A method includes: accessing an initial image depicting a verified assembly unit; and detecting an initial constellation of features in the initial image. The method further includes: accessing a first image depicting an unverified assembly unit; detecting a first constellation of features in the first image; characterizing differences between corresponding features in the initial constellation of features and the first constellation of features; identifying a first dimension of a first feature of interest exhibiting a first difference exceeding a threshold difference; receiving manual verification the first feature of interest from the first constellation of features, the first dimension offset from a target dimension of the first feature of interest from the initial constellation of features; and defining a first nominal feature range for the first feature of interest, the range bounded by the first dimension and the target dimension.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
G06T 7/174 - SegmentationEdge detection involving the use of two or more images
G06T 7/33 - Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
A method includes: accessing an initial image depicting a verified assembly unit; and detecting an initial constellation of features in the initial image. The method further includes: accessing a first image depicting an unverified assembly unit; detecting a first constellation of features in the first image; characterizing differences between corresponding features in the initial constellation of features and the first constellation of features; identifying a first dimension of a first feature of interest exhibiting a first difference exceeding a threshold difference; receiving manual verification the first feature of interest from the first constellation of features, the first dimension offset from a target dimension of the first feature of interest from the initial constellation of features; and defining a first nominal feature range for the first feature of interest, the range bounded by the first dimension and the target dimension.
G06T 7/62 - Analysis of geometric attributes of area, perimeter, diameter or volume
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 20/70 - Labelling scene content, e.g. deriving syntactic or semantic representations
One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.
One variation of a method for automatically generating a common measurement across multiple assembly units includes: displaying a first image—recorded at an optical inspection station—within a user interface; receiving manual selection of a particular feature in a first assembly unit represented in the first image; receiving selection of a measurement type for the particular feature; extracting a first real dimension of the particular feature in the first assembly unit from the first image according to the measurement type; for each image in a set of images, identifying a feature—analogous to the particular feature—in an assembly unit represented in the image and extracting a real dimension of the feature in the assembly unit from the image according to the measurement type; and aggregating the first real dimension and a set of real dimensions extracted from the set of images into a digital container.
G06T 11/60 - Editing figures and textCombining figures or text
G06F 3/04842 - Selection of displayed objects or displayed text elements
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
5.
METHODS FOR AUTOMATICALLY GENERATING A COMMON MEASUREMENT ACROSS MULTIPLE ASSEMBLY UNITS
A method includes: identifying a first set of key features in a first inspection image characterizing geometric properties of a set of predefined features; extracting a first set of real dimensions of the first set of key features from the first inspection image; projecting the first set of real dimensions proximal the first set of key features onto the first inspection image; receiving confirmation of a first subset of key features, in the first set of key features, from a user; identifying the first subset of key features in a second inspection image; identifying a second set of key features in the second inspection image characterizing properties of the set of predefined features, the second set of key features distinct from unconfirmed features in the first set of key features; and extracting a second set of real dimensions of the second set of key features from the second inspection image.
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
One variation of an optical inspection kit includes: an enclosure defining an imaging volume; an optical sensor adjacent the imaging volume and defining a field of view directed toward the imaging volume; a nest module defining a receptacle configured to locate a surface of interest on a first unit of a first part within the imaging volume at an image plane of the optical sensor; a dark-field lighting module adjacent and perpendicular to the nest module and including a dark-field light source configured to output light across a light plane and a directional light filter configured to pass light output by the dark-field light source normal to the light plane and to reject light output by the dark-field light source substantially nonparallel to the light plane; and a bright-field light source proximal the optical sensor and configured to output light toward the surface of interest.
H04N 9/82 - Transformation of the television signal for recording, e.g. modulation, frequency changingInverse transformation for playback the individual colour picture signal components being recorded simultaneously only
H04N 23/56 - Cameras or camera modules comprising electronic image sensorsControl thereof provided with illuminating means
H04N 23/90 - Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
H05B 47/16 - Controlling the light source by timing means
7.
METHODS FOR AUTOMATICALLY GENERATING A COMMON MEASUREMENT ACROSS MULTIPLE ASSEMBLY UNITS
A method includes: identifying a first set of key features in a first inspection image characterizing geometric properties of a set of predefined features; extracting a first set of real dimensions of the first set of key features from the first inspection image; projecting the first set of real dimensions proximal the first set of key features onto the first inspection image; receiving confirmation of a first subset of key features, in the first set of key features, from a user; identifying the first subset of key features in a second inspection image; identifying a second set of key features in the second inspection image characterizing properties of the set of predefined features, the second set of key features distinct from unconfirmed features in the first set of key features; and extracting a second set of real dimensions of the second set of key features from the second inspection image.
