DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 07 August 2025 have been fully considered but they are not persuasive.
The Applicant argues that Hyatt et al. does not appear to disclose, “wherein the first camera and the second camera are identified based on a first activation time for the first camera being connected to the machine vision system being within a threshold interval from a second activation time for the second camera being connected to the machine vision system.” The Examiner respectfully disagrees. The Examiner is interpreting the activation times for the individual cameras being connected to the machine vision system (e.g., within a threshold interval from one another) as being the time when the cameras capture the images and those images are sent to the machine vision system (as in the sending time is the connection time because a communication link is being established at that time). Furthermore, the images from the first and second cameras are linked because their sending time (connection times) overlap and the threshold is being read as images that are captured at the same time. Hyatt et al. discloses this idea with “simultaneous” inspection by individual inspection assemblies that perform inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time (Figs. 7A-8; paragraphs [0040] and [0128]). Therefore, when the claim limitations are given their broadest reasonable interpretation, Hyatt et al. meets the claimed limitations and the rejection is maintained. The Examiner suggests further defining the connection process as being when the first camera being connected to a first port of a computer vision system at a first address within a threshold value of a second address of a second port of the machine vision system that the second camera is connected to (paragraph [0007] of the Applicant’s Specification).
The Applicant argues that Hyatt et al. does not appear to disclose, “identifying a first subset of cameras from the plurality of cameras that provided outputs in the plurality of outputs that include images of a specified portion of an object at or above a specified confidence interval for a machine learning model to determine whether the specified portion satisfies a pass/fail criterion.” The Examiner respectfully disagrees. Hyatt et al. discloses in Fig. 8 a method for visual inspection of items on a production line. The setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features. The analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line (paragraph [0120]). These profiles that are setup and stored in the database are used for the machine learning model to determine whether an item passes or fails inspection. There will be thresholds and/or instructions in the profiles that will provide guidance for the machine learning model, which means that once these machine learning models are trained with the profiles, the machine learning model will be able to determine with confidence whether the item passes or fails inspection. Hyatt et al. also discloses in Fig. 6B and Fig. 8 different inspection configurations, which will affect how the system 115 operates during inspection (paragraphs [0112]-[0118]). The different configurations will determine how the cameras are used and whether some images will have overlapping views (for example, CFG1-CFG5). Once the system is setup and the correct file is loaded, the inspection can take place accordingly (Fig. 8). Therefore, when the claim limitations are given their broadest reasonable interpretation, Hyatt et al. meets the claimed limitations and the rejection is maintained.
The Applicant argues that Hyatt et al. does not appear to disclose, “combining output form a virtual device,” “a job defined for the virtual device,” and “activating the quality assurance device according to an analysis of the first portion and the second portion visible in the combined output received from the virtual device according to the job.” The Examiner respectfully disagrees. As described above, Hyatt et al. can select different inspection configurations, which will affect how the system 115 operates during inspection (paragraphs [0112]-[0118]). Once of those configurations can inspect the same item simultaneously from overlapping or adjoining points of view with multiple inspection assemblies 110 or multiple camera assemblies 111 (Figs. 7A-7E; CFG5). As shown in Figs. 7D and 7E, the three captured views 745, 755, and 756 respectively of inspection assemblies 110A, 110B, and 110n are seamlessly joined (“stitched”) together to present a single view 752 of item 25 (paragraph [0109]). The images joined together reads on a combined output of a virtual device. That image is then used in the machine learning model to determine whether the item passes or fails inspection. Therefore, when the claim limitations are given their broadest reasonable interpretation, Hyatt et al. meets the claimed limitations and the rejection is maintained.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-7, 9, 10, and 12-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Hyatt et al. (U.S. Patent Application Publication 2022/0005183).
