72 results about "Context based" patented technology

The invention discloses a vertical federal learning feature selection method based on context awareness, and belongs to the technical field of artificial intelligence and data privacy protection. According to the method, firstly, an unsupervised sparse network is utilized at a client to initialize the importance of local features so as to accelerate convergence and reduce calculation complexity; and then, obtaining the embedded representation of each client in a pre-training stage, and screening the embedded representation by combining context features through a server side, thereby indirectly identifying key features. In the feature selection stage, the client side performs local feature screening according to the significant embedded index issued by the server, and the influence of irrelevant features on calculation and communication is further reduced. According to the invention, an attention mechanism is introduced to dynamically evaluate contributions of different participants, so that fair weight distribution is realized. According to the method, through staged joint optimization, the communication and calculation cost in the federation training process is effectively reduced, and meanwhile, the prediction precision and interpretability of the model are improved.

System and method including an artificially intelligent assistant are described. An example method includes, in response to initiation of an artificially intelligent assistant at a head-wearable device, capturing contextual data. The contextual data includes one or more of image data, audio data, and / or sensor data. The method includes determining, based on the contextual data, a contextual cue, and providing a portion of the contextual data and a portion of the contextual cue to the artificially intelligent assistant. The method includes determining, by the artificially intelligent assistant, a user request based on the portion of the contextual data and the contextual cue, and receiving a response to the user request. The response is generated using a machine learning model. The method further includes causing the head-wearable device to present the response.

The invention relates to the technical field of data processing, in particular to a large model reasoning optimization method based on context incremental updating, which comprises the following steps: processing a multi-modal data stream through timestamp alignment and a filtering algorithm, extracting features by adopting a shared encoder and a private encoder, and realizing feature decoupling through a depth information bottleneck principle. A dynamic emotion map is constructed by using Gaussian process regression and a random process algorithm, and self-adaptive updating control is realized by combining meta-learning and Bayesian optimization. Incremental state management is realized by adopting a neural Turing machine, reasoning consistency is guaranteed through a generative adversarial network, model parameters are optimized in combination with a digital twin system and reinforcement learning, and a mental health service response is finally generated through a conditional generation model and hierarchical reinforcement learning. According to the method, the problem of asynchronism of multi-modal emotion feature dynamic evolution and context increment updating is effectively solved, accumulated drift of emotion state tracking is eliminated, and the continuity of reasoning logic is guaranteed.

The invention relates to a home violation risk identification method, system and device based on artificial intelligence and a medium. The method comprises the following steps: preprocessing audio data to obtain a voice segment; performing automatic voice recognition on the voice segments to obtain a dialogue text sequence, and performing acoustic feature extraction to obtain a time sequence acoustic feature sequence; performing key feature extraction based on context semantics and risk knowledge on the dialogue text sequence to generate a semantic feature vector; performing deep emotion mode learning on the time sequence acoustic feature sequence to generate an acoustic feature vector; performing multi-modal fusion on the semantic feature vector and the acoustic feature vector to obtain a fusion feature vector; and carrying out collaborative risk judgment on the fused feature vector to generate a result containing high, medium and low risk levels and corresponding judgment confidence coefficients. By adopting the method, the limitation of single modal analysis can be overcome, and the home violence risk can be identified more comprehensively and accurately.

This application proposes a model-based question-answering method and apparatus for generating context based on grammatical structure. The method includes: selecting multiple initial code fragments related to the question information from a target code library; obtaining a source code file containing any one of the initial code fragments; locating a first code block node corresponding to the initial code fragment from the abstract syntax tree of the source code file; locating all ancestor nodes of the first code block node from the abstract syntax tree and extracting the code structure information of each ancestor node; generating a target code fragment based on the code structure information of the first code block node and all ancestor nodes; concatenating multiple target code fragments to obtain a target context; and inputting the target context into a question-answering model so that the model outputs answer information based on the target context. The embodiments of this application can effectively improve the question-answering performance of the model.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and / or the like for performing context-based document classification prediction using a hierarchical attention-based keyword classifier machine learning framework. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform context-based document classification prediction using at least one of techniques using contextual keyword classifications, techniques using attention-based keyword classifier machine learning framework, techniques using a greedy matching indicator, and / or the like.

