2830 results about "Smart device" patented technology

XCB poly-radio devices as finders, locators, scanners, sensors, and Bluetooth (BT) radio topology reporters as part of a global lost-and-found services system. XCB devices function as “smart objects” and combine dual transceivers operating in the telecommunications and the low power ISM radio spectra, with default power management overrides under local control of the BT modem. “Familiar zones” are defined, and a “familiarity index” versus a “strangeness index” based on meta-analysis of BT radio contact logs are used to assign location. Location, in concert with other sensor data, is coupled to conditional logic rules programmable by the user or the system administrator. Locations, determined by monitoring familiar and unfamiliar BT radio topology, trigger executable commands to smartphones, to smart devices generally, and to remote machines, and can for example, generate alerts to a user if radiotagged assets, children or pets have strayed, are lost, or are left behind. When tied to cloud predictive services or increased edge computing capacity, incipient loss of radiotag objects can be detected before an owner knows the smart object is lost. In another embodiment, XCB devices having a novel chip architecture for improved power management of cellular and MCU circuits mediated by BT radio modem that achieves “situational awareness” by monitoring the BT signal environment around the device.

The invention provides a multi-source data real-time fusion processing method and system for a mobile intelligent device, and relates to the technical field of data processing.The method comprises the steps that 1, multi-dimensional original data streams are collected in real time through a heterogeneous sensor array integrated by the mobile intelligent device, data streams of different sensors are aligned by applying a space-time synchronization mechanism, and the data streams of different sensors are obtained; generating an original data set with consistent time and space; 2, dynamic interpolation compensation operation is executed on the original data set, and a dynamic calibration framework is constructed based on the internal topological relation of the data flow to form a dynamic sensing domain; and generating an evolution sequence according to the data unit evolution behavior of the domain boundary, generating a space correction value through the evolution sequence and the offset feature of the preset reference, and generating preprocessed data fused with the space correction value in combination with real-time data correlation analysis. According to the method, dynamic adjustment is triggered through anomaly detection, the fusion parameters are updated through the sliding window, and real-time efficient fusion processing of multi-source data of the mobile intelligent device is achieved.

Provide a system. [Solution] A system including: a means for utilizing generative AI to provide information that serves as an alternative to vision; a means for accessing the AI ​​via a smart device or headset; a means for acquiring environmental information in real time; a means for recognizing surrounding objects and people; and a means for conveying information in the form of voice, audio messages, haptic feedback, etc.

The invention relates to the technical field of the Internet of Things, in particular to an intelligent device communication method and system based on the Internet of Things. The method comprises the following steps: acquiring spectrum occupation data, electromagnetic interference mode data and terminal equipment energy state data of a plurality of communication channels in an industrial environment through a preset distributed spectrum sensing network; performing adaptive spectrum analysis processing on the spectrum occupancy data and the electromagnetic interference mode data to obtain channel quality evaluation data including interference source characteristics and channel attenuation characteristics; and according to the channel quality evaluation data and a pre-acquired historical channel performance memory bank, performing priority ranking on the available communication channels to obtain dynamic channel priority data based on time continuity. The spectrum resource allocation condition in the industrial environment is monitored in real time through the distributed spectrum sensing network, the current interference can be identified, and the interference can be pre-judged through an advanced prediction model.

A semantic sensing system includes a processor, a memory and at least one sensing element, wherein the processor is configured to apply semantic drift or entropy to determine affirmative and non-affirmative circumstances based on inputs from the at least one sensing element to cause the system to perform semantic augmentation towards a first endpoint supervisor in relation with the affirmative and non-affirmative determinations.

An artificial intelligence based Offensive Cybersecurity Appliance or OCA for launching a cyber-offensive countermeasure aimed at effectively mitigating sophisticated cyber threats such as real-time Ransomware as a Service (RaaS) directives and emergent killware instigated by malicious-threat actors or cyber attackers. The system covertly uses a vast array of penetrative counter cyber attacks, based on an Advanced Persistent Threat or APT model, to circumvent encountered information security controls instituted by security-based components of a cyber attacker's Local Area Network or LAN. Intrinsically, the penetrative counter cyber attacks are constituted of multiple blended artificial intelligence based cyber attacks. The OCA subsequently uses these blended artificial intelligence based cyber attacks to render the hardware (including the cyber attacker's computer system used to instigate the cyber attack, discovered peripheral smart devices, and network-edge device such as a modem-router) interconnected to the cyber attacker's LAN inoperable by destroying the electronic components associated with that hardware.

