative AI model" is an artificial intelligence system that automatically generates knowledge from data and proposes optimal information and services based on the user's behavior and emotions.

[0515] A "prompt" is an instruction or question used as input to a generative AI model, and it forms the basis for the model's responses and information generation.

[0516] This invention is a user-adaptive information delivery system that optimizes the user experience by utilizing user behavior data, preference data, environmental data, and emotional data. This system consists of a server, terminals, and advanced analytical functions including an emotional engine.

[0517] The server centrally manages diverse data sent from users and learns user behavior patterns and emotional states based on this data. Specifically, a system equipped with an emotion engine analyzes the user's voice tone and facial expression data to recognize the user's emotions in real time. This recognized emotional data is sent to the server along with behavioral data. The server uses machine learning algorithms to analyze this data, accumulates the user's emotional history, and updates models that predict the user's expectations in future situations.

[0518] The terminal functions as a device that presents information transmitted from the server to the user. It uses holograms, voice interfaces, and visual interfaces to intuitively and effectively provide the user with the most suitable information and services. For example, if analysis indicates that the user is experiencing stress, the terminal can recommend relaxation music or videos.

[0519] Users can make choices regarding the information and services provided through their devices and provide feedback on their preferences and feelings. This feedback information is sent via the device to a server and stored in a database. This process helps improve future predictive models.

[0520] For example, if the system detects an emotion in a user while they are working, the server will consider their past stress levels and work performance and send a notification through their device to encourage them to take a break at an appropriate time. In this way, it is possible to improve the user's work-life balance and support efficient work progress.

[0521] Furthermore, the system uses a generative AI model to create prompts and suggest the most suitable information and services for the user. Examples of prompts include, "Please provide ways to relax, taking into account the user's current emotional state," and "Please suggest ways to streamline the next task based on the user's past data."

[0522] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0523] Step 1:

[0524] Users perform daily activities through their devices, generating behavioral data, preference data, environmental data, and emotional data. This data is collected through the device's sensors and application APIs. For example, a smartphone's microphone and camera capture voice tone and facial expressions, which are then compiled together with the user's location information and activity logs.

[0525] Step 2:

[0526] The terminal sends the collected data to the server. This transmission occurs in real time, continuously providing data to the server. The input is the user's collected data, and the output is data packets that are forwarded to the server. For example, each data point is encrypted and sent over a secure channel.

[0527] Step 3:

[0528] The server begins data analysis based on the received data. Using an emotion engine, it extracts emotional data from voice tone and facial expressions, and analyzes behavioral patterns using machine learning algorithms. The input is data from the terminal, and the output is a model of the user's emotional state and behavioral prediction. Specifically, the server stores the emotional history in a database and updates the prediction model with new data.

[0529] Step 4:

[0530] The server generates prompt messages using a generative AI model based on the analysis results. These prompt messages are designed to provide users with appropriate information and services. The input is the analyzed data and the predictive model, and the output is the prompt message. For example, a prompt such as "Please provide ways to relax, taking into account the user's current emotional state" might be generated.

[0531] Step 5:

[0532] The terminal provides information and services to the user based on generated prompt messages. These are presented in an intuitively understandable format through holograms and voice interfaces. Input is the prompt message from the server, and output is the interface content the user sees. For example, relaxing music is streamed, and relaxing images are displayed visually.

[0533] Step 6:

[0534] Users can react to the information and services presented and provide feedback. The device sends this feedback to the server, where it is stored in a database. The input is the user's feedback data, and the output is the data sent to the server. Specifically, the user evaluates their satisfaction with the displayed content, and this is used to predict improvements for the next time.

[0535] (Application Example 2)

[0536] 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."

[0537] Online shopping and virtual stores face the challenge of providing information tailored to individual user interests and emotional states, often resulting in a uniform user experience. Traditional systems recommend products based solely on explicit user preference data, making it difficult to provide dynamic information that responds to emotional changes or fleeting interests.

[0538] 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.

[0539] In this invention, the server includes means for acquiring data on user behavior, preferences, environment, and emotions; means for predicting user behavior patterns, emotional states, and interests based on the acquired data; and means for presenting the most suitable information and services to the user using holograms, voice, and visual interfaces based on the prediction results. This enables a personalized and superior user experience by providing appropriate products and services in response to the user's real-time emotional state.

[0540] "User behavior data" refers to data that shows specific actions taken by users, such as product browsing history, purchase history, and the flow of operations.

[0541] "Preference data" refers to information about user preferences and preferred attributes, such as data on favorite product categories and brands.

