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 .
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.
Claim(s) 1, 5-8, 12-15, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dandibhotla et al (US 20170103506 A1), hereinafter Dandibhotla in view of Schmidt et al (US 20230343066 A1), hereinafter Schmidt, and further in view of Bardot (US 20220215948 A1).
-Regarding claim 1, Dandibhotla discloses a system for analyzing system health of individual electronic components using image mapping, the system comprising: at least one non-transitory storage device; and at least one processing device coupled to the at least one non-transitory storage device, wherein the at least one processing device is configured to (Abstract; FIGS. 1-2; [0021], “one or more computing systems that make up a component health monitoring system …general-purpose or special-purpose computer microprocessor configured to execute … programs stored in a main memory and/or in an onboard or external storage device. Various memory modules …”; [0034]; [0037]): continuously monitor one or more components during an execution of a process ([0027], “The component health monitoring system … display notifications to a machine operator regarding the monitored parameters for various components and/or communicate the notifications … generated continuously”; [0028], “component health monitoring procedure may occur on a continuous or periodic basis …”); generate a component health image for a component of the one or more components based on the execution of the process (FIG. 2, steps 210-220; [0028]; [0029], “machine operating conditions … feature sets …”; [0031]-[0032]); compare the component health image based on the execution of the process to one or more previous component health images for the component based on one or more previous executions of the process (FIG. 2, steps 210-218, [0029], “retrieve reference images of the work implement with healthy components having locations and dimensions that fall within acceptable limits …”; [0031]-[0032]); based on the comparison of the component health image based on the execution of the process to the one or more previous component health images for the component based on one or more previous executions of the process (FIG. 2, steps 210-218; [0029], “determine directional changes in image intensity”), and based on a similar feature of the component that characterize a portion of an image including the component with dimensions, determine a component health action ([0031], “in need of replacement or repair, or identifying an area where a component is missing …”; [0032]), wherein the component health action comprises (i) causing a transmission of an alert in an instance in which the component health image similar feature that characterize a portion of an image including the component with dimensions is outside of a threshold range (FIG. 2, step 220; [0018]; [0027], “comparison with one or more threshold values indicates the need to generate the notification”); [0032]; [0033], “a notification to an operator or other personnel when a target image does not fall within the classification of feature sets characterizing healthy components”; [0036], “a visual and/or audible alert”; FIG. 1), and (ii) causing an automated check-up of the component for potential errors (FIG. 2; [0028], “component health monitoring procedure may occur on a continuous or periodic basis without requiring initiation by an operator or other personnel”; [0030], “distinguished … a distribution of intensity gradients and edge directions … where the component is missing … dimensions within acceptable thresholds, or missing components …”; [0032]-[0033]; [0035], “… rate at which components are wearing out …”).
Dandibhotla does not disclose determining a component health image similarity score for the comparison.
In the same field of endeavor, Schmidt teaches a method to identify maintenance assets using sets of machine-health diagnostic images and link individual machine-health diagnostic images to the identified maintenance assets (Schmidt: Abstract; FIGS. 1-7D). Schmidt further teaches determining a component health image similarity score for the comparison of feature vectors of machine-health diagnostic image to corresponding feature vector of ground truth (Schmidt: FIG. 3A; [0036]-[0037]; [0091]; [0092], “determine whether the two feature vectors have at least a threshold degree of similarity”)
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Dandibhotla with the teaching of Schmid by using similarity measure compare component health image features in order to provide reliable monitoring for the health of a component.
Dandibhotla in view of Schmid does not teach wherein the component health image comprises a plurality of visual indicators associated with a plurality of capabilities of the component.
However, Bardot is an analogous art pertinent to the problem to be solved in this application and teaches a method for medical data health monitoring (Bardot: FIGS. 1-28). Bardot further teaches wherein the component health image comprises a plurality of visual indicators associated with a plurality of capabilities of the component (Bardot: FIG. 24, Mas 2403, dashboard 2480; [0466], “image format … 1 image every 10-30 seconds”; [0471], “ … provide continuous … monitor data quality … providing visual indicator(s) of various aspects of data health … data quality problems can be determined … automatically … ”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Dandibhotla in view of Schmid with the teaching of Bardot by visualizing component health image in order to provide a good user experience.