G06T 11/60 - Editing figures and textCombining figures or text
G06F 3/04842 - Selection of displayed objects or displayed text elements
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
8.
Method for monitoring manufacture of assembly units
One variation of a method for monitoring manufacture of assembly units includes: receiving selection of a target location hypothesized by a user to contain an origin of a defect in assembly units of an assembly type; accessing a feature map linking non-visual manufacturing features to physical locations within the assembly type; for each assembly unit, accessing an inspection image of the assembly unit recorded by an optical inspection station during production of the assembly unit, projecting the target location onto the inspection image, detecting visual features proximal the target location within the inspection image, and aggregating non-visual manufacturing features associated with locations proximal the target location and representing manufacturing inputs into the assembly unit based on the feature map; and calculating correlations between visual and non-visual manufacturing features associated with locations proximal the target location and the defect for the set of assembly units.
One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.
A method includes accessing feature values representing a historical population of assembly units assembled on an assembly line; and accessing a failure status of the assembly unit at a target test on the assembly line. The method also includes, for each feature: deriving a correlation between values of the feature and failure status at the target test; deriving an effective limit of the feature based on scope of feature values in the historical population of assembly units; and calculating an action score for the feature based on the correlation and a width of the effective limit. The method further includes: selecting a particular feature exhibiting greatest action score; defining a preemptive test for the particular feature upstream of the target test during a next assembly period; and assigning a target limit, narrower than an effective limit of the particular feature, to the preemptive test.
G05B 19/418 - Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.
G06F 3/04842 - Selection of displayed objects or displayed text elements
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
G06T 11/60 - Editing figures and textCombining figures or text
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
12.
Method for monitoring manufacture of assembly units
One variation of a method for monitoring manufacture of assembly units includes: receiving selection of a target location hypothesized by a user to contain an origin of a defect in assembly units of an assembly type; accessing a feature map linking non-visual manufacturing features to physical locations within the assembly type; for each assembly unit, accessing an inspection image of the assembly unit recorded by an optical inspection station during production of the assembly unit, projecting the target location onto the inspection image, detecting visual features proximal the target location within the inspection image, and aggregating non-visual manufacturing features associated with locations proximal the target location and representing manufacturing inputs into the assembly unit based on the feature map; and calculating correlations between visual and non-visual manufacturing features associated with locations proximal the target location and the defect for the set of assembly units.
One variation of an optical inspection kit includes: an enclosure defining an imaging volume; an optical sensor adjacent the imaging volume and defining a field of view directed toward the imaging volume; a nest module defining a receptacle configured to locate a surface of interest on a first unit of a first part within the imaging volume at an image plane of the optical sensor; a dark-field lighting module adjacent and perpendicular to the nest module and including a dark-field light source configured to output light across a light plane and a directional light filter configured to pass light output by the dark-field light source normal to the light plane and to reject light output by the dark-field light source substantially nonparallel to the light plane; and a bright-field light source proximal the optical sensor and configured to output light toward the surface of interest.
H04N 9/82 - Transformation of the television signal for recording, e.g. modulation, frequency changingInverse transformation for playback the individual colour picture signal components being recorded simultaneously only
H04N 23/56 - Cameras or camera modules comprising electronic image sensorsControl thereof provided with illuminating means
H04N 23/90 - Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
One variation of a method for automatically generating a common measurement across multiple assembly units includes: displaying a first image—recorded at an optical inspection station—within a user interface; receiving manual selection of a particular feature in a first assembly unit represented in the first image; receiving selection of a measurement type for the particular feature; extracting a first real dimension of the particular feature in the first assembly unit from the first image according to the measurement type; for each image in a set of images, identifying a feature—analogous to the particular feature—in an assembly unit represented in the image and extracting a real dimension of the feature in the assembly unit from the image according to the measurement type; and aggregating the first real dimension and a set of real dimensions extracted from the set of images into a digital container.
G06T 11/60 - Editing figures and textCombining figures or text
G06F 3/04842 - Selection of displayed objects or displayed text elements
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
15.
