0-=DETAILED ACTION
Response to Amendment
This Action is responsive to Applicant’s response filed on 04/13/2026. All claims are still pending
in the present application. This Action is made FINAL.
Amendment
Applicant submitted amendments on 04/13/2026. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Response to Arguments
During prosecution, claim scope not solely on the basis of claim language, but also on giving claims their broadest reasonable construction in light of the specification as it would be interpreted by one of ordinary skill in the art. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364 (Fed. Cir. 2004). See also Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004) (“Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim.”). Additionally, “[t]hough understanding the claim language may be aided by the explanations contained in the written description, it is important not to import into a claim limitations that are not a part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment.” Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875 (Fed. Cir. 2004).
In regards to Argument(s) 1-4, Applicant(s) state(s) that, Bufi et al (US 2022/0366558 A1) in view of Lopez et al (US 2020/0026590 A1) and Yates et al (US 2023/0324900 A1) does not disclose/teach/suggest an image depicts at least two parts of a monitored device, that a first part is subject to at least one first failure mode, and that a second part is subject to at least one second failure mode, the at least one second failure mode being different than the at least one first failure mode. Applicant further state(s) that identifying the first part and the second part in the image, detecting whether the first part is assumed to comply with the at least one first failure mode using at least a first engine and whether the second part is assumed to comply with the at least one second failure mode using at least a second engine, verifying whether each part complies with its respective failure mode, and taking at least one action aimed at avoiding a malfunction of the device, therefore, the rejection of 35 U.S.C. 103 should be removed, (Emphasis added, Remarks, page(s) 9-11).
Applicant’s arguments have been fully considered but are moot in view of the new ground(s) of rejection in view of Bufi et al (US 2022/0366558 A1) in view of Shentu et al (US 2021/0276185 A1) and Lopez et al (US 2020/0026590 A1).
In regards to Argument(s) 5-6, Applicant(s) state(s) that, Lopez is directed to predicting component failure using sensor-derived historical data rather than image-based inspection, and therefore does not teach taking a single image, identifying multiple parts within the image, applying separate failure analyses to the identified parts within the same image-based monitoring pipeline, or integrating such teachings into Bufi's image-based inspection framework, therefore, the rejection of 35 U.S.C. 103 should be removed, (Emphasis added, Remarks, page(s) 9-11).
With respect to the 35 USC 103 rejection, Applicant’s arguments have been considered but they are not persuasive. Examiner agrees that Lopez is not directed to image acquisition, image segmentation, or identifying multiple parts within a captured image. However, the present rejection does not rely upon Lopez for those teaching. Rather, Lopez is relied upon solely for teaching the claimed analytical engine architecture. Specifically, Lopez teaches that "a separate model may be used for each component in a product" (Paragraph [0009] – [0010]), that "each of the plurality of models generated may correspond to a particular type of failure" (Paragraph [0022] – [0025]), and further teaches a model generator and model composer for generating and combining analytical models (Figure 2; and Paragraph [0036] – [0038]). Under the broadest reasonable interpretation, and consistent with Applicant's own definition of an "engine" (Specification Paragraph [0030]), these disclosures reasonably correspond to the claimed first engine and second engine, each configured to perform a respective failure analysis.
Accordingly, Applicant's arguments improperly attribute to Lopez limitations for which Examiner does not rely upon Lopez. A rejection under 35 U.S.C. § 103 must be evaluated based upon the combined teachings of the applied references rather than whether any individual reference teaches every claimed limitation. See MPEP § 2143; In re Keller, 642 F.2d 413, 425 (CCPA 1981); and In re Merck & Co., 800 F.2d 1091, 1097 (Fed. Cir. 1986).
In regards to Argument(s) 7, Applicant(s) state(s) that, Yates does not remedy the deficiencies of the previously applied combination because Yates does not teach identifying first and second parts within the same image, separately evaluating those parts using respective engines, or verifying compliance of the respective failure modes as recited in claim 1, therefore, the rejection of 35 U.S.C. 103 should be removed, (Emphasis added, Remarks, page(s) 9-11).