The subject technology relates to context-based response formulation for retrieval-augmented generation systems. An example method facilitating context-based response formulation for retrieval-augmented generation systems includes supplementing a query provided to a machine learning model with supplemental entity data, resulting in an augmented query, where the supplemental entity data is determined based on context information associated with the query and expected entity types associated with a determined intent of the query. The method can further include determining an estimated degree of error associated with a document retrieved by the machine learning model in response to the augmented query, and facilitating, in response to the estimated degree of error being lower than a threshold degree of error, generating a response to the query based on the document.

The invention discloses a scientific calculation and language generation collaborative reasoning method and system based on a physical separation hybrid expert architecture and a storage medium, and relates to the technical field of large model and scientific calculation fusion. According to the method, a novel architecture named PiMoE is provided, and intelligent collaboration of a language task and a numerical calculation task on Token granularity is achieved by integrating a frozen high-precision scientific calculation expert module, a text-to-calculation alignment module and a dynamic token router module. Wherein scientific calculation experts pre-train and freeze parameters on specific field data, and calculation precision and interpretability are ensured; the text-to-calculation module learns to map natural language input into numerical representation which can be processed by experts; the token router then dynamically decides, based on context semantics, that each Token should be generated by an expert or LLM. The training process adopts a three-stage decoupling strategy: in the first stage, independently training and freezing an expert model; in the second stage, a text-numerical value alignment module is optimized; and in the third stage, a router is trained to realize dynamic scheduling of experts and LLMs. During reasoning, the system is seamlessly switched between language generation and scientific calculation according to the semantic context, so that high precision of complex calculation is guaranteed, and semantic reasoning and generation capabilities of LLM are kept. According to the method, the problems that a large model is insufficient in precision, uncontrollable and unextensible in a scientific calculation scene are effectively solved, and deep fusion and dynamic collaboration of language understanding and numerical reasoning are realized.

The application discloses a zero-shot abnormal image detection method based on a learnable prompt. A learnable prompt generation module based on context optimization is designed, which contains a learnable prompt and an image abnormal state prompt that can be optimized. A multi-level visual coding feature of a to-be-detected image is obtained by using an image coding network of a visual language large model, and a text feature of a learnable prompt embedding is obtained by using a text coding network. A multi-level cosine similarity between the visual coding feature and the text feature is calculated to construct an image abnormal area calculation module, so that an abnormal area of the to-be-detected image is obtained. The learnable prompt avoids the complexity and instability of manually designed prompts, improves the accuracy of image abnormal detection, guarantees the effectiveness and efficiency of zero-shot learning, and greatly reduces the cost of pre-training of a visual language large model to a downstream task.

A method, non-transitory computer-readable storage medium and system is disclosed for using context-based dictionaries to search through multimedia data using input that specifies tags, words, phrases, descriptions, environments, emotions, sentiments, multimedia objects or content, or other relevant attributes. The system retrieves original content, analyzes and processes it, and presents to the user synchronized multimedia content and text content that is automatically tagged for searching. The system creates dictionaries containing word definitions and information that have been customized according to context; in addition, the system creates textual and non-linguistic attributes that enable and enhance searching functions; moreover, it enables modification of the dictionary entries as well as its searching functions through a feedback loop that may include input from human users and artificial intelligence programs; furthermore, the system may be used to create or modify a linguistic or a multimedia instantiation of a story.

The invention discloses an industrial control instruction stream anomaly detection method and device based on deep learning, and relates to the technical field of energy industry internet security. The method comprises the following steps: restoring to generate an interactive session with a complete context based on a directionally collected industrial control instruction and an equipment state data stream; performing semantic structured processing on the instruction in the interaction session to generate structured semantic information which can be understood by a machine; taking the interaction session and the corresponding structured semantic information as a training sample, and constructing and training an instruction stream semantic model and an instruction stream execution effect prediction model; processing the real-time industrial control instruction stream, and inputting the processed real-time industrial control instruction stream into the instruction stream semantic model and the instruction stream execution effect prediction model to obtain a prediction result; and judging an anomaly detection result of the real-time industrial control instruction flow according to the output prediction result and an anomaly judgment rule.