Method for monitoring manufacture of assembly units
One variation of a method for monitoring manufacture of assembly units includes: receiving selection of a target location hypothesized by a user to contain an origin of a defect in assembly units of an assembly type; accessing a feature map linking non-visual manufacturing features to physical locations within the assembly type; for each assembly unit, accessing an inspection image of the assembly unit recorded by an optical inspection station during production of the assembly unit, projecting the target location onto the inspection image, detecting visual features proximal the target location within the inspection image, and aggregating non-visual manufacturing features associated with locations proximal the target location and representing manufacturing inputs into the assembly unit based on the feature map; and calculating correlations between visual and non-visual manufacturing features associated with locations proximal the target location and the defect for the set of assembly units.
One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.
One variation of an optical inspection kit includes: an enclosure defining an imaging volume; an optical sensor adjacent the imaging volume and defining a field of view directed toward the imaging volume; a nest module defining a receptacle configured to locate a surface of interest on a first unit of a first part within the imaging volume at an image plane of the optical sensor; a dark-field lighting module adjacent and perpendicular to the nest module and including a dark-field light source configured to output light across a light plane and a directional light filter configured to pass light output by the dark-field light source normal to the light plane and to reject light output by the dark-field light source substantially nonparallel to the light plane; and a bright-field light source proximal the optical sensor and configured to output light toward the surface of interest.
H04N 9/82 - Transformation of the television signal for recording, e.g. modulation, frequency changingInverse transformation for playback the individual colour picture signal components being recorded simultaneously only
G01B 11/02 - Measuring arrangements characterised by the use of optical techniques for measuring length, width, or thickness
G01N 21/88 - Investigating the presence of flaws, defects or contamination
One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.
One variation of a method for monitoring manufacture of assembly units includes: receiving selection of a target location hypothesized by a user to contain an origin of a defect in assembly units of an assembly type; accessing a feature map linking non-visual manufacturing features to physical locations within the assembly type; for each assembly unit, accessing an inspection image of the assembly unit recorded by an optical inspection station during production of the assembly unit, projecting the target location onto the inspection image, detecting visual features proximal the target location within the inspection image, and aggregating non-visual manufacturing features associated with locations proximal the target location and representing manufacturing inputs into the assembly unit based on the feature map; and calculating correlations between visual and non-visual manufacturing features associated with locations proximal the target location and the defect for the set of assembly units.
A method includes: displaying a first image of a first assembly unit within a user interface; locating a first virtual origin at a first feature on the first assembly unit; displaying a first subregion of the first image within the user interface responsive to a change in a view window of the first image; recording a geometry and a position of the first subregion relative to the first virtual origin; locating a second virtual origin at a second feature—analogous to the first feature—on a second assembly unit represented in the second image; projecting the geometry and the position of the first subregion onto the second image according to the second virtual origin to define a second subregion of the second image; and, in response to receipt of a command to advance from the first image to the second image, displaying the second subregion within the user interface.
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.
One variation of a method for predicting manufacturing defects includes: accessing a set of inspection images of a set of assembly units recorded by an optical inspection station; for each inspection image in the set of inspection images, detecting a set of features in the inspection image and generating a vector representing the set of features in a multi-dimensional feature space; grouping neighboring vectors in the multi-dimensional feature space into a set of vector groups; and, in response to receipt of a first inspection result indicting a defect in a first assembly unit, in the set of assembly units, associated with a first vector in a first vector group, in the set of vector groups, labeling the first vector group with the defect and flagging a second assembly unit associated with a second vector, in the first vector group, as exhibiting characteristics of the defect.
One variation of a method for predicting manufacturing defects includes: accessing a set of inspection images of a set of assembly units recorded by an optical inspection station; for each inspection image in the set of inspection images, detecting a set of features in the inspection image and generating a vector representing the set of features in a multi- dimensional feature space; grouping neighboring vectors in the multi-dimensional feature space into a set of vector groups; and, in response to receipt of a first inspection result indicting a defect in a first assembly unit, in the set of assembly units, associated with a first vector in a first vector group, in the set of vector groups, labeling the first vector group with the defect and flagging a second assembly unit associated with a second vector, in the first vector group, as exhibiting characteristics of the defect.
One variation of an optical inspection kit includes: an enclosure defining an imaging volume; an optical sensor adjacent the imaging volume and defining a field of view directed toward the imaging volume; a nest module defining a receptacle configured to locate a surface of interest on a first unit of a first part within the imaging volume at an image plane of the optical sensor; a dark-field lighting module adjacent and perpendicular to the nest module and including a dark-field light source configured to output light across a light plane and a directional light filter configured to pass light output by the dark-field light source normal to the light plane and to reject light output by the dark-field light source substantially nonparallel to the light plane; and a bright-field light source proximal the optical sensor and configured to output light toward the surface of interest.
