128 results about "Applied artificial intelligence" patented technology

Systems and methods for interactive image compositing may integrate image searching using visual search criteria (e.g., color, composition, tonal distribution, or lighting effects), non-destructive image compositing, and high-performance rendering using a fast gradient solver to provide an efficient workflow for users. An image compositing application, executable by a CPU and/or GPU, may employ a sketch-and-refine approach, whereby a user draws a rough perimeter around an object of interest in an image and (following an initial application of the fast gradient solver to a lower-resolution version of the image) receives a preview of how it would fit into a composition. The fast gradient solver may differentiate between the object and its background by applying artificial intelligence techniques and/or dependent on user-supplied hints. In a second stage, the fast gradient solver may refine the solution for a final rendering of the composite image by operating on successively higher-resolution versions of the image.

A computer system for generating a diagnostic tool by applying artificial intelligence to an instrument for diagnosis of a disorder, such as autism. For autism, the instrument can be a caregiver-directed set of questions designed for an autism classification tool or an observation of the subject in a video, video conference, or in person and associated set of questions about behavior that are designed for use in a separate autism classification tool. The computer system can have one or more processors and memory to store one or more computer programs having instructions for generating a highly statistically accurate set of diagnostic items selected from the instrument, which are tested against a first test using a technique using artificial intelligence and a second test against an independent source. Also, a computer implemented method and a non-transitory computer-readable storage medium are disclosed.

A framework for safely operating autonomous machinery, such as vehicles and other heavy equipment, in an in-field or off-road environment, includes detecting, identifying, classifying and tracking objects and/or terrain characteristics from on-board sensors that capture images in front and around the autonomous machinery as it performs agricultural or other activities. The framework generates commands for navigational control of the autonomous machinery in response to perceived objects and terrain impacting safe operation. The framework processes image data and range data in multiple fields of view around the autonomous equipment to discern objects and terrain, and applies artificial intelligence techniques in one or more neural networks to accurately interpret this data for enabling such safe operation.

Artificial intelligence (AI) technology can be used in combination with composable communication goal statements and an ontology to facilitate a user's ability to quickly structure story outlines in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired communication goal. The determined content can be arranged in a computed story outline that is created at runtime, and NLG can be performed on the computed story outline to generate the narrative story.

Identifying and detecting threats to an enterprise system groups log lines from enterprise data sources and/or from incoming data traffic. The process applies artificial intelligence processing to the statistical outlier in the event of the statistical outliers comprises a sparsely labelled real data set, by receiving the sparsely labelled real data set for identifying malicious data and comprising real labelled feature vectors and generating a synthetic data set comprising a plurality of synthetic feature vectors derived from the real, labelled feature vectors. The process further identifies the sparsely labelled real data set as a local data set and the synthetic data set as a global set. The process further applies a transfer learning framework for mixing the global data set with the local data set for increasing the precision recall area under curve (PR AUC) for reducing false positive indications occurring in analysis of the threats to the enterprise.

To provide users with more flexibility for controlling narrative generation from visualizations of data, the inventors disclose how selective control can be provided over various aspects of the narrative generation process, such as selectively enabled and disabled narrative analytics for analyzing visualization data. For example, narrative analytics relating to segment analysis and trendline analysis with respect to line charts can be selectively enabled and disabled as part of the narrative generation process.

Disclosed herein is an NLP system that is able to extract meaning from a natural language message using improved parsing techniques. Such an NLP system can be used in concert with an NLG system to interactively interpret messages and generate response messages in an interactive conversational stream. The parsing can include (1) named entity recognition that contextualizes the meanings of words in a message with reference to a knowledge base of named entities understood by the NLP and NLG systems, (2) syntactically parsing the message to determine a grammatical hierarchy for the named entities within the message, (3) reduction of recognized named entities into aggregations of named entities using the determined grammatical hierarchy and reduction rules to further clarify the message's meaning, and (4) mapping the reduced aggregation of named entities to an intent or meaning, wherein this intent/meaning can be used as control instructions for an NLG process.