An AI-driven system for personalized advertising in real time, where: ◯ an analytics unit to monitor and analyze user behavior in real time across multiple digital platforms such as websites, mobile applications, social media, and smart devices; the unit collects data on user engagement, browsing patterns, time spent, and content preferences to enable targeted, personalized advertising; ◯ a prediction module that predicts preferences and interests of a user, operatively connected to the user interaction data collection unit, wherein the prediction module uses machine learning models such as deep learning, recurrent neural networks (RNNs) and transformer-based architectures to predict interests of users based on historical interactions and inferred preferences; ◯ an emotion and sentiment analysis unit that assesses the user's mood in real time through computer vision, natural language processing (NLP) and voice analysis, whereby the analysis of facial expressions, voice pitch and linguistic mood is used to determine emotional states and receptivity to advertising content; ◯ an embodiment of a context awareness component in operational communication with the emotion analysis and mood unit, in some cases further augmented by various environmental and situational data such as the device type and its physical location, date and time, and the content processed in the device, processing methods, etc., in establishing adaptability and automatic ad placement to be as relevant as possible to the user and their status as prescribed; ◯ Use reinforcement learning algorithms and generative AI models to drive advertising with personalization engines. Creative elements, messaging, and presentations are dynamically adjusted in real time based on user responses to ensure advertising is personalized and always optimized for best performance; o a privacy-focused AI system with federated learning, differential privacy methods, and on-device AI processing to reduce targeted advertising while complying with international data protection laws such as the General Data Protection Regulation (GDPR) and California consumer privacy laws; o an operationally adapted ad delivery mechanism to engage with real-time bidding (RTB) networks, programmatic advertising exchanges and demand-side platforms (DSPs) and place advertisements through digital advertising networks, which guarantees the delivery of tailored advertising to the most relevant audience in real time; and o a contextual feedback loop in which the machine learning models used in the preference and interest prediction module and in advertising personalization The engine is continuously updated to reflect the latest user engagement data, improving personalization over time and optimizing advertising performance.

A semantic sensing system includes a processor, a memory and at least one sensing element, wherein the processor is configured to apply semantic drift or entropy to determine affirmative and non-affirmative circumstances based on inputs from the at least one sensing element to cause the system to perform semantic augmentation towards a first endpoint supervisor in relation with the affirmative and non-affirmative determinations.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a linkage method and system of an apartment network and intelligent equipment, and the method comprises the steps: collecting network operation data and equipment operation data of a target apartment in real time; based on the equipment operation data, determining currently achievable candidate service scenes of the target apartment; fusing the network operation data and the equipment operation data into a joint operation map of the target apartment through the space-time diagram attention network; a multi-agent depth deterministic strategy gradient algorithm is adopted to carry out network resource allocation and equipment control decision making on the joint operation map, scene adaptive optimization is realized in combination with candidate service scenes, and a linkage strategy of a target apartment is obtained; and generating a decision instruction based on the linkage strategy, and adopting the decision instruction to realize remote control of the network equipment and the intelligent equipment. Through deep fusion of the space-time diagram attention network and multi-agent reinforcement learning, dynamic collaborative management and control of the network and equipment are realized, and the resource utilization rate and the service response speed are improved.

The invention relates to an intelligent device safe networking method and system based on a dynamic secret key, and the method comprises the steps: obtaining a device identifier and an initial secret key of an intelligent device, recording a current timestamp when the intelligent device is started, and generating a dynamic secret key through a Hash algorithm in combination with the device identifier and the initial secret key; generating a corresponding networking request based on the dynamic key, and sending the dynamic key and the networking request to a corresponding gateway; verifying the received networking request and the dynamic key through the gateway to obtain a verification result, and sending the verification result to the intelligent device; and generating a corresponding session key through the intelligent device according to a preset Diffie-Hellman algorithm in combination with the verification result, performing encrypted communication with the gateway based on the session key, and sending corresponding encrypted data to the gateway. According to the invention, flexible key management and efficient encryption communication can be realized.

Embodiments described herein relate to systems and methods for an individualized intervention to generate and provide wellness pattern aware interventions. The system generates digital instructions and output for an online web application, device hosted application, smart device, or similar system. In one embodiment, the system provides the individualized intervention that matches device capacity, user context, and the best intervention type for a multi feel state longitudinal journey over a multi-day duration.

