system

Abstract

We provide the system. [Solution] Device and means for acquiring data on the user's surrounding environment and activities, Functional means for encrypting acquired data and transmitting it to a central device via a communication network, A method and means for a central device to analyze transmitted data and generate output information based on the user's activity status, A terminal device means for presenting the generated output information to the user in audio or visual form, 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional interactive artificial intelligence systems have a problem in that they cannot start a conversation without an explicit instruction or question from a user, and it is difficult to promote convenience and natural conversation in the user's daily life. Also, since it has been difficult to provide information timely and appropriately based on the user situation, it is necessary to realize a personalized conversation experience based on the user's activity situation.

Means for Solving the Problems

[0005] This invention provides a device that acquires data on the user's surrounding environment and activities, thereby understanding the user's situation in real time. The acquired data is encrypted and transmitted to a central device via a communication network. The central device analyzes the transmitted data using machine learning algorithms and generates output information based on the user's activity status. This information is presented to the user at the optimal timing, either audibly or visually, thereby promoting a natural conversational experience for the user.

[0006] "Users" refers to individuals or organizations that use the system.

[0007] "Surrounding environment" refers to the physical location and circumstances in which the user currently exists.

[0008] "Activity data" refers to data that includes information related to the user's behavior, movements, schedule, etc.

[0009] A "device" is a hardware or software component designed to perform a specific function.

[0010] "Encryption" is the act of transforming data using a specific algorithm in order to securely protect the data.

[0011] A "communication network" refers to a collection of computers and devices connected for the purpose of sending and receiving data.

[0012] The term "central system" refers to the computational infrastructure used for data analysis and information generation.

[0013] "Analysis" refers to the process of converting received data into information and obtaining usable conclusions and insights.

[0014] A "machine learning algorithm" is a computational method that automatically learns patterns and knowledge from data to perform predictions and classifications.

[0015] "Output information" refers to the information generated as a result of analysis and provided to the user.

[0016] "Timing" means the point or period when a specific action or event occurs. [[ID=VIII]]

[0017] [[ID=IX]] "Presented audibly or visually" refers to making information audible to the user as sound or visible as vision on a screen or the like.

[0018] "Dialogue experience" represents the overall process and quality of communication between the user and the system.

Brief Description of Drawings

[0019] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0020] 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.

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

[0022] 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.

[0023] 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.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] 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).

[0026] 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."

[0027] [First Embodiment]

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

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0034] 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.

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

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] This invention is a system in which a terminal and a server work together to provide personalized information to the user. The terminal is equipped with sensors and a microphone necessary to acquire data on the user's surrounding environment and activities, and has the function to collect this data in real time. Specifically, the terminal collects the user's GPS information, date, time, voice data, etc., and also retrieves schedule information from calendar applications, etc.

[0041] The data collected by the device undergoes initial processing locally to remove unnecessary noise. Following this, the data is encrypted to protect privacy and transmitted to the server via the communication network. The server analyzes all received data, using machine learning algorithms to understand the user's behavior patterns and current context. Through this analysis, a foundation is formed for generating useful output information for the user.

[0042] As a concrete example, the device determines the user's location during their commute, and the server generates promotional information for nearby cafes based on that information. This information is then presented as a notification at an appropriate time so that the user can refresh themselves along the way.

[0043] The server sends the generated output information back to the terminal, which then presents it to the user through voice announcements and screen displays, enabling appropriate and natural dialogue. Through this cycle, the system provides the user with a rich and personalized dialogue experience.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The device collects data on the user's surroundings and activities using sensors and a microphone. This data includes location information from GPS, voice input, time, date, and schedule information from scheduling applications.

[0047] Step 2:

[0048] The device performs initial processing on the collected data. Specifically, it performs noise reduction on the audio data and verifies that the location information is accurate. At this stage, it also filters out unimportant data and selects only the necessary data.

[0049] Step 3:

[0050] The terminal encrypts the processed data and sends it to the server while maintaining privacy. This transmission is performed using a secure communication protocol.

[0051] Step 4:

[0052] The server decodes and analyzes the received data. Here, machine learning algorithms are applied to recognize user behavior patterns and understand the current context. This process accurately grasps the user's needs and interests.

[0053] Step 5:

[0054] The server generates optimal output information for the user based on the analysis results. This information is personalized according to the user's current activities and interests, and may include, for example, weather forecasts, schedule reminders, and new product information.

[0055] Step 6:

[0056] The server sends the generated output information to the terminal. The data sent at this time is organized in a format that allows users to easily consume the information.

[0057] Step 7:

[0058] The device presents received information to the user via voice announcement or display on the screen. The timing and format of the presentation are optimized according to the user's situation.

[0059] Step 8:

[0060] Users can respond to the presented information using voice or touch interfaces. This response helps in further data collection and analysis in the next cycle.

[0061] (Example 1)

[0062] 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."

[0063] Traditionally, information presentation systems for users have faced challenges in adequately generating and providing personalized information, making it difficult to deliver timely information tailored to users' activities and circumstances. Furthermore, there is a need for effective means to perform highly accurate behavioral pattern analysis while protecting user privacy.

[0064] 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.

[0065] In this invention, the server includes terminal device means for acquiring environmental and activity data based on the user's location, time, and voice; functional means for denoising and encrypting the acquired data and transmitting it to a central processing unit via communication means; and method means for the central processing unit to analyze the received data, understand the user's behavior patterns and context through a learning algorithm, and generate output information. This makes it possible to provide users with personalized information appropriately in real time.

[0066] A "terminal device" is a device used to acquire user environment and activity data, and has the function of acquiring location information, voice data, etc.

[0067] "Communication means" refers to the technologies and protocols used to securely transmit acquired data to a central processing unit, and includes data encryption and transmission.

[0068] A "central processing unit" is a device that receives and analyzes data transmitted from terminal devices to understand user behavior patterns, and generates output information using learning algorithms.

[0069] A "learning algorithm" is an analytical method used to analyze received data and understand user behavior patterns and context, and is based on artificial intelligence technology.

[0070] A "generative artificial intelligence model" is a technology that generates prompt messages using received data and analysis results, with the aim of providing users with the most relevant information.

[0071] A "control unit" is a device or mechanism that determines the optimal timing for presenting generated output information to the user, and it operates based on the user's current actions and location information.

[0072] This invention is a system that presents personalized information to users through the cooperation of a terminal device and a central processing unit. The terminal device is equipped with a location information acquisition device, a voice collection device, and an accelerometer, which are used to acquire the user's environment and activity data in real time. This information is acquired on a general-purpose computing device such as a smartphone or tablet.

[0073] The terminal first performs initial processing on the collected data, such as noise reduction and format conversion. This initial processing includes noise reduction from audio data and formatting of GPS data. Once processing is complete, the data is encrypted and transmitted to the central processing unit via a communication method. SSL / TLS technology is used to ensure the security of the data during this process.

[0074] The central processing unit (CCU) is responsible for analyzing the received data. It uses machine learning algorithms to understand user behavior patterns and current situations, and generates appropriate information. This includes predictive analytics based on user location and past behavior history, utilizing software libraries such as TENSORFLOW® and PyTorch.

[0075] The generated information is constructed into specific messages using a generative artificial intelligence model. For example, if a user is in a specific location, information about appropriate nearby locations is generated based on a specific prompt such as, "Tell me about recommended cafes on my next commute route." This generated information is then notified to the user at the optimal time.

[0076] The server sends the generated information to the terminal device, which then presents it to the user via voice assistance functions and a display. Google® Text-to-Speech is used for speech synthesis, providing natural-sounding guidance. Through this entire process, the system can provide users with a highly personalized information experience that improves their quality of life.

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

[0078] Step 1:

[0079] The device communicates with sensors to acquire user environment data. Location information is obtained from the GPS sensor, and voice data is obtained from the microphone. Inputs include the current time, location, and voice data, which are output as acquired data.

[0080] Step 2:

[0081] The device performs initial processing on the acquired data, including noise reduction and format conversion. Specifically, it removes unwanted background noise from the audio data and formats the location information into a standard format. The input is the acquired data, and the output is the formatted data.

[0082] Step 3:

[0083] The terminal encrypts the formatted data and sends it to the server using a communication method. The data is encrypted using SSL / TLS and protected from eavesdropping by third parties. The input is formatted data, and the output is encrypted data.

[0084] Step 4:

[0085] The server receives encrypted data, decrypts it, and performs analysis. It uses machine learning algorithms to extract user behavior patterns and current context. The input is encrypted data, and the output is behavioral prediction information as a result of the analysis.

[0086] Step 5:

[0087] The server uses an AI model based on the analysis results to create prompt messages and generate specific information for the user. For example, it might create a prompt for "recommended cafes along your next commute route." The input is behavioral prediction information, and the output is the generated prompt message and related information.

[0088] Step 6:

[0089] The server sends the generated information back to the terminal and controls the timing of information presentation to optimize it. The input is the generated prompt message, and the output is the determination of when the information should be presented to the user.

[0090] Step 7:

[0091] The device presents the received information to the user using voice announcements and screen displays. Google Text-to-Speech is used for speech synthesis, ensuring the information is conveyed in a natural manner. Input consists of the information to be presented and the timing of its presentation, while output is either voice or visual display for the user.

[0092] (Application Example 1)

[0093] 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."

[0094] In today's world, consumers are exposed to a vast amount of information daily, making it difficult to accurately identify truly useful information. Furthermore, while there is a need for more personalized information to stimulate customer interest and purchasing intent in physical stores, existing methods are insufficient. Therefore, a system is needed that provides personalized product information in real time based on customer location and behavioral patterns.

[0095] 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.

[0096] In this invention, the server includes device means for acquiring data on the user's surrounding environment and activities, functional means for encrypting the acquired data and transmitting it to a central device via a communication network, and method means for the central device to analyze the transmitted data and generate product information based on the user's activity status. This makes it possible to provide personalized product information to users in real time when they visit physical stores, thereby increasing their purchasing intent.

[0097] "User's surrounding environment and activity data" refers to information about the user's location and behavior, and specifically includes location information, operating status, and schedule information.

[0098] "Device" refers to hardware or software components designed for data acquisition, processing, and communication.

[0099] Encryption is the process of transforming data in a specific way so that it can only be understood by authorized recipients.

[0100] A "communication network" is a digital or analog infrastructure used to send and receive information.

[0101] A "central processing unit" is a processing device that analyzes collected data and generates information based on that analysis.

[0102] A "machine learning algorithm" is a mathematical method that uses large amounts of data to find patterns and gives it the ability to make predictions and decisions.

[0103] "Product information" refers to details and special offers related to products that consumers may be interested in.

[0104] A "portable information terminal" is an information processing device that a user can carry with them, and examples include smartphones and tablets.

[0105] This invention is a system for providing personalized product information in real-world stores. The system aims to present useful information to users by linking a mobile device with a central device.

[0106] Mobile information terminals acquire location information, schedule information, and other data in real time using multiple sensors (e.g., GPS modules and accelerometers) and calendar applications to capture data on the user's surrounding environment and activities. This makes it possible to quantitatively understand the user's trends and interests.

[0107] The acquired data undergoes initial processing, such as noise reduction, on the device before being securely encrypted. The encrypted data is then transmitted via the communication network to a central server. This server utilizes machine learning algorithms such as Python and TensorFlow to analyze user behavior patterns and generate highly relevant product information.

[0108] The product information obtained from the analysis is then transmitted back to the mobile device and presented to the user visually. For example, while a user is moving through a shopping mall, specific product sale information or coupons are displayed in real time, stimulating the user's desire to purchase. The information presented is controlled by a generation AI model to be delivered at the optimal timing.