[0542] "Environmental data" refers to information related to the user's surroundings, including data such as the type of device being used, the time of day, and location information.

[0543] "Emotional data" refers to data that indicates the user's emotional state, representing the nature of emotions recognized in real time through information obtained from voice and facial expressions.

[0544] A "hologram" refers to a technology that generates three-dimensional images, or the resulting images themselves, and is used to present visual information to users.

[0545] A "voice interface" is a method for users and information systems to communicate using voice-based input and output.

[0546] A "visual interface" is a method of presenting information visually through graphics or displays, and is used to provide information to users.

[0547] "Means of presenting the most appropriate information and services to the user" refers to system components that include methods for presenting personalized information and services based on the user's behavioral patterns and emotional state.

[0548] To realize this application, the system consists of a server, a terminal, and an emotion engine. The server is responsible for collecting data on user behavior, preferences, environment, and emotions. The server comprehensively analyzes this diverse data and uses machine learning algorithms to predict user behavior patterns and emotional states with high accuracy. This involves real-time emotion recognition using the user's voice tone and facial expression data analyzed by the emotion engine.

[0549] The terminal uses holograms, audio, and visual interfaces to provide users with the most relevant information and services based on analysis results sent from the server. This means that when a user browses products in a virtual store, they are presented with product and promotional information tailored to their current interests and emotions.

[0550] A concrete example is a scenario where a user is using smart glasses while virtual shopping. If the user smiles while browsing fashion items, the emotion engine recognizes this as a positive emotion and sends that data to the server. The server then selects highly relevant new products and special offers and presents them to the user visually as holograms through the device. This allows the user to gain inspiration for new purchases.

[0551] An example of a prompt message might be, "Design a system that analyzes user facial expression data and recommends fashion items based on the user's interests." In this way, the present invention aims to improve the personalized user experience by comprehensively utilizing user behavior and emotional information.

[0552] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0553] Step 1:

[0554] The server collects data on user behavior, preferences, environment, and emotions. This includes user operation history, voice tone, and facial expression data. Based on this input data, the server begins to understand the user's behavior patterns and emotional state. The data is centrally managed and organized for the next analysis step.

[0555] Step 2:

[0556] The server applies machine learning algorithms to the collected data. This process analyzes behavioral patterns and emotional states to build a model that predicts the user's next interests and emotions. Inputs include historical user data and real-time data, and the predictive model is updated as output. This step, performed by the server, enables highly accurate predictions, forming the foundation for future information provision.

[0557] Step 3:

[0558] The server generates data to present the user with the most relevant information and services based on an updated predictive model. This output data includes suggestions for products and services related to the user's interests. Specifically, the server selects products based on sentiment data and sends that information to the terminal.

[0559] Step 4:

[0560] The terminal receives suggestion information sent from the server and presents it to the user through holograms, audio, and visual interfaces. This process displays information in a way that easily captures the user's attention and facilitates interaction. Specifically, products are positioned so that the hologram display is within the user's line of sight, and audio guidance is also provided.

[0561] Step 5:

[0562] Users review the information presented by the device and provide feedback on products and services that interest them. This feedback, including the user's choices and reactions, is used for future data analysis. In this step, the feedback is collected, sent back to the server, and stored in the database. The user's specific choices and requests are treated as information that contributes to future service improvements.

[0563] 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.

[0564] 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.

[0565] 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.

[0566] [Fourth Embodiment]

[0567] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0568] 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.

[0569] 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).

[0570] 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.

[0571] 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.

[0572] 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).

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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".

[0580] This invention is a system that efficiently collects and analyzes user behavior data, preference data, and environmental data to provide users with optimal information and services. This system is primarily composed of server, terminal, and user interaction.

[0581] The server aggregates data acquired from individual users and analyzes it using the latest machine learning algorithms. Based on the data analysis, the server recognizes user behavior patterns and preferences and continuously updates a model that predicts future needs. The predictive model is generated based on the user's past behavior history and environmental information.

[0582] The terminal is responsible for displaying information and services tailored to the user as a holographic interface, based on prediction results sent from the server. The terminal can provide information to the user and engage in natural dialogue through visual and auditory means. Speech recognition technology can accurately understand the user's voice instructions, and natural language processing technology can be used to generate responses.

[0583] Users interact with the presented information and services. When users input feedback into their devices, this feedback information is sent to the server and stored in a database. This information is used to make future predictions, improving the overall accuracy and personalization capabilities of the system.