Applicant’s arguments have been fully considered but are moot in view of the new ground(s) of rejection in view of Bufi et al (US 2022/0366558 A1) in view of Shentu et al (US 2021/0276185 A1) and Lopez et al (US 2020/0026590 A1).
Office Action Summary
Claim(s) 1, 3, 5, 7, 12-13, 15, 17, 19, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bufi et al (US 2022/0366558 A1) in view of Shentu et al (US 2021/0276185 A1), and further in view of Lopez et al (US 2020/0026590 A1).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bufi et al (US 2022/0366558 A1) in view of Shentu et al (US 2021/0276185 A1) and Lopez et al (US 2020/0026590 A1), and further in view of Jones et al (US 2015/0019187 A1).
Claim(s) 6 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bufi et al (US 2022/0366558 A1) in view of Shentu et al (US 2021/0276185 A1) and Lopez et al (US 2020/0026590 A1), and further in view of Lehmann et al (US 2014/0372348 A1).
Claim(s) 14 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bufi et al (US 2022/0366558 A1) in view of Shentu et al (US 2021/0276185 A1) and Lopez et al (US 2020/0026590 A1), and further in view of Yates et al (US 2023/0324900 A1).
Claim(s) 22-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bufi et al (US 2022/0366558 A1) in view of Shentu et al (US 2021/0276185 A1) and Lopez et al (US 2020/0026590 A1), and further in view of Zhou et al (US 2024/0185594 A1).
Claim(s) 2, 9-11, 18, and 20 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim Rejections - 35 USC § 103
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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 3, 5, 7, 12-13, 15, 17, 19, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bufi et al (US 2022/0366558 A1) in view of Shentu et al (US 2021/0276185 A1), and further in view of Lopez et al (US 2020/0026590 A1).
Regarding claim(s) 1, 19, and 21, Bufi teaches a computer program product comprising a non-transitory computer readable medium retaining program instructions, which instructions when read by a processor (Figure 1: Computing system 116; and Paragraph [0071]), cause the processor to perform:
receiving an image captured by a camera, the image depicting at least two parts of a monitored device(Paragraph [0012]: “a camera for acquiring image data of an article under inspection, a node computing device for receiving the image data from the camera and analyzing the image data using a defect detection model trained to detect at least one defect type”);
taking at least one action subject to the first part complying with the at least one first failure mode or the second part complying with the at least one second failure mode(Paragraph [0037]: “The method may further include generating a stop inspection command at the PLC device upon determining the defect data is unacceptable. The method may further include generating an alarm command at the PLC device upon determining the defect data is unacceptable and sending the alarm command to an alarm system configured to generate and output an alarm”).
Bufi fails to teach second failure mode or not; and
However, Shentu teaches to receiving an image captured by a camera, the image depicting at least two parts of a monitored device, a first part of the at least two parts subject to at least one first failure mode, and a second part of the at least two parts subject to at least one second failure mode being different than the at least one first failure mode (Figure 1; Paragraph [0008]: “the segmentation prediction identifies one or more distinct objects in the first scene and determining if the failure mode of the first set of failure modes is present in the first scene based on the segmentation prediction […]”; Paragraph [0009]: “The second set of failure modes may comprise at least one of: a failure mode in which the robot picked up two objects, a failure mode in which an object picked up by the robot is damaged, a failure mode in which an object is deformed”; and Paragraph [0057]: “different segmentation predictions may be generated based on each camera view, illustrating further importance for additional means of error detection. Segmentation results […] may further be used to detect failure modes in accordance with some embodiments of the present technology”);