Regarding claim 1, Hyatt et al. discloses a method, comprising: identifying a first camera and a second camera of a plurality of cameras associated with a machine vision system, wherein the first camera and the second camera are identified based on a first activation time for the first camera being connected to the machine vision system being within a threshold interval from a second activation time for the second camera being connected to the machine vision system (Figs. 7A-8; paragraph [0040] – as used herein “simultaneous” inspection by individual inspection assemblies implies that the inspection assemblies are performing inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time; the threshold is that the images are taken at the same time; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth); displaying, in a graphical user interface, a proposal to operate the first camera and the second camera as a virtual device in performing a job in the machine vision system using the first camera and the second camera (Figs. 7A-8; paragraph [0111] – process 800 includes the embodiments of Figs. 7A-7C – in step 802 performed typically only once as part of an initial setup, controller 130 identifies that more than one inspection assembly 110 or camera assembly 111 is connected to controller 130 – controller 130 then gives a user the option, via UI 132 of controller 130, to define the configuration of the inspection assemblies 110 or camera assemblies 111); and in response to receiving confirmation of the proposal: creating the job in the machine vision system (Figs. 7A-8; paragraph [0119] – system 115 typically requires a one-time setup step 806 for each item or aspect of item or stage of item that is to be inspected – in the setup step 806, at least two or more defect free samples of a manufactured item of the same type are placed in succession within field of view 104 or the combined field of view 704 of the inspection assemblies 110 or camera assemblies 111 that will inspect the particular item or aspect; paragraph [0120] – the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features – the analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line – profile 136 describes the item and the setup of system 115 for inspecting the item and is stored in DB 134; paragraph [0121] – in steps 808A-B, inspection of the item/s can commence with loading of the correct item profile 136 to be associated with each inspection assembly 110 – loading the item profile 136 includes configuring the inspection assemblies 110 and controller 130 based on configuration information contained in the profile 136); receiving a first analysis image from the first camera and a second analysis image from the second camera (paragraph [0126] – in the inspection step 809, the items under inspection are imaged by cameras 102 of each inspection assembly 110 or camera assembly 111 – the images received from the inspection assemblies 110 or camera assembly 111, which may be referred to as inspection images, are processed by controller 130 using machine learning/AI algorithms to detect defects or for gating, counting or sorting of items and/or other inspection tasks based on the loaded profiles 136; paragraph [0127] – in steps 810A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result – optionally, a user can select the current capture view for specific inspection assemblies 110 using UI 132); combining the first analysis image and the second analysis image into a combined image (paragraph [0126] – in the inspection step 809, the items under inspection are imaged by cameras 102 of each inspection assembly 110 or camera assembly 111 – the images received from the inspection assemblies 110 or camera assembly 111, which may be referred to as inspection images, are processed by controller 130 using machine learning/AI algorithms to detect defects or for gating, counting or sorting of items and/or other inspection tasks based on the loaded profiles 136; paragraph [0127] – in steps 810A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result – optionally, a user can select the current capture view for specific inspection assemblies 110 using UI 132; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth; paragraph [0132] – in step 810B for CFG2 and CFG5 individual outputs will be provided for each aspect of the inspected item as imaged by each inspection assembly 110 or camera assembly 111 and a correlated result for the item may be provided summarizing the results from all of the inspection assemblies 110 or camera assemblies 111); executing the job on the combined image (paragraph [0126] – in the inspection step 809, the items under inspection are imaged by cameras 102 of each inspection assembly 110 or camera assembly 111 – the images received from the inspection assemblies 110 or camera assembly 111, which may be referred to as inspection images, are processed by controller 130 using machine learning/AI algorithms to detect defects or for gating, counting or sorting of items and/or other inspection tasks based on the loaded profiles 136; paragraph [0127] – in steps 810A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result – optionally, a user can select the current capture view for specific inspection assemblies 110 using UI 132; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth; paragraph [0132] – in step 810B for CFG2 and CFG5 individual outputs will be provided for each aspect of the inspected item as imaged by each inspection assembly 110 or camera assembly 111 and a correlated result for the item may be provided summarizing the results from all of the inspection assemblies 110 or camera assemblies 111); and rendering an outcome based on an analysis of the combined image according to the job (paragraph [0126] – in the inspection step 809, the items under inspection are imaged by cameras 102 of each inspection assembly 110 or camera assembly 111 – the images received from the inspection assemblies 110 or camera assembly 111, which may be referred to as inspection images, are processed by controller 130 using machine learning/AI algorithms to detect defects or for gating, counting or sorting of items and/or other inspection tasks based on the loaded profiles 136; paragraph [0127] – in steps 810A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result – optionally, a user can select the current capture view for specific inspection assemblies 110 using UI 132; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth; paragraph [0132] – in step 810B for CFG2 and CFG5 individual outputs will be provided for each aspect of the inspected item as imaged by each inspection assembly 110 or camera assembly 111 and a correlated result for the item may be provided summarizing the results from all of the inspection assemblies 110 or camera assemblies 111).