[0584] As a concrete example, if a user uses this system for daily task management, the server analyzes the user's past calendar events and task management data and automatically generates a schedule that takes into account meetings and events scheduled for the next day. The terminal displays this schedule as a hologram, and the user can check and modify its contents using voice and gestures.

[0585] Thus, the present invention aims to enable efficient and intuitive service provision tailored to the individual needs of each user, and to improve the user experience by continuously learning the user's lifestyle and preferences.

[0586] The following describes the processing flow.

[0587] Step 1:

[0588] The server automatically acquires user behavior data, preference data, and environmental data from various digital devices. This includes location information, calendar events, voice input history, and application usage history.

[0589] Step 2:

[0590] The server analyzes the collected data in real time and identifies user behavior patterns. It uses machine learning algorithms to update models that predict future needs based on users' past behavior and preferences.

[0591] Step 3:

[0592] The server generates recommendations and services based on predicted needs. This information is designed to optimize the user's life and includes, for example, schedule suggestions and recommendations for using specific services.

[0593] Step 4:

[0594] The terminal visually presents the user with recommended information received from the server in hologram format. The interface used here is intuitive and also supports voice commands through the use of speech recognition technology.

[0595] Step 5:

[0596] Users provide feedback on the presented information and services using voice or gestures. This feedback includes, for example, approving, rejecting, or requesting changes to proposals.

[0597] Step 6:

[0598] The device sends user feedback to the server. This information is stored in a database and used to improve the predictive model for future use.

[0599] Step 7:

[0600] The server adjusts its analysis algorithms based on user feedback, preparing to make more accurate predictions. This continuously improves overall system performance and user experience.

[0601] (Example 1)

[0602] 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".

[0603] In today's information society, users are exposed to vast amounts of information, making it difficult to efficiently find information and services that match their interests and behaviors. Furthermore, providing information tailored to individual user needs requires accurate predictions based on various data, but there is a lack of systems that can effectively achieve this. Additionally, there is a need to continuously improve systems by incorporating user feedback.

[0604] 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.

[0605] In this invention, the server includes means for acquiring information about user behavior, preferences, and environment; means for generating a computational model for predicting user behavior patterns and interests based on the acquired information; and means for providing personalized information and services to the user based on the prediction results. This makes it possible to efficiently provide information that meets the user's needs and improve the user experience.

[0606] "Information about user behavior" refers to data that includes users' daily activities, travel history, and usage history.

[0607] "Information about preferences" refers to data that shows the areas and tastes that a user is interested in.

[0608] "Environmental information" refers to data about the physical or social environment in which the user resides.

[0609] A "computational model" is a mathematical or statistical framework used to make predictions or classifications based on acquired data.

[0610] "Personalized information and services" refer to specialized information and services provided to meet the specific needs of a user.

[0611] "Speech recognition technology" is a technology that converts the voice spoken by a user into text and understands it.

[0612] "Natural language processing technology" is a technology for analyzing user interactions using natural language and generating responses.

[0613] A "generative AI model" is an algorithm that uses artificial intelligence to create content and information for users.

[0614] This invention is a system that efficiently provides personalized information and services using diverse user data. This system mainly consists of a server, terminals, and users.

[0615] The server collects information about user behavior, preferences, and environment. This includes data from smartphones and wearable devices. The collected data is then processed using a database management system (e.g., a typical relational database), followed by data cleaning and integration.

[0616] Next, the server uses the collected data to build a computational model based on a generative AI model. This process utilizes machine learning frameworks such as Python's Scikit-learn and TensorFlow. This model is used to predict user behavior patterns and interests, thereby enabling the generation of information and services tailored to user needs.

[0617] On the other hand, the terminal receives information provided by the server and presents it to the user. The terminal is equipped with a visual display device using a holographic interface, allowing for intuitive visualization of information. Furthermore, it can engage in natural conversations with the user using speech recognition and natural language processing technologies. This allows the user to interact with the system through voice commands and easily obtain the necessary information.

[0618] After receiving the information or service provided, users send feedback to their devices. This feedback information is then sent back to the server and stored in a database. The stored feedback is used to improve the overall accuracy and personalization capabilities of the system by being utilized in subsequent predictions.

[0619] For example, if a user uses this system for daily task management, the server analyzes the user's past schedules and task management data, and automatically generates an optimal schedule considering the tasks scheduled for the following day. The terminal displays this schedule as a hologram, and the user can check and modify its contents using voice and gestures. Such features contribute to improving user convenience.