The invention belongs to the technical field of commodity theft prevention, and particularly relates to an anti-theft early warning system and method for supermarket commodities based on artificial intelligence. The method comprises the following steps: when a user purchases commodities, a shopping cart logo and commodity images are obtained; and an artificial intelligence learning algorithm is applied to conduct image identification on the commodity image, conduct statistics on the number of purchased commodities; when the user settles the commodities, the shopping cart logo and bar codes ofthe settled commodities are obtained, and the quantity of the settled commodities is counted; the quantity of the purchased commodities and the quantity of the settled commodities are checked. If verification is inconsistent, the shopping cart logo is set to be an abnormal shopping cart logo; if an abnormal shopping cart is detected when the user passes through an access control terminal, an alarmis issued. The anti-theft early warning system and method for supermarket commodities based on the artificial intelligence check the quantity of purchased commodities put into the same shopping cartand the quantity of the settled commodities, and alarms the abnormal shopping cart passing through the access control terminal to achieve a purpose of theft prevention.

A method for assessing the integrity of a rock mass, the method including impacting the rock mass, capturing an acoustic signal generated as a result of the impact, deriving a frequency distribution for the captured acoustic signal, processing data from the frequency distribution by means of a neural network process applying artificial intelligence to assess the inputted data, and presenting a signal from the neural network process which is indicative of the integrity of the rock mass.

An approach is provided dynamically prioritizing question requests based on extracted question data. In the approach, performed by an information handling system, a number of question requests to a question and answering (QA) system are received from a computer network, and a plurality of question priority parameters are identified, including one or more question topics and a plurality question context parameters, by performing natural language processing (NLP) analysis of each question request. The approach determines a target priority value for each question request based on the plurality of question priority parameters identified for said question request. By evaluating the target priority values for the plurality of question requests, processing of the question requests is prioritized, such as by applying an artificial intelligence (AI) learned models and rule-based logic at the information handling system to evaluate the target priority values for the plurality of question requests.

Disclosed herein are example embodiments of an improved narrative generation system where an analysis service that executes data analysis logic that supports story generation can include configuration-driven analytics, such as at least one of a configuration-driven peaks analytic, a configuration-driven jumps analytic, a configuration-driven runs analytic, and/or a configuration-driven streaks analytic. In an example embodiment, the analysis service can be segregated from an authoring service that executes authoring logic for story generation through an interface. Accordingly, when the authoring service needs analysis from the analysis service, it can invoke the analysis service through the interface. By exposing the analysis service to the authoring service through the shared interface, the details of the logic underlying the analysis service are shielded from the authoring service (and vice versa where the details of the authoring service are shielded from the analysis service). Through parameterization of operating variables, the analysis service can thus be designed as a generalized data analysis service that can operate in a number of different content verticals with respect to a variety of different story types.

The invention relates to the field of ultrasonic medical images, and provides an artificial intelligence ultrasonic auxiliary system and application thereof. The system is mainly applied to an ultrasonic machine, an image system and telemedicine. The artificial intelligence ultrasonic auxiliary system comprises an image collection module. The system comprises an image transmission module, an imageframing module, an automatic classification module, an automatic film selection module, a teaching auxiliary module, a film reading prompt module and a quality control analysis module, realizes automatic classification of cases, automatically selects a standard section for negative cases, and provides functions of automatic film reading prompt, teaching assistance, quality control analysis and the like for suspected cases. After an ultrasonic auxiliary system based on artificial intelligence is implanted into an ultrasonic machine, an image information system and a remote medical system, doctors with low annual resources can quickly start, and the defect of basic medical resources is overcome.

Various techniques are described for using machine-learning artificial intelligence to improve how trading data can be processed to detect improper trading behaviors such as trade spoofing. In an example embodiment, semi-supervised machine learning is applied to positively labeled and unlabeled training data to develop a classification model that distinguishes between trading behavior likely to qualify as trade spoofing and trading behavior not likely to qualify as trade spoofing. Also, clustering techniques can be employed to segment larger sets of training data and trading data into bursts of trading activities that are to be assessed for potential trade spoofing status.

A workload assessment and model training method includes acquire a test item data set including a plurality of test items, each testing item including a plurality of test data; extracting a feature vector of each of the plurality of test data; calculating the difficulty coefficient of the test item according to the feature vector of the extracted test data; inputting the eigenvector, the difficulty coefficient and the project version scheduling of the test item into a preset loss function; When the calculated loss function value is less than or equal to the preset threshold, the workload evaluation model training ends. The invention also provides a workload evaluation method, an electronic device and a storage medium. The invention is the first time to apply artificial intelligence to thefirst column of the software testing field, the workload evaluation model trained has remarkable feature extraction ability, the model evaluation accuracy is high, and can accurately predict the workload of the testing items (scheduling and the number of test participants in the testing), and has strong objectivity.

