45 results about "Behavioral pattern" patented technology

A behavioral authorship verification system captures and analyzes multi-modal behavioral patterns during content creation to authenticate human authorship. The system comprises a processor executing behavioral analysis modules that generate comprehensive behavioral fingerprints distinguishing genuine human authors from AI-generated content and impostor authorship. A sentence progression mapping module detects sentence boundaries and captures intermediate composition states including additions, deletions, and modifications. A multi-modal input analysis module monitors keystroke dynamics including flight time and dwell time while detecting paste events and input method transitions. A behavioral pattern recognition engine generates user-specific baselines from historical sessions and computes deviation scores using statistical distance metrics. An anomaly correlation module aggregates behavioral deviation signals using weighted fusion algorithms to detect sophisticated mimicry attempts. An authorship scoring engine synthesizes outputs into unified confidence scores while maintaining temporal authorship chains. The system enables real-time authorship verification during content creation rather than post-hoc analysis.

Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method generates a first sparse similarity matrix for nearest neighbors of a plurality of data points. The data points each characterize a pattern of activity associated with an account. The method generates a second sparse similarity matrix for random neighbors of the plurality of data points. The method recursively clusters the plurality of data points based on the first sparse similarity matrix. The method quasi-supervises the recursive clustering based on the second sparse similarity matrix to stop the iterative clustering when the data points are split into N clusters. The value of N is not pre-determined. The method detects that the individual data point has changed clusters, indicating anomalous activity. And, the method generates an electronic alert that the anomalous activity is associated with the account.

The invention relates to a place recommendation method based on a shared hypergraph mask. The method comprises the following steps: selecting a public data set and constructing a recommendation model; constructing a trajectory hypergraph by using long and short trajectories of a user, inputting the trajectory hypergraph into the model, calculating user characterization and trajectory characterization of the long and short trajectories through an auto-encoder module in the model, and calculating fusion characterization of the user characterization and the trajectory characterization; a loss function # imgabs0 # is constructed by adopting a hypergraph, global and local loss functions # imgabs1 # and # imgabs2 # are constructed by using user characterization of long and short tracks and track characterization through a double-level contrast learning module in a model, local tracks obtained after position information is embedded are embedded into Eu, a prediction result # imgabs3 # is obtained through a Transformer encoder, and a loss function # imgabs4 # is constructed at the same time, and four losses are constructed through the prediction result # imgabs3 # and the loss function # imgabs4 #. The loss function forms a model overall loss function # imgabs5 # for training the model, and a final trained recommendation model is obtained. By adopting the method, discrete data can be effectively processed, the characteristics of long and short term behavior modes can be accurately captured, and deeper and more accurate interest point recommendation can be provided.

Systems, methods, and other embodiments associated with quasi-supervised clustering for activity pattern characterization and anomalous activity detection are described. In one embodiment, a method generates a first sparse similarity matrix for nearest neighbors of a plurality of data points. The data points each characterize a pattern of activity associated with an account. The method generates a second sparse similarity matrix for random neighbors of the plurality of data points. The method recursively clusters the plurality of data points based on the first sparse similarity matrix. The method quasi-supervises the recursive clustering based on the second sparse similarity matrix to stop the iterative clustering when the data points are split into N clusters. The value of N is not pre-determined. The method detects that the individual data point has changed clusters, indicating anomalous activity. And, the method generates an electronic alert that the anomalous activity is associated with the account.

The invention is suitable for the technical field of smart home control, and provides a skill generation method, control equipment and a control system for an intelligent agent with a body. The method comprises the following steps: acquiring a first behavior sequence formed by interaction events of a user and smart home equipment in a first time period; performing multi-dimensional mining on the first behavior sequence, and mining a plurality of behavior modes; for any behavior mode, generating a corresponding skill package based on the behavior mode; and when a trigger source is detected, the intelligent agent controls the corresponding smart home equipment to execute corresponding actions according to the generated skill packages. According to the invention, behavior modes can be mined from multiple dimensions, complex behaviors can be understood, and self-adaptive smart home personalized services can be realized.