identifying in the image the first part and the second part (Paragraph [0008]: “producing a segmentation prediction based on the one or more first images, wherein the segmentation prediction identifies one or more distinct objects in the first scene”; Paragraph [0032]: “[…] the system can guide motions and/or actions taken by robotic arm […] A computer vision system may provide information that can be used to decipher geometries, material properties, distinct items (i.e., segmentation) […]”; and Paragraph [0057]: “Machine learning techniques may be utilized for generating segmentation predictions […] Image segmentation may be used to identify distinct objects in a scene, which may include a robotic device in addition to objects […]”, Examiner’s Note: Under the broadest reasonable interpretation, the “identified distinct objects” correspond to the claimed "first part" and "second part" because Applicant does not define the claimed "part" as requiring any particular structural limitation. Rather, the claim requires identifying two parts in the image, and Shentu's segmentation process expressly identifies multiple distinct objects within the captured image. Therefore, Shentu teaches "identify in the image the first part and the second part");
detecting whether the first part is assumed to comply with the at least one first failure mode(Paragraph [0008]: “the segmentation prediction identifies one or more distinct objects in the first scene and determining if the failure mode of the first set of failure modes is present in the first scene based on the segmentation prediction […]”; and Paragraph [0009]: “The second set of failure modes may comprise at least one of: a failure mode in which the robot picked up two objects, a failure mode in which an object picked up by the robot is damaged, a failure mode in which an object is deformed”);
verifying whether the first part complies with the at least one first failure mode or not, and verify whether the second part complies with the at least one second failure mode or not (Figure 3; Paragraph [0044]: “The one or more images are then processed using deep learning techniques, in some embodiments, and used to determine if a failure mode is present in step 310. If a failure mode is present, the system may report that the failure mode is present in step 315B”; Paragraph [0045]: “If a failure mode is not present […] camera is used to collect one or more images of an intermediary scene […] The images are then processed and used to determine if a failure mode is present in step 320. If it is determined that a failure mode is present, the system may report that the failure mode is present in step 325B”; and Paragraph [0046]: “If it is determined that a failure mode is not present in step 320, […] cameras are used to collect one or more images of a final scene […] The images are then processed and used to determine if a failure mode is present in step 330. If it is determined that a failure mode is present, the system may report that a failure mode is present in step 335B”); and
taking at least one action subject to the first part complying with the at least one first failure mode or the second part complying with the at least one second failure mode, the at least one action aimed at avoiding a malfunction of the device (Figure 3; and Paragraph [0044]: “[…] determine if a failure mode is present in step 310. If a failure mode is present, the system may report that the failure mode is present in step 315B […] the system may direct the robot to cease motion upon detecting that a failure mode is present so that the error can be addressed. Alternatively, instead of or in addition to reporting that a failure mode was detected, process 300 may further include resolving an error associated with the error mode).
Bufi teaches an image-based inspection system including receiving an image captured by a camera, a processor configured to analyze the captured image, and identifying defects in an article under inspection. Shentu teaches processing captured images using image segmentation to identify distinct objects, determining whether different sets of failure modes are present for the identified objects, verifying whether the detected failure modes are present or absent, and performing responsive actions based on the verification results (e.g., reporting the failure, stopping robot motion, or resolving the detected error).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Bufi and Shentu before the effective filing date of the claimed invention. The motivation for this combination of references would have been to more accurately decipher shapes and location of objects in a scene and because multiple cameras are used from different angles to improve segmentation results. Incorporating Shentu's image segmentation techniques into Bufi's image-based inspection system would have predictably improved identification of multiple parts and the corresponding failure mode analysis. This motivation for the combination of Bufi and Shentu is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Bufi and Shentu fail to teach to detecting whether the first part is assumed to comply with the at least one first failure mode, comprising using at least a first engine, and whether the second part is assumed to comply with the at least one second failure mode, comprising using at least a second engine.