H04N 9/82 - Transformation of the television signal for recording, e.g. modulation, frequency changingInverse transformation for playback the individual colour picture signal components being recorded simultaneously only
G01B 11/02 - Measuring arrangements characterised by the use of optical techniques for measuring length, width, or thickness
G01N 21/88 - Investigating the presence of flaws, defects or contamination
One variation of an optical inspection kit includes: an enclosure defining an imaging volume; an optical sensor adjacent the imaging volume and defining a field of view directed toward the imaging volume; a nest module defining a receptacle configured to locate a surface of interest on a first unit of a first part within the imaging volume at an image plane of the optical sensor; a dark-field lighting module adjacent and perpendicular to the nest module and including a dark-field light source configured to output light across a light plane and a directional light filter configured to pass light output by the dark-field light source normal to the light plane and to reject light output by the dark-field light source substantially nonparallel to the light plane; and a bright-field light source proximal the optical sensor and configured to output light toward the surface of interest.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware; computer software for use in collecting,
storing, viewing, sorting, manipulating, and analyzing data
of the manufacturing process and the parts, components, or
assemblies being manufactured; computer software for
identifying, diagnosing, analyzing, improving, optimizing,
predicting, and managing issues with equipment, processes,
production, and products in manufacturing; computer software
to monitor, control, manage, improve, and optimize
equipment, processes, production, and products in
manufacturing; computer software to integrate manufacturing
machine operations, track issues, and generate production
reports; computer software for empirical modeling and
statistical analysis of data obtained from and the
monitoring of process controls systems and production issues
in manufacturing; computer software for identifying,
diagnosing, analyzing, improving, optimizing, and managing
manufacturing issues, namely, issues regarding process,
workmanship, quality, and design; computer hardware,
electric sensors, motion sensors, cameras, lights, video
recorders, microphones, radio transmitters and receivers,
motion and light detectors, lasers, x-rays and robotic
machinery, with all of the aforementioned being for use in
connection with monitoring, controlling, managing,
improving, and optimizing, the assembly line, test stations,
and other processes in manufacturing; computer software for
use in processing and managing bill of materials, supply
chain, part quality, product quality, customer complaints,
returns, recalls and repairs; computer software for use in
processing, managing, and extracting insights from bill of
materials, supply chain, part quality, product quality,
customer complaints, returns, recalls and repairs; computer
software for use in comparing and analyzing idealized models
of hardware to actual parts, components, and products;
computer software for use comparing and predicting the
performance of parts, process, and products. Software as a service (SaaS) services featuring software for
use in collecting, storing, viewing, sorting, manipulating,
and analyzing data of the manufacturing process and the
parts, components, or assemblies being manufactured and for
developing insights about the manufacturing process and the
parts, components, or assemblies being manufactured;
software as a service (SaaS) services featuring software for
identifying, diagnosing, analyzing, improving, optimizing,
and managing issues with equipment, processes, production,
and products in manufacturing; software as a service (SaaS)
services featuring software to monitor, control, manage,
improve, and optimize equipment, processes, production, and
products in manufacturing; software as a service (SaaS)
services featuring software to integrate manufacturing
machine operations, track issues, and generate production
reports; software as a service (SaaS) services featuring
software for empirical modeling and statistical analysis of
data obtained from and the monitoring of process controls
systems and production issues in manufacturing; software as
a service (SaaS) services featuring software for
identifying, diagnosing, analyzing, improving, optimizing,
and managing manufacturing issues, namely, issues regarding
process, workmanship, quality, and design; product design
and development for others; engineering services;
engineering services in the field of manufacturing and the
improvement and optimization of manufacturing production and
processes; software as a service (SaaS) services featuring
software for use in processing and managing bill of
materials, supply chain, part quality, product quality,
customer complaints, returns, recalls and repairs; software
as a service (SaaS) services featuring software for use in
processing, managing, and extracting insights from bill of
materials, supply chain, part quality, product quality,
customer complaints, returns, recalls and repairs; software
as a service (SaaS) services featuring software for use in
comparing and analyzing idealized models of hardware to
actual parts, components, and products; software as a
service (SaaS) services featuring software for use comparing
and predicting the performance of parts, process, and
products.
27.