[0109] A concrete example of its use is in a clothing store within a shopping mall, where, when a user is near a specific item, discount information for that item and recommendations for related products are instantly displayed on their smartphone screen. In this case, an example of a prompt message might be, "When a user shows interest in a particular category, please tell me how to generate and provide special offers related to that category."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The device begins acquiring information about the user's surroundings. This information includes GPS location data, date and time, and information from scheduling applications. The input is sensor and application data from the device, and the output is denoised pure data. This process begins when the GPS module captures location information as the user starts moving around the store.

[0113] Step 2:

[0114] The data acquired by the terminal undergoes basic processing in real time. This involves noise reduction and formatting standardization, converting the data into a processable format. The input is raw data, and the output is formatted data. This process equalizes temporary fluctuations in sensor data and removes noise.

[0115] Step 3:

[0116] The terminal encrypts the processed data and securely transmits it to the server via the communication network. The input is formatted data, and the output is encrypted data that reaches the server. The encryption algorithm protects the data from unauthorized access.

[0117] Step 4:

[0118] The server analyzes the received data using machine learning algorithms. It understands user behavior patterns and situations from the data and generates product information relevant to that context. The input is encrypted user data, and the output is product information based on the analysis results. It is also possible to utilize generative AI models and use generative prompts at the stage of outputting the analysis results.

[0119] Step 5:

[0120] The server sends the generated product information back to the terminal. The input is product information, and the output is the data received by the terminal. The server optimizes the timing and content of data transmission so that product information is presented to the user at the appropriate time.

[0121] Step 6:

[0122] The terminal presents received product information to the user visually or audibly. Input is data from the server, and output is displayed on the user interface or through an audio device. For example, specific actions may be taken, such as displaying discount information as a pop-up on the screen when approaching a particular product area.

[0123] 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.

[0124] This invention is a system that integrates an emotion engine to gain a deeper understanding of user emotions and enable personalized interactions. This system includes a terminal, a server, and an emotion engine, all of which work together.

[0125] First, the device collects the user's voice and facial data using sensors. This includes the user's speech and facial features captured by the camera. This data is sent in real time to the emotion engine, where emotion recognition takes place. The emotion engine uses various algorithms to analyze the user's voice tone, facial expressions, and even their word choice, and generates data to understand their emotions.

[0126] The emotion data generated by the emotion engine is sent to the server via the device. The server analyzes this data in combination with previously sent environment and activity data. Machine learning algorithms understand the user's current context and generate appropriate output information. This output information reflects the user's emotional state, allowing for personalization such as recommending relaxing content if the user is feeling stressed.

[0127] The output information generated by the server is sent back to the terminal and presented to the user either audibly or visually. The timing and content of the presentation are optimized based on the analysis results of the emotion engine, allowing for responses tailored to the user's emotions.

[0128] For example, if the emotion engine detects that the user is smiling when their face is visible to the camera, the server can determine this to be a positive emotional state and provide the user with information about new products or promotions. This enables natural interaction with the user and allows for the provision of information tailored to individual needs and circumstances.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The device collects the user's voice and facial data through its microphone and camera. This includes the content of the user's speech, voice tone, facial movements, and expressions.

[0132] Step 2:

[0133] The device passes the collected data to the emotion engine. The emotion engine analyzes the audio and video data to estimate the user's emotional state in real time. For example, it identifies emotions such as joy, sadness, and anger from the pitch and volume of the voice and the movement of facial muscles.

[0134] Step 3:

[0135] The emotion engine returns the estimated emotion data to the device, encrypts this data, and sends it to the server. This ensures that the emotion information is securely protected as it moves on to the next processing stage.

[0136] Step 4:

[0137] The server comprehensively analyzes the transmitted emotional data in combination with previously acquired user environment and activity data. Based on this, the server understands the user's state and generates appropriate output information. For example, if the server determines that the user is tired, it may provide information such as recommending relaxing music.

[0138] Step 5:

[0139] The server sends the generated output information back to the terminal. The transmitted information is then organized into a format that is easily accepted by the user.

[0140] Step 6:

[0141] The device presents the received information to the user. This presentation can be done audibly, visually, or both, and the method and timing of the presentation are chosen considering the user's emotional state.

[0142] Step 7:

[0143] Users can provide feedback on the information presented. This feedback is reflected in the emotion engine and server analysis, and used to improve future information provision.

[0144] (Example 2)

[0145] 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".

[0146] In modern information technology, accurately understanding a user's emotional state and providing appropriate information based on that understanding is a significant challenge. Traditional systems relied heavily on user behavior history and basic pattern recognition, resulting in a lack of individualized responses that reflected the user's real-time emotions. This has led to a growing demand for more personalized service delivery.

[0147] 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.

[0148] In this invention, the server includes a sensor device means for acquiring the user's voice and facial expressions, a functional means for encrypting the acquired voice and facial expression data and transmitting it to a central processing unit via a communication path, and a method means for the central processing unit to analyze the transmitted data using an emotion analysis algorithm and generate output information based on the user's emotional state. This enables highly accurate analysis of the user's real-time emotional state and provides optimal information and support based on that analysis.

[0149] A "sensor device" is a device used to acquire a user's voice and facial expressions in real time, and includes a microphone and a camera.

[0150] A "communication path" is the network infrastructure used to securely transmit data from a terminal to a central processing unit.

[0151] The "Central Processing Unit" is a computer system that is primarily responsible for processing data transmitted by users and generating appropriate output information.

[0152] An "emotion analysis algorithm" is a computational method used to analyze a user's voice and facial expression data and infer their emotional state from it.

[0153] "Output information" refers to the content of information and services that the central processing unit generates based on the results of sentiment analysis and provides to the user.

[0154] This invention is a system that accurately understands the emotional state of users and provides personalized information. The main components of the system are a terminal, a server, and an emotion analysis engine, which work together in coordination.

[0155] The device is designed to collect user voice and facial expression data. Specifically, a highly sensitive microphone captures the user's voice, and a camera captures the user's facial features. These sensor devices acquire data in real time and transmit it to the device. The hardware used includes common smartphones and tablet devices, which also function as sensors.

[0156] The terminal encrypts the acquired data and sends it to the server via a secure communication path. The server passes the received data to an emotion analysis engine. This analysis engine utilizes deep learning technology to extract voice tone and facial expressions as features and infer emotional states. In particular, highly accurate analysis is possible using open-source machine learning frameworks (e.g., TensorFlow).

[0157] The server generates appropriate output information based on the analysis results. Specifically, information is personalized according to the user's emotions; for example, a user feeling stressed is provided with relaxing music. This generation process utilizes cloud-based database software (e.g., MySQL®) and performs advanced contextual analysis based on historical data and trends.

[0158] For example, if a user displays a depressed expression, the system can analyze it and suggest a music playlist that promotes relaxation. It is also possible to create prompts tailored to the user's emotions using a generative AI model. An example of a prompt would be: "This system analyzes the user's emotions to provide personalized information. When the user looks at their smartphone camera and smiles, please suggest what information should be displayed."

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] The device collects user voice and facial expression data using sensor devices. Specifically, the device's microphone captures the user's speech as input, and the camera records the user's facial expressions. This information is stored on the device as digital data. This data is necessary for real-time analysis.

[0162] Step 2:

[0163] The terminal encrypts the collected voice and facial expression data. The digital data obtained in step 1 is used as input, and an encryption algorithm is applied to ensure security. Encrypted data is generated as output and sent to the server via the communication path. HTTPS is used as the communication protocol here.

[0164] Step 3:

[0165] The server decrypts the received encrypted data and passes it to the sentiment analysis engine. The input is the encrypted data sent in step 2, which is then decrypted back to its original format using the secure key. The output is the raw data available for analysis. This is then input into the sentiment analysis engine to prepare it for analysis.

[0166] Step 4:

[0167] The emotion analysis engine infers the user's emotional state using raw data. The input is the raw data obtained in step 3, and the emotion analysis algorithm (deep learning technology) analyzes the characteristics of voice tone and facial expressions. As output, a digital signal indicating the user's emotional state is generated.

[0168] Step 5:

[0169] The server generates appropriate output information based on the sentiment analysis results. The input is the sentiment state information obtained in step 4, which is analyzed in combination with a cloud-based database to determine an appropriate action plan. The output is personalized information presented to the user.

[0170] Step 6:

[0171] The terminal presents the user with output information sent from the server. The input is the information generated in step 5, which is presented visually through the voice assistant or display. Specific actions include the terminal playing healing music or displaying new product information.

[0172] (Application Example 2)

[0173] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0174] Traditional customer service struggled to immediately understand and respond to customers' emotions. As a result, providing personalized services that customers desired was difficult, hindering improvements in customer satisfaction. Furthermore, the lack of timely information provision based on the customer's situation and emotions led to a decline in the accuracy of service delivery.

[0175] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes an instrument means for acquiring data on the user's surroundings and actions, a functional means for encrypting the acquired data and transmitting it to a central device via a communication means, a method means for the central device to analyze the transmitted data and generate output information based on the user's behavioral state and emotional state, an emotion engine means for analyzing the user's emotions in real time and providing optimal output information based on the user's emotional state, and an instrument means for presenting visual information to the service provider based on the emotion analysis results and supporting customer service. This makes it possible to grasp the user's emotional state in real time and provide personalized customer service and timely information based on it.

[0176] A "device" is a device used to acquire data about the user's surroundings and actions.

[0177] A "communication means" is a medium that has the function of encrypting acquired data and transmitting it to a central device.

[0178] The "central device" is a core processing unit that analyzes transmitted data and generates output information based on the user's behavioral and emotional states.

[0179] An "emotion engine" is software that analyzes a user's emotions in real time and provides optimal output information based on that state.

[0180] A "device" is a machine that presents visual information to service providers based on emotion analysis results, thereby supporting customer service interactions.

[0181] "Output information" refers to information and instructions that are generated based on the user's behavioral and emotional states and provided to the user.

[0182] This invention realizes a system that grasps the emotional state of users in real time and provides personalized customer service based on that understanding. This system operates in stores visited by users, via smart glasses worn by employees.

[0183] The hardware consists of smart glasses equipped with a camera and microphone to detect the user's facial expressions and voice. The software environment incorporates an emotion engine, and deep learning frameworks such as TensorFlow and PyTorch are used to analyze data in real time.

[0184] First, the smart glasses, acting as the terminal, collect the user's facial expression data and voice tone. The collected data is transmitted to a central device via communication. The central device analyzes this data to identify the user's behavioral and emotional states. Through analysis using an emotion engine, optimal output information is generated according to the emotions the user is expressing.

[0185] This output information is visually displayed on smart glasses and provided to employees. For example, if a customer picks up a product and smiles, special promotional information or details related to that product will be overlaid on the glasses, enabling employees to provide appropriate service.

[0186] An example of a prompt message is: "A customer is smiling after picking up a new product. The emotion engine has analyzed this as positive interest. What promotional information should be displayed in this situation?" This allows the emotion engine to accurately grasp the user's needs and support efficient and effective customer service.

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] The smart glasses, acting as the terminal, collect the user's facial expressions and voice tone. Using the built-in camera and microphone, it detects the user's facial expressions and voice tone in real time as they view products in the store. The input at this stage consists of customer facial image data and voice data. These are then converted into a format suitable for emotion analysis.

[0190] Step 2:

[0191] The terminal encrypts the collected data and transmits it to the central device via a communication method. An encryption algorithm is used to ensure data security. The input consists of facial image data and audio data acquired in step 1, and the output is encrypted data for transmission.

[0192] Step 3:

[0193] The server analyzes the received data. Here, using an emotion engine, it identifies customer emotions in real time using algorithms that leverage TensorFlow and PyTorch. The input is encrypted data that is transmitted, and the output is numerical data based on the user's emotional state and interests.

[0194] Step 4:

[0195] The server generates optimal output information based on the analyzed sentiment data, tailored to the user's emotions and behavior. Using a generative AI model, it suggests promotional information to customers who have shown interest in a specific product. The input is the sentiment analysis results, and the output is the applicable promotional information and product details.