However, Lopez teaches teach to detecting whether the first part is assumed to comply with the at least one first failure mode, comprising using at least a first engine, and whether the second part is assumed to comply with the at least one second failure mode, comprising using at least a second engine (Figure 1; Paragraph [0009] – Paragraph [0010]: “To predict failure, a model may need to be constructed for each component […] A separate model may be used for each component in a product […]”; Paragraph [0022] – Paragraph [0025]: “Generate a plurality of models instructions 110, when executed by a processor […] to generate a plurality of models to model the failure of a component […] each of the plurality of models generated may correspond to a particular type of failure […] Model A represents the best model for predicting component failure due to overheating and Model B represents the best model for predicting component failure due to degradation […]”; and Paragraph [0036] – Paragraph [0038]: “a model generator refers to machine readable instructions to generate a plurality of models using features […] a model composer refers to machine readable instructions to assemble a plurality of models […]”); and
verify whether the first part complies with the at least one first failure mode or not, and verify whether the second part complies with the at least one second failure mode or not (Paragraph [0022] – Paragraph [0025]: “Generate a plurality of models instructions 110, when executed by a processor […] to generate a plurality of models to model the failure of a component […] each of the plurality of models generated may correspond to a particular type of failure […] Model A represents the best model for predicting component failure due to overheating and Model B represents the best model for predicting component failure due to degradation […]”; and Paragraph [0036] – Paragraph [0038]: “a model generator refers to machine readable instructions to generate a plurality of models using features […] a model composer refers to machine readable instructions to assemble a plurality of models […]”).
Bufi teaches an image-based inspection system including receiving an image captured by a camera, a processor configured to analyze the captured image, and inspection of an article under inspection. Additionally, Shentu teaches processing captured images using image segmentation to identify distinct objects, determining whether different sets of failure modes are present for the identified objects, verifying whether the detected failure modes are present or absent, and performing responsive actions based on the verification results, such as reporting the detected failure, stopping robot motion, or resolving the detected error. Furthermore, Lopez teaches performing analytical evaluations using a plurality of analytical models corresponding to different components and different failure types, including separate models for different failure analyses, thereby corresponding to the claimed first engine and second engine for independently evaluating different failure conditions.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the image-based inspection system of Bufi with the image segmentation techniques of Shentu in order to "more accurately decipher shapes and location of objects in a scene" and because "multiple cameras are used from different angles to improve segmentation results," thereby enabling separate analytical models, as taught by Lopez, to independently evaluate different failure conditions associated with the identified objects. Since Lopez teaches that "a separate model may be used for each component in a product" and that "each of the plurality of models generated may correspond to a particular type of failure," the combined teachings would have predictably improved the accuracy and reliability of automated failure detection and analysis. This motivation for the combination of Bufi, Shentu, and Lopez is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim(s) 3, Bufi as modified by Shentu and Lopez teaches the system according to claim 1, where Bufi teaches wherein the at least one first failure mode and the at least one second failure mode are static failure modes (Paragraph [0034]: “[…] object detection model configured to receive the image data as an input and generate defect data describing a detected defect as an output […] determining at the PLC device whether the defect data is acceptable or unacceptable by comparing the defect data to tolerance data”; and Paragraph [0211]: “The defect classification includes assigning a detected defect to a defect class. Defect classes are associated with particular defect types (e.g. dent, paint, scratch, etc.) the defect detection model 154 is trained to detect”, Examiner’s Note: Under the broadest reasonable interpretation, these detected defect conditions represent existing physical conditions of the inspected article at the time the image is captured, rather than predicted future conditions, and therefore reasonably correspond to the claimed static failure modes).
Regarding claim(s) 5, Bufi as modified by Shentu and Lopez teaches the system according to claim 1, where Bufi teach wherein the at least one first failure mode is a static failure mode (Paragraph [0034]: “[…] object detection model configured to receive the image data as an input and generate defect data describing a detected defect as an output […] determining at the PLC device whether the defect data is acceptable or unacceptable by comparing the defect data to tolerance data”; and Paragraph [0211]: “The defect classification includes assigning a detected defect to a defect class. Defect classes are associated with particular defect types (e.g. dent, paint, scratch, etc.) the defect detection model 154 is trained to detect”) and where Lopez teaches the at least one second failure mode is a dynamic failure mode (Paragraph [0030]: “The data extracted may include data indicating the time at which the component failed and what caused the component to fail. For example, in the case of an overheating failure, the extracted data may include the time at which the component stopped working and the temperature at which the component failed”, Examiner’s Note: Under the broadest reasonable interpretation, the overheating failure analysis reasonably corresponds to the claimed dynamic failure mode because it is based upon operating conditions that vary over time and are analyzed using historical sensor-derived data for failure prediction).