Methods for automatically generating a common measurement across multiple assembly units
A method includes: displaying a first image of a first assembly unit within a user interface; locating a first virtual origin at a first feature on the first assembly unit; displaying a first subregion of the first image within the user interface responsive to a change in a view window of the first image; recording a geometry and a position of the first subregion relative to the first virtual origin; locating a second virtual origin at a second feature—analogous to the first feature—on a second assembly unit represented in the second image; projecting the geometry and the position of the first subregion onto the second image according to the second virtual origin to define a second subregion of the second image; and, in response to receipt of a command to advance from the first image to the second image, displaying the second subregion within the user interface.
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
One variation of a method for automatically generating a common measurement across multiple assembly units includes: displaying a first image—recorded at an optical inspection station—within a user interface; receiving manual selection of a particular feature in a first assembly unit represented in the first image; receiving selection of a measurement type for the particular feature; extracting a first real dimension of the particular feature in the first assembly unit from the first image according to the measurement type; for each image in a set of images, identifying a feature—analogous to the particular feature—in an assembly unit represented in the image and extracting a real dimension of the feature in the assembly unit from the image according to the measurement type; and aggregating the first real dimension and a set of real dimensions extracted from the set of images into a digital container.
G06F 3/0484 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware; computer software; computer software for
use in collecting, storing, viewing, sorting, manipulating,
and analyzing data of the manufacturing process and the
parts, components, or assemblies being manufactured;
computer software for identifying, diagnosing, analyzing,
improving, optimizing, and managing issues with equipment,
processes, production, and products in manufacturing;
computer software to monitor, control, manage, improve, and
optimize equipment, processes, production, and products in
manufacturing; computer software to integrate manufacturing
machine operations, track issues, and generate production
reports; computer software for empirical modeling and
statistical analysis of data obtained from and the
monitoring of process controls systems and production issues
in manufacturing; computer software for identifying,
diagnosing, analyzing, improving, optimizing, and managing
manufacturing issues, namely, issues regarding process,
workmanship, quality, and design; computer hardware,
electronic sensors, motions sensors, cameras, video
recorders, microphones, transmitters, receivers, motion and
light detectors, lasers, x-rays and electronic apparatuses
for use in connection with monitoring, controlling,
managing, improving, and optimizing the assembly line, test
stations, and other processes in manufacturing. Software as a service (SaaS) services; software as a service
(SaaS) services featuring software for use in collecting,
storing, viewing, sorting, manipulating, and analyzing data
of the manufacturing process and the parts, components, or
assemblies being manufactured; computer software for
identifying, diagnosing, analyzing, improving, optimizing,
and managing issues with equipment, processes, production,
and products in manufacturing; software as a service (SaaS)
services featuring software to monitor, control, manage,
improve, and optimize equipment, processes, production, and
products in manufacturing; software as a service (SaaS)
services featuring software to integrate manufacturing
machine operations, track issues, and generate production
reports; software as a service (SaaS) services featuring
software for empirical modeling and statistical analysis of
data obtained from and the monitoring of process controls
systems and production issues in manufacturing; software as
a service (SaaS) services featuring software for
identifying, diagnosing, analyzing, improving, optimizing,
and managing manufacturing issues, namely, issues regarding
process, workmanship, quality, and design; design,
development and consulting services related thereto in the
field of manufacturing and the improvement and optimization
of manufacturing production and processes; engineering
services; engineering services in the field of manufacturing
and the improvement and optimization of manufacturing
production and processes.