-Regarding claim 8, Dandibhotla discloses computer program product for analyzing system health of individual electronic components using image mapping, the computer program product comprising at least one non- transitory computer-readable medium having computer-readable program code portions embodied therein, the computer-readable program code portions comprising (Abstract; FIGS. 1-2; [0021], “one or more computing systems that make up a component health monitoring system …general-purpose or special-purpose computer microprocessor configured to execute … programs stored in a main memory and/or in an onboard or external storage device. Various memory modules …”; [0034]; [0037]): an executable portion configured to continuously monitor one or more components during an execution of a process ([0027], “The component health monitoring system … display notifications to a machine operator regarding the monitored parameters for various components and/or communicate the notifications … generated continuously”; [0028], “component health monitoring procedure may occur on a continuous or periodic basis …”); an executable portion configured to generate a component health image for a component based on the execution of the process (FIG. 2, steps 210-220; [0028]; [0029], “machine operating conditions … feature sets …”; [0031]-[0032]); an executable portion configured to compare the component health image based on the execution of the process to one or more previous component health images for the component based on one or more previous executions of the process (FIG. 2, steps 210-218, [0029], “retrieve reference images of the work implement with healthy components having locations and dimensions that fall within acceptable limits …”; [0031]-[0032]); an executable portion configured to determine a similar feature of the component that characterize a portion of an image including the component with dimensions based on the comparison of the component health image based on the execution of the process to the one or more previous component health images for the component based on one or more previous executions of the process (FIG. 2, steps 210-218; [0029], “determine directional changes in image intensity”), and an executable portion configured to determine a component health action based on a similar feature of the component that characterize a portion of an image including the component with dimensions ([0031], “in need of replacement or repair, or identifying an area where a component is missing …”; [0032]), wherein the component health action comprises (i) causing a transmission of an alert in an instance in which the component health image similar feature that characterize a portion of an image including the component with dimensions is outside of a threshold range (FIG. 2, step 220; [0018]; [0027], “comparison with one or more threshold values indicates the need to generate the notification”); [0032]; [0033], “a notification to an operator or other personnel when a target image does not fall within the classification of feature sets characterizing healthy components”; [0036], “a visual and/or audible alert”; FIG. 1), and (ii) causing an automated check-up of the component for potential errors (FIG. 2; [0028], “component health monitoring procedure may occur on a continuous or periodic basis without requiring initiation by an operator or other personnel”; [0030], “distinguished … a distribution of intensity gradients and edge directions … where the component is missing … dimensions within acceptable thresholds, or missing components …”; [0032]-[0033]; [0035], “… rate at which components are wearing out …”).
Dandibhotla does not disclose determining a component health image similarity score for the comparison.
In the same field of endeavor, Schmidt teaches a method to identify maintenance assets using sets of machine-health diagnostic images and link individual machine-health diagnostic images to the identified maintenance assets (Schmidt: Abstract; FIGS. 1-7D). Schmidt further teaches determining a component health image similarity score for the comparison of feature vectors of machine-health diagnostic image to corresponding feature vector of ground truth (Schmidt: FIG. 3A; [0036]-[0037]; [0091]; [0092], “determine whether the two feature vectors have at least a threshold degree of similarity”)
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Dandibhotla with the teaching of Schmid by using similarity measure compare component health image features in order to provide reliable monitoring for the health of a component.
Dandibhotla in view of Schmid does not teach wherein the component health image comprises a plurality of visual indicators associated with a plurality of capabilities of the component.
However, Bardot is an analogous art pertinent to the problem to be solved in this application and teaches a method for medical data health monitoring (Bardot: FIGS. 1-28). Bardot further teaches wherein the component health image comprises a plurality of visual indicators associated with a plurality of capabilities of the component (Bardot: FIG. 24, Mas 2403, dashboard 2480; [0466], “image format … 1 image every 10-30 seconds”; [0471], “ … provide continuous … monitor data quality … providing visual indicator(s) of various aspects of data health … data quality problems can be determined … automatically … ”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Dandibhotla in view of Schmid with the teaching of Bardot by visualizing component health image in order to provide a good user experience.