Regarding claim 2, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the first camera and the second camera are identified further based on: identifying an overlap in a first image produced by the first camera with a second image produced by the second camera (Hyatt et al.: Figs. 7A-8; paragraph [0040] – as used herein “simultaneous” inspection by individual inspection assemblies implies that the inspection assemblies are performing inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time; the threshold is that the images are taken at the same time; paragraph [0105] – in Fig. 7B three camera assemblies 111A, 111B, and 111n are shown for simultaneously imaging overlapping or adjoining aspects of item 25 such that controller 130 can combine the images provided from FOVs 104A, 104B, and 104n of each inspection assembly into a single FOV 704 to provide a single inspection result for item 25; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth).
Regarding claim 3, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the first camera and the second camera are identified further based on: activating a light source; and observing a threshold change in contrast or brightness in a first image produced by the first camera and the threshold change in contrast or brightness in a second image produced by the second camera while the light source is activated compared to when the light source is inactive (Hyatt et al.: paragraph [0010] – for example, two inspection systems placed close together will each affect the other’s lighting requirements resulting in a doubly complex integration; lighting conditions are worked out during the setup process; paragraph [0014] – each inspection assembly comprises a customizable mounting assembly, and a camera assembly which comprises an inspection camera and light source – the inspection cameras and lighting sources from each inspection assembly are controlled by the controller; paragraph [0021] – prior to inspection, the appropriate profile is selected for the item to be inspected – items to be inspected preferably comprise any item type, shape or material, set in any lighting environment; paragraph [0065] – light source 106 comprises LEDs or other known light source – the intensity (brightness) of light source 106 can be adjusted – optionally, the color of light source 106 can be adjusted – optionally, light source 106 comprises multiple controllable segments, each of which can be activated or provided with the same or different intensity and/or color; paragraph [0069] – controller 130 activates camera 102 and light source 106 or any of camera assembly 111 components or controllable segments as described above, which may or may not be activated depending on the item being imaged or the inspection lighting environment; paragraph [0070] – controller 130 preferably alters the intensity or color of light source 106 depending on the item being imaged or the inspection lighting environment – controller 103 preferably alters the intensity or color of light source 106 for regions of particular interest within the illuminated area – controller 130 preferably alters the intensity or color of light source 106 so that images taken by camera 102 of item 20 are not over or under exposed).
Regarding claim 4, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the first camera and the second camera are identified further based on: the first camera being connected to a first port of a computer vision system at a first address within a threshold value of a second address of a second port of the machine vision system that the second camera is connected to (Hyatt et al.: paragraph [0062] – controller 130 may be locally connected to the plurality of inspection assemblies 110 or may be remotely connected, e.g., via the cloud; paragraph [0111] – in step 802 performed typically only once as part of an initial setup, controller 130 identifies that more than one inspection assembly 110 or camera assembly 111 is connected to controller 130 – controller 130 then gives a user the option, via UI 132 of controller 130, to define the configuration of the inspection assemblies 110 or camera assemblies 111; during setup it will be determined which cameras will be used for each configuration based on their locations and/or addresses – for example, when trying to create a combined image, cameras closest to one another with overlapping fields of view will be selected).
Regarding claim 5, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein the first camera is grouped into a second virtual device with a third camera that is not grouped in the virtual device with the second camera (Hyatt et al.: Figs. 7A-8; paragraph [0040] – as used herein “simultaneous” inspection by individual inspection assemblies implies that the inspection assemblies are performing inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time; the threshold is that the images are taken at the same time; paragraph [0062] – controller 130 may be locally connected to the plurality of inspection assemblies 110 or may be remotely connected, e.g., via the cloud; paragraph [0105] – in Fig. 7B three camera assemblies 111A, 111B, and 111n are shown for simultaneously imaging overlapping or adjoining aspects of item 25 such that controller 130 can combine the images provided from FOVs 104A, 104B, and 104n of each inspection assembly into a single FOV 704 to provide a single inspection result for item 25; paragraph [0111] – in step 802 performed typically only once as part of an initial setup, controller 130 identifies that more than one inspection assembly 110 or camera assembly 111 is connected to controller 130 – controller 130 then gives a user the option, via UI 132 of controller 130, to define the configuration of the inspection assemblies 110 or camera assemblies 111; during setup it will be determined which cameras will be used for each configuration based on their locations and/or addresses – for example, when trying to create a combined image, cameras closest to one another with overlapping fields of view will be selected; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth).
