DETAILED ACTION
Claims 1-17 are pending in this application and have been examined under the priority date of 04/21/2021 in reference to the provisional application.
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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/02/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-17 are rejected under 35 U.S.C. 101 as being directed to an abstract idea without significantly more.
Regarding claims 1 and 11, these claims are directed to an abstract idea, mental process or step of mere data gathering. Specifically, the claims recite the limitations which are classified as follows:
“capturing a plurality of images of a system (Data gathering),
the plurality of images defining different components of the system captured from a plurality of points of view (abstract idea);
based on the plurality of images, detecting a plurality of findings associated with at least one of the components (mental process);
determining a first component associated with a first finding of the plurality of findings (mental process);
identifying a set of images of the plurality of images, each image in the set of images including the first finding (mental process);
determining quality metrics associated with each of the images in the set of images (mental process);
making a comparison of the quality metric to a quality threshold associated with the first component (mental process);
and based on the comparison, selecting a subset of images for display to an operator associated with the system (Data gathering), wherein each image in the subset of images defines the first finding (abstract idea), and the respective quality metric of each image in the subset meets or exceeds the quality threshold (mental process);
Under step 2A prong 1 the limitations above could reasonably be performed by a human gathering images, viewing the images, and making a series of comparisons and assessments about the content of the images. Further, under step 2A prong 2 claim 1 does not recite additional elements that amount to significantly more or meaningfully translate the claim into practical application, claim 11 recites the additional elements of a processor and a memory, however neither provide meaningful translation of the claim into practical application or amount to significantly more. Under step 2B, the claim does not include recited elements that amount to significantly more than the judicial exception. (See MPEP section 2106 for reference)
Dependent claims 2-10 and 12-17 follow the same logic and are drawn to the same abstract idea, mental process or steps of mere data gathering.
Regarding claims 2 and 12, the claims add the limitations; assigning a value related to each of a plurality of quality parameters, each quality parameter representative of a feature of the images that can be distinguished by a human eye (mental process).
The limitations recited by claims 2 and 12 constitute a mental process or abstract idea without significantly more. Claim 12 further adds the additional elements of a memory and a processor which neither translate the claim into practical application or amount to significantly more.
Regarding claims 3 and 13, the claims add the limitations; wherein the plurality of quality parameters define an object access parameter, the object access parameter indicative of a degree to which the first component is covered, such that the first finding is blocked in the respective image from view by the human eye (abstract idea or mental process of making a determination).
The limitations recited by claims 3 and 13 constitute a mental process or abstract idea without significantly more.
Regarding claims 4 and 14, the claims add the limitations; wherein determining the quality metrics further comprises:
determining a weight associated with each of the plurality of quality parameters, wherein the weight is based on the first component (Mental process);
and aggregating the plurality of quality parameters in accordance with their respective weights, so as to compute the quality metrics (Mental process).
The limitations recited by claims 4 and 14 constitute a mental process or abstract idea without significantly more. Claim 14 further adds the additional elements of a memory and a processor which neither translate the claim into practical application or amount to significantly more.
Regarding claim 5 the claim adds the limitation; wherein the weight is further based on context information associated with the images, the context information indicating an environment of the first component when the images are captured (mental process based off of viewing an image).
The limitations recited by claim 5 constitute a mental process or abstract idea without significantly more.
Regarding claim 6 the claim adds the limitation; wherein the quality threshold is based on context information associated with the images, the context information indicating an environment of the first component when the images are captured (mental process of making a determination based off of viewing an image).
The limitations recited by claim 6 constitute a mental process or abstract idea without significantly more.
Regarding claims 7 and 15, the claims add the limitations; based on the quality metrics, ranking each image in the subset of images so as to define a first image having the highest quality metric (mental process of making a determination and ranking images);
and displaying the first image having the highest quality metric (step of mere data gathering, additionally this could be done by a human selecting the image and displaying it).
The limitations recited by claims 7 and 15 constitute a mental process or abstract idea without significantly more. Claim 15 further adds the additional elements of a memory and a processor which neither translate the claim into practical application or amount to significantly more.
Regarding claims 8 and 16, the claims add the limitations;
identify a point of view associated with each image in the subset of images, the point of view defined by a direction from which the first component is viewable in the respective image, so as define multiple point of view classifications (Mental process of making a determination);
and based on the quality metrics, rank each image in the subset of images with respect each of the multiple point of view classifications (Mental process of making a determination).
The limitations recited by claims 8 and 16 constitute a mental process or abstract idea without significantly more. Claim 16 further adds the additional elements of a memory and a processor which neither translate the claim into practical application or amount to significantly more.
Regarding claims 9 and 17, the claims add the limitations;
receive a user actuation (Mere data gathering or human initiated step of clicking a button);
and responsive to the user actuation, select a second image from a second point of view classification that is different than the first point of view classification, wherein the monitor is further configured to, responsive to the user actuation, display the second image instead of the first image (Mere data gathering, a human could reasonably select and collect the images to display them on a screen).
The limitations recited by claims 9 and 17 constitute a mental process, abstract idea, or step of mere data gathering without significantly more. Claim 17 further adds the additional elements of a memory, a processor and a monitor which neither translate the claim into practical application or amount to significantly more.
Regarding claim 10 the claim adds the limitation; based on the quality metrics, determining that the second image has a higher rank as compared to the other images associated with the second point of view classification (Mental process of making a determination based off of looking at an image).
The limitations recited by claim 10 constitute a mental process or abstract idea without significantly more.