Context rules, machine learning, and user interface in context-based search techniques are described. In an implementation, inputs are received via a user interface describing a plurality of contexts associated with user consumption of digital content. A plurality of context rules are generated based on the inputs and a plurality of rule search results are generated based on context data. The context data details the plurality of contexts associated with user consumption of the digital content. One or more machine learning models are trained based on the context data. A determination is made that a transition point has been reached, and in response, a transition is performed between use of the plurality of context rules and use of the one or more machine-learning models in generating a plurality of subsequent search results.

A self-supervised learning framework for empowering instance discrimination in medical imaging using Context-Aware instance Discrimination (CAiD), in which the trained deep models are then utilized for the processing of medical imaging. An exemplary system receives a plurality of medical images; trains a self-supervised learning framework to increasing instance discrimination for medical imaging using a Context-Aware instance Discrimination (CAiD) model using the received plurality of medical images; generates multiple cropped image samples and augments samples using image distortion; applies instance discrimination learning a mapping back to a corresponding original image; reconstructs the cropped image samples and applies an auxiliary context-aware learning loss operation; and generates as output, a pre-trained CAiD model based on the application of both (i) the instance discrimination learning and (ii) the auxiliary context-aware learning loss operation.

This application discloses a self-supervised group behavior recognition method based on context-aware relation prediction coding, belonging to the field of video analysis technology. The method includes: acquiring group behavior video data; constructing a network structure for a self-learning model of group behavior features based on context-aware relation prediction coding, the network structure including a group label generator, an attention mechanism encoder, and a hybrid context Transformer decoder; performing self-supervised training on the network structure to obtain a target network for self-learning group behavior features; and fine-tuning the target network based on a small number of samples to obtain a target network for achieving group behavior recognition. This application captures contextual relationships through context-aware relation prediction coding, thereby obtaining a more comprehensive and accurate self-learning representation of group behavior. It is suitable for efficient and accurate group behavior recognition in situations lacking annotations.

The invention provides a semantic deviation optimization method for retrieval enhancement generation based on context fusion knowledge, and relates to the technical field of natural language process.The semantic deviation optimization method comprises the steps that for input target query, an initial large language model is guided through structured prompt to generate a hypothetical context set; optimizing the initial large language model through knowledge migration optimization according to the hypothetical context set to obtain an optimized large language model, so that the optimized large language model outputs the optimized hypothetical context set according to input target query; according to the optimized hypothetical context set, screening out a target document set from a candidate document set by adopting a double-encoder architecture; and according to the optimized hypothetical context set and the target document set, generating a reply text corresponding to the target query through the optimized large language model.

A method of generating a hint of a large language model includes receiving (101) a textual user input for controlling a lighting system, obtaining (103) contextual information indicative of one or more contextual characteristics of a context of the user, a subset of configuration data related to the context of the user and / or an example subset of lighting control behaviors related to the context of the user is selected (105) based on the contextual characteristic. The method further includes generating (107) a hint of the large language model and communicating (109) the hint to the large language model. The prompt includes the textual user input and the selected one or more subsets. The prompt excludes configuration data that is not selected a portion of the configuration data subset, and excludes examples that are not selected a portion of the example subset.

Systems and methods for providing brand-safe media based on context are disclosed. One computer-implemented method may include: receiving, at a computer system, caption data associated with a most recent segment of a live media stream; storing, using a processor associated with the computer system, the caption data to a data cache containing previously stored caption data associated with prior segments of the live media stream; providing, using the processor, the caption data to a trained machine learning model responsive to determining that the caption data is relevant to a program broadcast in the live media stream; generating, using the processor and based on output received from the trained machine learning model, a metadata tag to associate with the caption data; and utilizing the metadata tag to identify one or more segments of media content to include in the live media stream during a break in the program.

