19 results about "Contextual awareness" patented technology

A system and method for generating and delivering personalized promotional offers in real time in response to user voice input. The system processes natural language queries and analyzes prior purchase history, inferred preferences, historical interactions, environmental context including device type, location, and time, and ongoing behavioral signals. Machine learning models adapt future offers based on accumulated user data to provide evolving personalization. The system supports multilingual speech recognition and can operate across smartphones, voice assistants, wearables, smart televisions, extended reality environments, and large language models. Offers may be delivered through audio, visual, or haptic interfaces to enable integration into commerce and advertising platforms. The combination of speech-triggered interaction, adaptive personalization, and contextual awareness enables dynamic, AI-driven promotional flows that operate consistently across multiple platforms and languages.

An embodiment includes identifying, from among the plurality of digital content datasets, a set of candidate textual items based on relevance to a specified subtopic using one or more natural language processing techniques. The embodiment groups candidate textual items into a predetermined number of groups using relevance scores and feature vectors. The embodiment trains a pre-trained encoder-decoder model using a designated group of selected textual items, where the pre-trained encoder-decoder model is pretrained to generate textual content according to a particular style of writing. The embodiment generates, using the pre-trained encoder-decoder model, machine-authored textual content in the particular style of writing resulting in an article about the specified subtopic based on the designated group of selected textual items. The embodiment also includes transmitting the article to a remote web server as an update for a website.

The invention relates to the technical field of intelligent medical information processing, and discloses an intelligent voice interaction robot based on natural language processing, which comprises a robot body and an interaction system mounted on the robot body, the interaction system comprises a voice receiving module, an optimization recognition module, a semantic analysis module, an intention recognition module, a task execution module and a content output module. According to the method, the robustness in a complex medical environment and a special user group is improved through a noise suppression and self-adaptive interaction strategy; through deep semantic analysis and a multi-task risk identification model, precise judgment and context perception of medical intentions and risk levels thereof are realized; and through task execution based on risk grading and multiple compliance check, high unification of intelligence and differentiation of medical service content generation and medical safety is ensured, so that efficient and reliable technical support is provided for medical fields such as medical consultation, pre-inquiry and triage processes.

Various systems (100) and methods (500) are provided for displaying information indicating whether a distance between a location of a feature in a region of interest of a subject and a location of a medical device (140) in the region of interest is within a safe range corresponding to the feature. The location of the feature in the region of interest may be determined (520). The position of the medical device (140) in the region of interest may be determined (530). The distance between the position of the feature and the position of the medical device (140) may be determined (540). Whether the distance is within the safety range may be determined (550). A display (218) may be controlled to display an ultrasound image (560) of the region of interest including the feature and the medical device (140) and to display the information (570) indicating whether the distance is within the safe range.

A method is provided. The method is executed by an interface engine implemented as a computer program within a computing environment. The interface engine executes detection and automation of computer activity. The method includes recording the computer activity across user interfaces (Uls) and automatically processing the computer activity utilizing a generative Al model to extract patterns. The patterns include portions of the computer activity that are similar or resilient to changes. The method includes determining existing automations according to the patterns.

The invention discloses a smart home control method, device and equipment, a medium and a product. The method comprises the following steps: acquiring multi-modal sensing data, and converting the multi-modal sensing data into a discrete feature descriptor sequence; constructing first prompt information according to the situation state definition information and the discrete feature descriptor sequence, and guiding a micro language model to generate a current situation state based on the first prompt information; and generating a control instruction sequence corresponding to the controlled household equipment according to the current situation state, and controlling the controlled household equipment to execute corresponding operation according to the control instruction sequence. By introducing a hierarchical context awareness mechanism, conversion from an original perception signal to a discrete semantic state is completed by using low-power-consumption signal processing and a miniature semantic model, and accurate recognition of a complex life situation under a low-computing-power condition is realized by using a zero-sample logical reasoning capability of the miniature semantic model. And the energy efficiency ratio and the response speed of edge calculation are improved.

This invention relates to the field of intelligent customer service, specifically disclosing a method and system for constructing a customer service knowledge base based on a knowledge graph. The method includes: identifying implicit needs by analyzing the spatiotemporal correlation between user actions and customer service dialogues, generating enhanced knowledge units that integrate explicit and implicit knowledge; constructing a knowledge graph, achieving adaptive optimization of the knowledge graph through real-time monitoring of relationship strength changes, forming a dynamic knowledge graph; capturing the user's current contextual information, matching it with the dynamic knowledge graph to generate contextualized knowledge subgraphs, and using multi-path reasoning to generate personalized response content; the system includes modules for implementing the above steps. This invention effectively solves the shortcomings of traditional customer service knowledge bases in implicit knowledge mining, dynamic updating, and deep contextual awareness, achieving improvements in knowledge acquisition from partial to comprehensive, knowledge base from static to dynamic, and responses from general to personalized, significantly enhancing the cognitive depth and service quality of intelligent customer service systems.