[0620] A possible input prompt for the generating AI model could be, "Based on the user's behavioral and preference data, predict the next day's schedule and display it as a hologram." This prompt allows the system to provide information tailored to the user's specific needs.

[0621] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0622] Step 1:

[0623] The server collects information about user behavior, preferences, and environment. Sensor data from smartphones and wearable devices is taken as input. Specifically, it collects GPS data, app usage history, and sensor activity data in real time. This data is integrated, stored in a database, and then processed with noise reduction and missing value imputation before outputting the refined data.

[0624] Step 2:

[0625] The server constructs a predictive algorithm using a generative AI model based on organized data stored in the database. The input consists of the user's past behavior and preference data. Specifically, it uses Python's Scikit-learn and TensorFlow to apply machine learning algorithms and learn behavioral patterns. The resulting model predicts the user's future behavior and needs.

[0626] Step 3:

[0627] The server uses a constructed predictive model to generate personalized information and services for the user. The predictive model and the user's current environment information are used as input. Specifically, the generative model automatically generates the user's schedule and suggests optimal tasks for the next day. It also prepares information in text format and converts it into a format suitable for the user. This result is then output and sent to the terminal.

[0628] Step 4:

[0629] The terminal presents information to the user visually and audibly, based on information sent from the server. Input includes personalized information and service details from the server. Specifically, it visualizes schedules using a holographic interface and uses speech recognition technology to understand user voice commands and generate responses. The user can receive the information, review its contents, and make changes as needed.

[0630] Step 5:

[0631] Users input feedback on the presented information and services into the terminal. This input can be via voice commands or text. Specifically, users express their opinions via voice or text and evaluate convenience and satisfaction. This feedback is sent to the server by the terminal.

[0632] Step 6:

[0633] The server receives feedback sent from the terminal and stores it in a database. Input includes user feedback and change history. Specifically, the stored feedback is used to improve future predictions and to refine new generative AI models. This process continuously improves the system's accuracy and personalization capabilities, enabling the delivery of better services.

[0634] (Application Example 1)

[0635] 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".

[0636] In today's world, there is a demand for information and services that accurately meet the diverse preferences and needs of consumers. However, general product information systems struggle to personalize information by fully considering the preferences and behavioral data of each individual consumer, hindering improvements in the customer experience. Therefore, there is a need for new methods that provide personalized information and products in real time based on consumer behavioral data and preferences.

[0637] 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.

[0638] In this invention, the server includes means for acquiring user behavior data, preference data, and environmental data; means for predicting the user's behavior patterns and interests based on the acquired data; means for presenting personalized information and services to the user based on the prediction results; and means for visually displaying product information to the user using an augmented reality interface. This makes it possible to provide personalized information that meets the individual needs of each consumer.

[0639] "User behavior data" refers to information collected based on consumers' location, movements, and activity logs.

[0640] "Data representing preferences" refers to data that includes consumers' interests and preferences, purchase history, browsing history, and so on.

[0641] "Environmental data" refers to information about the physical or social environment surrounding consumers, including time, weather, and congestion levels.

[0642] "Behavioral patterns" refer to consistent consumer activity and behavioral tendencies that can be inferred from past data.

[0643] "Personalized information and services" refer to information and services that are selected and provided based on the individual consumer's behavioral data and preferences.

[0644] An "augmented reality interface" is a technology that overlays digital information onto the real world and displays it visually.

[0645] "Means of visual display" refers to methods that allow information to be viewed directly with the eyes through devices such as liquid crystal displays and smart glasses.

[0646] The system of this invention functions through a server, a user's terminal, and user interaction. First, the server collects user behavior data, preference data, and environmental data in real time. This data is acquired through the user's smart glasses or mobile terminal. The server analyzes this data using machine learning algorithms to generate a model that predicts the user's behavior patterns and interests. Google Cloud ML Engine is used for this purpose.

[0647] Based on this, the server personalizes recommended products and information and creates a set of information to display via an augmented reality interface. This visual information is displayed on smart glasses and other digital devices using Unity. The terminal visually presents product information and service details as holograms, and the user interacts with them naturally using voice and gestures. This interaction is realized through speech recognition and natural language processing technologies via Dialogflow.

[0648] When a user provides feedback on products or information through the interface, the device collects that feedback and sends it to a server. This feedback is stored in the user's data set and used to make predictions for the future. For example, when a user visits a clothing store, new products are recommended based on items they have purchased in the past. In this case, the prompt might be in the format of, "How can we select recommended products based on the user's past purchase history and preference data and display them as holograms on smart glasses?"