The invention belongs to the technical field of artificial intelligence, and discloses an artificial intelligence technology assistance-based education consultation service method, which comprises the steps of obtaining original voice data in an education consultation scene, and removing background noise through time-frequency masking processing to obtain high-quality de-noised voice data; performing character transcription on the de-noised voice data, performing adaptive text segmentation by using a pre-constructed term index database, and identifying and replacing terms which are wrongly transcribed; the method comprises the following steps of: normalizing oral statements in a text through anaphora resolution based on a context memory mechanism and an omission and completion technology based on dependency syntactic analysis; generating a preliminary scheme framework containing problem diagnosis, core suggestion and implementation steps by utilizing a large language model based on the standardized text; according to the method, the accuracy of voice recognition and the continuity of semantic understanding in education consultation can be effectively improved, and automatic and structured output of professional consultation schemes is realized.

This relates to a computer-implemented method for retrieving information from a knowledge graph (1). [Solution] An embedding machine learning system (10) generates embeddings (300) of the natural language input query (3) and embeddings (201, 202, 203) of classes and properties of the knowledge graph ontology (2). By comparing each embedding, the relevant classes and properties of the ontology (2) for the input query (3) are identified. Subsequently, a context-based ontology (2A) is determined as a subset of the ontology (2) containing the identified relevant classes and properties. A first machine learning system (20) determines a structured query (3a) based on the natural language input query (3) and the supplied context-based ontology (2A), and by executing the determined structured query (3a) against the knowledge graph (1), the information queried by the input query (3) is obtained from the knowledge graph (1).

This invention discloses an airworthiness document review method based on dynamic active learning and knowledge graph intelligent agents. It collects and processes multimodal heterogeneous data sources to obtain a standardized knowledge base. Based on this standardized knowledge base, it constructs an aviation domain knowledge system using dynamic active learning and human-machine collaboration mechanisms. It then constructs a knowledge graph and performs knowledge enhancement. Finally, it dynamically constructs a review context based on a retrieval enhancement generation method combined with the knowledge graph, and injects this context into an intelligent agent for intelligent data review and report generation. This invention achieves automated review of airworthiness documents through a dynamic active learning-intelligent agent review method, significantly improving the efficiency of manual review. It utilizes RAG technology to inject domain knowledge into the intelligent agent, improving the accuracy of general-purpose large language models in airworthiness review tasks. Through knowledge semantic driving and human-in-the-loop knowledge enhancement, it alleviates the problem of scarcity of high-quality data in the domain. Finally, it collaboratively utilizes models with different parameter scales, saving computational resources.

Methods, systems, devices, and non-transitory computer readable media for generating context-based text content are provided. The disclosed technology can include receiving content data comprising content associated with one or more data modalities. One or more associated with the content data can be determined. Based on inputting the content data and context data based on the one or more contexts into one or more machine-learned models, one or more context-based text segments based on the content data can be generated. The one or more machine-learned models can be configured to generate the one or more context-based text segments based on recognition of one or more features of the content data and the context data. Furthermore, context-based text content based on the one or more context-based text segments can be generated.

Systems and methods for suggesting related groups and recipients when replying messages in messaging applications. In response to the first received message, the system identifies a membership group including a sender and a recipient. Interface elements representing these common groups are displayed as selectable suggestions. The receiving user may select a group that is included in the reply with other users. The suggested groups are determined based on recent interactions, bi-directional contacts, and message content. The user may also create a new group for ongoing messaging according to the suggestions. By recommending shared groups and related recipients, the system enables efficient context-based selection upon reply. The suggestions aim at simplifying receiver selection through an intuitive interface and a machine learning algorithm. This improves the user experience of seamless messaging discussion with appropriate recipients.

