Abstract: Computerized detection of one or more user interface objects is performed by processing an image file containing one or more user interface objects of a user interface generated by an application program. Sub-control objects can be detected in the image file, where each sub-control object can form a portion of a user interface object that receives user input. Extraneous sub-control objects can be detected. Sub-control objects that overlap with or that are within a predetermined vicinity of an identified set of sub-control objects can be removed. Sub-control objects in the identified set of sub-control objects can be correlated to combine one or more of the sub-control objects in the identified set of sub-control objects to generate control objects that correspond to certain of the user interface objects of the user interface generated by the application program.

Abstract: Robotic process automation (RPA) systems with improved playback capabilities. Certain embodiments can provide resilient playback of software automation processes by providing enhanced capability to locate user interface controls within a screen image of a user interface. In some embodiments, one or more of the user interface controls located within the screen image of the user interface can be used to manipulate the user interface so that other user interface controls become visible within the screen image. Other embodiments can, additionally or alternatively, provide resilient playback of software automation processes by providing the capability to record, compute and store parameters for user interface controls detected from a screen image of a user interface. These parameters can be used to assist in locating correct corresponding user interface controls within a screen image presented at playback of a software automation process.

Abstract: Computerized detection of one or more user interface objects is performed by processing an image file containing one or more user interface objects of a user interface generated by an application program. Sub-control objects can be detected in the image file, where each sub-control object can form a portion of a user interface object that receives user input. Extraneous sub-control objects can be detected. Sub-control objects that overlap with or that are within a predetermined vicinity of an identified set of sub-control objects can be removed. Sub-control objects in the identified set of sub-control objects can be correlated to combine one or more of the sub-control objects in the identified set of sub-control objects to generate control objects that correspond to certain of the user interface objects of the user interface generated by the application program.

Abstract: A User Interface (UI) interface object detection system employs an initial dataset comprising a set of images, that may include synthesized images, to train a Machine Learning (ML) engine to generate an initial trained model. A data point generator is employed to generate an updated synthesized image set which is used to further train the ML engine. The data point generator may employ images generated by an application program as a reference by which to generate the updated synthesized image set. The images generated by the application program may be tagged in advance. Alternatively, or in addition, the images generated by the application program may be captured dynamically by a user using the application program.

Abstract: Computerized detection of one or more user interface objects is performed by processing an image file containing one or more user interface objects of a user interface generated by an application program. Sub-control objects can be detected in the image file, where each sub-control object can form a portion of a user interface object that receives user input. Extraneous sub-control objects can be detected. Sub-control objects that overlap with or that are within a predetermined vicinity of an identified set of sub-control objects can be removed. Sub-control objects in the identified set of sub-control objects can be correlated to combine one or more of the sub-control objects in the identified set of sub-control objects to generate control objects that correspond to certain of the user interface objects of the user interface generated by the application program.

Abstract: Computerized detection of one or more user interface objects is performed by processing an image file containing one or more user interface objects of a user interface generated by an application program. Sub-control objects can be detected in the image file, where each sub-control object can form a portion of a user interface object that receives user input. Extraneous sub-control objects can be detected. Sub-control objects that overlap with or that are within a predetermined vicinity of an identified set of sub-control objects can be removed. Sub-control objects in the identified set of sub-control objects can be correlated to combine one or more of the sub-control objects in the identified set of sub-control objects to generate control objects that correspond to certain of the user interface objects of the user interface generated by the application program.

Abstract: A modular kitchen-connected indoor stationary solar cooking device (102) is disclosed. The solar cooking device (102) includes a housing (202), a thermal battery (204) disposed in the housing (202) and adapted to store thermal energy, and a first heater (206) disposed to be in contact with the thermal battery (204). The first heater (206) is coupled to a solar array (104) and adapted to receive solar energy for charging the thermal battery (204). The solar cooking device (102) includes a second heater (208) disposed to be in contact with the thermal battery (204). The second heater (208) is coupled to a mains supply and adapted to receive electrical supply for charging the thermal battery (204). The solar cooking device (102) includes a heat control assembly (210) disposed on a cooktop (802) and adapted to accommodate a cooking vessel. The heat control assembly (210) is adapted to rotate for controlling a heat supply for cooking in the cooking vessel.

Abstract: Robotic process automation (RPA) systems with improved playback capabilities. Certain embodiments can provide resilient playback of software automation processes by providing enhanced capability to locate user interface controls within a screen image of a user interface. In some embodiments, one or more of the user interface controls located within the screen image of the user interface can be used to manipulate the user interface so that other user interface controls become visible within the screen image. Other embodiments can, additionally or alternatively, provide resilient playback of software automation processes by providing the capability to record, compute and store parameters for user interface controls detected from a screen image of a user interface. These parameters can be used to assist in locating correct corresponding user interface controls within a screen image presented at playback of a software automation process.

Abstract: Robotic process automation (RPA) systems with improved playback capabilities. Certain embodiments can provide resilient playback of software automation processes by providing the capability to record, compute and store parameters for user interface controls detected from a screen image of a user interface. These parameters can be used to assist in locating correct corresponding user interface controls within a screen image presented at playback of a software automation process. Other embodiments can, additionally or alternatively, provide resilient playback of software automation processes by providing enhanced capability to locate user interface controls within a screen image of a user interface. In some embodiments, one or more of the user interface controls located within the screen image of the user interface can be used to manipulate the user interface so that other user interface controls become visible within the screen image.