Regarding claim(s) 7, Bufi as modified by Shentu and Lopez teaches the system according to claim 1, where Lopez teaches wherein the at least one first failure mode and the at least one second failure mode are dynamic failure modes (Paragraph [0030]: “The data extracted may include data indicating the time at which the component failed and what caused the component to fail. For example, in the case of an overheating failure, the extracted data may include the time at which the component stopped working and the temperature at which the component failed”, Examiner’s Note: Under the broadest reasonable interpretation, the overheating failure analysis reasonably corresponds to the claimed dynamic failure mode because it is based upon operating conditions that vary over time and are analyzed using historical sensor-derived data for failure prediction).
Regarding claim(s) 12, Bufi as modified by Shentu and Lopez teaches the system according to claim 1 further comprising:
Where Bufi teaches a camera configured to capture the image of the monitored device (Paragraph [0033] – Paragraph [0034]: “The image data may include a plurality of images acquired during rotation of the article […] The method may include acquiring image data of an article under inspection using a camera, providing the image data to a node computing device, analyzing the image data at the node computing device using a defect detection model trained to detect at least one defect type”).
Regarding claim(s) 13, Bufi as modified by Shentu and Lopez teaches the system according to claim 1, where Lopez teaches wherein the at least one action comprises alerting a user (Paragraph [0056]: “generating a health report at 448 may include generating a predicted type of failure for a particular component. Thus, the health report may serve to alert a user that a component is predicted to fail. Further, the health report may tell the user how the component is most likely to fail (e.g. overheating versus a mechanical failure)”).
Regarding claim(s) 15, Bufi as modified by Shentu and Lopez teaches the system according to claim 1, where Lopez teaches wherein the at least one action comprises analyzing a trend of the failure mode (Paragraph [0030]: “The data extracted may include data indicating the time at which the component failed and what caused the component to fail. For example, in the case of an overheating failure, the extracted data may include the time at which the component stopped working and the temperature at which the component failed”, Examiner’s Note: Under the broadest reasonable interpretation, and consistent with Applicant's own definition of "trend" or "trend of failure mode" (Specification, Paragraph [0028]), this analysis teaches analyzing a trend of the failure mode because it evaluates the behavior of a failure mode over time and the circumstances under which a fault progresses to failure).
Regarding claim(s) 17, Bufi as modified by Shentu and Lopez teaches the system according to claim 1, where Bufi teaches wherein the at least one action comprises at least one item selected from the group consisting of:
changing a capture rate for the camera and setting an analysis rate for at least a portion of further images captured by the camera (Paragraph [0228]: “The frame rate refers to the frame rate of inference per single image. The frame rate may be determined by numerous factors (e.g. image size, number of layers, number of anchor boxes, algorithms within network, operations within network, floating point precision, layer fusion, etc.). A higher FPS inference by the node device 148 may decrease inspection time to levels more suitable to industrial manufacturing inspection processes”);
where Shentu teach sending a message to a person in charge (Paragraph [0038]: “detects an error at any of the aforementioned steps, it may send a report or error message and/or pause robotic motion until the error is resolved”; and Paragraph [0044]: “A report may be generated and communicated to a person or system responsible for addressing errors, in some examples. Furthermore, the system may direct the robot to cease motion upon detecting that a failure mode is present so that the error can be addressed”);
storing an alert in a storage device (Paragraph [0038]: “detects an error at any of the aforementioned steps, it may send a report or error message and/or pause robotic motion until the error is resolved”; and Paragraph [0044]: “A report may be generated and communicated to a person or system responsible for addressing errors, in some examples. Furthermore, the system may direct the robot to cease motion upon detecting that a failure mode is present so that the error can be addressed”; and Paragraph [0065]: “Storage system 710 may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information”, Examiner’s Note: Under the broadest reasonable interpretation, the generated report or error message reasonably corresponds to the claimed alert, and storing such generated alert information in the disclosed storage system reasonably corresponds to storing an alert in a storage device); and
updating a database (Paragraph [0028]: “the weights in a neural network are continually updated in response to errors, failures, or mistakes. In order to create a robust, working model, training data is used to initially dial in the weights until a sufficiently strong model is found or the learning process gets stuck and is forced to stop. In some implementations, the weights may continue to update throughout use, even after the training period is over, while in other implementations, they may not be allowed to update after the training period”).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bufi et al (US 2022/0366558 A1) in view of Shentu et al (US 2021/0276185 A1) and Lopez et al (US 2020/0026590 A1), and further in view of Jones et al (US 2015/0019187 A1).