This invention discloses a method, apparatus, device, and medium for digital human live streaming interactive processing that integrates a large language model. It relates to the technical field of digital human live streaming interactive processing, including multimodal data acquisition and preprocessing steps, sentiment analysis and semantic understanding steps, response content generation and personalization enhancement steps, and multimodal interactive output steps. By constructing a multimodal data acquisition and preprocessing mechanism, it integrates input information from three modalities: voice, text, and video / image, achieving comprehensive perception of the audience's interactive intent in the live streaming scenario. Based on this, it utilizes a large language model for deep semantic understanding and contextual awareness, and combines a multimodal sentiment analysis module to comprehensively infer the audience's tone, speech rate, facial micro-expressions, and bullet screen text, enabling the digital human to accurately capture the audience's fine-grained emotional state changes, thereby generating highly human-like, context-appropriate response content.

A method for contextual awareness of a vehicle using perception-inspired event generation includes: receiving perception input data from vehicle sensors; and processing the perception input data to classify and generate parameters related to external entities near the vehicle. The method includes generating a hierarchical event structure that classifies and prioritizes the perception input data by categorizing external entities into regions of interest and prioritizing external entities within those regions based on a risk level value. Higher risk level values ​​indicate higher priority within the region of interest. The method also includes developing vehicle behavior planning based on the hierarchical event structure.

The invention relates to a multi-language learning interaction system, and the system comprises a multi-mode input processing module which receives voice, text, image and motion data; the situation sensing module is used for constructing a three-dimensional scene model through an environment sensor; the multi-language knowledge graph database is used for storing cross-language semantic networks and culture rules; the AI interaction engine is used for generating a dynamic task based on the user capability portrait; the AR feedback module is used for superposing interactive virtual language elements in a physical space; and the adaptive evaluation system optimizes the learning path in real time. The method is characterized in that a real scene is dynamically bound through a sensor array and a knowledge graph to generate a training task, and the problem of learning disjunction is solved; a sensitive semantic recognition engine is used for inserting culture annotations in real time, and the cross-culture error rate is reduced; gamification modes are dynamically switched based on physiological signal analysis to maintain learning efficiency; and full-function off-line support is realized by adopting a knowledge distillation technology.

A method to use a pull request scoring and prioritization (PRSP) engine to incorporate contextual awareness when reviewing a pull request (PR) of code from a developer is described. Using The PRSP engine addresses the contextual complexities of software development and frees up reviewer time by incorporating contextual awareness into a quality and scoring process. The PRSP engine uses a machine learning model to develop scores for the PR which are used to calculate a quality for the PR. Based on the quality, the PRSP engine after review by a PR reviewer either merges the code of the PR with a main code or sends the code back to the developer to be corrected.

The envisioned Artificial Intelligence Long-term Memory System presents a significant advancement in artificial intelligence (AI) by addressing the challenge of transient memory in AI language models. This innovation introduces a hardware-centric approach to augment the memory faculties of AI language models, enabling them to store, access, refine, and incorporate specific data points for deeper user engagement. The enhancements focus on long-term memory, facilitating AI models to remember and build upon past interactions, thus offering a more natural and intuitive interaction between computers and users. The potential applications span from personal computing to complex medical diagnostics, marking a pivotal step towards AI models functioning with contextual awareness and memory retention akin to human interaction.

Processing logic maintains a data item tag hierarchy in view of user context information and identifies, from the data item tag hierarchy, a highest ranked data item tag of a plurality of data item tags associated with a data item, the plurality of data item tags representing a content of the data item. The processing logic further storing the data item on a memory device at a first shared storage location together with one or more additional data items with which the highest ranked data item tag is also associated.

This invention relates to an automatic segmentation method and system for aortic dissection images. Utilizing the temporal continuity of two-dimensional CT image sequences, and combining label combination processing, dynamic anchor point selection, and a bidirectional propagation mechanism, it achieves cross-frame matching to supplement global information in long sequences, solving the problem of incomplete single-frame labels. Simultaneously, this application employs an adaptive label propagation strategy formed by label combination processing, dynamic anchor point selection, and the bidirectional propagation mechanism to correct propagation errors and focus on feature learning of similar combinations, balancing efficiency and accuracy. Furthermore, by combining spatial attention and channel attention mechanisms to enhance contextual awareness of true and false cavity boundaries in single-frame labels, it improves high-quality local features, achieving high-precision segmentation of three-dimensional CT images.

A surgical hub may be provided for prioritizing data on a display using situational awareness of the medical instrument. A first surgical task using the medical instrument during a medical procedure may be determined based on the context data. A second surgical task using the medical instrument may be determined based on the first surgical task and the context data. A message may be transmitted that may instruct the display to prioritize displayed data associated with the second surgical task.

The invention relates to the technical field of medical care informatics, in particular to a health data acquisition and analysis platform for senile chronic disease management, which comprises semantic perception edge nodes, space-time alignment transmission middleware, a causal reasoning analysis engine and a clinical interpretable intervention generation layer, end-to-end closed loop optimization is realized through sensing end context awareness, transmission layer dynamic semantic packaging, analysis layer state transition graph neural network modeling and output layer clinical path driven intervention instruction generation. According to the method, the intervention accuracy and timeliness are remarkably improved, the user compliance is enhanced, and the system power consumption is reduced.

A method is provided. The method is executed by an interface engine implemented as a computer program within a computing environment. The interface engine executes detection and automation of computer activity. The method includes recording the computer activity across user interfaces (UIs) and automatically processing the computer activity utilizing a generative AI model to extract patterns. The patterns include portions of the computer activity that are similar or resilient to changes. The method includes determining existing automations according to the patterns.