[0196] Step 5:

[0197] The terminal visually displays the generated output information on the smart glasses' display. This allows employees to share appropriate information based on their interactions with customers. The input consists of promotional information and product details, while the output is a visual presentation of information to the customer.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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".

[0214] This invention is a system in which a terminal and a server work together to provide personalized information to the user. The terminal is equipped with sensors and a microphone necessary to acquire data on the user's surrounding environment and activities, and has the function to collect this data in real time. Specifically, the terminal collects the user's GPS information, date, time, voice data, etc., and also retrieves schedule information from calendar applications, etc.

[0215] The data collected by the device undergoes initial processing locally to remove unnecessary noise. Following this, the data is encrypted to protect privacy and transmitted to the server via the communication network. The server analyzes all received data, using machine learning algorithms to understand the user's behavior patterns and current context. Through this analysis, a foundation is formed for generating useful output information for the user.

[0216] As a concrete example, the device determines the user's location during their commute, and the server generates promotional information for nearby cafes based on that information. This information is then presented as a notification at an appropriate time so that the user can refresh themselves along the way.

[0217] The server sends the generated output information back to the terminal, which then presents it to the user through voice announcements and screen displays, enabling appropriate and natural dialogue. Through this cycle, the system provides the user with a rich and personalized dialogue experience.

[0218] The following describes the processing flow.

[0219] Step 1:

[0220] The device collects data on the user's surroundings and activities using sensors and a microphone. This data includes location information from GPS, voice input, time, date, and schedule information from scheduling applications.

[0221] Step 2:

[0222] The device performs initial processing on the collected data. Specifically, it performs noise reduction on the audio data and verifies that the location information is accurate. At this stage, it also filters out unimportant data and selects only the necessary data.

[0223] Step 3:

[0224] The terminal encrypts the processed data and sends it to the server while maintaining privacy. This transmission is performed using a secure communication protocol.

[0225] Step 4:

[0226] The server decodes and analyzes the received data. Here, machine learning algorithms are applied to recognize user behavior patterns and understand the current context. This process accurately grasps the user's needs and interests.

[0227] Step 5:

[0228] The server generates optimal output information for the user based on the analysis results. This information is personalized according to the user's current activities and interests, and may include, for example, weather forecasts, schedule reminders, and new product information.

[0229] Step 6:

[0230] The server sends the generated output information to the terminal. The data sent at this time is organized in a format that allows users to easily consume the information.

[0231] Step 7:

[0232] The device presents received information to the user via voice announcement or display on the screen. The timing and format of the presentation are optimized according to the user's situation.

[0233] Step 8:

[0234] Users can respond to the presented information using voice or touch interfaces. This response helps in further data collection and analysis in the next cycle.

[0235] (Example 1)

[0236] 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."

[0237] Traditionally, information presentation systems for users have faced challenges in adequately generating and providing personalized information, making it difficult to deliver timely information tailored to users' activities and circumstances. Furthermore, there is a need for effective means to perform highly accurate behavioral pattern analysis while protecting user privacy.

[0238] 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.

[0239] In this invention, the server includes terminal device means for acquiring environmental and activity data based on the user's location, time, and voice; functional means for denoising and encrypting the acquired data and transmitting it to a central processing unit via communication means; and method means for the central processing unit to analyze the received data, understand the user's behavior patterns and context through a learning algorithm, and generate output information. This makes it possible to provide users with personalized information appropriately in real time.

[0240] A "terminal device" is a device used to acquire user environment and activity data, and has the function of acquiring location information, voice data, etc.

[0241] "Communication means" refers to the technologies and protocols used to securely transmit acquired data to a central processing unit, and includes data encryption and transmission.

[0242] A "central processing unit" is a device that receives and analyzes data transmitted from terminal devices to understand user behavior patterns, and generates output information using learning algorithms.

[0243] A "learning algorithm" is an analytical method used to analyze received data and understand user behavior patterns and context, and is based on artificial intelligence technology.

[0244] A "generative artificial intelligence model" is a technology that generates prompt messages using received data and analysis results, with the aim of providing users with the most relevant information.

[0245] A "control unit" is a device or mechanism that determines the optimal timing for presenting generated output information to the user, and it operates based on the user's current actions and location information.

[0246] This invention is a system that presents personalized information to users through the cooperation of a terminal device and a central processing unit. The terminal device is equipped with a location information acquisition device, a voice collection device, and an accelerometer, which are used to acquire the user's environment and activity data in real time. This information is acquired on a general-purpose computing device such as a smartphone or tablet.

[0247] The terminal first performs initial processing on the collected data, such as noise reduction and format conversion. This initial processing includes noise reduction from audio data and formatting of GPS data. Once processing is complete, the data is encrypted and transmitted to the central processing unit via a communication method. SSL / TLS technology is used to ensure the security of the data during this process.

[0248] The central processing unit (CCU) is responsible for analyzing the received data. It uses machine learning algorithms to understand user behavior patterns and current situations, generating appropriate information. This includes predictive analytics based on user location and past behavior history, utilizing software libraries such as TensorFlow and PyTorch.

[0249] The generated information is constructed into specific messages using a generative artificial intelligence model. For example, if a user is in a specific location, information about appropriate nearby locations is generated based on a specific prompt such as, "Tell me about recommended cafes on my next commute route." This generated information is then notified to the user at the optimal time.

[0250] The server sends the generated information to the terminal device, which then presents it to the user via voice assistance or a display. Google Text-to-Speech is used for speech synthesis, providing natural-sounding guidance. Through this entire process, the system can provide users with a highly personalized information experience that improves their quality of life.

[0251] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0252] Step 1:

[0253] The device communicates with sensors to acquire user environment data. Location information is obtained from the GPS sensor, and voice data is obtained from the microphone. Inputs include the current time, location, and voice data, which are output as acquired data.

[0254] Step 2:

[0255] The device performs initial processing on the acquired data, including noise reduction and format conversion. Specifically, it removes unwanted background noise from the audio data and formats the location information into a standard format. The input is the acquired data, and the output is the formatted data.

[0256] Step 3:

[0257] The terminal encrypts the formatted data and sends it to the server using a communication method. The data is encrypted using SSL / TLS and protected from eavesdropping by third parties. The input is formatted data, and the output is encrypted data.

[0258] Step 4:

[0259] The server receives encrypted data, decrypts it, and performs analysis. It uses machine learning algorithms to extract user behavior patterns and current context. The input is encrypted data, and the output is behavioral prediction information as a result of the analysis.

[0260] Step 5:

[0261] The server uses an AI model based on the analysis results to create prompt messages and generate specific information for the user. For example, it might create a prompt for "recommended cafes along your next commute route." The input is behavioral prediction information, and the output is the generated prompt message and related information.

[0262] Step 6:

[0263] The server sends the generated information back to the terminal and controls the timing of information presentation to optimize it. The input is the generated prompt message, and the output is the determination of when the information should be presented to the user.

[0264] Step 7:

[0265] The device presents the received information to the user using voice announcements and screen displays. Google Text-to-Speech is used for speech synthesis, ensuring the information is conveyed in a natural manner. Input consists of the information to be presented and the timing of its presentation, while output is either voice or visual display for the user.

[0266] (Application Example 1)

[0267] 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."

[0268] In today's world, consumers are exposed to a vast amount of information daily, making it difficult to accurately identify truly useful information. Furthermore, while there is a need for more personalized information to stimulate customer interest and purchasing intent in physical stores, existing methods are insufficient. Therefore, a system is needed that provides personalized product information in real time based on customer location and behavioral patterns.

[0269] 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.

[0270] In this invention, the server includes device means for acquiring data on the user's surrounding environment and activities, functional means for encrypting the acquired data and transmitting it to a central device via a communication network, and method means for the central device to analyze the transmitted data and generate product information based on the user's activity status. This makes it possible to provide personalized product information to users in real time when they visit physical stores, thereby increasing their purchasing intent.

[0271] "User's surrounding environment and activity data" refers to information about the user's location and behavior, and specifically includes location information, operating status, and schedule information.

[0272] "Device" refers to hardware or software components designed for data acquisition, processing, and communication.

[0273] Encryption is the process of transforming data in a specific way so that it can only be understood by authorized recipients.

[0274] A "communication network" is a digital or analog infrastructure used to send and receive information.

[0275] A "central processing unit" is a processing device that analyzes collected data and generates information based on that analysis.

[0276] A "machine learning algorithm" is a mathematical method that uses large amounts of data to find patterns and gives it the ability to make predictions and decisions.

[0277] "Product information" refers to details and special offers related to products that consumers may be interested in.

[0278] A "portable information terminal" is an information processing device that a user can carry with them, and examples include smartphones and tablets.

[0279] This invention is a system for providing personalized product information in real-world stores. The system aims to present useful information to users by linking a mobile device with a central device.

[0280] Mobile information terminals acquire location information, schedule information, and other data in real time using multiple sensors (e.g., GPS modules and accelerometers) and calendar applications to capture data on the user's surrounding environment and activities. This makes it possible to quantitatively understand the user's trends and interests.

[0281] The acquired data undergoes initial processing, such as noise reduction, on the device before being securely encrypted. The encrypted data is then transmitted via the communication network to a central server. This server utilizes machine learning algorithms such as Python and TensorFlow to analyze user behavior patterns and generate highly relevant product information.

[0282] The product information obtained as a result of the analysis is transmitted back to the mobile information terminal and visually presented to the user. For example, when the user is moving within a shopping mall, specific product sales information and coupons are displayed in real time, stimulating the user's purchasing desire. Note that the information to be presented is controlled to be provided at the optimal timing by the generative AI model.

[0283] As a specific example of use, in a clothing store within a shopping mall, when the user is near a specific item, discount information for that item and recommendations for related products are immediately displayed on the smartphone screen. At this time, an example of a prompt sentence is something like "Please teach me a method to generate and provide special offers related to a category in a situation where the user shows interest in a specific category."

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The terminal starts acquiring the user's surrounding environment information. This information includes GPS location information, date and time, information from the schedule application, etc. The input is the terminal's sensor and application data, and the output is the noise-removed pure data. This process is started when the user begins to move within the store and the GPS module captures the location information.

[0287] Step 2:

[0288] The data acquired by the terminal undergoes basic processing in real time. Here, noise removal and format unification are performed, and it is converted into a processable data format. The input is the raw data, and the output is the data with a proper format. In this process, temporary fluctuations in the sensor data are equalized and noise is removed.

[0289] Step 3:

[0290] The terminal encrypts the processed data and securely transmits it to the server via the communication network. The input is formatted data, and the output is encrypted data that reaches the server. The encryption algorithm protects the data from unauthorized access.

[0291] Step 4:

[0292] The server analyzes the received data using machine learning algorithms. It understands user behavior patterns and situations from the data and generates product information relevant to that context. The input is encrypted user data, and the output is product information based on the analysis results. It is also possible to utilize generative AI models and use generative prompts at the stage of outputting the analysis results.

[0293] Step 5:

[0294] The server sends the generated product information back to the terminal. The input is product information, and the output is the data received by the terminal. The server optimizes the timing and content of data transmission so that product information is presented to the user at the appropriate time.

[0295] Step 6:

[0296] The terminal presents received product information to the user visually or audibly. Input is data from the server, and output is displayed on the user interface or through an audio device. For example, specific actions may be taken, such as displaying discount information as a pop-up on the screen when approaching a particular product area.

[0297] 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.

[0298] This invention is a system that integrates an emotion engine to gain a deeper understanding of user emotions and enable personalized interactions. This system includes a terminal, a server, and an emotion engine, all of which work together.

[0299] First, the device collects the user's voice and facial data using sensors. This includes the user's speech and facial features captured by the camera. This data is sent in real time to the emotion engine, where emotion recognition takes place. The emotion engine uses various algorithms to analyze the user's voice tone, facial expressions, and even their word choice, and generates data to understand their emotions.