The invention provides a user portrait recommendation method based on a high-order structure and semantic enhancement, and the method specifically comprises the following steps: S1, introducing a multi-hop adjacent matrix to capture a high-order behavior pattern in an interaction graph for the interaction graph of a user and a project through a high-order structure maintenance module of user grouping, and employing a low-rank approximation and clustering method to obtain a high-order behavior pattern in the interaction graph; grouping the users based on the behavior similarity; s2, extracting a representative keyword set from items interacted by the same group of users, and obtaining group-level keywords of the users; s3, through a portrait perception recommendation module based on cross-view comparative learning, user semantic embedding and project semantic embedding based on keywords are constructed; obtaining user structure embedding and project structure embedding according to a collaborative structure between a user and a project in the interaction graph; then, alignment of project semantic embedding and project structure embedding is achieved through cross-view comparative learning; and S4, calculating a user-project combination score and generating a recommendation result. According to the invention, the recommendation performance is enhanced.

An embodiment of the present invention provides a management server for improving behavioral habits, comprising: a communication module; a memory for storing a behavioral habit improvement service provider program; and a processor for executing the program. The processor provides a user interface to a user terminal, based on the execution of the program, which inputs behavioral pattern information including behavioral habits, triggering conditions, and solutions, as well as time information of fulfilling the behavioral pattern information. It matches the behavioral pattern information, time information, and user identification information input from the user terminal and stores them in a database. It generates behavioral pattern feedback that displays behavioral habits, triggering conditions, and solutions on a single screen and provides behavioral pattern feedback through the user interface.

A system is provided.SOLUTION: A system comprising: means for collecting location information of a user; means for transmitting the collected location information to a server; means having a generative AI for analyzing the collected location information and learning a behavior pattern of the user; means for detecting a behavior deviating from the analyzed behavior pattern; and means for notifying a registered contact when the deviating behavior is detected.SELECTED DRAWING: Figure 1

The disclosure describes a method of generating a target profile including the target's sequence of events (SOE) for a task. Such target profile sequence of events is derived from several source group's transactions, where any source group's transactions cannot be shared with other source groups but the derived target group's profile is the only information that is shared. Source-side information is periodically extracted for a plurality of sources that each interact with a plurality of targets. The information includes source stages, resources, and stage transition events for a task with a target. Source information is used to generate a set of normalized stages, and a set of normalized events for transitioning between the stages of the set of normalized stages. An artificial intelligence (AI) model is trained using the source information. The AI model can generate a target profile with target process information inferred using the trained model. The target process information can include the target's identifiers for each stage, an estimated duration of the stage, deliverables for the stage, and one or more stage transition events for the stage.

To detect ransomware infections of an endpoint at an early stage and prevent data encryption and data exploitation even if ransomware countermeasures are not sufficiently implemented on the endpoint.SOLUTION: A storage access monitoring method according to the present invention acquires, as operation information, the operation status of a volume 40 constituting a storage device 30 from which an endpoint (host 10) can read / write data through a network 50, and determines whether or not a behavior related to the latest operation information is abnormal by performing any one or more of a comparative analysis step of comparing the latest operation information with past operation information, a pattern comparison step of determining the presence or absence of a behavioral pattern indicating the possibility of an effect of the ransomware in the latest operation information, and a trend comparison step of determining whether or not an operation related to the latest operation information is different from a normal operation related to the past operation information.SELECTED DRAWING: Figure 5

This invention relates to a slow disk simulation method and system based on data augmentation, belonging to the field of data processing technology. It solves the problems of insufficient realism, simplistic behavioral patterns, and inability to accurately reflect the complex characteristics of real slow disks in existing slow disk simulation methods. The method includes: calculating the correlation coefficients between key indicators based on multiple key indicators of the slow disk by collecting corresponding time-series data, and constructing a feature matrix model; performing data augmentation on the time-series data of each key indicator based on a dynamic time warping algorithm to generate augmented time-series data; validating the augmented time-series data based on the feature matrix model, and storing it in the augmented time-series dataset if the verification passes; matching the real-time time-series data of each key indicator of the target disk with the augmented time-series dataset, and injecting delays into the I / O requests of the target disk based on the matched augmented time-series data when a match is successful, to simulate slow disk behavior. This achieves slow disk simulation covering various real-world faults.