Regarding claim(s) 4, Bufi as modified by Shentu and Lopez teaches the system according to Claim 3, but do not specifically teach wherein a static failure mode of the at least one first part causes the at least one second failure mode. However, Jones teach wherein a static failure mode of the at least one first part causes the at least one second failure mode (Figure 7; and Paragraph [0072]: “The cascading effect layout model may also illustrate linkages 706 (only one linkage being called out) between the nodes 702, which may illustrate how a failure of one system of the complex system may directly or indirectly result in failure of one or more other systems of the complex system”, Examiner’s Note: Under the broadest reasonable interpretation, the linked systems reasonably correspond to the claimed first part and second part, while the disclosed failure propagation reasonably corresponds to a static failure mode of the first part causing the second failure mode of the second part).
Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was made to incorporate the failure propagation analysis of Jones into the image-based monitoring system of Bufi as modified by Shentu and Lopez in order to account for interdependencies between monitored parts and improve evaluation of system-level reliability. Applying known failure propagation relationships to an image-based monitoring system merely represents the predictable use of prior-art elements according to their established functions to improve automated failure analysis and responsive decision-making. This motivation for the combination of Bufi, Shentu, Lopez, and Jones is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Claim(s) 6 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bufi et al (US 2022/0366558 A1) in view of Shentu et al (US 2021/0276185 A1) and Lopez et al (US 2020/0026590 A1), and further in view of Lehmann et al (US 2014/0372348 A1).
Regarding claim(s) 6, Bufi as modified by Shentu and Lopez teaches the system according to Claim 5, where Bufi teaches wherein the processor (Figure 1: Computing system 116; and Paragraph [0071]) is adapted to analyze a static failure mode in the at least one first part (Paragraph [0034]: “[…] object detection model configured to receive the image data as an input and generate defect data describing a detected defect as an output […] determining at the PLC device whether the defect data is acceptable or unacceptable by comparing the defect data to tolerance data”; and Paragraph [0211]: “The defect classification includes assigning a detected defect to a defect class. Defect classes are associated with particular defect types (e.g. dent, paint, scratch, etc.) the defect detection model 154 is trained to detect”)
Bufi fails to teach motion of the at least one second part that does not comply with an expected motion (read as “desired route”). However, Lehmann teaches motion of the at least one second part that does not comply with an expected motion (Paragraph [0066]: “The present process provides a crowd model which models the behavior of the crowd based on fluid dynamics […] each sub-population having a characteristic behavior, such as a desired route or a stochastic behavioral pattern”; and Paragraph [0082] – Paragraph [0083]: “auxiliary low-order stochastic models and tolerances which governs the dynamics of temporal variations in parametric values of the crowd model coefficients which are determined to be expected, typical, or normal […] The present system identifies a potential anomaly by detecting an abrupt change in the parameters of the crowd model […] if the abrupt change invalidates the predictions of the auxiliary stochastic model […]”).