[0649] With the above configuration, users can receive individually customized product information in real time, regardless of time or location. This invention can improve the quality of the consumer experience and increase the efficiency of services for businesses.

[0650] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0651] Step 1:

[0652] The server collects behavioral data, preference data, and environmental data received from the user's smart glasses or mobile device. This data input is real-time data from when the user uses the device. The server stores this input data in a database and prepares it for subsequent analysis.

[0653] Step 2:

[0654] The server uses machine learning algorithms to predict user behavior patterns and interests based on the collected data. The input is the data collected in step 1, and the output is a predictive model of user interests and preferences. In this step, Google Cloud ML Engine is used to analyze the data and continuously update the predictive model.

[0655] Step 3:

[0656] The server selects personalized information and recommended products for the user based on the results of the predictive model. The input is the output of the predictive model obtained in step 2, and this is used to select specific information. This information is personalized taking into account the user's preferences, and the output is recommended information and a list of products.

[0657] Step 4:

[0658] The device receives recommendation information sent from the server and prepares to display it visually to the user. This display preparation process takes the output information from step 3 as input and generates data for the augmented reality interface as output. This data is processed using Unity and displayed as a hologram on the smart glasses.

[0659] Step 5:

[0660] Users visually view information and products and interact with them using voice or gestures. User input consists of voice commands and hand movements used during the interaction, which transmit the user's intentions to the device. Using Dialogflow, user instructions are understood through speech recognition and natural language processing technologies.

[0661] Step 6:

[0662] User feedback is sent from the device to the server and stored as data for future use. The input here is the feedback provided by the user to the device, which can be used to improve the accuracy of future predictions and recommendations. The output is an updated user database, and the feedback is also used as prompt text for the generative AI model.

[0663] 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.

[0664] This invention provides a user-adaptive information delivery system that utilizes emotional data in addition to user behavioral data, preference data, and environmental data. The system includes a server, terminals, and advanced analytical functions including an emotional engine to optimize the user experience.

[0665] The server centrally manages diverse data sent by the user and uses it to learn the user's behavior patterns and emotional states. The emotion engine analyzes the user's voice tone and facial expression data to recognize the user's emotions in real time. The recognized emotion data, along with behavioral data, is sent to the server and used as material for analysis. The server uses machine learning algorithms to accumulate and analyze the user's emotional history and updates models that predict the user's expectations in future situations.

[0666] The terminal plays a role in presenting information sent from the server to the user in an intuitive and effective manner. It utilizes holograms, audio, and visual interfaces to present the most suitable information and services to the user. For example, if the terminal determines that the user is experiencing stress, it might recommend relaxation music or videos to help them relax.

[0667] Users make choices based on the information and services provided through their devices, and in some cases, provide feedback on their preferences and emotions to the device. This feedback information is then sent to a server, stored in a database, and used to improve the predictive model for future use.

[0668] For example, if an emotion is detected through a terminal while a user is working, the server considers the user's past stress levels and work performance and sends a notification to the terminal prompting them to take a break at an appropriate time. This improves the user's work-life balance and enables more efficient work. In this way, the present invention is designed to comprehensively manage user emotions and behavior and improve the user experience.

[0669] The following describes the processing flow.

[0670] Step 1:

[0671] When users use their devices in their daily lives, they provide data that includes emotions to the device through voice and camera. The device collects this data in real time and sends it to the emotion engine.

[0672] Step 2:

[0673] The emotion engine analyzes the user's voice tone and facial expression data to recognize their current emotional state. This allows it to identify emotions such as "happy" or "stressed."

[0674] Step 3:

[0675] The device transmits user behavior data, preference data, and environmental data to the server along with recognized emotion data. This allows for centralized data management and comprehensive analysis.

[0676] Step 4:

[0677] The server analyzes all received data using the latest machine learning algorithms to predict future needs based on user behavior patterns and emotional history. This prediction takes into account user preferences and emotional patterns.

[0678] Step 5:

[0679] The server generates user-optimized information and services based on the prediction results. In doing so, consideration is given to providing suggestions that align with the user's emotions.

[0680] Step 6:

[0681] The device presents information obtained from the server to the user through holograms and voice interfaces. It delivers information intuitively and effectively, tailored to the user's emotional state.

[0682] Step 7:

[0683] Users react to the information presented and provide feedback to their devices as needed. This feedback may include comments such as "This suggestion was helpful" or "I would like to see other options."