Regarding claim 6, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein a first output of the first camera and a second output of the second camera are saved to a shared canvas (Hyatt et al.: Figs. 7A-8; paragraph [0040] – as used herein “simultaneous” inspection by individual inspection assemblies implies that the inspection assemblies are performing inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time; the threshold is that the images are taken at the same time; paragraph [0105] – in Fig. 7B three camera assemblies 111A, 111B, and 111n are shown for simultaneously imaging overlapping or adjoining aspects of item 25 such that controller 130 can combine the images provided from FOVs 104A, 104B, and 104n of each inspection assembly into a single FOV 704 to provide a single inspection result for item 25; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth).
Regarding claim 7, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 1 including that wherein a machine learning model identifies the first camera and the second camera from the plurality of cameras (Hyatt et al.: Figs. 7A-8; paragraph [0013] – ease of deployment and operation is enabled by a combination of machine learning, computer vision, and other algorithms that dynamically adapt to assess the item to be inspected, the target area of inspection, and the characteristics of the surrounding environment (such as but not limited to lighting) effecting the inspection setup; paragraph [0040] – as used herein “simultaneous” inspection by individual inspection assemblies implies that the inspection assemblies are performing inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time; the threshold is that the images are taken at the same time; paragraph [0062] – controller 130 may be locally connected to the plurality of inspection assemblies 110 or may be remotely connected, e.g., via the cloud; paragraph [0105] – in Fig. 7B three camera assemblies 111A, 111B, and 111n are shown for simultaneously imaging overlapping or adjoining aspects of item 25 such that controller 130 can combine the images provided from FOVs 104A, 104B, and 104n of each inspection assembly into a single FOV 704 to provide a single inspection result for item 25; paragraph [0111] – in step 802 performed typically only once as part of an initial setup, controller 130 identifies that more than one inspection assembly 110 or camera assembly 111 is connected to controller 130 – controller 130 then gives a user the option, via UI 132 of controller 130, to define the configuration of the inspection assemblies 110 or camera assemblies 111; during setup it will be determined which cameras will be used for each configuration based on their locations and/or addresses – for example, when trying to create a combined image, cameras closest to one another with overlapping fields of view will be selected; paragraph [0120] – the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth).
Regarding claim 9, Hyatt et al. discloses a method, comprising: receiving a plurality of outputs from a corresponding plurality of cameras associated with a machine vision system (Figs. 7A-8; paragraph [0040] – as used herein “simultaneous” inspection by individual inspection assemblies implies that the inspection assemblies are performing inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time; the threshold is that the images are taken at the same time; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth); analyzing a combined output of the plurality of outputs (paragraph [0126] – in the inspection step 809, the items under inspection are imaged by cameras 102 of each inspection assembly 110 or camera assembly 111 – the images received from the inspection assemblies 110 or camera assembly 111, which may be referred to as inspection images, are processed by controller 130 using machine learning/AI algorithms to detect defects or for gating, counting or sorting of items and/or other inspection tasks based on the loaded profiles 136; paragraph [0127] – in steps 810A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result – optionally, a user can select the current capture view for specific inspection assemblies 110 using UI 132; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth; paragraph [0132] – in step 810B for CFG2 and CFG5 individual outputs will be provided for each aspect of the inspected item as imaged by each inspection assembly 110 or camera assembly 111 and a correlated result for the item may be provided summarizing the results from all of the inspection assemblies 110 or camera assemblies 111); identifying a first subset of cameras from the plurality of cameras that provided outputs in the plurality of outputs that include images of a specified portion of an object at or above a specified confidence interval for a machine learning model to determine whether the specified portion satisfies a pass/fail criterion (paragraph [0120] - the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features – the analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line; paragraph [0126] – in the inspection step 809, the items under inspection are imaged by cameras 102 of each inspection assembly 110 or camera assembly 111 – the images received from the inspection assemblies 110 or camera assembly 111, which may be referred to as inspection images, are processed by controller 130 using machine learning/AI algorithms to detect defects or for gating, counting or sorting of items and/or other inspection tasks based on the loaded profiles 136; paragraph [0127] – in steps 810A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result – optionally, a user can select the current capture view for specific inspection assemblies 110 using UI 132; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth; paragraph [0132] – in step 810B for CFG2 and CFG5 individual outputs will be provided for each aspect of the inspected item as imaged by each inspection assembly 110 or camera assembly 111 and a correlated result for the item may be provided summarizing the results from all of the inspection assemblies 110 or camera assemblies 111); identifying a second subset of cameras from the first subset of cameras that provide overlapping coverage of the specified portion with one another (paragraph [0120] - the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features – the analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line; paragraph [0126] – in the