[0300] The emotion data generated by the emotion engine is sent to the server via the device. The server analyzes this data in combination with previously sent environment and activity data. Machine learning algorithms understand the user's current context and generate appropriate output information. This output information reflects the user's emotional state, allowing for personalization such as recommending relaxing content if the user is feeling stressed.

[0301] The output information generated by the server is sent back to the terminal and presented to the user either audibly or visually. The timing and content of the presentation are optimized based on the analysis results of the emotion engine, allowing for responses tailored to the user's emotions.

[0302] For example, if the emotion engine detects that the user is smiling when their face is visible to the camera, the server can determine this to be a positive emotional state and provide the user with information about new products or promotions. This enables natural interaction with the user and allows for the provision of information tailored to individual needs and circumstances.

[0303] The following describes the processing flow.

[0304] Step 1:

[0305] The terminal collects the user's voice data and facial expression data through the microphone and camera. This includes the user's utterance content, voice tone, facial movements and expressions, etc.

[0306] Step 2:

[0307] The terminal passes the collected data to the emotion engine. The emotion engine analyzes the voice and video data and estimates the user's emotional state in real time. For example, it identifies emotions such as joy, sadness, and anger from the pitch and intensity of the voice, and the movements of the facial muscles.

[0308] Step 3:

[0309] The emotion engine returns the estimated emotion data to the terminal, encrypts this data, and sends it to the server. In this way, the emotion information is securely protected and proceeds to the next processing stage.

[0310] Step 4:

[0311] The server comprehensively analyzes the transmitted emotion data in combination with the previously obtained user's environmental and activity data. Based on this, the server grasps the user's state and generates appropriate output information. For example, when it is determined that the user is tired, information such as recommending relaxing music is prepared.

[0312] Step 5:

[0313] The server sends the generated output information back to the terminal. The transmitted information is organized in a form that is easy for the user to accept.

[0314] Step 6:

[0315] The terminal presents the received information to the user. This presentation is done by voice, visually, or both, and the method and timing of the presentation are selected considering the user's emotional state.

[0316] Step 7:

[0317] Users can provide feedback on the information presented. This feedback is reflected in the emotion engine and server analysis, and used to improve future information provision.

[0318] (Example 2)

[0319] 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".

[0320] In modern information technology, accurately understanding a user's emotional state and providing appropriate information based on that understanding is a significant challenge. Traditional systems relied heavily on user behavior history and basic pattern recognition, resulting in a lack of individualized responses that reflected the user's real-time emotions. This has led to a growing demand for more personalized service delivery.

[0321] 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.

[0322] In this invention, the server includes a sensor device means for acquiring the user's voice and facial expressions, a functional means for encrypting the acquired voice and facial expression data and transmitting it to a central processing unit via a communication path, and a method means for the central processing unit to analyze the transmitted data using an emotion analysis algorithm and generate output information based on the user's emotional state. This enables highly accurate analysis of the user's real-time emotional state and provides optimal information and support based on that analysis.

[0323] A "sensor device" is a device used to acquire a user's voice and facial expressions in real time, and includes a microphone and a camera.

[0324] A "communication path" is the network infrastructure used to securely transmit data from a terminal to a central processing unit.

[0325] The "Central Processing Unit" is a computer system that is primarily responsible for processing data transmitted by users and generating appropriate output information.

[0326] An "emotion analysis algorithm" is a computational method used to analyze a user's voice and facial expression data and infer their emotional state from it.

[0327] "Output information" refers to the content of information and services that the central processing unit generates based on the results of sentiment analysis and provides to the user.

[0328] This invention is a system that accurately understands the emotional state of users and provides personalized information. The main components of the system are a terminal, a server, and an emotion analysis engine, which work together in coordination.

[0329] The device is designed to collect user voice and facial expression data. Specifically, a highly sensitive microphone captures the user's voice, and a camera captures the user's facial features. These sensor devices acquire data in real time and transmit it to the device. The hardware used includes common smartphones and tablet devices, which also function as sensors.

[0330] The terminal encrypts the acquired data and sends it to the server via a secure communication path. The server passes the received data to an emotion analysis engine. This analysis engine utilizes deep learning technology to extract voice tone and facial expressions as features and infer emotional states. In particular, highly accurate analysis is possible using open-source machine learning frameworks (e.g., TensorFlow).

[0331] The server generates appropriate output information based on the analysis results. Specifically, information is personalized according to the user's emotions; for example, a user feeling stressed is provided with relaxing music. This generation process utilizes cloud-based database software (e.g., MySQL) and performs advanced contextual analysis based on historical data and trends.

[0332] For example, if a user displays a depressed expression, the system can analyze it and suggest a music playlist that promotes relaxation. It is also possible to create prompts tailored to the user's emotions using a generative AI model. An example of a prompt would be: "This system analyzes the user's emotions to provide personalized information. When the user looks at their smartphone camera and smiles, please suggest what information should be displayed."

[0333] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0334] Step 1:

[0335] The device collects user voice and facial expression data using sensor devices. Specifically, the device's microphone captures the user's speech as input, and the camera records the user's facial expressions. This information is stored on the device as digital data. This data is necessary for real-time analysis.

[0336] Step 2:

[0337] The terminal encrypts the collected voice and facial expression data. The digital data obtained in step 1 is used as input, and an encryption algorithm is applied to ensure security. Encrypted data is generated as output and sent to the server via the communication path. HTTPS is used as the communication protocol here.

[0338] Step 3:

[0339] The server decrypts the received encrypted data and passes it to the sentiment analysis engine. The input is the encrypted data sent in step 2, which is then decrypted back into its original format using the secure key. The output is the raw data available for analysis. This is then input into the sentiment analysis engine to prepare it for analysis.

[0340] Step 4:

[0341] The emotion analysis engine infers the user's emotional state using raw data. The input is the raw data obtained in step 3, and the emotion analysis algorithm (deep learning technology) analyzes the characteristics of voice tone and facial expressions. As output, a digital signal indicating the user's emotional state is generated.

[0342] Step 5:

[0343] The server generates appropriate output information based on the sentiment analysis results. The input is the sentiment state information obtained in step 4, which is analyzed in combination with a cloud-based database to determine an appropriate action plan. The output is personalized information presented to the user.

[0344] Step 6:

[0345] The terminal presents the user with output information sent from the server. The input is the information generated in step 5, which is presented visually through the voice assistant or display. Specific actions include the terminal playing healing music or displaying new product information.

[0346] (Application Example 2)

[0347] 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."

[0348] Traditional customer service struggled to immediately understand and respond to customers' emotions. As a result, providing personalized services that customers desired was difficult, hindering improvements in customer satisfaction. Furthermore, the lack of timely information provision based on the customer's situation and emotions led to a decline in the accuracy of service delivery.

[0349] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes an instrument means for acquiring data on the user's surroundings and actions, a functional means for encrypting the acquired data and transmitting it to a central device via a communication means, a method means for the central device to analyze the transmitted data and generate output information based on the user's behavioral state and emotional state, an emotion engine means for analyzing the user's emotions in real time and providing optimal output information based on the user's emotional state, and an instrument means for presenting visual information to the service provider based on the emotion analysis results and supporting customer service. This makes it possible to grasp the user's emotional state in real time and provide personalized customer service and timely information based on it.

[0350] A "device" is a device used to acquire data about the user's surroundings and actions.

[0351] A "communication means" is a medium that has the function of encrypting acquired data and transmitting it to a central device.

[0352] The "central device" is a core processing unit that analyzes transmitted data and generates output information based on the user's behavioral and emotional states.

[0353] An "emotion engine" is software that analyzes a user's emotions in real time and provides optimal output information based on that state.

[0354] A "device" is a machine that presents visual information to service providers based on emotion analysis results, thereby supporting customer service interactions.

[0355] "Output information" refers to information and instructions that are generated based on the user's behavioral and emotional states and provided to the user.

[0356] This invention realizes a system that grasps the emotional state of users in real time and provides personalized customer service based on that understanding. This system operates in stores visited by users, via smart glasses worn by employees.

[0357] The hardware consists of smart glasses equipped with a camera and microphone to detect the user's facial expressions and voice. The software environment incorporates an emotion engine, and deep learning frameworks such as TensorFlow and PyTorch are used to analyze data in real time.

[0358] First, the smart glasses, acting as the terminal, collect the user's facial expression data and voice tone. The collected data is transmitted to a central device via communication. The central device analyzes this data to identify the user's behavioral and emotional states. Through analysis using an emotion engine, optimal output information is generated according to the emotions the user is expressing.

[0359] This output information is visually displayed on smart glasses and provided to employees. For example, if a customer picks up a product and smiles, special promotional information or details related to that product will be overlaid on the glasses, enabling employees to provide appropriate service.

[0360] An example of a prompt message is: "A customer is smiling after picking up a new product. The emotion engine has analyzed this as positive interest. What promotional information should be displayed in this situation?" This allows the emotion engine to accurately grasp the user's needs and support efficient and effective customer service.

[0361] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0362] Step 1:

[0363] The smart glasses, acting as the terminal, collect the user's facial expressions and voice tone. Using the built-in camera and microphone, it detects the user's facial expressions and voice tone in real time as they view products in the store. The input at this stage consists of customer facial image data and voice data. These are then converted into a format suitable for emotion analysis.

[0364] Step 2:

[0365] The terminal encrypts the collected data and transmits it to the central device via a communication method. An encryption algorithm is used to ensure data security. The input is the facial image data and audio data acquired in step 1, and the output is the encrypted data for transmission.

[0366] Step 3:

[0367] The server analyzes the received data. Here, using an emotion engine and algorithms leveraging TensorFlow and PyTorch, it identifies customer emotions in real time. The input is encrypted data transmitted, and the output is numerical data based on the user's emotional state and interests.

[0368] Step 4:

[0369] The server generates optimal output information based on the analyzed sentiment data, tailored to the user's emotions and behavior. Using a generative AI model, it suggests promotional information to customers who have shown interest in a specific product. The input is the sentiment analysis results, and the output is the applicable promotional information and product details.

[0370] Step 5:

[0371] The terminal visually displays the generated output information on the smart glasses' display. This allows employees to share appropriate information based on their interactions with customers. The input consists of promotional information and product details, while the output is a visual presentation of information to the customer.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] [Third Embodiment]

[0376] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0377] 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.

[0378] 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).

[0379] 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.

[0380] 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.

[0381] 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).

[0382] 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.

[0383] 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.

[0384] 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.

[0385] 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.

[0386] 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.

[0387] 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".

[0388] This invention is a system in which a terminal and a server work together to provide personalized information to the user. The terminal is equipped with sensors and a microphone necessary to acquire data on the user's surrounding environment and activities, and has the function to collect this data in real time. Specifically, the terminal collects the user's GPS information, date, time, voice data, etc., and also retrieves schedule information from calendar applications, etc.

[0389] The data collected by the device undergoes initial processing locally to remove unnecessary noise. Following this, the data is encrypted to protect privacy and transmitted to the server via the communication network. The server analyzes all received data, using machine learning algorithms to understand the user's behavior patterns and current context. Through this analysis, a foundation is formed for generating useful output information for the user.

[0390] As a concrete example, the device determines the user's location during their commute, and the server generates promotional information for nearby cafes based on that information. This information is then presented as a notification at an appropriate time so that the user can refresh themselves along the way.

[0391] The server sends the generated output information back to the terminal, which then presents it to the user through voice announcements and screen displays, enabling appropriate and natural dialogue. Through this cycle, the system provides the user with a rich and personalized dialogue experience.

[0392] The following describes the processing flow.

[0393] Step 1:

[0394] The device collects data on the user's surroundings and activities using sensors and a microphone. This data includes location information from GPS, voice input, time, date, and schedule information from scheduling applications.