[0684] Step 8:

[0685] The device sends user feedback to the server, where it is stored in a database. The server analyzes this feedback to improve the accuracy of future predictive models.

[0686] (Example 2)

[0687] 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".

[0688] Conventional information delivery systems could only provide limited information based on user behavior and preference data, and could not adequately consider the user's emotional state. As a result, it was difficult to provide appropriate information and services that met user expectations in a timely manner, limiting the improvement of the user experience. Furthermore, the insufficient utilization of user feedback made it difficult to improve the system's predictive accuracy.

[0689] 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.

[0690] In this invention, the server includes means for acquiring user behavior data, preference data, environmental data, and emotional data; means for learning the user's behavior patterns and emotional state based on the acquired data and predicting the user's expectations; and means for presenting the user with customized information and services in an intuitive and effective manner based on the prediction results. This enables the provision of appropriate information that takes into account the user's emotional state, and by providing optimal services to individual users, the user experience can be improved.

[0691] "User behavior data" refers to information about a user's activity history, location information, and services used, and is used to analyze user behavior patterns.

[0692] "Preference data" refers to information about a user's past interests, preferences, and selection history, and is data used to understand the individual needs of each user.

[0693] "Environmental data" refers to information about the user's surroundings and conditions, including time of day, weather, and location information.

[0694] "Emotional data" refers to information about a user's psychological state extracted from their voice tone, facial expressions, and other biosignals, and is used to identify the user's emotions.

[0695] An "emotion engine" is a collection of algorithms and technologies that analyze a user's voice and facial expression data to recognize their emotions in real time.

[0696] A "generative AI model" is an artificial intelligence system that automatically generates knowledge from data and proposes optimal information and services based on the user's behavior and emotions.

[0697] A "prompt" is an instruction or question used as input to a generative AI model, and it forms the basis for the model's responses and information generation.

[0698] This invention is a user-adaptive information delivery system that optimizes the user experience by utilizing user behavior data, preference data, environmental data, and emotional data. This system consists of a server, terminals, and advanced analytical functions including an emotional engine.

[0699] The server centrally manages diverse data sent from users and learns user behavior patterns and emotional states based on this data. Specifically, a system equipped with an emotion engine analyzes the user's voice tone and facial expression data to recognize the user's emotions in real time. This recognized emotional data is sent to the server along with behavioral data. The server uses machine learning algorithms to analyze this data, accumulates the user's emotional history, and updates models that predict the user's expectations in future situations.

[0700] The terminal functions as a device that presents information transmitted from the server to the user. It uses holograms, voice interfaces, and visual interfaces to intuitively and effectively provide the user with the most suitable information and services. For example, if analysis indicates that the user is experiencing stress, the terminal can recommend relaxation music or videos.

[0701] Users can make choices regarding the information and services provided through their devices and provide feedback on their preferences and feelings. This feedback information is sent via the device to a server and stored in a database. This process helps improve future predictive models.

[0702] For example, if the system detects an emotion in a user while they are working, the server will consider their past stress levels and work performance and send a notification through their device to encourage them to take a break at an appropriate time. In this way, it is possible to improve the user's work-life balance and support efficient work progress.

[0703] Furthermore, the system uses a generative AI model to create prompts and suggest the most suitable information and services for the user. Examples of prompts include, "Please provide ways to relax, taking into account the user's current emotional state," and "Please suggest ways to streamline the next task based on the user's past data."

[0704] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0705] Step 1:

[0706] Users perform daily activities through their devices, generating behavioral data, preference data, environmental data, and emotional data. This data is collected through the device's sensors and application APIs. For example, a smartphone's microphone and camera capture voice tone and facial expressions, which are then compiled together with the user's location information and activity logs.

[0707] Step 2:

[0708] The terminal sends the collected data to the server. This transmission occurs in real time, continuously providing data to the server. The input is the user's collected data, and the output is data packets that are forwarded to the server. For example, each data point is encrypted and sent over a secure channel.

[0709] Step 3:

[0710] The server begins data analysis based on the received data. Using an emotion engine, it extracts emotional data from voice tone and facial expressions, and analyzes behavioral patterns using machine learning algorithms. The input is data from the terminal, and the output is a model of the user's emotional state and behavioral prediction. Specifically, the server stores the emotional history in a database and updates the prediction model with new data.

[0711] Step 4:

[0712] The server generates prompt messages using a generative AI model based on the analysis results. These prompt messages are designed to provide users with appropriate information and services. The input is the analyzed data and the predictive model, and the output is the prompt message. For example, a prompt such as "Please provide ways to relax, taking into account the user's current emotional state" might be generated.