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware; computer software for use in collecting, storing, viewing, sorting, manipulating, and analyzing data of the manufacturing process and the parts, components, or assemblies being manufactured; computer software for identifying, diagnosing, analyzing, improving, optimizing, predicting, and managing issues with equipment, processes, production, and products in manufacturing; computer software to monitor, control, manage, improve, and optimize equipment, processes, production, and products in manufacturing; computer software to integrate manufacturing machine operations, track issues, and generate production reports; computer software for statistical analysis of data obtained from and the monitoring of process controls systems and production issues in manufacturing; computer software for identifying, diagnosing, analyzing, improving, optimizing, and managing manufacturing issues, namely, issues regarding process, workmanship, quality, and design; computer hardware, electric sensors, motion sensors, cameras, lights, radio transmitters and receivers, motion and light detectors, and robotic machinery, with all of the aforementioned being for use in connection with monitoring, controlling, managing, improving, and optimizing, the assembly line, test stations, and other processes in manufacturing; computer software for use in processing and managing part quality and product quality; computer software for use in extracting insights from bill of materials, supply chain, part quality, product quality, returns, recalls and repairs; computer software for use in comparing and analyzing idealized models of hardware to actual parts, components, and products; computer software for use comparing and predicting the performance of parts, process, and products Software as a service (SaaS) services featuring software for use in collecting, storing, viewing, sorting, manipulating, and analyzing data of the manufacturing process and the parts, components, or assemblies being manufactured and for developing insights about the manufacturing process and the parts, components, or assemblies being manufactured; software as a service (SaaS) services featuring software for identifying, diagnosing, analyzing, improving, optimizing, and managing issues with equipment, processes, production, and products in manufacturing; software as a service (SaaS) services featuring software to monitor, control, manage, improve, and optimize equipment, processes, production, and products in manufacturing; software as a service (SaaS) services featuring software to integrate manufacturing machine operations, track issues, and generate production reports; software as a service (SaaS) services featuring software for statistical analysis of data obtained from and the monitoring of process controls systems and production issues in manufacturing; software as a service (SaaS) services featuring software for identifying, diagnosing, analyzing, improving, optimizing, and managing manufacturing issues, namely, issues regarding process, workmanship, quality, and design; engineering services; engineering services in the field of manufacturing and the improvement and optimization of manufacturing production and processes; software as a service (SaaS) services featuring software for use in processing and managing part quality and product quality; software as a service (SaaS) services featuring software for use in extracting insights from bill of materials, supply chain, part quality, product quality, returns, recalls and repairs; software as a service (SaaS) services featuring software for use in comparing and analyzing idealized models of hardware to actual parts, components, and products; software as a service (SaaS) services featuring software for use comparing and predicting the performance of parts, process, and products
09 - Scientific and electric apparatus and instruments
42 - Scientific, technological and industrial services, research and design
Goods & Services
Computer hardware; computer software for use in collecting, storing, viewing, sorting, manipulating, and analyzing data of the manufacturing process and the parts, components, or assemblies being manufactured; computer software for identifying, diagnosing, analyzing, improving, optimizing, predicting, and managing issues with equipment, processes, production, and products in manufacturing; computer software to monitor, control, manage, improve, and optimize equipment, processes, production, and products in manufacturing; computer software to integrate manufacturing machine operations, track issues, and generate production reports; computer software for statistical analysis of data obtained from and the monitoring of process controls systems and production issues in manufacturing; computer software for identifying, diagnosing, analyzing, improving, optimizing, and managing manufacturing issues, namely, issues regarding process, workmanship, quality, and design; computer hardware, electric sensors, motion sensors, cameras, lights, radio transmitters and receivers, motion and light detectors, and robotic machinery, with all of the aforementioned being for use in connection with monitoring, controlling, managing, improving, and optimizing, the assembly line, test stations, and other processes in manufacturing; computer software for use in processing and managing part quality and product quality; computer software for use in extracting insights from bill of materials, supply chain, part quality, product quality, returns, recalls and repairs; computer software for use in comparing and analyzing idealized models of hardware to actual parts, components, and products; computer software for use comparing and predicting the performance of parts, process, and products Software as a service (SaaS) services featuring software for use in collecting, storing, viewing, sorting, manipulating, and analyzing data of the manufacturing process and the parts, components, or assemblies being manufactured and for developing insights about the manufacturing process and the parts, components, or assemblies being manufactured; software as a service (SaaS) services featuring software for identifying, diagnosing, analyzing, improving, optimizing, and managing issues with equipment, processes, production, and products in manufacturing; software as a service (SaaS) services featuring software to monitor, control, manage, improve, and optimize equipment, processes, production, and products in manufacturing; software as a service (SaaS) services featuring software to integrate manufacturing machine operations, track issues, and generate production reports; software as a service (SaaS) services featuring software for statistical analysis of data obtained from and the monitoring of process controls systems and production issues in manufacturing; software as a service (SaaS) services featuring software for identifying, diagnosing, analyzing, improving, optimizing, and managing manufacturing issues, namely, issues regarding process, workmanship, quality, and design; engineering services; engineering services in the field of manufacturing and the improvement and optimization of manufacturing production and processes; software as a service (SaaS) services featuring software for use in processing and managing part quality and product quality; software as a service (SaaS) services featuring software for use in extracting insights from bill of materials, supply chain, part quality, product quality, returns, recalls and repairs; software as a service (SaaS) services featuring software for use in comparing and analyzing idealized models of hardware to actual parts, components, and products; software as a service (SaaS) services featuring software for use comparing and predicting the performance of parts, process, and products