[0395] Step 2:

[0396] The device performs initial processing on the collected data. Specifically, it performs noise reduction on the audio data and verifies that the location information is accurate. At this stage, it also filters out unimportant data and selects only the necessary data.

[0397] Step 3:

[0398] The terminal encrypts the processed data and sends it to the server while maintaining privacy. This transmission is performed using a secure communication protocol.

[0399] Step 4:

[0400] The server decodes and analyzes the received data. Here, machine learning algorithms are applied to recognize user behavior patterns and understand the current context. This process accurately grasps the user's needs and interests.

[0401] Step 5:

[0402] The server generates optimal output information for the user based on the analysis results. This information is personalized according to the user's current activities and interests, and may include, for example, weather forecasts, schedule reminders, and new product information.

[0403] Step 6:

[0404] The server sends the generated output information to the terminal. The data sent at this time is organized in a format that allows users to easily consume the information.

[0405] Step 7:

[0406] The device presents received information to the user via voice announcement or display on the screen. The timing and format of the presentation are optimized according to the user's situation.

[0407] Step 8:

[0408] Users can respond to the presented information using voice or touch interfaces. This response helps in further data collection and analysis in the next cycle.

[0409] (Example 1)

[0410] 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."

[0411] Traditionally, information presentation systems for users have faced challenges in adequately generating and providing personalized information, making it difficult to deliver timely information tailored to users' activities and circumstances. Furthermore, there is a need for effective means to perform highly accurate behavioral pattern analysis while protecting user privacy.

[0412] 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.

[0413] In this invention, the server includes terminal device means for acquiring environmental and activity data based on the user's location, time, and voice; functional means for denoising and encrypting the acquired data and transmitting it to a central processing unit via communication means; and method means for the central processing unit to analyze the received data, understand the user's behavior patterns and context through a learning algorithm, and generate output information. This makes it possible to provide users with personalized information appropriately in real time.

[0414] A "terminal device" is a device used to acquire user environment and activity data, and has the function of acquiring location information, voice data, etc.

[0415] "Communication means" refers to the technologies and protocols used to securely transmit acquired data to a central processing unit, and includes data encryption and transmission.

[0416] A "central processing unit" is a device that receives and analyzes data transmitted from terminal devices to understand user behavior patterns, and generates output information using learning algorithms.

[0417] A "learning algorithm" is an analytical method used to analyze received data and understand user behavior patterns and context, and is based on artificial intelligence technology.

[0418] A "generative artificial intelligence model" is a technology that generates prompt messages using received data and analysis results, with the aim of providing users with the most relevant information.

[0419] A "control unit" is a device or mechanism that determines the optimal timing for presenting generated output information to the user, and it operates based on the user's current actions and location information.

[0420] This invention is a system that presents personalized information to users through the cooperation of a terminal device and a central processing unit. The terminal device is equipped with a location information acquisition device, a voice collection device, and an accelerometer, which are used to acquire the user's environment and activity data in real time. This information is acquired on a general-purpose computing device such as a smartphone or tablet.

[0421] The terminal first performs initial processing on the collected data, such as noise reduction and format conversion. This initial processing includes noise reduction from audio data and formatting of GPS data. Once processing is complete, the data is encrypted and transmitted to the central processing unit via a communication method. SSL / TLS technology is used to ensure the security of the data during this process.

[0422] The central processing unit (CCU) is responsible for analyzing the received data. It uses machine learning algorithms to understand user behavior patterns and current situations, generating appropriate information. This includes predictive analytics based on user location and past behavior history, utilizing software libraries such as TensorFlow and PyTorch.

[0423] The generated information is constructed into specific messages using a generative artificial intelligence model. For example, if a user is in a specific location, information about appropriate nearby locations is generated based on a specific prompt such as, "Tell me about recommended cafes on my next commute route." This generated information is then notified to the user at the optimal time.

[0424] The server sends the generated information to the terminal device, which then presents it to the user via voice assistance or a display. Google Text-to-Speech is used for speech synthesis, providing natural-sounding guidance. Through this entire process, the system can provide users with a highly personalized information experience that improves their quality of life.

[0425] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0426] Step 1:

[0427] The device communicates with sensors to acquire user environment data. Location information is obtained from the GPS sensor, and voice data is obtained from the microphone. Inputs include the current time, location, and voice data, which are output as acquired data.

[0428] Step 2:

[0429] The device performs initial processing on the acquired data, including noise reduction and format conversion. Specifically, it removes unwanted background noise from the audio data and formats the location information into a standard format. The input is the acquired data, and the output is the formatted data.

[0430] Step 3:

[0431] The terminal encrypts the formatted data and sends it to the server using a communication method. The data is encrypted using SSL / TLS and protected from eavesdropping by third parties. The input is formatted data, and the output is encrypted data.

[0432] Step 4:

[0433] The server receives encrypted data, decrypts it, and performs analysis. It uses machine learning algorithms to extract user behavior patterns and current context. The input is encrypted data, and the output is behavioral prediction information as a result of the analysis.

[0434] Step 5:

[0435] The server uses an AI model based on the analysis results to create prompt messages and generate specific information for the user. For example, it might create a prompt for "recommended cafes along your next commute route." The input is behavioral prediction information, and the output is the generated prompt message and related information.

[0436] Step 6:

[0437] The server sends the generated information back to the terminal and controls the timing of information presentation to optimize it. The input is the generated prompt message, and the output is the determination of when the information should be presented to the user.

[0438] Step 7:

[0439] The device presents the received information to the user using voice announcements and screen displays. Google Text-to-Speech is used for speech synthesis, ensuring the information is conveyed in a natural manner. Input consists of the information to be presented and the timing of its presentation, while output is either voice or visual display for the user.

[0440] (Application Example 1)

[0441] 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."

[0442] In today's world, consumers are exposed to a vast amount of information daily, making it difficult to accurately identify truly useful information. Furthermore, while there is a need for more personalized information to stimulate customer interest and purchasing intent in physical stores, existing methods are insufficient. Therefore, a system is needed that provides personalized product information in real time based on customer location and behavioral patterns.

[0443] 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.

[0444] In this invention, the server includes device means for acquiring data on the user's surrounding environment and activities, functional means for encrypting the acquired data and transmitting it to a central device via a communication network, and method means for the central device to analyze the transmitted data and generate product information based on the user's activity status. This makes it possible to provide personalized product information to users in real time when they visit physical stores, thereby increasing their purchasing intent.

[0445] "User's surrounding environment and activity data" refers to information about the user's location and behavior, and specifically includes location information, operating status, and schedule information.

[0446] "Device" refers to hardware or software components designed for data acquisition, processing, and communication.

[0447] Encryption is the process of transforming data in a specific way so that it can only be understood by authorized recipients.

[0448] A "communication network" is a digital or analog infrastructure used to send and receive information.

[0449] A "central processing unit" is a processing device that analyzes collected data and generates information based on that analysis.

[0450] A "machine learning algorithm" is a mathematical method that uses large amounts of data to find patterns and gives it the ability to make predictions and decisions.

[0451] "Product information" refers to details and special offers related to products that consumers may be interested in.

[0452] A "portable information terminal" is an information processing device that a user can carry with them, and examples include smartphones and tablets.

[0453] This invention is a system for providing personalized product information in real-world stores. The system aims to present useful information to users by linking a mobile device with a central device.

[0454] Mobile information terminals acquire location information, schedule information, and other data in real time using multiple sensors (e.g., GPS modules and accelerometers) and calendar applications to capture data on the user's surrounding environment and activities. This makes it possible to quantitatively understand the user's trends and interests.

[0455] The acquired data undergoes initial processing, such as noise reduction, on the device before being securely encrypted. The encrypted data is then transmitted via the communication network to a central server. This server utilizes machine learning algorithms such as Python and TensorFlow to analyze user behavior patterns and generate highly relevant product information.

[0456] The product information obtained from the analysis is then transmitted back to the mobile device and presented to the user visually. For example, while a user is moving through a shopping mall, specific product sale information or coupons are displayed in real time, stimulating the user's desire to purchase. The information presented is controlled by a generation AI model to be delivered at the optimal timing.

[0457] A concrete example of its use is in a clothing store within a shopping mall, where, when a user is near a specific item, discount information for that item and recommendations for related products are instantly displayed on their smartphone screen. In this case, an example of a prompt message might be, "When a user shows interest in a particular category, please tell me how to generate and provide special offers related to that category."

[0458] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0459] Step 1:

[0460] The device begins acquiring information about the user's surroundings. This information includes GPS location data, date and time, and information from scheduling applications. The input is sensor and application data from the device, and the output is denoised pure data. This process begins when the GPS module captures location information as the user starts moving around the store.

[0461] Step 2:

[0462] The data acquired by the terminal undergoes basic processing in real time. This involves noise reduction and formatting standardization, converting the data into a processable format. The input is raw data, and the output is formatted data. This process equalizes temporary fluctuations in sensor data and removes noise.

[0463] Step 3:

[0464] The terminal encrypts the processed data and securely transmits it to the server via the communication network. The input is formatted data, and the output is encrypted data that reaches the server. The encryption algorithm protects the data from unauthorized access.

[0465] Step 4:

[0466] The server analyzes the received data using machine learning algorithms. It understands user behavior patterns and situations from the data and generates product information relevant to that context. The input is encrypted user data, and the output is product information based on the analysis results. It is also possible to utilize generative AI models and use generative prompts at the stage of outputting the analysis results.

[0467] Step 5:

[0468] The server sends the generated product information back to the terminal. The input is product information, and the output is the data received by the terminal. The server optimizes the timing and content of data transmission so that product information is presented to the user at the appropriate time.

[0469] Step 6:

[0470] The terminal presents received product information to the user visually or audibly. Input is data from the server, and output is displayed on the user interface or through an audio device. For example, specific actions may be taken, such as displaying discount information as a pop-up on the screen when approaching a particular product area.

[0471] 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.

[0472] This invention is a system that integrates an emotion engine to gain a deeper understanding of user emotions and enable personalized interactions. This system includes a terminal, a server, and an emotion engine, all of which work together.

[0473] First, the device collects the user's voice and facial data using sensors. This includes the user's speech and facial features captured by the camera. This data is sent in real time to the emotion engine, where emotion recognition takes place. The emotion engine uses various algorithms to analyze the user's voice tone, facial expressions, and even their word choice, and generates data to understand their emotions.

[0474] The emotion data generated by the emotion engine is sent to the server via the device. The server analyzes this data in combination with previously sent environment and activity data. Machine learning algorithms understand the user's current context and generate appropriate output information. This output information reflects the user's emotional state, allowing for personalization such as recommending relaxing content if the user is feeling stressed.

[0475] The output information generated by the server is sent back to the terminal and presented to the user either audibly or visually. The timing and content of the presentation are optimized based on the analysis results of the emotion engine, allowing for responses tailored to the user's emotions.

[0476] For example, if the emotion engine detects that the user is smiling when their face is visible to the camera, the server can determine this to be a positive emotional state and provide the user with information about new products or promotions. This enables natural interaction with the user and allows for the provision of information tailored to individual needs and circumstances.

[0477] The following describes the processing flow.

[0478] Step 1:

[0479] The device collects the user's voice and facial data through its microphone and camera. This includes the content of the user's speech, voice tone, facial movements, and expressions.

[0480] Step 2:

[0481] The device passes the collected data to the emotion engine. The emotion engine analyzes the audio and video data to estimate the user's emotional state in real time. For example, it identifies emotions such as joy, sadness, and anger from the pitch and volume of the voice and the movement of facial muscles.

[0482] Step 3:

[0483] The emotion engine returns the estimated emotion data to the device, encrypts this data, and sends it to the server. This ensures that the emotion information is securely protected as it moves on to the next processing stage.

[0484] Step 4:

[0485] The server comprehensively analyzes the transmitted emotional data in combination with previously acquired user environment and activity data. Based on this, the server understands the user's state and generates appropriate output information. For example, if the server determines that the user is tired, it may provide information such as recommending relaxing music.