[0713] Step 5:

[0714] The terminal provides information and services to the user based on generated prompt messages. These are presented in an intuitively understandable format through holograms and voice interfaces. Input is the prompt message from the server, and output is the interface content the user sees. For example, relaxing music is streamed, and relaxing images are displayed visually.

[0715] Step 6:

[0716] Users can react to the information and services presented and provide feedback. The device sends this feedback to the server, where it is stored in a database. The input is the user's feedback data, and the output is the data sent to the server. Specifically, the user evaluates their satisfaction with the displayed content, and this is used to predict improvements for the next time.

[0717] (Application Example 2)

[0718] 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".

[0719] Online shopping and virtual stores face the challenge of providing information tailored to individual user interests and emotional states, often resulting in a uniform user experience. Traditional systems recommend products based solely on explicit user preference data, making it difficult to provide dynamic information that responds to emotional changes or fleeting interests.

[0720] 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.

[0721] In this invention, the server includes means for acquiring data on user behavior, preferences, environment, and emotions; means for predicting user behavior patterns, emotional states, and interests based on the acquired data; and means for presenting the most suitable information and services to the user using holograms, voice, and visual interfaces based on the prediction results. This enables a personalized and superior user experience by providing appropriate products and services in response to the user's real-time emotional state.

[0722] "User behavior data" refers to data that shows specific actions taken by users, such as product browsing history, purchase history, and the flow of operations.

[0723] "Preference data" refers to information about user preferences and preferred attributes, such as data on favorite product categories and brands.

[0724] "Environmental data" refers to information related to the user's surroundings, including data such as the type of device being used, the time of day, and location information.

[0725] "Emotional data" refers to data that indicates the user's emotional state, representing the nature of emotions recognized in real time through information obtained from voice and facial expressions.

[0726] A "hologram" refers to a technology that generates three-dimensional images, or the resulting images themselves, and is used to present visual information to users.

[0727] A "voice interface" is a method for users and information systems to communicate using voice-based input and output.

[0728] A "visual interface" is a method of presenting information visually through graphics or displays, and is used to provide information to users.

[0729] "Means of presenting the most appropriate information and services to the user" refers to system components that include methods for presenting personalized information and services based on the user's behavioral patterns and emotional state.

[0730] To realize this application, the system consists of a server, a terminal, and an emotion engine. The server is responsible for collecting data on user behavior, preferences, environment, and emotions. The server comprehensively analyzes this diverse data and uses machine learning algorithms to predict user behavior patterns and emotional states with high accuracy. This involves real-time emotion recognition using the user's voice tone and facial expression data analyzed by the emotion engine.

[0731] The terminal uses holograms, audio, and visual interfaces to provide users with the most relevant information and services based on analysis results sent from the server. This means that when a user browses products in a virtual store, they are presented with product and promotional information tailored to their current interests and emotions.

[0732] A concrete example is a scenario where a user is using smart glasses while virtual shopping. If the user smiles while browsing fashion items, the emotion engine recognizes this as a positive emotion and sends that data to the server. The server then selects highly relevant new products and special offers and presents them to the user visually as holograms through the device. This allows the user to gain inspiration for new purchases.

[0733] An example of a prompt message might be, "Design a system that analyzes user facial expression data and recommends fashion items based on the user's interests." In this way, the present invention aims to improve the personalized user experience by comprehensively utilizing user behavior and emotional information.

[0734] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0735] Step 1:

[0736] The server collects data on user behavior, preferences, environment, and emotions. This includes user operation history, voice tone, and facial expression data. Based on this input data, the server begins to understand the user's behavior patterns and emotional state. The data is centrally managed and organized for the next analysis step.

[0737] Step 2:

[0738] The server applies machine learning algorithms to the collected data. This process analyzes behavioral patterns and emotional states to build a model that predicts the user's next interests and emotions. Inputs include historical user data and real-time data, and the predictive model is updated as output. This step, performed by the server, enables highly accurate predictions, forming the foundation for future information provision.

[0739] Step 3:

[0740] The server generates data to present the user with the most relevant information and services based on an updated predictive model. This output data includes suggestions for products and services related to the user's interests. Specifically, the server selects products based on sentiment data and sends that information to the terminal.

[0741] Step 4:

[0742] The terminal receives suggestion information sent from the server and presents it to the user through holograms, audio, and visual interfaces. This process displays information in a way that easily captures the user's attention and facilitates interaction. Specifically, products are positioned so that the hologram display is within the user's line of sight, and audio guidance is also provided.

