Abstract: Disclosed herein is a system providing a mixed reality combination system that pairs augmented reality technology and an inertial measurement unit sensor with 3D printed objects such that user motions tracked by the inertial measurement unit as the user interacts with the 3D printed object is reflected in a virtual environment display of dynamic 3D imagery and augmented reality imagery.

Abstract: A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.

Abstract: A data processing system is configured to pre-process data for a machine learning classifier. The data processing system includes an input port that receives one or more data items, an extraction engine that extracts a plurality of data signatures and structure data, a logical rule set generation engine configured to generate a data structure, select a particular data signature of the data structure, identify each instance of the particular data signature in the data structure, segment the data structure around instances of the particular data signature, identify one or more sequences of data signatures connected to the particular data signature, and generate a logical ruleset. A classification engine executes one or more classifiers against the logical ruleset to classify the one or more data items received by the input port.

Abstract: A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.

Abstract: A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.

Abstract: Disclosed herein is system, including a hand-held tool, for example, a surgical scalpel, integrated with a 9 degree-of-freedom inertial measurement unit and a method for tracking the location of the hand-held instrument during manual or robotically-assisted procedures. The system and method has application in the surgical field, wherein instrumented surgical instruments may be precisely tracked throughout a surgical procedure.

Abstract: A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.

Abstract: A hierarchical deep-learning object detection framework provides a method for identifying objects of interest in high-resolution, high pixel count images, wherein the objects of interest comprise a relatively a small pixel count when compared to the overall image. The method uses first deep-learning model to analyze the high pixel count images, in whole or as a patchwork, at a lower resolution to identify objects, and a second deep-learning model to analyze the objects at a higher resolution to classify the objects.

Abstract: Provided is a storage container including a base and a cartridge container attachable to the base. The cartridge container defines a plurality of internal chambers and includes a plurality of individually operable doors, each of the plurality of doors corresponding to one of the internal chambers. The storage container further includes a plurality of individually addressable actuators disposed in at least one of the base and the cartridge container, each of the plurality of actuators corresponding to one of the doors. A microprocessor is programmed or configured to receive at least one instruction from a server computer, and transmit a command, based on the at least one instruction, to actuate one of the plurality of actuators. Actuating one of the plurality of actuators causes the corresponding door to move from a closed position to an opened position. Systems and methods are provided for remote and immediate dispensing of articles.

Abstract: Provided is a storage container including a base and a cartridge container attachable to the base. The cartridge container defines a plurality of internal chambers and includes a plurality of individually operable doors, each of the plurality of doors corresponding to one of the internal chambers. The storage container further includes a plurality of individually addressable actuators disposed in at least one of the base and the cartridge container, each of the plurality of actuators corresponding to one of the doors. A microprocessor is programmed or configured to receive at least one instruction from a server computer, and transmit a command, based on the at least one instruction, to actuate one of the plurality of actuators. Actuating one of the plurality of actuators causes the corresponding door to move from a closed position to an opened position. Systems and methods are provided for remote and immediate dispensing of articles.

Abstract: A data processing system is configured to pre-process data for a machine learning classifier. The data processing system includes an input port that receives one or more data items, an extraction engine that extracts a plurality of data signatures and structure data, a logical rule set generation engine configured to generate a data structure, select a particular data signature of the data structure, identify each instance of the particular data signature in the data structure, segment the data structure around instances of the particular data signature, identify one or more sequences of data signatures connected to the particular data signature, and generate a logical ruleset. A classification engine executes one or more classifiers against the logical ruleset to classify the one or more data items received by the input port.

Abstract: The invention generally relates to a shopping cart that could have: (i) a slidably movable lower container; (ii) a telescoping container; (iii) dual handles; (iv) a stand having a number of holders and a writing surface formed therein; (v) a wheel having a brake coupled thereto; (vi) a generally A-shaped frame; and/or (vii) a retractable child seat assembly.