[0486] Step 5:

[0487] The server sends the generated output information back to the terminal. The transmitted information is then organized into a format that is easily accepted by the user.

[0488] Step 6:

[0489] The device presents the received information to the user. This presentation can be done audibly, visually, or both, and the method and timing of the presentation are chosen considering the user's emotional state.

[0490] Step 7:

[0491] Users can provide feedback on the information presented. This feedback is reflected in the emotion engine and server analysis, and used to improve future information provision.

[0492] (Example 2)

[0493] 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."

[0494] In modern information technology, accurately understanding a user's emotional state and providing appropriate information based on that understanding is a significant challenge. Traditional systems relied heavily on user behavior history and basic pattern recognition, resulting in a lack of individualized responses that reflected the user's real-time emotions. This has led to a growing demand for more personalized service delivery.

[0495] 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.

[0496] In this invention, the server includes a sensor device means for acquiring the user's voice and facial expressions, a functional means for encrypting the acquired voice and facial expression data and transmitting it to a central processing unit via a communication path, and a method means for the central processing unit to analyze the transmitted data using an emotion analysis algorithm and generate output information based on the user's emotional state. This enables highly accurate analysis of the user's real-time emotional state and provides optimal information and support based on that analysis.

[0497] A "sensor device" is a device used to acquire a user's voice and facial expressions in real time, and includes a microphone and a camera.

[0498] A "communication path" is the network infrastructure used to securely transmit data from a terminal to a central processing unit.

[0499] The "Central Processing Unit" is a computer system that is primarily responsible for processing data transmitted by users and generating appropriate output information.

[0500] An "emotion analysis algorithm" is a computational method used to analyze a user's voice and facial expression data and infer their emotional state from it.

[0501] "Output information" refers to the content of information and services that the central processing unit generates based on the results of sentiment analysis and provides to the user.

[0502] This invention is a system that accurately understands the emotional state of users and provides personalized information. The main components of the system are a terminal, a server, and an emotion analysis engine, which work together in coordination.

[0503] The device is designed to collect user voice and facial expression data. Specifically, a highly sensitive microphone captures the user's voice, and a camera captures the user's facial features. These sensor devices acquire data in real time and transmit it to the device. The hardware used includes common smartphones and tablet devices, which also function as sensors.

[0504] The terminal encrypts the acquired data and sends it to the server via a secure communication path. The server passes the received data to an emotion analysis engine. This analysis engine utilizes deep learning technology to extract voice tone and facial expressions as features and infer emotional states. In particular, highly accurate analysis is possible using open-source machine learning frameworks (e.g., TensorFlow).

[0505] The server generates appropriate output information based on the analysis results. Specifically, information is personalized according to the user's emotions; for example, a user feeling stressed is provided with relaxing music. This generation process utilizes cloud-based database software (e.g., MySQL) and performs advanced contextual analysis based on historical data and trends.

[0506] For example, if a user displays a depressed expression, the system can analyze it and suggest a music playlist that promotes relaxation. It is also possible to create prompts tailored to the user's emotions using a generative AI model. An example of a prompt would be: "This system analyzes the user's emotions to provide personalized information. When the user looks at their smartphone camera and smiles, please suggest what information should be displayed."

[0507] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0508] Step 1:

[0509] The device collects user voice and facial expression data using sensor devices. Specifically, the device's microphone captures the user's speech as input, and the camera records the user's facial expressions. This information is stored on the device as digital data. This data is necessary for real-time analysis.

[0510] Step 2:

[0511] The terminal encrypts the collected voice and facial expression data. The digital data obtained in step 1 is used as input, and an encryption algorithm is applied to ensure security. Encrypted data is generated as output and sent to the server via the communication path. HTTPS is used as the communication protocol here.

[0512] Step 3:

[0513] The server decrypts the received encrypted data and passes it to the sentiment analysis engine. The input is the encrypted data sent in step 2, which is then decrypted back into its original format using the secure key. The output is the raw data available for analysis. This is then input into the sentiment analysis engine to prepare it for analysis.

[0514] Step 4:

[0515] The emotion analysis engine infers the user's emotional state using raw data. The input is the raw data obtained in step 3, and the emotion analysis algorithm (deep learning technology) analyzes the characteristics of voice tone and facial expressions. As output, a digital signal indicating the user's emotional state is generated.

[0516] Step 5:

[0517] The server generates appropriate output information based on the sentiment analysis results. The input is the sentiment state information obtained in step 4, which is analyzed in combination with a cloud-based database to determine an appropriate action plan. The output is personalized information presented to the user.

[0518] Step 6:

[0519] The terminal presents the user with output information sent from the server. The input is the information generated in step 5, which is presented visually through the voice assistant or display. Specific actions include the terminal playing healing music or displaying new product information.

[0520] (Application Example 2)

[0521] 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."

[0522] Traditional customer service struggled to immediately understand and respond to customers' emotions. As a result, providing personalized services that customers desired was difficult, hindering improvements in customer satisfaction. Furthermore, the lack of timely information provision based on the customer's situation and emotions led to a decline in the accuracy of service delivery.

[0523] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes an instrument means for acquiring data on the user's surroundings and actions, a functional means for encrypting the acquired data and transmitting it to a central device via a communication means, a method means for the central device to analyze the transmitted data and generate output information based on the user's behavioral state and emotional state, an emotion engine means for analyzing the user's emotions in real time and providing optimal output information based on the user's emotional state, and an instrument means for presenting visual information to the service provider based on the emotion analysis results and supporting customer service. This makes it possible to grasp the user's emotional state in real time and provide personalized customer service and timely information based on it.

[0524] A "device" is a device used to acquire data about the user's surroundings and actions.

[0525] A "communication means" is a medium that has the function of encrypting acquired data and transmitting it to a central device.

[0526] The "central device" is a core processing unit that analyzes transmitted data and generates output information based on the user's behavioral and emotional states.

[0527] An "emotion engine" is software that analyzes a user's emotions in real time and provides optimal output information based on that state.

[0528] A "device" is a machine that presents visual information to service providers based on emotion analysis results, thereby supporting customer service interactions.

[0529] "Output information" refers to information and instructions that are generated based on the user's behavioral and emotional states and provided to the user.

[0530] This invention realizes a system that grasps the emotional state of users in real time and provides personalized customer service based on that understanding. This system operates in stores visited by users, via smart glasses worn by employees.

[0531] The hardware consists of smart glasses equipped with a camera and microphone to detect the user's facial expressions and voice. The software environment incorporates an emotion engine, and deep learning frameworks such as TensorFlow and PyTorch are used to analyze data in real time.

[0532] First, the smart glasses, acting as the terminal, collect the user's facial expression data and voice tone. The collected data is transmitted to a central device via communication. The central device analyzes this data to identify the user's behavioral and emotional states. Through analysis using an emotion engine, optimal output information is generated according to the emotions the user is expressing.

[0533] This output information is visually displayed on smart glasses and provided to employees. For example, if a customer picks up a product and smiles, special promotional information or details related to that product will be overlaid on the glasses, enabling employees to provide appropriate service.

[0534] An example of a prompt message is: "A customer is smiling after picking up a new product. The emotion engine has analyzed this as positive interest. What promotional information should be displayed in this situation?" This allows the emotion engine to accurately grasp the user's needs and support efficient and effective customer service.

[0535] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0536] Step 1:

[0537] The smart glasses, acting as the terminal, collect the user's facial expressions and voice tone. Using the built-in camera and microphone, it detects the user's facial expressions and voice tone in real time as they view products in the store. The input at this stage consists of customer facial image data and voice data. These are then converted into a format suitable for emotion analysis.

[0538] Step 2:

[0539] The terminal encrypts the collected data and transmits it to the central device via a communication method. An encryption algorithm is used to ensure data security. The input is the facial image data and audio data acquired in step 1, and the output is the encrypted data for transmission.

[0540] Step 3:

[0541] The server analyzes the received data. Here, using an emotion engine and algorithms leveraging TensorFlow and PyTorch, it identifies customer emotions in real time. The input is encrypted data transmitted, and the output is numerical data based on the user's emotional state and interests.

[0542] Step 4:

[0543] The server generates optimal output information based on the analyzed sentiment data, tailored to the user's emotions and behavior. Using a generative AI model, it suggests promotional information to customers who have shown interest in a specific product. The input is the sentiment analysis results, and the output is the applicable promotional information and product details.

[0544] Step 5:

[0545] The terminal visually displays the generated output information on the smart glasses' display. This allows employees to share appropriate information based on their interactions with customers. The input consists of promotional information and product details, while the output is a visual presentation of information to the customer.

[0546] 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.

[0547] 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.

[0548] 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.

[0549] [Fourth Embodiment]

[0550] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0551] 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.

[0552] 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).

[0553] 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.

[0554] 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.

[0555] 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).

[0556] 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.

[0557] 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.

[0558] 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.

[0559] 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.

[0560] 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.

[0561] 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.

[0562] 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".

[0563] This invention is a system in which a terminal and a server work together to provide personalized information to the user. The terminal is equipped with sensors and a microphone necessary to acquire data on the user's surrounding environment and activities, and has the function to collect this data in real time. Specifically, the terminal collects the user's GPS information, date, time, voice data, etc., and also retrieves schedule information from calendar applications, etc.

[0564] The data collected by the device undergoes initial processing locally to remove unnecessary noise. Following this, the data is encrypted to protect privacy and transmitted to the server via the communication network. The server analyzes all received data, using machine learning algorithms to understand the user's behavior patterns and current context. Through this analysis, a foundation is formed for generating useful output information for the user.

[0565] As a concrete example, the device determines the user's location during their commute, and the server generates promotional information for nearby cafes based on that information. This information is then presented as a notification at an appropriate time so that the user can refresh themselves along the way.

[0566] The server sends the generated output information back to the terminal, which then presents it to the user through voice announcements and screen displays, enabling appropriate and natural dialogue. Through this cycle, the system provides the user with a rich and personalized dialogue experience.

[0567] The following describes the processing flow.

[0568] Step 1:

[0569] The device collects data on the user's surroundings and activities using sensors and a microphone. This data includes location information from GPS, voice input, time, date, and schedule information from scheduling applications.

[0570] Step 2:

[0571] The device performs initial processing on the collected data. Specifically, it performs noise reduction on the audio data and verifies that the location information is accurate. At this stage, it also filters out unimportant data and selects only the necessary data.

[0572] Step 3:

[0573] The terminal encrypts the processed data and sends it to the server while maintaining privacy. This transmission is performed using a secure communication protocol.

[0574] Step 4:

[0575] The server decodes and analyzes the received data. Here, machine learning algorithms are applied to recognize user behavior patterns and understand the current context. This process accurately grasps the user's needs and interests.

[0576] Step 5:

[0577] The server generates optimal output information for the user based on the analysis results. This information is personalized according to the user's current activities and interests, and may include, for example, weather forecasts, schedule reminders, and new product information.

[0578] Step 6:

[0579] The server sends the generated output information to the terminal. The data sent at this time is organized in a format that allows users to easily consume the information.

[0580] Step 7:

[0581] The device presents received information to the user via voice announcement or display on the screen. The timing and format of the presentation are optimized according to the user's situation.

[0582] Step 8:

[0583] Users can respond to the presented information using voice or touch interfaces. This response helps in further data collection and analysis in the next cycle.

[0584] (Example 1)

[0585] 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".

[0586] Traditionally, information presentation systems for users have faced challenges in adequately generating and providing personalized information, making it difficult to deliver timely information tailored to users' activities and circumstances. Furthermore, there is a need for effective means to perform highly accurate behavioral pattern analysis while protecting user privacy.

[0587] 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.

