40944 results about "Machine learning" patented technology

The invention discloses a man-machine interaction method and system based on artificial intelligence. The method includes the following steps of receiving input information input by a user through an application terminal, obtaining the intent information of the user according to the input information of the user, distributing the input information to at least one interaction service subsystem according to the intent information, receiving the return result returned by the interaction service subsystems, generating a user return result according to the return result through a preset decision strategy, and providing the user return result to the user. By means of the method and system, the man-machine interaction system is virtualized instead of being instrumentalized, and the user can obtain the relaxed and pleasure interaction experience in the intelligent interaction process through chat, research and other service. The search in the form of keywords is improved into the search based on natural languages, the user can express demands through flexible and free natural languages, and the multi-round interaction process is closer to the interaction experience among humans.

Systems and methods of generating and modifying a playlist using contextual information are disclosed. For example, a user may provide an input seed such as a song name or artist name. The input seed is compared to database items and a playlist is generated as a result. In some examples, the contextual information is used to enhance the comparison and to select better content objects for a user's playlist. Meanwhile, in some examples the content objects in the playlist may be arranged in an enhanced order using ranking and/or contextual information.

A triggering mechanism for generating task reminders based on contextual information associated with the tasks is provided. Contextual information may be extracted from a variety of sources and may be related to one or more tasks. Based on the contextual information, task reminders may be generated and may be presented in a manner that makes the task reminders more useful to an end user.

A method and system for anatomical object detection using marginal space deep neural networks is disclosed. The pose parameter space for an anatomical object is divided into a series of marginal search spaces with increasing dimensionality. A respective sparse deep neural network is trained for each of the marginal search spaces, resulting in a series of trained sparse deep neural networks. Each of the trained sparse deep neural networks is trained by injecting sparsity into a deep neural network by removing filter weights of the deep neural network.

Exemplary systems and methods for malware attack detection and identification are provided. A malware detection and identification system can comprise a controller. The controller can comprise an analysis environment configured to transmit network data to a virtual machine, flag input values associated with the network data from untrusted sources, monitor the flagged input values within the virtual machine, identify an outcome of one or more instructions that manipulate the flagged input values, and determine if the outcome of the one or more instructions comprise an unauthorized activity.

Advanced features for interactive television applications are described, including a back feature, a forward feature, a history feature, a go to feature, an extras feature, a reminder feature, a favorites feature, a parental control feature, and a search feature. Features may be inter-resource. Support for multiple data paths, Internet access, interactive services, and user profiles are also described.

Embodiments of the invention address the problem of detecting bots in network traffic based on a classification model learned during a training phase using machine learning algorithms based on features extracted from network data associated with either known malicious or known non-malicious client and applying the learned classification model to features extracted in real-time from current network data. The features represent communication activities between the known malicious or known non-malicious client and a number of servers in the network.

A method of operation of a navigation system includes: generating a training data from a randomly sampled uncategorized point of interest; generating a trained classifier model by training a classifier model using the training data; generating a category identifier, a confidence score, or a combination thereof for an uncategorized point of interest using the trained classifier model; generating a categorized point of interest by assigning the category identifier to the uncategorized point of interest; calculating a weighted confidence score based on a weighted F-measure for the category identifier, a pair of the category identifier and the confidence score; and consolidating the categorized point of interest based on the weighted confidence score for the category identifier being meeting or exceeding a threshold for displaying on a device.

An interactive voice actuated control system for a testing machine such as a tensile testing machine is described. Voice commands are passed through a user-command predictor and integrated with a graphical user interface control panel to allow hands-free operation. The user-command predictor learns operator command patterns on-line and predicts the most likely next action. It assists less experienced operators by recommending the next command, and it adds robustness to the voice command interpreter by verbally asking the operator to repeat unlikely commanded actions. The voice actuated control system applies to industrial machines whose normal operation is characterized by a nonrandom series of commands.

Systems and methods to leverage and manage data and knowledge in a M2M or Internet of Things (IoT) network are provided. More particularly, a cognitive intelligence platform for an IoT network that provides autonomic decision support system at or near real-time and executes a dynamic runtime is provided. The hardware, software and communication design of the platform replicates the structural and operational model of the human nervous system to achieve cognitive intelligence through adaptation, collaborative learning, knowledge sharing and self-adjustment. Further, in one embodiment, the cognitive intelligence platform has three logical processing layers of increasing complexity, each of which has agents that use statistical and machine learning techniques and algorithms to resolve situational needs and update knowledge. Furthermore, each processing layer of the platform has a basic level of intelligence and additionally the hierarchy of layers aggregates the learning and intelligence at each layer.

The present invention, in one embodiment, is directed to a method and system for accelerating and improving the history matching of a well bore and/or reservoir simulation model using a neural network. The neural network provides a correlation between the calculated history match error and a selected set of parameters that characterize the well bore and/or the reservoir. The neural network iteratively varies a selection and/or the value of the parameters to provide at least one set of history match parameters having a value that provides a minimum for the calculated history matching error.

Financial data including general ledger activity and underlying journal entries are examined to determine whether risks of material misstatement due to fraudulent financial reporting can be identified. The financial data is analyzed statistically and modeled over time, comparing actual data values with predicted data values to identify anomalies in the financial data. The anomalous financial data is then analyzed using clustering algorithms to identify common characteristics of the various transactions underlying the anomalies. The common characteristics are then compared with characteristics derived from data known to derive from fraudulent activity, and the common characteristics are reported, along with a weight or probability that the anomaly associated with the common characteristic is an identification of risks of material misstatement due to fraud. Large volumes of financial data are therefore efficiently processed to accurately identify risks of material misstatement due to fraud in connection with financial audits, or for actual detection of fraud in connection with forensic and investigative accounting activities. The analysis is enhanced by using flow analysis methods to select subsets of financial data to examine for anomalies. Flow analysis methods are also used to reveal useful business information found in money flow graphs of financial data.

The invention discloses a human face super-resolution reconstruction method based on a generative adversarial network and sub-pixel convolution, and the method comprises the steps: A, carrying out the preprocessing through a normally used public human face data set, and making a low-resolution human face image and a corresponding high-resolution human face image training set; B, constructing the generative adversarial network for training, adding a sub-pixel convolution to the generative adversarial network to achieve the generation of a super-resolution image and introduce a weighted type loss function comprising feature loss; C, sequentially inputting a training set obtained at step A into a generative adversarial network model for modeling training, adjusting the parameters, and achieving the convergence; D, carrying out the preprocessing of a to-be-processed low-resolution human face image, inputting the image into the generative adversarial network model, and obtaining a high-resolution image after super-resolution reconstruction. The method can achieve the generation of a corresponding high-resolution image which is clearer in human face contour, is more specific in detail and is invariable in features. The method improves the human face recognition accuracy, and is better in human face super-resolution reconstruction effect.