-Regarding claim 15, Dandibhotla discloses a computer-implemented method for analyzing system health of individual electronic components using image mapping, the method comprising (Abstract; FIGS. 1-2; [0021], “one or more computing systems that make up a component health monitoring system …general-purpose or special-purpose computer microprocessor configured to execute … programs stored in a main memory and/or in an onboard or external storage device. Various memory modules …”; [0034]; [0037]): continuously monitoring one or more components during an execution of a process ([0027], “The component health monitoring system … display notifications to a machine operator regarding the monitored parameters for various components and/or communicate the notifications … generated continuously”; [0028], “component health monitoring procedure may occur on a continuous or periodic basis …”); generating a component health image for a component of the one or more components based on the execution of the process (FIG. 2, steps 210-220; [0028]; [0029], “machine operating conditions … feature sets …”; [0031]-[0032]); compare the component health image based on the execution of the process to one or more previous component health images for the component based on one or more previous executions of the process (FIG. 2, steps 210-218, [0029], “retrieve reference images of the work implement with healthy components having locations and dimensions that fall within acceptable limits …”; [0031]-[0032]); determining a similar feature of the component that characterize a portion of an image including the component with dimensions based on the comparison of the component health image based on the execution of the process to the one or more previous component health images for the component based on one or more previous executions of the process (FIG. 2, steps 210-218; [0029], “determine directional changes in image intensity”), and determining a component health action based on a similar feature of the component that characterize a portion of an image including the component with dimensions ([0031], “in need of replacement or repair, or identifying an area where a component is missing …”; [0032]), wherein the component health action comprises (i) causing a transmission of an alert in an instance in which the component health image similar feature that characterize a portion of an image including the component with dimensions is outside of a threshold range (FIG. 2, step 220; [0018]; [0027], “comparison with one or more threshold values indicates the need to generate the notification”); [0032]; [0033], “a notification to an operator or other personnel when a target image does not fall within the classification of feature sets characterizing healthy components”; [0036], “a visual and/or audible alert”; FIG. 1), , and (ii) causing an automated check-up of the component for potential errors (FIG. 2; [0028], “component health monitoring procedure may occur on a continuous or periodic basis without requiring initiation by an operator or other personnel”; [0030], “distinguished … a distribution of intensity gradients and edge directions … where the component is missing … dimensions within acceptable thresholds, or missing components …”; [0032]-[0033]; [0035], “… rate at which components are wearing out …”).
Dandibhotla does not disclose determining a component health image similarity score for the comparison.
In the same field of endeavor, Schmidt teaches a method to identify maintenance assets using sets of machine-health diagnostic images and link individual machine-health diagnostic images to the identified maintenance assets (Schmidt: Abstract; FIGS. 1-7D). Schmidt further teaches determining a component health image similarity score for the comparison of feature vectors of machine-health diagnostic image to corresponding feature vector of ground truth (Schmidt: FIG. 3A; [0036]-[0037]; [0091]; [0092], “determine whether the two feature vectors have at least a threshold degree of similarity”)
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Dandibhotla with the teaching of Schmid by using similarity measure compare component health image features in order to provide reliable monitoring for the health of a component.
Dandibhotla in view of Schmid does not teach wherein the component health image comprises a plurality of visual indicators associated with a plurality of capabilities of the component.
However, Bardot is an analogous art pertinent to the problem to be solved in this application and teaches a method for medical data health monitoring (Bardot: FIGS. 1-28). Bardot further teaches wherein the component health image comprises a plurality of visual indicators associated with a plurality of capabilities of the component (Bardot: FIG. 24, Mas 2403, dashboard 2480; [0466], “image format … 1 image every 10-30 seconds”; [0471], “ … provide continuous … monitor data quality … providing visual indicator(s) of various aspects of data health … data quality problems can be determined … automatically … ”).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Dandibhotla in view of Schmid with the teaching of Bardot by visualizing component health image in order to provide a good user experience.
-Regarding claims 5, 12, and 18, Dandibhotla in view of Schmidt, and further in view of Bardot teaches the system of claim 1, the computer program product of claim 8, and the method of claim 15.
Dandibhotla does not disclose identifying at least one of the one or more previous component health images for the component based on a similarity to the component health image.
In the same field of endeavor, Schmidt teaches a method to identify maintenance assets using sets of machine-health diagnostic images and link individual machine-health diagnostic images to the identified maintenance assets (Schmidt: Abstract; FIGS. 1-7D). Schmidt further teaches applying image similarity to the set of machine-health diagnostic images to generate a feature vector characterizing a machine that has been captured by the set of machine-health diagnostic images (Schmidt: FIGS. 3A-3G; [0035]-[0036])
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Dandibhotla with the teaching of Schmid by identify at least one of the one or more previous component health images for the component based on a similarity to the component health image in order to provide reliable monitoring for the health of a component.
-Regarding claims 6, 13 and 19, Dandibhotla in view of Schmidt, and further in view of Bardot teaches the system of claim 1, the computer program product of claim 8, and the method of claim 15. The combination further teaches generate an array of one or more component metric images, wherein the array of the one or more component metric images is used to generate at least one of the one or more previous component health images for the component (Dandibhotla: [0023], “The reference image may include an image of the work implement with the component mounted on the work implement and having dimensions that fall within acceptable thresholds. … a reference image may also include an image of the work implement with one or more components such as GET's missing from the work implement, or an image of the work implement with a component mounted on the work implement and having dimensions that fall outside of acceptable thresholds”).