The invention relates to the field of hospital patient safety management, and particularly discloses a hospital patient safety management system and method based on data analysis, and the method comprises the steps: firstly obtaining the hospitalization record of a patient, the heart rate values, collected by an intelligent device, of the patient at a plurality of preset time points, and the blood pressure values, collected by the intelligent device, of the patient at a plurality of preset time points; and then feature extraction and correlation analysis are carried out on the three by using a deep learning technology, and finally whether a patient safety early warning is sent out is judged through a classifier, so that the patient nursing quality is improved, and the response speed to the health condition of the patient is improved.

A wireless smart wearable device includes a first portion and a second portion that can communicate with each other wirelessly. The first portion and the second portion respectively includes a first processor and a second processor, a first wireless communication module and a second wireless communication module, a first camera and a second camera, a first image acquisition module and a second image acquisition module, and a first clock and a second clock. The first image acquisition module transmits a first hardware trigger signal based on the first clock to the first camera, and the second image acquisition module transmits a second hardware trigger signal based on the second clock to the second camera. At least one processor enables the first wireless communication module and the second wireless communication module to continuously perform wireless communication with each other and / or wireless communication between both of them and a smart device, and determines a clock difference to achieve synchronization between the first and second hardware trigger signals.

A smart device and method for certified sterilization of personal computing / communications devices (e.g., smartphones, tablets, phablets) and other like personal effects provides an interior sterilization chamber fully or partially enclosed by a housing. The smart device includes an object tray or receptacle situated within the chamber for sterilization of any objects secured therein. Within the sterilization chamber, illuminators direct ultraviolet-C (UV-C) or other like disinfecting radiation at the external surfaces of the object according to a predetermined sterilization cycle or program. The selected sterilization cycle is executed by controlling the activation and / or articulation of the illuminators and / or the object tray or receptacle (either or both of which may be articulated to maximize the irradiated surface area of the object). When a sterilization cycle is complete, the control processors generate a unique token memorializing the successful completion of the cycle upon the sterilized object (e.g., a uniquely identified mobile device).

Various embodiments described herein relate to systems, methods, and non-transitory computer-readable media structured to perform server-to-device secure data exchange using a device access token. In an embodiment, a smart device receives, from a requestor entity provided to the smart device, an account data provisioning request for an account. Based on the account data provisioning request, an account identifier for the account is determined. In some arrangements, the account identifier comprises or is associated with a device access token. Based on the device access token, a data element associated with the account is determined. In some embodiments, the data element is accessible to the requestor entity only if it is not access-restricted based on the device access token. Based on the data element, an executable graphic rendering instruction is generated. The executable graphic rendering instruction is executed, which includes generating and displaying, on a user interface of the smart device, a dynamic account status indicator relating to the account.

The invention discloses a source-load interaction method based on an electricity utilization information acquisition system, and relates to the field of power distribution network operation and optimization. The method comprises the following four steps: S1, power consumption data acquisition: dynamically acquiring user power consumption, distributed energy output and power grid state data through intelligent equipment, and ensuring real-time accuracy; s2, load characteristic analysis: constructing a quantitative model to analyze characteristics such as a user load elastic coefficient and adjustable potential, establishing a supply and demand balance evaluation system in combination with distributed energy fluctuation, and determining peak and valley time periods and gaps; s3, generating an interactive strategy, and generating and issuing dynamic strategies such as electricity price excitation, load regulation and energy scheduling based on an analysis result; and S4, executing and feeding back the strategy, monitoring the execution effect in real time, and optimizing the strategy through a closed loop mechanism. According to the method, the problems of staticization, single mode and low efficiency of a traditional method are solved, the power grid supply and demand balance capability, the renewable energy consumption rate and the strategy accuracy are improved, and low-carbon and intelligent operation of the power distribution network is supported.

An authentication method includes registering in an authentication service associated with an application, a ID of a wearable device, disposing the wearable device proximate to a smart device that does not have the application, to provide the ID and an identifier for the application, wherein the smart device stores a document, receiving in the authentication service from the smart device, a communication including the ID, the identifier, and the document, wherein the smart device receives the application in response to the identifier, determining in the authentication service, whether an authentication service is approved in response to the ID, digitally signing in the authentication service, the document to form a digitally signed document, in response to the document and to determining that the authentication service is approved, outputting with the authentication service, the digitally signed document to the smart device.