[0588] In this invention, the server includes terminal device means for acquiring environmental and activity data based on the user's location, time, and voice; functional means for denoising and encrypting the acquired data and transmitting it to a central processing unit via communication means; and method means for the central processing unit to analyze the received data, understand the user's behavior patterns and context through a learning algorithm, and generate output information. This makes it possible to provide users with personalized information appropriately in real time.

[0589] A "terminal device" is a device used to acquire user environment and activity data, and has the function of acquiring location information, voice data, etc.

[0590] "Communication means" refers to the technologies and protocols used to securely transmit acquired data to a central processing unit, and includes data encryption and transmission.

[0591] A "central processing unit" is a device that receives and analyzes data transmitted from terminal devices to understand user behavior patterns, and generates output information using learning algorithms.

[0592] A "learning algorithm" is an analytical method used to analyze received data and understand user behavior patterns and context, and is based on artificial intelligence technology.

[0593] A "generative artificial intelligence model" is a technology that generates prompt messages using received data and analysis results, with the aim of providing users with the most relevant information.

[0594] A "control unit" is a device or mechanism that determines the optimal timing for presenting generated output information to the user, and it operates based on the user's current actions and location information.

[0595] This invention is a system that presents personalized information to users through the cooperation of a terminal device and a central processing unit. The terminal device is equipped with a location information acquisition device, a voice collection device, and an accelerometer, which are used to acquire the user's environment and activity data in real time. This information is acquired on a general-purpose computing device such as a smartphone or tablet.

[0596] The terminal first performs initial processing on the collected data, such as noise reduction and format conversion. This initial processing includes noise reduction from audio data and formatting of GPS data. Once processing is complete, the data is encrypted and transmitted to the central processing unit via a communication method. SSL / TLS technology is used to ensure the security of the data during this process.

[0597] The central processing unit (CCU) is responsible for analyzing the received data. It uses machine learning algorithms to understand user behavior patterns and current situations, generating appropriate information. This includes predictive analytics based on user location and past behavior history, utilizing software libraries such as TensorFlow and PyTorch.

[0598] The generated information is constructed into specific messages using a generative artificial intelligence model. For example, if a user is in a specific location, information about appropriate nearby locations is generated based on a specific prompt such as, "Tell me about recommended cafes on my next commute route." This generated information is then notified to the user at the optimal time.

[0599] The server sends the generated information to the terminal device, which then presents it to the user via voice assistance or a display. Google Text-to-Speech is used for speech synthesis, providing natural-sounding guidance. Through this entire process, the system can provide users with a highly personalized information experience that improves their quality of life.

[0600] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0601] Step 1:

[0602] The device communicates with sensors to acquire user environment data. Location information is obtained from the GPS sensor, and voice data is obtained from the microphone. Inputs include the current time, location, and voice data, which are output as acquired data.

[0603] Step 2:

[0604] The device performs initial processing on the acquired data, including noise reduction and format conversion. Specifically, it removes unwanted background noise from the audio data and formats the location information into a standard format. The input is the acquired data, and the output is the formatted data.

[0605] Step 3:

[0606] The terminal encrypts the formatted data and sends it to the server using a communication method. The data is encrypted using SSL / TLS and protected from eavesdropping by third parties. The input is formatted data, and the output is encrypted data.

[0607] Step 4:

[0608] The server receives encrypted data, decrypts it, and performs analysis. It uses machine learning algorithms to extract user behavior patterns and current context. The input is encrypted data, and the output is behavioral prediction information as a result of the analysis.

[0609] Step 5:

[0610] The server uses an AI model based on the analysis results to create prompt messages and generate specific information for the user. For example, it might create a prompt for "recommended cafes along your next commute route." The input is behavioral prediction information, and the output is the generated prompt message and related information.

[0611] Step 6:

[0612] The server sends the generated information back to the terminal and controls the timing of information presentation to optimize it. The input is the generated prompt message, and the output is the determination of when the information should be presented to the user.

[0613] Step 7:

[0614] The device presents the received information to the user using voice announcements and screen displays. Google Text-to-Speech is used for speech synthesis, ensuring the information is conveyed in a natural manner. Input consists of the information to be presented and the timing of its presentation, while output is either voice or visual display for the user.

[0615] (Application Example 1)

[0616] 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".

[0617] In today's world, consumers are exposed to a vast amount of information daily, making it difficult to accurately identify truly useful information. Furthermore, while there is a need for more personalized information to stimulate customer interest and purchasing intent in physical stores, existing methods are insufficient. Therefore, a system is needed that provides personalized product information in real time based on customer location and behavioral patterns.

[0618] 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.

[0619] In this invention, the server includes device means for acquiring data on the user's surrounding environment and activities, functional means for encrypting the acquired data and transmitting it to a central device via a communication network, and method means for the central device to analyze the transmitted data and generate product information based on the user's activity status. This makes it possible to provide personalized product information to users in real time when they visit physical stores, thereby increasing their purchasing intent.

[0620] "User's surrounding environment and activity data" refers to information about the user's location and behavior, and specifically includes location information, operating status, and schedule information.

[0621] "Device" refers to hardware or software components designed for data acquisition, processing, and communication.

[0622] Encryption is the process of transforming data in a specific way so that it can only be understood by authorized recipients.

[0623] A "communication network" is a digital or analog infrastructure used to send and receive information.

[0624] A "central processing unit" is a processing device that analyzes collected data and generates information based on that analysis.

[0625] A "machine learning algorithm" is a mathematical method that uses large amounts of data to find patterns and gives it the ability to make predictions and decisions.

[0626] "Product information" refers to details and special offers related to products that consumers may be interested in.

[0627] A "portable information terminal" is an information processing device that a user can carry with them, and examples include smartphones and tablets.

[0628] This invention is a system for providing personalized product information in real-world stores. The system aims to present useful information to users by linking a mobile device with a central device.

[0629] Mobile information terminals acquire location information, schedule information, and other data in real time using multiple sensors (e.g., GPS modules and accelerometers) and calendar applications to capture data on the user's surrounding environment and activities. This makes it possible to quantitatively understand the user's trends and interests.

[0630] The acquired data undergoes initial processing, such as noise reduction, on the device before being securely encrypted. The encrypted data is then transmitted via the communication network to a central server. This server utilizes machine learning algorithms such as Python and TensorFlow to analyze user behavior patterns and generate highly relevant product information.

[0631] The product information obtained from the analysis is then transmitted back to the mobile device and presented to the user visually. For example, while a user is moving through a shopping mall, specific product sale information or coupons are displayed in real time, stimulating the user's desire to purchase. The information presented is controlled by a generation AI model to be delivered at the optimal timing.

[0632] A concrete example of its use is in a clothing store within a shopping mall, where, when a user is near a specific item, discount information for that item and recommendations for related products are instantly displayed on their smartphone screen. In this case, an example of a prompt message might be, "When a user shows interest in a particular category, please tell me how to generate and provide special offers related to that category."

[0633] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0634] Step 1:

[0635] The device begins acquiring information about the user's surroundings. This information includes GPS location data, date and time, and information from scheduling applications. The input is sensor and application data from the device, and the output is denoised pure data. This process begins when the GPS module captures location information as the user starts moving around the store.

[0636] Step 2:

[0637] The data acquired by the terminal undergoes basic processing in real time. This involves noise reduction and formatting standardization, converting the data into a processable format. The input is raw data, and the output is formatted data. This process equalizes temporary fluctuations in sensor data and removes noise.

[0638] Step 3:

[0639] The terminal encrypts the processed data and securely transmits it to the server via the communication network. The input is formatted data, and the output is encrypted data that reaches the server. The encryption algorithm protects the data from unauthorized access.

[0640] Step 4:

[0641] The server analyzes the received data using machine learning algorithms. It understands user behavior patterns and situations from the data and generates product information relevant to that context. The input is encrypted user data, and the output is product information based on the analysis results. It is also possible to utilize generative AI models and use generative prompts at the stage of outputting the analysis results.

[0642] Step 5:

[0643] The server sends the generated product information back to the terminal. The input is product information, and the output is the data received by the terminal. The server optimizes the timing and content of data transmission so that product information is presented to the user at the appropriate time.

[0644] Step 6:

[0645] The terminal presents received product information to the user visually or audibly. Input is data from the server, and output is displayed on the user interface or through an audio device. For example, specific actions may be taken, such as displaying discount information as a pop-up on the screen when approaching a particular product area.

[0646] 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.

[0647] This invention is a system that integrates an emotion engine to gain a deeper understanding of user emotions and enable personalized interactions. This system includes a terminal, a server, and an emotion engine, all of which work together.

[0648] First, the device collects the user's voice and facial data using sensors. This includes the user's speech and facial features captured by the camera. This data is sent in real time to the emotion engine, where emotion recognition takes place. The emotion engine uses various algorithms to analyze the user's voice tone, facial expressions, and even their word choice, and generates data to understand their emotions.

[0649] The emotion data generated by the emotion engine is sent to the server via the device. The server analyzes this data in combination with previously sent environment and activity data. Machine learning algorithms understand the user's current context and generate appropriate output information. This output information reflects the user's emotional state, allowing for personalization such as recommending relaxing content if the user is feeling stressed.

[0650] The output information generated by the server is sent back to the terminal and presented to the user either audibly or visually. The timing and content of the presentation are optimized based on the analysis results of the emotion engine, allowing for responses tailored to the user's emotions.

[0651] For example, if the emotion engine detects that the user is smiling when their face is visible to the camera, the server can determine this to be a positive emotional state and provide the user with information about new products or promotions. This enables natural interaction with the user and allows for the provision of information tailored to individual needs and circumstances.

[0652] The following describes the processing flow.

[0653] Step 1:

[0654] The device collects the user's voice and facial data through its microphone and camera. This includes the content of the user's speech, voice tone, facial movements, and expressions.

[0655] Step 2:

[0656] The device passes the collected data to the emotion engine. The emotion engine analyzes the audio and video data to estimate the user's emotional state in real time. For example, it identifies emotions such as joy, sadness, and anger from the pitch and volume of the voice and the movement of facial muscles.

[0657] Step 3:

[0658] The emotion engine returns the estimated emotion data to the device, encrypts this data, and sends it to the server. This ensures that the emotion information is securely protected as it moves on to the next processing stage.

[0659] Step 4:

[0660] The server comprehensively analyzes the transmitted emotional data in combination with previously acquired user environment and activity data. Based on this, the server understands the user's state and generates appropriate output information. For example, if the server determines that the user is tired, it may provide information such as recommending relaxing music.

[0661] Step 5:

[0662] The server sends the generated output information back to the terminal. The transmitted information is then organized into a format that is easily accepted by the user.

[0663] Step 6:

[0664] The device presents the received information to the user. This presentation can be done audibly, visually, or both, and the method and timing of the presentation are chosen considering the user's emotional state.

[0665] Step 7:

[0666] Users can provide feedback on the information presented. This feedback is reflected in the emotion engine and server analysis, and used to improve future information provision.

[0667] (Example 2)

[0668] 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".

[0669] In modern information technology, accurately understanding a user's emotional state and providing appropriate information based on that understanding is a significant challenge. Traditional systems relied heavily on user behavior history and basic pattern recognition, resulting in a lack of individualized responses that reflected the user's real-time emotions. This has led to a growing demand for more personalized service delivery.

[0670] 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.

[0671] In this invention, the server includes a sensor device means for acquiring the user's voice and facial expressions, a functional means for encrypting the acquired voice and facial expression data and transmitting it to a central processing unit via a communication path, and a method means for the central processing unit to analyze the transmitted data using an emotion analysis algorithm and generate output information based on the user's emotional state. This enables highly accurate analysis of the user's real-time emotional state and provides optimal information and support based on that analysis.

[0672] A "sensor device" is a device used to acquire a user's voice and facial expressions in real time, and includes a microphone and a camera.

[0673] A "communication path" is the network infrastructure used to securely transmit data from a terminal to a central processing unit.