Abstract: Robotic process automation (RPA) systems with improved playback capabilities are disclosed. Certain embodiments can provide resilient playback of software automation processes by providing enhanced capabilities to locate user interface controls within a screen image of a user interface of an application program being utilized by the software automation processes. Using such capabilities, embodiments can provide resilient playback of software automation processes by providing the capability to resolve playback errors by detecting user interface controls within at least one screen image of a user interface that was captured when the software automation process was created. Advantageously, embodiments disclosed herein allow software automation processes to continue to operate over time and thus with greater resiliency and flexibility.

Abstract: Computerized detection of one or more user interface objects is performed by processing an image file containing one or more user interface objects of a user interface generated by an application program. Sub-control objects are detected in the image file, where each sub-control object forms a portion of a user interface object that receives user input. Extraneous sub-control objects are detected. Sub-control objects that overlap with or that are within a predetermined vicinity of an identified set of sub-control objects are removed. Sub-control objects in the identified set of sub-control objects are correlated to combine one or more of the sub-control objects in the identified set of sub-control objects to generate control objects that correspond to certain of the user interface objects of the user interface generated by the application program.

Abstract: A User Interface (UI) interface object detection system employs an initial dataset comprising a set of images, that may include synthesized images, to train a Machine Learning (ML) engine to generate an initial trained model. A data point generator is employed to generate an updated synthesized image set which is used to further train the ML engine. The data point generator may employ images generated by an application program as a reference by which to generate the updated synthesized image set. The images generated by the application program may be tagged in advance. Alternatively, or in addition, the images generated by the application program may be captured dynamically by a user using the application program.

Abstract: Methods and systems for building a universal always-on multimodal identification system. A universal representation to be used for executing one or more tasks, working on data with one or more signal modalities and comprising modal fusions signals at various levels is learned from a dataset that is targeted user or object agnostic. This universal representation is combined with a second stage task specific representation that is learned on-the-device using data from the particular user without sending the data to the cloud.

Abstract: This invention presents a universal framework for the discovery, understanding and matching of dress styles. In one embodiment, a computer-implemented method for building a universal dress style learner is disclosed, said method comprising: learning human skin models; detecting skin using the learned human skin models; collecting a set of dress images worn by a model; computing a set of style features based on the skin detected for at least one subset within the set of dress images; computing a set of clusters on the at least one subset of dress images based on at least one subset of the set of style features; validating the set of clusters for the at least one subset of style features; and computing a set of validated style features and a style basis.

Abstract: A unified framework to understand multimedia signals utilizes the loosely annotated multimedia data on the Web, analysis it in various signal domains, such as text, image, audio and combinations thereof, and builds an association graph called the “Multimedia Brain,” which basically comprises visual signals, audio signals, text phrases and the like that capture a multitude of objects, experiences and their attributes and the links among them that capture similar intent or functional and contextual relationships.

Abstract: The present disclosure describes a method and system called “Universal Learner (UL),” which provides a unified framework to understand multimedia signals. The UL utilizes the loosely annotated multimedia data on the Web, analyses it in various signal domains, such as text, image, audio and combinations thereof, and builds an association graph called the “Multimedia Brain,” which basically comprises visual signals, audio signals, text phrases and the like that capture a multitude of objects, experiences and their attributes and the links among them that capture similar intent or functional and contextual relationships.

Abstract: “A Universal Learner (UL)” provides a unified framework to understand multimedia signals. The UL utilizes the loosely annotated multimedia data on the Web, analyses it in various signal domains, such as text, image, audio and combinations thereof, and builds an association graph called the “Multimedia Brain,” which basically comprises visual signals, audio signals, text phrases and the like that capture a multitude of objects, experiences and their attributes and the links among them that capture similar intent or functional and contextual relationships.

Abstract: This invention presents a universal framework for the discovery, understanding and matching of dress styles. In one embodiment, a computer-implemented method for building a universal dress style learner is disclosed, said method comprising: learning human skin models; detecting skin using the learned human skin models; collecting a set of dress images worn by a model; computing a set of style features based on the skin detected for at least one subset within the set of dress images; computing a set of clusters on the at least one subset of dress images based on at least one subset of the set of style features; validating the set of clusters for the at least one subset of style features; and computing a set of validated style features and a style basis.

Abstract: Methods and systems for discovering styles via color and pattern co-occurrence are disclosed. According to one embodiment, a computer-implemented method comprises collecting a set of fashion images, selecting at least one subset within the set of fashion images, the subset comprising at least one image containing a fashion item, and computing a set of segments by segmenting the at least one image into at least one dress segment. Color and pattern representations of the set of segments are computed by using a color analysis method and a pattern analysis method respectively. A graph is created wherein each graph node corresponds to one of a color representation or a pattern representation computed for the set of segments. Weights of edges between nodes of the graph indicate a degree of how the corresponding colors or patterns complement each other in a fashion sense.

Abstract: The present disclosure describes a method and system called “Universal Learner (UL),” which provides a unified framework to understand multimedia signals. The UL utilizes the loosely annotated multimedia data on the Web, analyses it in various signal domains, such as text, image, audio and combinations thereof, and builds an association graph called the “Multimedia Brain,” which basically comprises visual signals, audio signals, text phrases and the like that capture a multitude of objects, experiences and their attributes and the links among them that capture similar intent or functional and contextual relationships.