inspection step 809, the items under inspection are imaged by cameras 102 of each inspection assembly 110 or camera assembly 111 – the images received from the inspection assemblies 110 or camera assembly 111, which may be referred to as inspection images, are processed by controller 130 using machine learning/AI algorithms to detect defects or for gating, counting or sorting of items and/or other inspection tasks based on the loaded profiles 136; paragraph [0127] – in steps 810A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result – optionally, a user can select the current capture view for specific inspection assemblies 110 using UI 132; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth; paragraph [0132] – in step 810B for CFG2 and CFG5 individual outputs will be provided for each aspect of the inspected item as imaged by each inspection assembly 110 or camera assembly 111 and a correlated result for the item may be provided summarizing the results from all of the inspection assemblies 110 or camera assemblies 111); identifying a reduced number of cameras from the second subset as a third subset of cameras that provide total coverage of the specified portion (paragraph [0120] - the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features – the analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line; paragraph [0126] – in the inspection step 809, the items under inspection are imaged by cameras 102 of each inspection assembly 110 or camera assembly 111 – the images received from the inspection assemblies 110 or camera assembly 111, which may be referred to as inspection images, are processed by controller 130 using machine learning/AI algorithms to detect defects or for gating, counting or sorting of items and/or other inspection tasks based on the loaded profiles 136; paragraph [0127] – in steps 810A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result – optionally, a user can select the current capture view for specific inspection assemblies 110 using UI 132; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth; paragraph [0132] – in step 810B for CFG2 and CFG5 individual outputs will be provided for each aspect of the inspected item as imaged by each inspection assembly 110 or camera assembly 111 and a correlated result for the item may be provided summarizing the results from all of the inspection assemblies 110 or camera assemblies 111); outputting, to a user interface, the third subset of cameras as a virtual device for the machine vision system to use a combined output from (paragraph [0120] - the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features – the analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line; paragraph [0126] – in the inspection step 809, the items under inspection are imaged by cameras 102 of each inspection assembly 110 or camera assembly 111 – the images received from the inspection assemblies 110 or camera assembly 111, which may be referred to as inspection images, are processed by controller 130 using machine learning/AI algorithms to detect defects or for gating, counting or sorting of items and/or other inspection tasks based on the loaded profiles 136; paragraph [0127] – in steps 810A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result – optionally, a user can select the current capture view for specific inspection assemblies 110 using UI 132; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth; paragraph [0132] – in step 810B for CFG2 and CFG5 individual outputs will be provided for each aspect of the inspected item as imaged by each inspection assembly 110 or camera assembly 111 and a correlated result for the item may be provided summarizing the results from all of the inspection assemblies 110 or camera assemblies 111); in response to receiving confirmation of the virtual device, generating a job for the virtual device to perform in analyzing an object according to a set of criteria (Figs. 7A-8; paragraph [0119] – system 115 typically requires a one-time setup step 806 for each item or aspect of item or stage of item that is to be inspected – in the setup step 806, at least two or more defect free samples of a manufactured item of the same type are placed in succession within field of view 104 or the combined field of view 704 of the inspection assemblies 110 or camera assemblies 111 that will inspect the particular item or aspect; paragraph [0120] – the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features – the analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line – profile 136 describes the item and the setup of system 115 for inspecting the item and is stored in DB 134; paragraph [0121] – in steps 808A-B, inspection of the item/s can commence with loading of the correct item profile 136 to be associated with each inspection assembly 110 – loading the item profile 136 includes configuring the inspection assemblies 110 and controller 130 based on configuration information contained in the profile 136); and recording an analysis result of the virtual device for the job (Figs. 7A-8; paragraph [0040] – as used herein “simultaneous” inspection by individual inspection assemblies implies that the inspection assemblies are performing inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time; the threshold is that the images are taken at the same time; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth).
Regarding claim 10, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 9 including that the method further comprises: activating a quality assurance device associated with the virtual device according to the analysis result of the combined output received from the virtual device (Hyatt et al.: paragraph [0120] - the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features – the analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line; paragraph [0127] – in steps 810 A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably provides notifications or indications regarding inspected items found to be free of defects or to contain defects – exemplary methods of notification include but are not limited to visual indication, audio indication, or a combination of these – a visual indication might for example comprise a green (item defect-free) or red (item defective) rectangle surrounding the view of an item on UI 132 or a green or red flash of light, or a relevant icon presented on UI 132 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result).