-Regarding claims 7, 14 and 20, Dandibhotla in view of Schmidt, and further in view of Bardot teaches the system of claim 1, the computer program product of claim 8, and the method of claim 15. The combination further teaches generating the component health image for the component based on the execution of the process (Dandibhotla: FIGS. 1-2; [0012], “images captured by optical devices may be transmitted to an image processor that is part of a component health monitoring system onboard the first machine …”; [0020]; [0022]).
Claim(s) 2, 9, and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dandibhotla et al (US 20170103506 A1), hereinafter Dandibhotla in view of Schmidt et al (US 20230343066 A1), hereinafter Schmidt, and further in view of Bardot (US 20220215948 A1), in view of Baras et al (US 20170295014 A1), hereinafter Baras.
-Regarding claims 2, 9 and 16, Dandibhotla in view of Schmidt, and further in view of Bardot teaches the system of claim 1, the computer program product of claim 8, and the method of claim 15.
Dandibhotla in view of Schmidt, and further in view of Bardot does not teach comparing the component health image to an average component health image based at least partially on the one or more previous component health images.
However, Baras is an analogous art pertinent to the problem to be solved in this application and teaches a method for authenticating communication devices (Baras: Abstract: FIGS. 1-22). Baras further teaches comparing querying images to an average enrolled images to produce a final similarity score (Baras: [0067], “compute a similarity score with the scanner pattern of each pair {average scanner of the enrolled images, the scanner pattern of a query image}”; FIG. 11).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Dandibhotla in view of Schmidt, and further in view of Bardot with the teaching of Baras by comparing the component health image to an average component health image based at least partially on the one or more previous component health images in order to improve accuracy of classification for healthy components.
Claim(s) 3, 10, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dandibhotla et al (US 20170103506 A1), hereinafter Dandibhotla in view of Schmidt et al (US 20230343066 A1), hereinafter Schmidt, and further in view of Bardot (US 20220215948 A1), in view of Murayama et al (2016 APSIPA), hereinafter Murayama.
-Regarding claims 3, 10, and 17, Dandibhotla in view of Schmidt, and further in view of Bardot teaches the system of claim 1, the computer program product of claim 8, and the method of claim 15.
Dandibhotla in view of Schmidt, and further in view of Bardot does not teach wherein the component health image similarity score is produced via a standard distribution of the one or more previous component health images for the component.
However, Murayama is an analogous art pertinent to the problem to be solved in this application and teaches divergence similarity between two color images based on Jensen-Shannon divergence (Murayama: Abstract; FIGS. 1-5). Murayama further teaches that similarity score is produced distribution of images (Murayama: Page 2, Sec II.; equation (11) ).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Dandibhotla in view of Schmidt, and further in view of Bardot with the teaching of Murayama by using standard distribution of the one or more previous component health images for the component for determining similarity score in order to provide reliable classification for healthy components (Murayama: Page 4, Sec IV.).
Claim(s) 4 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dandibhotla et al (US 20170103506 A1), hereinafter Dandibhotla in view of Schmidt et al (US 20230343066 A1), hereinafter Schmidt, and further in view of Bardot (US 20220215948 A1), in view of Zhang (CN 115019072 A).
-Regarding claims 4 and 11, Dandibhotla in view of Schmidt, and further in view of Bardot teaches the system of claim 1 and the computer program product of claim 8.
Dandibhotla in view of Schmidt, and further in view of Bardot does not teach modifying at least one of the one or more previous component health images for the component to conform to a format of the component health image of the component. However, this is a common practice for calculating similarity of images.
However, Zhang is an analogous art pertinent to the problem to be solved in this application and teaches a simulation image processing method using similarity (Zhang: Abstract; FIGS. 1-9). Zhang further teaches converting the simulation image and the original image into an image of the same preset format, to generate a target simulation image and a target original image; obtaining the similarity of the target simulation image and the target original image (Zhang: Abstract; FIG. 1, steps 102-103; Page 7, Embodiment 1; Page 8, 4th paragraph).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify the teaching of Dandibhotla in view of Schmidt, and further in view of Bardot with the teaching of Zhang by modifying at least one of the one or more previous component health images for the component to conform to a format of the component health image of the component in order to improve efficiency of classification for healthy components (Zhang: Page 8, 4th paragraph).
Response to Arguments
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
The claim interpretation under 35 U.S.C. 112(f) has been withdrawn by considering applicant’s remarks, and claim interpretation of “processing device” based on plain meaning as a hardware component.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Auerbach (US 20230350199 A1) teaches using visual indicators for component status, battery health, etc.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
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/XIAO LIU/Primary Examiner, Art Unit 2664
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