247 results about "Learning object" patented technology

A system and method for authors of on-line learning material and learning objects is provided to develop and store their learning objects while also protecting their digital rights during the marketing and distribution of the learning objects. The digital content may include such asset content as video, music, text, educational content, or the like. Various stages of the invention include: (i) author registration that permits authors to access and become a registered user of the system, (ii) author content creation that permits authors to compose and create a Shareable Content Object (SCO) which represents one or more assets, (iii) DRM packaging that permits name tagging and security encasement, and (iv) ingesting and eStore publishing.

A computer-implemented adaptive learning method is disclosed. The method is intended for performance within the context of a task being carried out by a user. At least one of a sequence of elements presented to the user as part of the task is designated as a learning item. A learning object is selected in dependence upon the designated learning item, information relating to previous performance of the learning method in relation to the user, and a predetermined scheme devised to manage an overall learning process for the user. Presentation of the selected learning object to the user is intended to advance the user's knowledge of the designated learning item in some way. Once the learning object has been presented to the user, the information is updated in dependence upon the presented learning object and / or how the user interacts with or responds to the presented learning object.

A system and method for training a student employ a simulation station that displays output to the student and receives input. The computer system has a rules engine operating on it and computer accessible data storage storing (i) learning object data including learning objects configured to provide interaction with the student at the simulation system and (ii) rule data defining a plurality of rules accessed by the rules engine. The rules data includes, for each rule, respective (a) if-portion data defining a condition of data and (o) then-portion data defining an action to be performed at the simulation station. The rules engine causes the computer system to perform the action when the condition of data is present in the data storage. For at least some of the rules, the action comprises output of one of the learning objects so as to interact with the student. The system may be networked with middleware and adapters that map data received over the network to rules engine memory.

Embodiments of the present invention relate to systems and methods for improving the training of machine learning systems to recognize certain objects within a given image by supplementing an existing sparse set of real-world training images with a comparatively dense set of realistic training images. Embodiments may create such a dense set of realistic training images by training a machine learning translator with a convolutional autoencoder to translate a dense set of synthetic images of an object into more realistic training images. Embodiments may also create a dense set of realistic training images by training a generative adversarial network (“GAN”) to create realistic training images from a combination of the existing sparse set of real-world training images and either Gaussian noise, translated images, or synthetic images. The created dense set of realistic training images may then be used to more effectively train a machine learning object recognizer to recognize a target object in a newly presented digital image.

This disclosure presents methods, systems, computer-readable media, and apparatuses for optically tracking the location of one or more objects. The techniques may involve accumulation of initial image data, establishment of a dataset library containing image features, and tracking using a plurality of modules or trackers, for example an optical flow module, decision forest module, and color tracking module. Tracking outputs from the optical flow, decision forest and / or color tracking modules are synthesized to provide a final tracking output. The dataset library may be updated in the process.

A system and method for training a student employ a simulation station that displays output to the student and receives input. The computer system has a rules engine operating on it and computer accessible data storage storing (i) learning object data including learning objects configured to provide interaction with the student at the simulation system and (ii) rule data defining a plurality of rules accessed by the rules engine. The rules data includes, for each rule, respective (a) if-portion data defining a condition of data and (b) then-portion data defining an action to be performed at the simulation station. The rules engine causes the computer system to perform the action when the condition of data is present in the data storage. For at least some of the rules, the action comprises output of one of the learning objects so as to interact with the student.

Conventional robot apparatus etc. can not perform name-learning naturally. Learning the name of an object is performed such a manner that the name of a target object is obtained through dialog with a human being, the name is stored in association with plural items of different characteristic data detected for the target object, and a new object is recognized based on the stored data and associative information, the name and characteristic data of the new person are obtained and this associative information is stored.

The invention discloses a personalized network learning resource recommendation method which comprises the following steps: analyzing behavioral data of learners for visiting a network leaning system based on expanded theme pictures, acquiring leaning interest path changing patterns of the learners and groups of the learners on concepts and knowledge elements related to learning contents, and then realizing active personalized recommendation of proper learning resources to the learners according learning interest path changing patterns of the learner individuals and the groups of the learners individuals, sequence of learning objects of the expanded theme pictures and the like. The personalized network learning resource recommendation method can provide personalized learning service for network learners, thereby improving learning efficiency of the network learners.

The present invention is embodied in a system and process for automatically learning a reliable color-based tracking system. The tracking system is learned by using information produced by an initial object model in combination with an initial tracking function to probabilistically determine the configuration of one or more target objects in a temporal sequence of images, and a data acquisition function for gathering observations relating to color in each image. The observations gathered by the data acquisition function include information that is relevant to parameters desired for a final color-based object model. A learning function then uses probabilistic methods to determine conditional probabilistic relationships between the observations and probabilistic target configuration information to learn a color-based object model automatically tailored to specific target objects. The learned object model is then used in combination with the final tracking function to probabilistically locate and track specific target objects in one or more sequential images.