Regarding claim 12, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 9 including that wherein the specified confidence interval includes an evaluation that at least a threshold number of pixels captured by a first camera in a first image of an object match a second image of the object captured by a second camera (Hyatt et al.: paragraph [0120] - the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features – the analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line; paragraph [0126] – in the inspection step 809, the items under inspection are imaged by cameras 102 of each inspection assembly 110 or camera assembly 111 – the images received from the inspection assemblies 110 or camera assembly 111, which may be referred to as inspection images, are processed by controller 130 using machine learning/AI algorithms to detect defects or for gating, counting or sorting of items and/or other inspection tasks based on the loaded profiles 136; paragraph [0127] – in steps 810A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result – optionally, a user can select the current capture view for specific inspection assemblies 110 using UI 132; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth; paragraph [0132] – in step 810B for CFG2 and CFG5 individual outputs will be provided for each aspect of the inspected item as imaged by each inspection assembly 110 or camera assembly 111 and a correlated result for the item may be provided summarizing the results from all of the inspection assemblies 110 or camera assemblies 111).
Regarding claim 13, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 9 including that wherein the pass/fail criterion includes at least one of: optical character recognition; barcode recognition; feature presence; and alignment verification between two identified features of the object (Hyatt et al.: paragraph [0120] - the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features – the analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line; paragraph [0127] – in steps 810 A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably provides notifications or indications regarding inspected items found to be free of defects or to contain defects - exemplary methods of notification include but are not limited to visual indication, audio indication, or a combination of these – a visual indication might for example comprise a green (item defect-free) or red (item defective) rectangle surrounding the view of an item on UI 132 or a green or red flash of light, or a relevant icon presented on UI 132 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result).
Regarding claim 14, Hyatt et al. discloses a method, comprising: identifying a first camera having a first field of view including a first portion of an object under inspection by a machine vision system and a second camera having a second field of view including a second portion of the object under inspection by the machine vision system, wherein the second portion is not included in the first field of view and the first portion is not included in the second field of view (Figs. 7A-8; paragraph [0040] – as used herein “simultaneous” inspection by individual inspection assemblies implies that the inspection assemblies are performing inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time; the threshold is that the images are taken at the same time; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth); combining a first output of the first camera with a second output of the second camera as a combined output from a virtual device (paragraph [0126] – in the inspection step 809, the items under inspection are imaged by cameras 102 of each inspection assembly 110 or camera assembly 111 – the images received from the inspection assemblies 110 or camera assembly 111, which may be referred to as inspection images, are processed by controller 130 using machine learning/AI algorithms to detect defects or for gating, counting or sorting of items and/or other inspection tasks based on the loaded profiles 136; paragraph [0127] – in steps 810A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result – optionally, a user can select the current capture view for specific inspection assemblies 110 using UI 132; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth; paragraph [0132] – in step 810B for CFG2 and CFG5 individual outputs will be provided for each aspect of the inspected item as imaged by each inspection assembly 110 or camera assembly 111 and a correlated result for the item may be provided summarizing the results from all of the inspection assemblies 110 or camera assemblies 111); identifying a quality assurance device for the machine vision system to use in association with a job defined for the virtual device (paragraph [0120] - the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features – the analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line; paragraph [0127] – in steps 810 A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably provides notifications or indications regarding inspected items found to be free of defects or to contain defects – exemplary methods of notification include but are not limited to visual indication, audio indication, or a combination of these – a visual indication might for example comprise a green (item defect-free) or red (item defective) rectangle surrounding the view of an item on UI 132 or a green or red flash of light, or a relevant icon presented on UI 132 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result); and activating the quality assurance device according to an analysis of the first portion and the second portion visible in the combined output received from the virtual device according to the job (paragraph [0120] - the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features – the analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line; paragraph [0127] – in steps 810 A-C, controller 130 provides an output indicating the result of inspection step 809 – UI 132 preferably provides notifications or indications regarding inspected items found to be free of defects or to contain defects – exemplary methods of notification include but are not limited to visual indication, audio indication, or a combination of these – a visual indication might for example comprise a green (item defect-free) or red (item defective) rectangle surrounding the view of an item on UI 132 or a green or red flash of light, or a relevant icon presented on UI 132 – UI 132 preferably shows all of the images currently captured from all of the inspection assemblies 110 including the inspection result).