This paper discloses the automated application of context-based image editing and personalized visual photograph preferences using artificial intelligence. A method for context-based insertion of three-dimensional (3D) objects into a photograph includes: analyzing the scene of the photograph using a machine learning model to determine the scene's contextual properties. The method also includes: selecting 3D assets from a set of 3D assets based on the contextual properties. The method further includes: posing the 3D assets using geometric information determined from the scene such that the posed 3D assets conform to one or more of the photograph's viewpoint, orientation, or surface geometry. The method also includes: adding 3D elements associated with the scene context, which are selected and positioned according to the contextual properties. The method further includes: generating an output photograph that includes the posed 3D assets and the 3D elements integrated into the photograph.

The invention discloses a context-based Chinese intelligent error correction and text optimization system, which belongs to the technical field of natural language processing, and comprises a context semantic coding module, a multi-dimensional error detection module, a literary style perception module, a language authentic degree evaluation module and a self-adaptive error correction optimization module, the context semantic coding module realizes multi-granularity semantic coding through a hierarchical attention mechanism; the multi-dimensional error detection module is used for detecting homophone errors, similar character errors, idiom misuse and grammar errors in parallel; the literary style perception module identifies the literary style type of the text and provides targeted optimization; the language tunnel degree evaluation module evaluates the language tunnel degree and recommends alternative expressions; the self-adaptive error correction optimization module synthesizes the output of each module to generate an error correction result, and realizes closed-loop parameter optimization based on user feedback, the error correction accuracy reaches 96%, and the Chinese writing quality is effectively improved.

Techniques are described for maintaining contextual data to support content-related actions. In an example, a system stories second content at a source. The source is associated with first content. The system sends, to a device, an object that indicates the first content. From the device at a first time, the system receives first data indicating a first request for the second content and including source information that indicates the source. From the device at a second time, the system receives second data indicating a second request for the second content, the second data including the source information, the first data and the second data received at a frequency indicated by the object. The system determines that the requests are associated with the first content based on the source information included in the received data, and stores third data indicating a presentation of the first content by the device.

Systems, computer program products, and methods are described herein for sequential cypher encryption based on contextual machine learning powered performance testing engine for production environment. The present disclosure is configured to create a first set of testing data via a machine learning model (MLM) within a performance testing engine; encrypt the first set of testing data with a first encryption key; insert the encrypted first set into a lower environment within an application; test the lower environment with the encrypted first set; create a second set of testing data via the MLM; transfer the first encryption key to the second set of testing data; delete the first set of testing data within the lower environment using the first encryption key; encrypt the second set of testing data; and insert the encrypted second set of testing data into the lower environment.

A method for information retrieval from a knowledge graph is provided. A computer-implemented method of retrieving information from a knowledge graph (1). An embedding (300) of a natural language input query (3) and embedding (201, 202, 203) of classes and attributes of an ontology (2) of a knowledge graph are generated by an embedding machine learning system (10). By comparing these inlays, relevant classes and attributes of the ontology (2) relative to the input query (3) are identified. Subsequently, context-based ontologies (3a) are determined as a subset of the ontologies (2), the subset comprising the identified relevant classes and attributes. A structured form query (3A) is determined by a first machine learning system (20) based on the natural language input query (3) and the provided context-based ontology (2A), and information queried by the input query (3) is retrieved from the knowledge graph (1) by executing the determined structured form query (3a) for the knowledge graph (1).

The invention relates to the technical field of medical image processing and data compression, and discloses a medical image lossless compression-oriented double-chessboard autoregression entropy coding method based on context prior learning, and the method comprises the steps: carrying out the lossy compression of an input single-channel gray medical image, and obtaining a lossy reconstructed image and a residual image thereof; performing feature extraction and context generation on the lossy reconstructed image by using a context priori learning network to realize structural perception modeling of residual features; dividing the residual image into complementary sub-images based on an autoregression entropy model of double-chessboard decomposition, performing pixel-level probability estimation by combining context prior and coded block information, and generating a residual bit stream through arithmetic coding; and finally, combining to obtain a lossless compressed bit stream of the medical image. The method effectively reduces the bit rate on the premise of ensuring the lossless reconstruction quality, is suitable for lossless compression of 2D and 3D medical images, and has the characteristics of high compression efficiency and strong robustness.