The invention is suitable for the technical field of people flow density detection, and provides a scenic spot people flow density detection and early warning method and system, and the method comprises the steps: collecting real-time passenger flow data through an intelligent device disposed at a key node of a scenic spot; preprocessing the collected data and then constructing a multi-dimensional feature analysis model, wherein the multi-dimensional features comprise people flow composition features, behavior pattern features and environment coupling features; constructing a sample set based on feature vectors output by the multi-dimensional feature analysis model, outputting the sample set to a deep learning model to calculate a people flow density index, and triggering graded early warning when the people flow density index exceeds a first preset threshold value; generating multi-language voice early warning information according to the graded early warning, and performing regionalized broadcasting through directional sound field equipment; and when the people flow density index exceeds a second preset threshold value, generating a tourist shunting scheme including optimized path planning, thereby effectively improving the accuracy of people flow density detection and the timeliness of early warning.

The invention relates to the technical field of intelligent equipment management, in particular to an intelligent protective glasses equipment management method and system based on a cloud platform. The method comprises the following steps: monitoring corresponding dial scanning data, gas monitoring data and equipment external environment data in real time through a corresponding sensor built in intelligent protective glasses equipment, and uploading the data to a cloud platform; acquiring an equipment dial plate scanning use frequency, and performing dial plate scanning fault analysis and gas reading abnormity evaluation to obtain an equipment dial plate scanning fault probability and an equipment gas reading abnormity index; and carrying out equipment intelligent management analysis on the intelligent protective glasses equipment based on the equipment dial scanning fault probability and the equipment gas reading abnormal index so as to generate a cloud platform equipment abnormity maintenance management plan corresponding to the intelligent protective glasses equipment. According to the invention, comprehensive equipment state monitoring and accurate maintenance management work can be provided for the intelligent protective glasses, so that the use performance and user experience of the intelligent protective glasses can be improved.

The present application relates to the field of autonomous driving, and in particular relates to an occupancy grid prediction method, an occupancy grid prediction apparatus for implementing the method, a computer storage medium for implementing the method, and a smart device having the occupancy grid prediction apparatus. The method comprises: acquiring a pure visual image of the surroundings of a vehicle body; inputting the pure visual image into an occupancy grid prediction model; and using the occupancy grid prediction model to determine an occupancy result of an object in the pure visual image as a model output. The model output is an occupancy result in a cylindrical form, and the occupancy grid prediction model is constructed on the basis of a training dataset comprising sample images and truth value information of the sample images. The truth value information is directly generated from point cloud data or converted from occupancy information in a voxel form generated on the basis of the point cloud data.

Various embodiments described herein relate to systems, methods, and non-transitory computer-readable media structured to perform server-to-device secure data exchange using a device access token. In an embodiment, a smart device receives, from a requestor entity provided to the smart device, an account data provisioning request for an account. Based on the account data provisioning request, an account identifier for the account is determined. In some arrangements, the account identifier comprises or is associated with a device access token. Based on the device access token, a data element associated with the account is determined. In some embodiments, the data element is accessible to the requestor entity only if it is not access-restricted based on the device access token. Based on the data element, an executable graphic rendering instruction is generated. The executable graphic rendering instruction is executed, which includes generating and displaying, on a user interface of the smart device, a dynamic account status indicator relating to the account.

A system to capture a two dimensional digital source image of a scene by a user, including a smart device having a memory device for storing an instruction, a processor in communication with the memory and configured to execute the instruction, a digital image capture device in communication with the processor, said processor configured to capture a first two dimensional digital source image of the scene, said processor configured to execute an instruction to generate a second two dimensional digital image of the scene from said first two dimensional digital image of the scene via a camera angle rotation of between 1-180 degrees of said first two dimensional digital image of the scene, and a display in communication with the processor, the display configured to display a multidimensional digital image.

The present invention is a wireless vehicle tank monitoring system using non-contact fluid level sensors with adjustable sensitivity to measure fluid levels in recreational vehicle holding tanks. A smart device application on a portable computer receives real-time data via Bluetooth Low Energy (BLE) communication. The user-friendly interface displays graphical tank states. The system optimizes power efficiency through deep sleep and selective notification techniques, extending battery life. Sensors' sensitivity adapts to different tank wall thicknesses for accurate readings. The integration of non-contact sensors and power-efficient communication ensures reliability, easy installation, and real-time data transmission. This cost-effective solution effectively manages fluid levels in recreational vehicles.