A mechanism is provided that automates the creation of learning objects from knowledge and learning content in various common formats. Importing is performed using a tool with custom parsers for common formats. The parsers split the content into learning objects, generate metadata, and relate metadata to the objects. The tool may also provide points of integration for making new parsers available through the tool, Candidate content may be presented to user by searching the local file system. Search engine output may be used to present the candidate list.

A method for visual recognition of an object in an electronic image includes extracting unique points of an object to be learned and / or a target object. The unique points are obtained by cross-correlating the image with a structure. Generally, the structure and / or the size of the structure may vary to detect extremum information associated with the learned object and / or target object. An icon corresponding to each of the unique points is extracted. The size of the icon corresponds to the scale of the unique point. After extraction of the various icons, an object becomes a collection of icons. Each of these icons is un-rotated and normalized or resized to a constant size so it can be compared with other icons. One of the unique properties of these icons is their stability over scale and angle. Thus, this invention allows the recognition of an image(s) or object(s) from large number of trained images or objects very quickly.

In a first exemplary embodiment of the present invention, an automated, computerized method for learning object recognition in an image is provided. According to a feature of the present invention, the method comprises the steps of providing a training set of standard images, calculating intrinsic images corresponding to the standard images and building a classifier as a function of the intrinsic images.

A statistical model learning device is provided to efficiently select data effective in improving the quality of statistical models. A data classification means 601 refers to structural information 611 generally possessed by a data which is a learning object, and extracts a plurality of subsets 613 from the training data 612. A statistical model learning means 602 utilizes the plurality of subsets 613 to create statistical models 614 respectively. A data recognition means 603 utilizes the respective statistical models 614 to recognize other data 615 different from the training data 612 and acquires each recognition result 616. An information amount calculation means 604 calculates information amounts of the other data 615 from a degree of discrepancy among the statistical models of the recognition results. A data selection means 605 selects the data with a large information amount and adds the same to the training data 612.

A method for visual recognition of an object in an electronic image includes extracting unique points of an object to be learned and / or a target object. The unique points are obtained by cross-correlating the image with a structure. Generally, the structure and / or the size of the structure may vary to detect extremum information associated with the learned object and / or target object. An icon corresponding to each of the unique points is extracted. The size of the icon corresponds to the scale of the unique point. After extraction of the various icons, an object becomes a collection of icons. Each of these icons is un-rotated and normalized or resized to a constant size so it can be compared with other icons. One of the unique properties of these icons is their stability over scale and angle. Thus, this invention allows the recognition of an image(s) or object(s) from large number of trained images or objects very quickly.

A system and method of generating digital educational content comprises (a) creating a digital learning object by receiving user selection of a template from a repository, the template representing a composition of one or more digital educational content elements within a screen, receiving user selection of a layout from a repository, the layout representing an on-screen arrangement of one or more educational content elements within said screen, receiving user input of data for said template, receiving user input of parameters for said template, inserting the user input of data into said template, inserting the user input of parameters into said template, receiving user input of metadata for said template, (b) applying said layout to said template containing said user input of data and (ii) said user input of parameters and (iii) said user input of meta-data, (c); storing said digital learning object in a repository of digital learning objects.

A feature amount extracting unit extracts feature amount vectors of an inputted image. A probability distribution calculator calculates a probability distribution of feature amount vectors in a calculation space range which is set by a calculation space range setting unit. A probability distribution storage stores the probability distribution in association with identifiers of learning objects. A posterior probability calculator calculates a posterior probability, which is a probability that a recognition object corresponds to each of the learning objects, using feature amount vectors calculated from an image of the recognition object and the probability distribution of the learning objects stored in the probability distribution storage. A posterior probability comparing unit compares posterior probabilities calculated and outputs a result of recognition of the recognition object.

A segmentation method receives a learning image and an objects of interest specification. A segmentation learning method creates a segmentation recipe output. It performs a segmentation application using the second image and the segmentation recipe to create a segmentation result output. The segmentation learning method includes an object region of interest segmentation learning step and an object type specific segmentation learning step. The segmentation application method includes an object region of interest segmentation step and an object type specific segmentation step. The learnable object segmentation method further comprises an online learning and a feedback learning step that allows the update of the segmentation recipe automatically or under user direction.

A system and process for automatically learning a reliable color-based tracking system is presented. The tracking system is learned by using information produced by an initial object model in combination with an initial tracking function to probabilistically determine the configuration of one or more target objects in a temporal sequence of images, and a data acquisition function for gathering observations relating to color in each image. The observations gathered by the data acquisition function include information that is relevant to parameters desired for a final color-based object model. A learning function then uses probabilistic methods to determine conditional probabilistic relationships between the observations and probabilistic target configuration information to learn a color-based object model automatically tailored to specific target objects. The learned object model is then used in combination with the final tracking function to probabilistically locate and track specific target objects in one or more sequential images.