Regarding claim 15, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 14 including that wherein identifying the first camera and the second camera includes: reducing a plurality of cameras associated with the machine vision system to a first subset of nearby cameras (Hyatt et al.: Figs. 7A-8; paragraph [0119] – system 115 typically requires a one-time setup step 806 for each item or aspect of item or stage of item that is to be inspected – in the setup step 806, at least two or more defect free samples of a manufactured item of the same type are placed in succession within field of view 104 or the combined field of view 704 of the inspection assemblies 110 or camera assemblies 111 that will inspect the particular item or aspect; paragraph [0120] – the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features – the analysis results in the creation of a profile 136 unique to the item, used for defect detection, gating, counting, or sorting and/or other inspection tasks on the production line – profile 136 describes the item and the setup of system 115 for inspecting the item and is stored in DB 134; paragraph [0121] – in steps 808A-B, inspection of the item/s can commence with loading of the correct item profile 136 to be associated with each inspection assembly 110 – loading the item profile 136 includes configuring the inspection assemblies 110 and controller 130 based on configuration information contained in the profile 136); and analyzing images for related FOVs (Hyatt et al.: Figs. 7A-8; paragraph [0040] – as used herein “simultaneous” inspection by individual inspection assemblies implies that the inspection assemblies are performing inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time; the threshold is that the images are taken at the same time; paragraph [0105] – in Fig. 7B three camera assemblies 111A, 111B, and 111n are shown for simultaneously imaging overlapping or adjoining aspects of item 25 such that controller 130 can combine the images provided from FOVs 104A, 104B, and 104n of each inspection assembly into a single FOV 704 to provide a single inspection result for item 25; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth).
Regarding claim 16, Hyatt et al. discloses all of the limitations as previously discussed with respect to claims 14 and 15 including that wherein related FOVs see threshold changes in lighting conditions when light source cycled between an active state and an inactive state (Hyatt et al.: paragraph [0010] – for example, two inspection systems placed close together will each affect the other’s lighting requirements resulting in a doubly complex integration; lighting conditions are worked out during the setup process; paragraph [0014] – each inspection assembly comprises a customizable mounting assembly, and a camera assembly which comprises an inspection camera and light source – the inspection cameras and lighting sources from each inspection assembly are controlled by the controller; paragraph [0021] – prior to inspection, the appropriate profile is selected for the item to be inspected – items to be inspected preferably comprise any item type, shape or material, set in any lighting environment; paragraph [0065] – light source 106 comprises LEDs or other known light source – the intensity (brightness) of light source 106 can be adjusted – optionally, the color of light source 106 can be adjusted – optionally, light source 106 comprises multiple controllable segments, each of which can be activated or provided with the same or different intensity and/or color; paragraph [0069] – controller 130 activates camera 102 and light source 106 or any of camera assembly 111 components or controllable segments as described above, which may or may not be activated depending on the item being imaged or the inspection lighting environment; paragraph [0070] – controller 130 preferably alters the intensity or color of light source 106 depending on the item being imaged or the inspection lighting environment – controller 103 preferably alters the intensity or color of light source 106 for regions of particular interest within the illuminated area – controller 130 preferably alters the intensity or color of light source 106 so that images taken by camera 102 of item 20 are not over or under exposed).
Regarding claim 17, Hyatt et al. discloses all of the limitations as previously discussed with respect to claims 14 and 15 including that wherein related FOVs include a shared element of the object in each output (Hyatt et al.: Figs. 7A-8; paragraph [0040] – as used herein “simultaneous” inspection by individual inspection assemblies implies that the inspection assemblies are performing inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time; the threshold is that the images are taken at the same time; paragraph [0105] – in Fig. 7B three camera assemblies 111A, 111B, and 111n are shown for simultaneously imaging overlapping or adjoining aspects of item 25 such that controller 130 can combine the images provided from FOVs 104A, 104B, and 104n of each inspection assembly into a single FOV 704 to provide a single inspection result for item 25; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth).
Regarding claim 18, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 14 including that wherein the combined output is saved in a shared canvas that the first camera and the second camera both write to (Hyatt et al.: Figs. 7A-8; paragraph [0040] – as used herein “simultaneous” inspection by individual inspection assemblies implies that the inspection assemblies are performing inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time; the threshold is that the images are taken at the same time; paragraph [0105] – in Fig. 7B three camera assemblies 111A, 111B, and 111n are shown for simultaneously imaging overlapping or adjoining aspects of item 25 such that controller 130 can combine the images provided from FOVs 104A, 104B, and 104n of each inspection assembly into a single FOV 704 to provide a single inspection result for item 25; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth).