The invention provides a smart home control method, a readable storage medium and a smart home system. The method comprises the steps that character characteristics of target home equipment are set at least according to equipment functions of the target home equipment, the character characteristics comprise character dimensions, and the character dimensions comprise at least one of gentle, cold, humor and fine; according to at least part of the voice data, the behavior data and the indoor environment data of the user, the emotional state of the user is determined, and the behavior data comprises use condition data of the user for the multiple intelligent devices; analyzing the emotional state and the character traits by using a large model, and at least determining an interaction strategy of the target home device, the interaction strategy comprising a response style and response content; and under the condition that the emotional interaction function of the target home equipment is started, at least controlling the target home equipment to interact with the user according to the interaction strategy. According to the invention, the personification and intelligence level of the smart home equipment is improved.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing location tagging. The radar system has sufficient spatial resolution to recognize different external environments associated with different locations (e.g., recognize different rooms or different locations within a same room). Using the radar system, the smart device can achieve spatial awareness and automatically activate user-programmed applications or settings associated with the different locations. In this way, the radar system enables the smart device to provide a location-specific shortcut for various applications or settings. With the location-specific shortcut, the smart device can improve the user's experience and reduce the need to repeatedly navigate cumbersome interfaces.

A firearm surveillance sight system is disclosed comprising a sight having a plurality of adjustably mounted cameras configured to provide a view of the environment surrounding the user via one or more smart devices. The sight is adjustably mounted to a mounting rail of a firearm, each of the one or more smart devices adjustably mounted to the mounting rail of the firearm or a helmet mounting rail positioned on a helmet of the user.

The invention discloses an intelligent Internet of Things home energy management system, and relates to the technical field of intelligent homes, the system comprises a multi-dimensional data sensing module which uses a sensor and an intelligent equipment interface to collect human physiological data, obtains environment and external data through an environment sensor and a weather API, and sends the environment and external data to a cloud server; historical physiological records, environmental regulation logs and user feedback data are called, timestamps and space coordinate information are added to all kinds of data at the same time, and the data are converted into data vectors and uploaded to a system database; through the multi-dimensional data sensing module, human body physiological data, environmental data and external data can be comprehensively collected, and a physiological state prediction model and a health risk identification model exclusive to a user are constructed in combination with historical records and user feedback, so that the personalized level of health management is improved, and the health risk of the user is improved. And through a chaos embedding biological rhythm prediction formula and a dynamic Bayesian health risk assessment formula, accurate prediction of the physiological state of the user and identification of the health risk are realized.

To provide a system.SOLUTION: A system includes: means for providing information to substitute a field of view by effectively utilizing generative AI; means for accessing AI via a smart device or a headset; means for acquiring information of an environment in real time; means for recognizing a peripheral object or person; and means for transferring information in the form of voice, audio message, or tactile feedback.SELECTED DRAWING: Figure 1

The present disclosure provides an artificial intelligence device comprising: a communication unit for communicating with the outside; an input / output unit for receiving a signal from a user and outputting a process result; and a control unit. The control unit: outputs a conversational interface for conversation with the user when the user accesses a video editing system; receives, through the conversational interface, a video uploaded by the user; outputs, through the conversational interface, a conversation for editing the video; extracts, from the conversation, a keyword for editing the video; and edits the video on the basis of the extracted keyword.

The invention discloses a multi-person voice separation and recognition system and method for real-time voice interaction, and belongs to the technical field of voice signal processing. The technical principle comprises the following steps: segmenting streaming voice through an audio acquisition module and storing the streaming voice into a circular queue; the preprocessing module converts the wav file into a standard wav file; the voice separation module adopts a SepFormer model to separate a speaker track; the voice recognition module executes end-to-end transcription on the voice through a Paraformer model; and the result management module binds the speaker number and the text and then performs structured output. The multi-thread asynchronous architecture enables acquisition, separation and identification to be processed in parallel, and low delay is realized by combining a circular queue; the SepFormer and the Paraformer are cooperated to improve the separation precision and the recognition accuracy of the overlapped voices; jSON / database multi-format output is supported, and the interaction requirements of real-time conferences and intelligent devices are met.