The invention discloses a machine learning-based industrial equipment fault preventive recognition method. By the adoption of an artificial intelligence algorithm of machine learning, a state forecasting model is continuously learned and updated, so that a working state of industrial equipment can be monitored and forecast in real time, and a user can find an abnormality of the industrial equipment at the first time and sound an alarm in time to avoid an accident. According to the machine learning-based industrial equipment fault preventive recognition method, a typical fault case in the whole full life circle of the industrial equipment is used as a learning object, and furthermore, by the combination of file data of the industrial equipment, and reliability data of a regional environment condition, particularly a key element, are used as the learning object to estimate the reliability of the industrial equipment from multiple angles, so that a forecast result is more accurate.

A computer-implemented adaptive learning method is disclosed. The method is intended for performance within the context of a task being carried out by a user. At least one of a sequence of elements presented to the user as part of the task is designated as a learning item. A learning object is selected in dependence upon the designated learning item, information relating to previous performance of the learning method in relation to the user, and a predetermined scheme devised to manage an overall learning process for the user. Presentation of the selected learning object to the user is intended to advance the user's knowledge of the designated learning item in some way. Once the learning object has been presented to the user, the information is updated in dependence upon the presented learning object and / or how the user interacts with or responds to the presented learning object.

The fuel injection pump control device controls a fuel-injector pressure from the injection supply eliminated by the variables of determined measured parameters. The fuel is injected and supplied to a target machine. A learning correction process-implementing device is provided for successive implementation of multiple kinds of learning correction process corresponding to the multiple implemented conditions. The implemented conditions are different in the strictness for a pressure parameter of pressurized parameter as a learning object and are used for the fuel injection pressure controller.

A system for generating a sequence of learning objects comprising an e-Learning course is provided. The system can include one or more processors and a memory unit for electronically storing data comprising a plurality of learning objects. The system further can include a sequence builder configured to execute on the at least one processor. The sequence builder, more particularly, can be configured to generate a sequence comprising at least a portion of the plurality of learning objects based upon a measurable user relevancy and / or a measurable query relevancy of each learning object contained in the sequence.

The present invention relates to a learning mat for children using a toy that is capable of performing learning through the toy having a shape of a character, which induces a child's curiosity while learning and further ensures dynamic physical activities of the child while the learning. The learning mat includes: a mat 100 having learning objects 111 to be learned by a child depicted on the front surface thereof and adapted to wirelessly transmit learning information by section as the learning contents of the learning object 111 touched by the child; and a toy-shaped learning information output member 200 serving as a mat-touching member for touching the learning objects 111 depicted on the mat 100 and wirelessly connected to the mat 100 so as to receive the learning information by section transmitted wirelessly from the mat 100 and to output the received learning information through a speaker, the toy-shaped learning information output member 200 having a toy-shaped housing 201 on the external appearance thereof.

The invention discloses a learning system based on the augmented reality technology capable of increasing the learning interest and overcoming difficulties in learning and a learning method thereof. The system comprises a processor and a reading material carrier, wherein augmented reality software is installed in the processor; the processor is connected with a display and a camera; a learning object and an augmented reality identifier are printed on the reading material carrier; and the reading material carrier is arranged in the data acquisition range of the camera.

In a first exemplary embodiment of the present invention, an automated, computerized method for learning object recognition in an image is provided. According to a feature of the present invention, the method comprises the steps of providing a training set of standard images, calculating intrinsic images corresponding to the standard images and building a classifier as a function of the intrinsic images.

The invention discloses a vehicle-mounted terminal multi-target recognition tracking prediction method, which is a vehicle-mounted terminal multi-target recognition tracking prediction method based onYOLOv5s (You Only Look Once v5s) and FairMOT (FairMOT) fusion, and detects vehicles in front of a road quickly and accurately in real time by using a YOLOv5s deep learning object detection technology. The YOLOv5s model is fused into the FairMOT architecture detection module to perform target detection and re-identification tracking in a single network so as to realize position detection, type identification and multi-target motion trajectory tracking of the traffic target in front of the vehicle on the road, thereby achieving prediction of driving behaviors of lane change, following, deceleration and the like of the traffic target in front of the vehicle.

A method to assess the competency of a healthcare practitioner, wherein the method provides a learning object repository comprising a plurality of previously-created virtual objects, creates a first template and a second template by the second module, wherein the first template comprises one or more previously-defined learning objectives, and wherein the second template comprises one or more previously-defined competency assessments related to the one or more selected learning objectives. The method provides the first template and the second template to the learning object repository. The method displays on a visual display device a virtual clinical world comprising a plurality of virtual objects retrieved from the learning object repository. Further according to the method, a practitioner selects a virtual patient from the virtual clinical world, selects a series of interactions with the patient, and selects patient data. The method tracks the selected patient interactions, and the selected patient data.