The invention discloses an air-combat tactic team simulating method based on an expert system and tactic-military-strategy fractalization.The method includes the following steps that simulation modeling is conducted with a knowledge establishing, knowledge expressing and processing mathematical method, and tactics and military strategies are correspondingly converted into data sets respectively and are stored and called in a library or sub-library mode; a fractal military-strategy library is constructed; a tactic index system for evaluating the combat effectiveness of a tactic team is built; an air-combat knowledge self-learning mechanism is built; a military-strategy inferring mechanism is built through an artificial intelligence technology; military-strategy reconstruction is conducted, wherein smooth transition between tactics and between military strategies is achieved.The method is tried and verified in scientific research projects, the blue-party intelligent virtual tactic team is built with the method, the double-party whole-process air-combat simulation confronting test can be achieved in the mode that red-party air combat simulating trained persons only need to conduct operation and commanding on a flight simulation platform of a red party, the hardware scale is reduced, and training difficulty can be improved.

The invention provides an expert system for remote sensing image processing. The expert system for remote sensing image processing comprises a user interface, a system interface, an inference machine, knowledge bases, a management system of the knowledge bases, a database, a database management system, a knowledge obtaining mechanism and an interpretation mechanism. The expert system earnestly and scientifically conclude and summarize and establish the knowledge bases by simulating visual interpretation experiences of a human expert, then quotes the knowledge bases to classify remote sensing data, utilizes a mode recognition method to obtain multiple characteristics of ground feature, provides evidence for remote sensing image interpretation, meanwhile applies an artificial intelligence technology, applies experiences and methods of the remote sensing image interpretation expert, simulates specific thinking process of remote sensing image visual interpretation, and performs remote sensing image interpretation to enable the computer interpretation level to reach the expert level.

The invention discloses a personalized network resource recommended method based on network connection speed, comprising the following steps of: using a custom browser, and recording connection speed of a user and a browsed website; using obtained connection speed and training an artificial neural network which can predict the connection speed of the user and any website; operating a relevant search engine to find all the websites owning the resources on internet for the network resources wanted to be accessed by the user; using the artificial neural network to predict the connection speed of the user and all the found website; and sequencing all the found websites according to the connection speed of the user from large to small, which is used as a recommended result of the resources. The personalized network resource recommended method effectively uses the historical network access records of the user, applies an artificial intelligent method to predict the connection speed of the user and all the websites, combines the personal network condition of the user in the access process of the network, leads the use of the network resources to be capable of furthest using the bandwidth of the user and provides better experience of internet for the user.

A method and apparatus that receive blockchain transaction data from a blockchain ledger to a transaction database, receive intelligence labels from a blockchain ecosystem intelligence database, select a blockchain transaction flow comprising blockchain transactions associated with a digital account source address, digital account intermediate addresses, a digital account destination address, and intermediate transactions, receive input trace parameters, where the input trace parameters include at least one of an objective directional setting, a tracing constraint, and a transaction filter, the transaction filter based on the transaction timestamps and the digital assets transferred, apply the intelligence labels to the digital account source address, the digital account intermediate addresses, and the digital account destination address, apply an artificial intelligence graph search algorithm to the blockchain transaction flow based on the input trace parameters, and generate a report including tracing destination summary statistics of the auto-traced path.

An approach is provided dynamically prioritizing question requests based on extracted question data. In the approach, performed by an information handling system, a number of question requests to a question and answering (QA) system are received from a computer network, and a plurality of question priority parameters are identified, including one or more question topics and a plurality question context parameters, by performing natural language processing (NLP) analysis of each question request. The approach determines a target priority value for each question request based on the plurality of question priority parameters identified for said question request. By evaluating the target priority values for the plurality of question requests, processing of the question requests is prioritized, such as by applying an artificial intelligence (AI) learned models and rule-based logic at the information handling system to evaluate the target priority values for the plurality of question requests.