Abstract: A computer implemented method includes performing a statistical analysis on a database of product shape information and identifying product characteristics based on statistical relationships among the shapes in the product database. A plurality of production rules that express the allowable variations of shapes defining the product characteristics is generated, and the generated rules are saved in a database for use in generating product designs according to the application of the rules.

Abstract: Parametric shape recognition is achieved through a decomposition of shapes into a hierarchy of subshapes ordered by their decreasing restrictions. Instances of each of the subshapes are individually located in the design shape and then reconstructed to form an instance of the entire shape. The basis for the hierarchy of subshapes can be specified by the designer or based on the default parameter relations that come from architectural and engineering knowledge. The levels of the hierarchy are defined so that the most constrained lines of a shape are those lines that the designer intended exactly. These most constrained lines have specified parametric relations to other line segments and those relations, if altered, will compromise the designer's intentions. Conversely, the lowest level of the hierarchy, which contains the least constrained line segments, only implies a specific connectivity between line segments, necessitating a vaster search.

Abstract: The invention generally relates to a shopping cart that could have: (i) a slidably movable lower container; (ii) a telescoping container; (iii) dual handles; (iv) a stand having a number of holders and a writing surface formed therein; (v) a wheel having a brake coupled thereto; (vi) a generally A-shaped frame; and/or (vii) a retractable child seat assembly.

Abstract: Parametric shape recognition is achieved through a decomposition of shapes into a hierarchy of subshapes ordered by their decreasing restrictions. Instances of each of the subshapes are individually located in the design shape and then reconstructed to form an instance of the entire shape. The basis for the hierarchy of subshapes can be specified by the designer or based on the default parameter relations that come from architectural and engineering knowledge. The levels of the hierarchy are defined so that the most constrained lines of a shape are those lines that the designer intended exactly. These most constrained lines have specified parametric relations to other line segments and those relations, if altered, will compromise the designer's intentions. Conversely, the lowest level of the hierarchy, which contains the least constrained line segments, only implies a specific connectivity between line segments, necessitating a vaster search.

Abstract: Parametric shape recognition is achieved through a decomposition of shapes into a hierarchy of subshapes ordered by their decreasing restrictions. Instances of each of the subshapes are individually located in the design shape and then reconstructed to form an instance of the entire shape. The basis for the hierarchy of subshapes can be specified by the designer or based on the default parameter relations that come from architectural and engineering knowledge. The levels of the hierarchy are defined so that the most constrained lines of a shape are those lines that the designer intended exactly. These most constrained lines have specified parametric relations to other line segments and those relations, if altered, will compromise the designer's intentions. Conversely, the lowest level of the hierarchy, which contains the least constrained line segments, only implies a specific connectivity between line segments, necessitating a vaster search.

Abstract: A method of recognizing a first shape in a second shape. The method includes decomposing the first shape into at least one subshape belonging to one of a plurality of subshape groups, and searching the second shape for a parametric transformation of the subshape.

Abstract: A solution to determining the move set ordering in pattern searching is disclosed that involves driving a pattern search algorithm by a metric other than the step size of the patterns. An instance of this metric is the amount of change in an objective function. Preprocessing algorithms are disclosed which quantify the effect each move has on the objective function. Those moves having a greater effect on the objective function are applied before moves having a lesser effect. We call this effect on the object function the sensitivity of the object function to a particular move and present several methods to quantify it. The sensitivity may be expressed as a function or the moves can be ranked and clustered with the pattern search being driven by the ranked moves or the function. The search may also be driven by an expected change in objective function. Because of the rules governing abstracts, this abstract should not be used to construe the claims.

Abstract: A solution to determining the move set ordering in pattern searching is disclosed that involves driving a pattern search algorithm by a metric other than the step size of the patterns. An instance of this metric is the amount of change in an objective function. Preprocessing algorithms are disclosed which quantify the effect each move has on the objective function. Those moves having a greater effect on the objective function are applied before moves having a lesser effect. We call this effect on the object function the sensitivity of the object function to a particular move and present several methods to quantify it. The sensitivity may be expressed as a function or the moves can be ranked and clustered with the pattern search being driven by the ranked moves or the function.