It would have been obvious to one of ordinary skill in the art, at the time of the invention, to combine the static, image-based failure analysis of Bufi with the dynamic behavior analysis of Shentu and Lopez, and to further apply the expected versus observed motion analysis taught by Lehmann, in order to improve failure detection accuracy in a system monitoring multiple parts. Such a combination represents the predictable use of known techniques, static inspection for non-moving parts and motion-based anomaly detection for moving parts, according to their established functions. This motivation for the combination of Bufi, Shentu, Lopez, and Lehmann is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim(s) 8, Bufi as modified by Shentu and Lopez teaches the system according to Claim 7, but do not specifically teach wherein the processor is adapted to analyze motion of the at least one first part that does not comply with an expected motion for the first part, and to analyze motion of the at least one second part that does not comply with an expected motion of the second part. However, Lehmann teach wherein the processor is adapted to analyze motion of the at least one first part that does not comply with an expected motion for the first part, and to analyze motion of the at least one second part that does not comply with an expected motion of the second part (Paragraph [0066]: “The present process provides a crowd model which models the behavior of the crowd based on fluid dynamics […] each sub-population having a characteristic behavior, such as a desired route or a stochastic behavioral pattern”; and Paragraph [0082] – Paragraph [0083]: “auxiliary low-order stochastic models and tolerances which governs the dynamics of temporal variations in parametric values of the crowd model coefficients which are determined to be expected, typical, or normal […] The present system identifies a potential anomaly by detecting an abrupt change in the parameters of the crowd model […] if the abrupt change invalidates the predictions of the auxiliary stochastic model […]”).
It would have been obvious to one of ordinary skill in the art, at the time of the invention, to combine the static, image-based failure analysis of Bufi with the dynamic behavior analysis of Shentu and Lopez, and to further apply the expected versus observed motion analysis taught by Lehmann, in order to improve failure detection accuracy in a system monitoring multiple parts. Such a combination represents the predictable use of known techniques, static inspection for non-moving parts and motion-based anomaly detection for moving parts, according to their established functions. This motivation for the combination of Bufi, Shentu, Lopez, and Lehmann is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Claim(s) 14 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bufi et al (US 2022/0366558 A1) in view of Shentu et al (US 2021/0276185 A1) and Lopez et al (US 2020/0026590 A1), and further in view of Yates et al (US 2023/0324900 A1).
Regarding claim(s) 14, Bufi as modified by Shentu and Lopez teaches the system according to claim 1, but do not specifically teach wherein the at least one action comprises scheduling a maintenance operation. However, Yates teaches wherein the at least one action comprises scheduling a maintenance operation (Paragraph [0092]: “In some embodiments, the system is configured to generate one or more actions based on a predicted failure. In some embodiments, the one or more actions include scheduling maintenance, rerouting electricity, ordering of replacement parts and/or assets, generating reports and/or initiating replacement of assets predicted to fail within a specified timeframe”).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Bufi, Shentu, Lopez and Yates before the effective filing date of the claimed invention. The motivation for this combination of references would have been to proactively schedule maintenance after a detected failure condition, thereby reducing equipment downtime and improving system reliability. Applying a known maintenance scheduling technique following automated failure detection merely represents the predictable use of prior-art elements according to their established functions. This motivation for the combination of Bufi, Shentu, Lopez and Yates is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim(s) 16, , Bufi as modified by Shentu and Lopez teaches the system according to claim 1, but do not specifically teach wherein the at least one action comprises suggesting a change to the operation mode of the monitored device or a part thereof. However, Yates teaches wherein the at least one action comprises suggesting a change to the operation mode of the monitored device or a part thereof (Paragraph [0092]: “In some embodiments, the system is configured to generate one or more actions based on a predicted failure. In some embodiments, the one or more actions include scheduling maintenance, rerouting electricity, ordering of replacement parts and/or assets, generating reports and/or initiating replacement of assets predicted to fail within a specified timeframe”).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Bufi, Shentu, Lopez and Yates before the effective filing date of the claimed invention. The motivation for this combination of references would have been to identifying potential failures before they occur allowing for the re-routing of power or in some cases hot asset replacement (reliability), ensures assets are utilized through their entire useful life (affordability), and stops failures—including those that could lead to wildfire ignitions or other safety incidents—from occurring all together (risk), as taught by Yates. This motivation for the combination of Bufi, Shentu, Lopez and Yates is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Claim(s) 22-24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bufi et al (US 2022/0366558 A1) in view of Shentu et al (US 2021/0276185 A1) and Lopez et al (US 2020/0026590 A1), and further in view of Zhou et al (US 2024/0185594 A1).