The embodiment of the invention provides an internet behavior analysis method and system applied to a smart library, and the method comprises the steps: carrying out the dynamic analysis of an internet behavior sequence of a user in the smart library, generating a behavior unit sequence containing an operation instruction unit and a resource access unit, and generating an association rule set between behavior units; performing behavior pattern evolution tracking based on the behavior unit sequence and the association rule set to obtain stage transition features and pattern variation nodes of the behavior pattern, and constructing a mapping relationship between the stage transition features and the pattern variation nodes and digital resource category attributes by combining the attribute features of the digital resources of the smart library; and according to the mapping relationship, generating an adaptation scheme of the user and the digital resource, the adaptation scheme covering the resource access path optimization suggestion and the resource association recommendation sequence, and finally pushing the adaptation scheme to the user terminal of the smart library to guide the user to perform digital resource access operation, thereby improving the resource acquisition efficiency of the user, and improving the user experience. And the smart library service quality is enhanced.

A system is provided.SOLUTION: A system comprising: means for automatically adjusting lighting, temperature, music, and video based on user behavior patterns and preferences; means for analyzing the behavior patterns and outside temperature and predictively adjusting appropriate room temperature; and means for providing interactive entertainment in response to user responses and selections.SELECTED DRAWING: Figure 1

This invention relates to the field of secure communication application technology, specifically to a host asset identification and profiling method based on multimodal network feature fusion enhancement. First, a data collection agent is deployed at the core node of the target network to collect multimodal data through traffic mirroring and log crawling, and a cross-modal aligned multi-source network data representation is constructed. Next, a TCN-BiLSTM model combined with an attention mechanism is used to extract deep periodic features, and a GraphSAGE model is used to generate graph embedding vectors bound to topological relationships. A pre-trained BERT model is used to obtain global protocol semantic feature vectors and communication intent distribution. Subsequently, a modality-specific mapping is used to unify dimensions, and a two-layer mechanism of intra-modal self-attention and inter-modal mutual attention is constructed to dynamically allocate weights, fusing multimodal features to extract core features of host assets. Finally, the fused features are mapped to structured text, and a CLM strategy combined with LoRA technology is used to fine-tune the LLM, generating a natural language asset profile containing role positioning, behavioral patterns, and topological relationship logic.

This invention discloses a method for detecting and classifying Android malware based on multi-feature fusion deep learning. The method is accomplished by obtaining a sample of an APK to be tested, feeding the sample into a trained model, and obtaining detection results. The invention uses deep learning to capture the data flow patterns of different types of malware from static taint paths. The taint paths are used as a set of features for detecting and classifying Android malware. Feature fusion is performed using a Wide & Deep model, where the Wide portion processes two types of features: sensitive API calls and dangerous permissions, and the Deep portion processes static taint paths. The fused features add semantic information from the static taint paths, enabling the learning of behavioral patterns of different malware, improving the accuracy and robustness of the model.

An object of a system according to an embodiment is to integrate data from different devices and extract a behavior pattern in a cross device.SOLUTION: A system according to an embodiment includes a behavior pattern extraction unit. The behavior pattern extraction unit integrates data from different devices and extracts a behavior pattern in a cross device.SELECTED DRAWING: Figure 1

The system according to this embodiment aims to provide individually optimized savings suggestions based on the user's purchase history, behavioral patterns, and asset status. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's purchase history, behavioral patterns, and asset status. The analysis unit analyzes the data collected by the collection unit. The generation unit generates prompts that take into account the latest financial information based on the data obtained by the analysis unit. The provision unit provides individually optimized savings suggestions based on the prompts generated by the generation unit.

We provide the system. [Solution] A device that receives information acquired from a video acquisition device, An artificial intelligence analysis device that analyzes behavioral patterns based on the aforementioned information and identifies specific behaviors, A device that detects identified behavior as problematic behavior if it exceeds a predetermined standard, A device that generates response procedures and warnings based on detected problematic behavior, A device that transmits the aforementioned warning to a portable terminal, A system that includes this.