Regarding claim 19, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 14 including that wherein the first camera and the second camera capture images of the object at non-overlapping times (Hyatt et al.: Fig. 5; paragraph [0078] – in the embodiment of Fig. 5, items 20A, 20B, and 20n are stages in the production process of an item 20 where the reproduction of item 20 includes the addition of parts 26 and 28 to item 20; each step of the process of that particular item will be captured as it goes through the assembly process, which means that each image will be captured at different times as the item moves through the assembly process).
Regarding claim 20, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 14 including that wherein a machine learning model identifies the first camera and the second camera from a plurality of cameras available to the machine vision system including additional cameras to the first camera and the second camera (Hyatt et al.: Figs. 7A-8; paragraph [0013] – ease of deployment and operation is enabled by a combination of machine learning, computer vision, and other algorithms that dynamically adapt to assess the item to be inspected, the target area of inspection, and the characteristics of the surrounding environment (such as but not limited to lighting) effecting the inspection setup; paragraph [0040] – as used herein “simultaneous” inspection by individual inspection assemblies implies that the inspection assemblies are performing inspection concurrently or alternatively that the controller recognizes that the same items have been placed in view of the inspection assemblies and synchronizes the inspection results or alternatively that the inspection assemblies are synchronized and capturing images at the same time; the threshold is that the images are taken at the same time; paragraph [0062] – controller 130 may be locally connected to the plurality of inspection assemblies 110 or may be remotely connected, e.g., via the cloud; paragraph [0105] – in Fig. 7B three camera assemblies 111A, 111B, and 111n are shown for simultaneously imaging overlapping or adjoining aspects of item 25 such that controller 130 can combine the images provided from FOVs 104A, 104B, and 104n of each inspection assembly into a single FOV 704 to provide a single inspection result for item 25; paragraph [0111] – in step 802 performed typically only once as part of an initial setup, controller 130 identifies that more than one inspection assembly 110 or camera assembly 111 is connected to controller 130 – controller 130 then gives a user the option, via UI 132 of controller 130, to define the configuration of the inspection assemblies 110 or camera assemblies 111; during setup it will be determined which cameras will be used for each configuration based on their locations and/or addresses – for example, when trying to create a combined image, cameras closest to one another with overlapping fields of view will be selected; paragraph [0120] – the setup images are analyzed by controller 130 using machine learning/artificial intelligence (AI) and computer vision algorithms to create a complete representation of the item, for example, to collect information regarding possible 2D shapes and 3D characteristics of the item or to find uniquely discriminative features of the item and the spatial relation between these unique features; paragraph [0128] – preferably every image of every item captured by the inspection assemblies 110 including the inspection output (decision) is stored in database 134 – preferably related inspection images are stored together or linked for easy retrieval of related images such as for CFG2, CFG4 and CFG5 – for example, for CFG5, a stitched together image of an item taken from multiple camera assemblies is stored to enable a user viewing the image for a defective section of the item (as captured from one of the inspection assemblies) to easily view the entire item such as to determine if there is some correlation between the aspects of the defective item – linking the images may optionally be performed by any suitable means such as but not limited to: a common serial number, a specific shared marking, saving in the same folder, and so forth).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 8 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Hyatt et al. (U.S. Patent Application Publication 2022/0005183).
Regarding claim 8, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 1, but fails to disclose wherein at least one camera of the plurality of cameras is an emulated camera. Official Notice is taken that both the concepts and advantages of using an emulated camera is well-known in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have had included an emulated camera in the method disclosed by Hyatt et al. in order to provide the method with the ability to replicate the aesthetic qualities of a camera within a digital workflow.
Regarding claim 11, Hyatt et al. discloses all of the limitations as previously discussed with respect to claim 9, but fails to disclose wherein the plurality of cameras includes a plurality of emulated cameras for which a single physical camera provides multiple associated outputs to emulate. Official Notice is taken that both the concepts and advantages of using emulated cameras is well-known in the art. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have had included emulated cameras in the method disclosed by Hyatt et al. in order to provide the method with the ability to replicate the aesthetic qualities of a plurality of cameras within a digital workflow.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Machine Vision References:
Basak et al. (U.S. Patent Application Publication 2022/0035490).
Alessandrini (U.S. Patent Application Publication 2021/0241469).
Oostendorp (U.S. Patent Application Publication 2013/0301902).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HEATHER R JONES whose telephone number is (571)272-7368. The examiner can normally be reached Mon. - Fri.: 9:00am - 5:00pm.
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/HEATHER R JONES/Primary Examiner, Art Unit 2481
November 15, 2025
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