[0674] The "Central Processing Unit" is a computer system that is primarily responsible for processing data transmitted by users and generating appropriate output information.

[0675] An "emotion analysis algorithm" is a computational method used to analyze a user's voice and facial expression data and infer their emotional state from it.

[0676] "Output information" refers to the content of information and services that the central processing unit generates based on the results of sentiment analysis and provides to the user.

[0677] This invention is a system that accurately understands the emotional state of users and provides personalized information. The main components of the system are a terminal, a server, and an emotion analysis engine, which work together in coordination.

[0678] The device is designed to collect user voice and facial expression data. Specifically, a highly sensitive microphone captures the user's voice, and a camera captures the user's facial features. These sensor devices acquire data in real time and transmit it to the device. The hardware used includes common smartphones and tablet devices, which also function as sensors.

[0679] The terminal encrypts the acquired data and sends it to the server via a secure communication path. The server passes the received data to an emotion analysis engine. This analysis engine utilizes deep learning technology to extract voice tone and facial expressions as features and infer emotional states. In particular, highly accurate analysis is possible using open-source machine learning frameworks (e.g., TensorFlow).

[0680] The server generates appropriate output information based on the analysis results. Specifically, information is personalized according to the user's emotions; for example, a user feeling stressed is provided with relaxing music. This generation process utilizes cloud-based database software (e.g., MySQL) and performs advanced contextual analysis based on historical data and trends.

[0681] For example, if a user displays a depressed expression, the system can analyze it and suggest a music playlist that promotes relaxation. It is also possible to create prompts tailored to the user's emotions using a generative AI model. An example of a prompt would be: "This system analyzes the user's emotions to provide personalized information. When the user looks at their smartphone camera and smiles, please suggest what information should be displayed."

[0682] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0683] Step 1:

[0684] The device collects user voice and facial expression data using sensor devices. Specifically, the device's microphone captures the user's speech as input, and the camera records the user's facial expressions. This information is stored on the device as digital data. This data is necessary for real-time analysis.

[0685] Step 2:

[0686] The terminal encrypts the collected voice and facial expression data. The digital data obtained in step 1 is used as input, and an encryption algorithm is applied to ensure security. Encrypted data is generated as output and sent to the server via the communication path. HTTPS is used as the communication protocol here.

[0687] Step 3:

[0688] The server decrypts the received encrypted data and passes it to the sentiment analysis engine. The input is the encrypted data sent in step 2, which is then decrypted back into its original format using the secure key. The output is the raw data available for analysis. This is then input into the sentiment analysis engine to prepare it for analysis.

[0689] Step 4:

[0690] The emotion analysis engine infers the user's emotional state using raw data. The input is the raw data obtained in step 3, and the emotion analysis algorithm (deep learning technology) analyzes the characteristics of voice tone and facial expressions. As output, a digital signal indicating the user's emotional state is generated.

[0691] Step 5:

[0692] The server generates appropriate output information based on the sentiment analysis results. The input is the sentiment state information obtained in step 4, which is analyzed in combination with a cloud-based database to determine an appropriate action plan. The output is personalized information presented to the user.

[0693] Step 6:

[0694] The terminal presents the user with output information sent from the server. The input is the information generated in step 5, which is presented visually through the voice assistant or display. Specific actions include the terminal playing healing music or displaying new product information.

[0695] (Application Example 2)

[0696] 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".

[0697] Traditional customer service struggled to immediately understand and respond to customers' emotions. As a result, providing personalized services that customers desired was difficult, hindering improvements in customer satisfaction. Furthermore, the lack of timely information provision based on the customer's situation and emotions led to a decline in the accuracy of service delivery.

[0698] In Application Example 2, the specific processing performed by the specific processing unit 290 of the data processing device 12 is realized by the following means. In this invention, the server includes an instrument means for acquiring data on the user's surroundings and actions, a functional means for encrypting the acquired data and transmitting it to a central device via a communication means, a method means for the central device to analyze the transmitted data and generate output information based on the user's behavioral state and emotional state, an emotion engine means for analyzing the user's emotions in real time and providing optimal output information based on the user's emotional state, and an instrument means for presenting visual information to the service provider based on the emotion analysis results and supporting customer service. This makes it possible to grasp the user's emotional state in real time and provide personalized customer service and timely information based on it.

[0699] A "device" is a device used to acquire data about the user's surroundings and actions.

[0700] A "communication means" is a medium that has the function of encrypting acquired data and transmitting it to a central device.

[0701] The "central device" is a core processing unit that analyzes transmitted data and generates output information based on the user's behavioral and emotional states.

[0702] An "emotion engine" is software that analyzes a user's emotions in real time and provides optimal output information based on that state.

[0703] A "device" is a machine that presents visual information to service providers based on emotion analysis results, thereby supporting customer service interactions.

[0704] "Output information" refers to information and instructions that are generated based on the user's behavioral and emotional states and provided to the user.

[0705] This invention realizes a system that grasps the emotional state of users in real time and provides personalized customer service based on that understanding. This system operates in stores visited by users, via smart glasses worn by employees.

[0706] The hardware consists of smart glasses equipped with a camera and microphone to detect the user's facial expressions and voice. The software environment incorporates an emotion engine, and deep learning frameworks such as TensorFlow and PyTorch are used to analyze data in real time.

[0707] First, the smart glasses, acting as the terminal, collect the user's facial expression data and voice tone. The collected data is transmitted to a central device via communication. The central device analyzes this data to identify the user's behavioral and emotional states. Through analysis using an emotion engine, optimal output information is generated according to the emotions the user is expressing.

[0708] This output information is visually displayed on smart glasses and provided to employees. For example, if a customer picks up a product and smiles, special promotional information or details related to that product will be overlaid on the glasses, enabling employees to provide appropriate service.

[0709] An example of a prompt message is: "A customer is smiling after picking up a new product. The emotion engine has analyzed this as positive interest. What promotional information should be displayed in this situation?" This allows the emotion engine to accurately grasp the user's needs and support efficient and effective customer service.

[0710] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0711] Step 1:

[0712] The smart glasses, acting as the terminal, collect the user's facial expressions and voice tone. Using the built-in camera and microphone, it detects the user's facial expressions and voice tone in real time as they view products in the store. The input at this stage consists of customer facial image data and voice data. These are then converted into a format suitable for emotion analysis.

[0713] Step 2:

[0714] The terminal encrypts the collected data and transmits it to the central device via a communication method. An encryption algorithm is used to ensure data security. The input is the facial image data and audio data acquired in step 1, and the output is the encrypted data for transmission.

[0715] Step 3:

[0716] The server analyzes the received data. Here, using an emotion engine and algorithms leveraging TensorFlow and PyTorch, it identifies customer emotions in real time. The input is encrypted data transmitted, and the output is numerical data based on the user's emotional state and interests.

[0717] Step 4:

[0718] The server generates optimal output information based on the analyzed sentiment data, tailored to the user's emotions and behavior. Using a generative AI model, it suggests promotional information to customers who have shown interest in a specific product. The input is the sentiment analysis results, and the output is the applicable promotional information and product details.

[0719] Step 5:

[0720] The terminal visually displays the generated output information on the smart glasses' display. This allows employees to share appropriate information based on their interactions with customers. The input consists of promotional information and product details, while the output is a visual presentation of information to the customer.

[0721] 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.

[0722] 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.

[0723] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0724] 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.

[0725] 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.

[0726] 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.

[0727] 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.

[0728] 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 based, for example, 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.

[0729] 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."

[0730] 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.

[0731] 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.

[0732] 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.

[0733] 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.

[0734] 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.

[0735] 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.

[0736] 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.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] 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.

[0741] 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.

[0742] The following is further disclosed regarding the embodiments described above.

[0743] (Claim 1)

[0744] Device and means for acquiring data on the user's surrounding environment and activities,

[0745] Functional means for encrypting acquired data and transmitting it to a central device via a communication network,

[0746] A method and means for a central device to analyze transmitted data and generate output information based on the user's activity status,

[0747] A terminal device means for presenting the generated output information to the user in audio or visual form,

[0748] A system that includes this.

[0749] (Claim 2)

[0750] The system according to claim 1, wherein the central device uses a machine learning algorithm to learn the user's behavior patterns.

[0751] (Claim 3)

[0752] The system according to claim 1, which includes a control unit for optimizing the timing of presenting generated output information to the user.

[0753] "Example 1"

[0754] (Claim 1)

[0755] A terminal device means for acquiring environmental and activity data based on the user's location, time, and voice,

[0756] Functional means for denoising and encrypting acquired data and then transmitting it to a central processing unit via communication means,

[0757] A method and means for a central processing unit to analyze received data, understand user behavior patterns and context through a learning algorithm, and generate output information,

[0758] A terminal device means for presenting the generated output information to the user by speech synthesis or visual display,

[0759] A system that includes this.

[0760] (Claim 2)

[0761] The system according to claim 1, wherein the central processing unit analyzes the user's behavior patterns using a learning algorithm and generates prompt sentences using a generative artificial intelligence model.

[0762] (Claim 3)

[0763] A control unit for determining the optimal timing based on the user's current actions and location when presenting generated output information to the user, according to claim 1.

[0764] "Application Example 1"

[0765] (Claim 1)

[0766] Device and means for acquiring data on the user's surrounding environment and activities,

[0767] Functional means for encrypting acquired data and transmitting it to a central device via a communication network,

[0768] A method and means for a central device to analyze transmitted data and generate product information based on the user's activity status,

[0769] A portable information terminal for visually presenting product information generated in the real world to a user,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, wherein a central device uses a machine learning algorithm to learn user behavior patterns and generates personalized suggestions related to the nearest commercial facility.

[0773] (Claim 3)

[0774] A control unit for optimizing the method for presenting generated product information to a user, according to claim 1.

[0775] "Example 2 of combining an emotion engine"

[0776] (Claim 1)

[0777] A sensor device means for acquiring the user's voice and facial expressions,

[0778] Functional means for encrypting acquired voice and facial expression data and transmitting it to a central processing unit via a communication path,

[0779] A method and means for a central processing unit to analyze transmitted data using an emotion analysis algorithm and generate output information based on the user's emotional state,

[0780] A terminal device means for presenting the generated output information to the user as audio or video,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, wherein the central processing unit uses machine learning algorithms to learn the user's behavior and emotional patterns.

[0784] (Claim 3)

[0785] The system according to claim 1, which includes a control unit for optimizing the timing of presenting output information generated according to the user's emotional state.

[0786] "Application example 2 when combining with an emotional engine"

[0787] (Claim 1)

[0788] A device or means for acquiring data on the user's surroundings and behavior,

[0789] Functional means for encrypting acquired data and transmitting it to a central device via communication means,

[0790] A method and means for a central device to analyze transmitted data and generate output information based on the user's behavioral state and emotional state,

[0791] A terminal device for presenting the generated output information to the user audibly or visually,

[0792] An emotion engine means for analyzing the user's emotions in real time and providing optimal output information based on the user's emotional state,

[0793] A device and means for presenting visual information to service providers based on emotion analysis results to support customer service,

[0794] A system that includes this.

[0795] (Claim 2)

[0796] The system according to claim 1, wherein the central device processes the user's behavior and emotional patterns using a machine learning algorithm.

[0797] (Claim 3)

[0798] A control unit for optimizing the timing of providing generated output information to a user based on the user's emotional state, according to claim 1. [Explanation of Symbols]

[0799] 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. Device and means for acquiring data on the user's surrounding environment and activities, Functional means for encrypting acquired data and transmitting it to a central device via a communication network, A method and means for a central device to analyze transmitted data and generate output information based on the user's activity status, A terminal device means for presenting the generated output information to the user in audio or visual form, A system that includes this.

2. The system according to claim 1, wherein the central device uses a machine learning algorithm to learn the user's behavior patterns.

3. The system according to claim 1, which includes a control unit for optimizing the timing of presenting generated output information to the user.

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