The present application discloses a data annotation method, device and storage medium based on graph structure and community discovery, which belongs to the field of data processing. The present application obtains customer transactions, product attributes, behavior trajectories and environmental data, and constructs a heterogeneous graph network including a basic physical layer (transaction association and product attribute mapping), a behavioral semantic layer (behavioral pattern and semantic association), and an environmental association layer (dynamic impact of the environment). A community discovery algorithm is used to mine customer groups with cross-departmental business value, and a three-level labeling system is constructed to quantify basic value attributes and fluctuation coefficients, behavioral patterns and product preferences, and dynamic trajectory characteristics. This achieves a unified understanding of customer behavior, in-depth mining of multi-dimensional customer relationships, dynamic evolution capture of customer value, and hierarchical customer cognition construction, solving the problems of data silos caused by task orientation in traditional methods, the constraints of dynamic value mining caused by a single data dimension, and the restrictions of hierarchical cognition construction caused by the flattening of the labeling system.

The present invention discloses a system (100) for determination of behavioral pattern based on browsing behavior, wherein the system (100) comprises a profiling unit (101) for profiling the browsing behavior of the first user in a user age group. The system (100) comprises a processing unit (102) for determining the correlation between the browsing behavior of the first user and other age-related behaviors. The system (100) further comprises a mapping unit (103) for creating of an age-related correlation map by correlating the browsing behavior and other age-related behaviors. Further, the system (100) comprises an analysis unit (104) for determining the similarities in the behavior of different age groups in a geographical location and a feedback unit (105) for reviewing the behavioral pattern determination for improving the performance of the system (100).

We provide the system. [Solution] Information collection means for collecting user transaction history data and behavioral data, A data analysis means for analyzing the collected data and identifying the user's past purchasing trends and behavioral patterns, A proposal generation means for extracting relevant facilities based on the user's current location information and generating preferential treatment according to the analysis results, A means for sending the generated discount information to the user's terminal, A prompt generation means that uses a generative AI model to create prompt sentences related to user trends, A system that includes this.

The invention discloses an indoor personnel video behavior process identification system based on target detection and an MS-TCN + + algorithm, accurate detection and identification of a personnel behavior process in an indoor free scene are realized through cooperative processing of target detection, behavior triggering and time sequence identification, and the system utilizes adaptive switching of high and low frame intervals to realize accurate identification of the personnel behavior process in an indoor free scene. Details when behaviors occur are fully captured, and the calculation amount in a non-behavior period is remarkably reduced, so that automatic segmentation and positioning of behavior segments in a real-time video stream are realized; through key limb detection and cross-frame integration based on YOLOv7, continuity and detection stability of target tracking are guaranteed, and detection errors caused by shielding, posture changes and multi-person interaction are effectively coped with; and in combination with multi-scale time sequence modeling and inter-frame consistency constraint of the MS-TCN + + model, the system can extract behavior laws in long time sequence data and suppress the influence of noise frames, so that the recognition accuracy and robustness are improved while the real-time performance is ensured.

This invention provides a fuzzy testing mutation scheduling optimization method based on the mayfly algorithm, belonging to the field of fuzzy testing. It includes: Step 1, constructing a lightweight mayfly population for each seed in the fuzzy testing process, where individuals in the population represent a probability distribution of a set of mutation operators; Step 2, selecting mutation operators for each seed in the queue according to a uniform probability distribution, performing mutation, and simultaneously collecting path coverage and crash detection as feedback information, and calculating fitness scores through normalization and weighting; Step 3, simulating diverse behavioral patterns of the mayfly population based on individual fitness scores, dynamically updating the probability distribution of mutation operators, and optimizing the probability distribution; Step 4, finally applying the optimized probability distribution to the next round of seed mutation to generate more effective test cases, and repeatedly executing the above steps to achieve continuous optimization of the mutation scheduling strategy, improving fuzzy testing coverage and vulnerability discovery capabilities.