[0743] Step 5:

[0744] Users review the information presented by the device and provide feedback on products and services that interest them. This feedback, including the user's choices and reactions, is used for future data analysis. In this step, the feedback is collected, sent back to the server, and stored in the database. The user's specific choices and requests are treated as information that contributes to future service improvements.

[0745] 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.

[0746] 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.

[0747] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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."

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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.

[0763] 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.

[0764] 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.

[0765] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0766] The following is further disclosed regarding the embodiments described above.

[0767] (Claim 1)

[0768] Means for acquiring user behavior data, preference data, and environmental data,

[0769] A means of predicting user behavior patterns and interests based on the acquired data,

[0770] A means for presenting customized information and services to the user based on the aforementioned prediction results,

[0771] A system that includes this.

[0772] (Claim 2)

[0773] The system according to claim 1, comprising means for realizing natural dialogue with a user using speech recognition technology and natural language processing technology.

[0774] (Claim 3)

[0775] The system according to claim 1, comprising means for collecting user feedback, storing it in a user database, and using it for future predictions.

[0776] "Example 1"

[0777] (Claim 1)

[0778] Means for obtaining information about user behavior, preferences, and environment,

[0779] A means for generating a computational model to predict user behavior patterns and interests based on the acquired information,

[0780] A means for providing personalized information and services to the user based on the aforementioned prediction results,

[0781] Means including a display device for presenting the aforementioned information by visual and auditory means,

[0782] A means for obtaining user feedback, storing it in the data storage device, and using it for future predictions,

[0783] A system that includes this.

[0784] (Claim 2)

[0785] The system according to claim 1, comprising means for enabling natural conversation with a user using speech recognition technology and natural language processing technology.

[0786] (Claim 3)

[0787] The system according to claim 1, comprising means for generating user-optimized information using a generative AI model.

[0788] "Application Example 1"

[0789] (Claim 1)

[0790] Means for acquiring user behavior data, preference data, and environmental data,

[0791] A means of predicting user behavior patterns and interests based on the acquired data,

[0792] A means for presenting personalized information and services to the user based on the aforementioned prediction results,

[0793] A means of visually displaying product information to the user using an augmented reality interface,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, comprising means for realizing natural dialogue with a user using speech recognition technology and natural language processing technology.

[0797] (Claim 3)

[0798] The system according to claim 1, comprising means for collecting feedback from users, storing it in a user data set, and using it for future predictions.

[0799] "Example 2 of combining an emotion engine"

[0800] (Claim 1)

[0801] Means for acquiring user behavior data, preference data, environmental data, and emotional data,

[0802] A means for learning user behavior patterns and emotional states based on the acquired data and predicting user expectations,

[0803] A means for presenting users with customized information and services in an intuitive and effective manner based on the aforementioned prediction results,

[0804] A method for collecting user feedback, storing it in a database, and using it for future predictions,

[0805] A system that includes this.

[0806] (Claim 2)

[0807] The system according to claim 1, comprising means for realizing natural dialogue with a user using speech recognition technology, natural language processing technology, and emotion recognition technology.

[0808] (Claim 3)

[0809] The system according to claim 1, comprising means for generating prompt sentences based on acquired behavioral data, preference data, environmental data, and emotional data using a generative AI model, and for proposing the most suitable information and services to the user.

[0810] "Application example 2 when combining with an emotional engine"

[0811] (Claim 1)

[0812] Means for acquiring data on user behavior, preferences, and environment,

[0813] A means for predicting user behavior patterns, emotional states, and interests based on the acquired data and emotional data,

[0814] A means of presenting the most suitable information and services to the user using a hologram, audio, and visual interface based on the aforementioned prediction results,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, comprising means for realizing a natural conversation with a user using speech analysis and natural language processing, and means for recognizing the user's emotions in real time and suggesting relevant products.

[0818] (Claim 3)

[0819] The system according to claim 1, comprising means for collecting user feedback, storing the data in the user's data storage area, and utilizing it in the next predictive model. [Explanation of symbols]

[0820] 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. Means for acquiring user behavior data, preference data, and environmental data, A means of predicting user behavior patterns and interests based on the acquired data, A means for presenting customized information and services to the user based on the aforementioned prediction results, A system that includes this.

2. The system according to claim 1, comprising means for realizing natural dialogue with a user using speech recognition technology and natural language processing technology.

3. The system according to claim 1, comprising means for collecting user feedback, storing it in a user database, and using it for future predictions.

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