Regarding claim(s) 22-24, Bufi as modified by Shentu and Lopez teaches the system according to claim 1, but do not specifically teach wherein verifying whether the first part complies with the at least one first failure mode or not, and verifying whether the second part complies with the at least one second failure mode or not is based at least in part on an interrelationship between the first part and the second part.
However, Zhou teaches wherein verifying whether the first part complies with the at least one first failure mode or not, and verifying whether the second part complies with the at least one second failure mode or not is based at least in part on an interrelationship between the first part and the second part (Paragraph [0018]: “each image of an assembly being validated (i.e., a test assembly) is taken in the same coordinate system or with a predetermined relationship maintained by the platform relative to the corresponding profile image”; Paragraph [0019]: “Each of the captured images is compared against the corresponding profile image to determine a matching score representative of whether the components were correctly installed”; Paragraph [0057]: “The profile assembly is the same type of device as the test assembly 15 and includes correct installation of the components (e.g., screws or the like) being validated”; Paragraph [0060]: “the processing device 115 compares a portion of the selected image to a portion of the corresponding profile image and computes a matching score”; and Paragraph [0062]: “multiple samples can compensate for minor displacements […] of the components being tested relative to the components in the profile image”).
Therefore, it would have been obvious to one of ordinary skill in the art to combine Bufi, Shentu, Lopez, and Zhou before the effective filing date of the claimed invention. The motivation for this combination of references would have been to improve verification accuracy by evaluating components with respect to their predetermined spatial relationship and corresponding profile configuration. As explained by Zhou, each test assembly image is maintained in a predetermined relationship with corresponding profile images (Paragraph [0018]), and corresponding portions of component images are compared to determine whether components are correctly installed (Paragraph [0019] and Paragraph [0060]). Applying these known image comparison techniques to the combined system would have predictably improved verification of multiple parts within the monitored device. This motivation for the combination of Bufi, Shentu, Lopez, and Zhou is supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Allowable Subject Matter
Claim(s) 2, 9-11, 18, and 20 is/are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Relevant Prior Art Directed to State of Art
Hofig (US 9,483,342 B2) are relevant prior art not applied in the rejection(s) above. Hofig discloses A method for supporting failure mode and effects analysis, the method comprising: storing a meta-model in a computer-readable storage medium, the meta-model comprising generic parts of a plurality of technical systems, generic failure modes, and associations between the generic parts and the generic failure modes, the associations indicating, for each generic part, one or more generic failure modes associated with the generic part, wherein each generic failure mode identifies a type of failure for a respective generic part; instantiating, with a processor, the generic parts and the generic failure modes to generate part instances and failure mode instances, respectively, the part instances and the failure mode instances specifying an individual technical system of the plurality of technical systems; storing the part instances and the failure mode instances in association with the plurality of technical systems; and associating each failure mode instance with a respective one of the part instances based on the associations between the generic failure modes and the generic parts stored in the meta-model, wherein at least one of the part instances is associated with more than one failure mode instance.
Bell et al (US 9,122,605 B2) are relevant prior art not applied in the rejection(s) above. Bell discloses A method for determining multiple simultaneous fault conditions occurring on complex systems comprising: providing a plurality of failure mode signatures, each failure signature indicative of a corresponding failure mode in a plurality of failure modes; receiving symptoms of a complex system from monitors; when the received symptoms are indicative of multiple simultaneous fault conditions, then: mapping each of the received symptoms to a position in an array to define a symptom signature; calculating an error code for each of the plurality of failure modes using the plurality of failure mode signatures and the symptom signature; determining a Hamming distance for each of the plurality of failure modes from the calculated error codes; identifying failure modes in the plurality of failure modes that have a lowest Hamming distance; grouping the identified failure modes with the lowest Hamming distance into a plurality of simultaneous fault conditions such that identified failure modes with the lowest Hamming distance and having a same error code are grouped together in a same fault condition; and assigning all remaining failure modes in the plurality of failure modes with other Hamming distances into one of a plurality simultaneous fault conditions.
Conclusion
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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/JONGBONG NAH/Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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