A system is provided.SOLUTION: A system comprising: means for collecting behavior data of a user; means for accumulating the collected behavior data; means for analyzing the accumulated behavior data and extracting a behavior pattern; means for suggesting a next behavior based on the extracted behavior pattern; and means for presenting the suggested behavior to the user.SELECTED DRAWING: Figure 1

This invention discloses a method for mining and guiding behavioral patterns based on user data, belonging to the field of data retrieval technology. The key technical points include: acquiring the user's current behavioral data; obtaining behavioral codes based on the current behavioral data and a medical effectiveness rule sub-library in a historical consensus database; the historical consensus database being composed of data stored in a blockchain; obtaining effective behavioral sequences based on the behavioral codes and an effect association rule sub-library in the historical consensus database; and determining the action parameters in the effective behavioral sequences based on a phased rule sub-library in the historical consensus database. This invention encodes behavior using user behavioral data and combines it with a historical consensus database to filter effective behavioral sequences that fit the behavioral codes. It does not passively rely on general medical guidelines but generates behavioral sequences based on group patterns that conform to medical standards and are adapted to the user's action habits and recovery speed, guiding the user through training.

The present application relates to the field of brain-computer interface technology, and in particular to a method, device and apparatus for predicting user behavior based on EEG in smart glasses. The method comprises: collecting multi-channel EEG data of smart glasses to obtain original signals; segmenting data based on original signals, obtaining behavioral features for sequence analysis, obtaining behavioral samples to construct a behavior library; performing time series mapping on the behavior library, obtaining behavioral paths to extract dependencies and construct sequence rules; generating prediction tags based on sequence rules to perform environmental analysis, obtaining scene features for correlation matching to construct fusion sequences to generate prediction results, perform feature decomposition, mark prediction intervals for rule construction to determine confidence, generate judgment sequences and extract behavioral patterns; obtain prediction parameters based on behavioral patterns; construct update rules based on prediction parameters, generate prediction strategies based on update rules, and output behavior prediction results based on prediction strategies. The accurate extraction of behavioral intention information in EEG signals is achieved.

This invention relates to the field of trusted computing technology and discloses a terminal access authentication method and system based on trusted computing. The method includes: performing a step-by-step extension measurement on the hardware root of trust to obtain an initial trusted chain construction result; performing time-series dependency analysis on the construction result to obtain a runtime timing benchmark; using the time interval between the client key exchange parameters and the micro-timestamp of the digital certificate in the protocol handshake message as a behavioral pattern signal to evaluate the perturbation factorization of the system event sequence to obtain an endogenous temporal perturbation factor; performing an overlap test on the behavioral pattern signal and the endogenous temporal perturbation factor to obtain an identity state linkage indicator; determining the dynamic trust risk level of identity authentication based on the identity state linkage indicator, and making an authentication decision on the identity authentication process based on the dynamic trust risk level to obtain an identity authentication result. This invention can improve the accuracy of identity authentication decisions and the efficiency of adaptive response in complex attack scenarios.

The system according to the embodiment aims to predict the behavioral patterns of a user and provide assistance for efficiently performing housework. [Solution] A system according to an embodiment includes a collection unit, a prediction unit, a proposal unit, an understanding unit, and an instruction unit. The collection unit collects user behavior data. The prediction unit predicts the user's behavior patterns based on the data collected by the collection unit. The proposal unit proposes a schedule based on the prediction results obtained by the prediction unit. The understanding unit understands the user's instructions. The instruction unit issues instructions to a household robot based on the instructions understood by the understanding unit.

The disclosure describes a method of generating a target profile including the target's sequence of events (SOE) for a task. Such target profile sequence of events is derived from several source group's transactions, where any source group's transactions cannot be shared with other source groups but the derived target group's profile is the only information that is shared. Source-side information is periodically extracted for a plurality of sources that each interact with a plurality of targets. The information includes source stages, resources, and stage transition events for a task with a target. Source information is used to generate a set of normalized stages, and a set of normalized events for transitioning between the stages of the set of normalized stages. An artificial intelligence (AI) model is trained using the source information. The AI model can generate a target profile with target process information inferred using the trained model. The target process information can include the target's identifiers for each stage, an estimated duration of the stage, deliverables for the stage, and one or more stage transition events for the stage.