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 1-2 and 7-10 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Zhang (US 20200050867 A1).
Regarding claim 1 Zhang discloses; A method comprising: capturing a plurality of images of a system,
the plurality of images defining different components of the system captured from a plurality of points of view (Zhang, [0039] the client obtains video data and the data is sent to the server, [0040] further clarifies that the server processes frames of images in the video data, therefore there are multiple images obtains in the video data, [0042] the video/image data is captures from multiple angles to show the same damaged portion of the vehicle body, indicating multiple points of view, [0044] the damaged portion of the vehicle (components) is identified and classified with its location and size);
based on the plurality of images, detecting a plurality of findings associated with at least one of the components (Zhang, [0043]-[0044] the server detects video images in captured video data to identify the damaged portion of the of the vehicle in the images, [0048] the network takes the image as input, the output of the network is a selection of multiple damaged regions and there confidences, where the confidence is a parameter indicating the degree of authenticity of the identified region.);
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(Zhang, Figure 1 showing the steps indicated, where the video is captured, components/damaged portions are identified and then classification is performed)
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(Zhang, Figure 5, showing the components from the image as inputs, and the parameters (type of damage) as outputs)
determining a first component associated with a first finding of the plurality of findings (Zhang, [0055] during analysis the video frames are analyzed to determine whether a vehicle component is damaged. [0056] a close up image set including images displaying the damaged portion and a component image set including images displaying a vehicle component to which the damaged portion belongs, [0057] the classifications including the damage type is included);
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(Zhang, [0055]-[0057])
identifying a set of images of the plurality of images, each image in the set of images including the first finding (Zhang, [0054] the server classifies the video images based on the detected damaged portion to determine the candidate image classification sets of the damaged portion. [0056] the determined candidate image classification sets may include a close up set of images displaying the damaged portion and a component image set including images displaying the damaged portion and a component image set including the vehicle component where the damaged portion occurs. [0057] the images may be classified into 3 difference categories/types);
determining quality metrics associated with each of the images in the set of images (Zhang, [0061] the damage portions are determined as specific type based upon how large of an area the damage is over, this is compared using a threshold, [0096] the server detects whether or not the clarity of an image (quality metric) is sufficient using a threshold);
making a comparison of the quality metric to a quality threshold associated with the first component (Zhang, [0061] the damage portions are determined as specific type based upon how large of an area the damage is over, this is compared using a threshold, [0096] the server detects whether or not the clarity of an image (quality metric) is sufficient using a threshold);
and based on the comparison, selecting a subset of images for display to an operator associated with the system (Zhang, [0075] states the server uses a vehicle loss assessment image to classify the images based on a present condition, [00139] where the region where the damage occurs is displayed and identified through detection of the region in the video images), wherein each image in the subset of images defines the first finding (Zhang, , [00083] the served send the region of the damaged portion to the client to display in real time, where the damage portion may be identified and displayed, and the use of an operate may facilitate observation), and the respective quality metric of each image in the subset meets or exceeds the quality threshold (Zhang, [0075] states the server uses a vehicle loss assessment image to classify the images based on a present condition).
Regarding claim 2 Zhang discloses; The method as recited in claim 1, wherein determining the quality metrics further comprises:
assigning a value related to each of a plurality of quality parameters (Zhang, [0061] the damage portions are determined as specific type based upon how large of an area the damage is over, this is compared using a threshold, where the size, width or area of the damage is compared to a threshold to determine the type of damage, further [0062]-[0068] discusses multiple value based metrics which can be assigned to each damage type including a ratio of the area of the damage, or a value corresponding to the pixels in the damaged region or span of the damage), each quality parameter representative of a feature of the images that can be distinguished by a human eye (Zhang, [0061]-[0068] the regions of damage are visible in the images and being classified based on size and pixel number, where each damaged regions (features) being assessed using parameters are visible to a human).
Regarding claim 7 Zhang discloses; The method as recited in claim 1, the method further comprising:
based on the quality metrics, ranking each image in the subset of images so as to define a first image having the highest quality metric (Zhang, [0090] multiple images which have the highest clarity are selected to be used for damage assessment);
and displaying the first image having the highest quality metric (Zhang, [0090] multiple images which have the highest clarity are selected to be used for damage assessment, where the images selected to the display (first images) hive the best quality of the batch).
Regarding claim 8 Zhang discloses; The method as recited in claim 7, the method further comprising:
identifying a point of view associated with each image in the subset of images (Zhang, [0078] the angle of the photographer (angle of the image capture) can be determined), the point of view defined by a direction from which the first component is viewable in the respective image (Zhang, [0078] the angle of the photographer (angle of the image capture) can be determined, [0081] the angle of view and location of filming/image capture may change, and this may be determined by the server), so as define multiple point of view classifications (Zhang, [0041] multiple videos are captured of the same component from multiple angles to obtain multiple points of view);
and based on the quality metrics, ranking each image in the subset of images with respect to each of the multiple point of view classifications (Zhang, [0090] multiple images which have the highest clarity and multiple filming angles are selected to be used for damage assessment, where the images selected to the display (first images) hive the best quality of the batch).
Regarding claim 9 Zhang discloses; The method as recited in claim 8, wherein the first image is associated with a first point of view classification, the method comprising:
responsive to a user actuation, selecting a second image from a second point of view classification that is different than the first point of view classification (Zhang, [0051] location regions in the image may be selected, [0057] multiple image frames may be selected and processed, [0076] multiple images may be selected for having good clarity and multiple angles);
and displaying the second image instead of the first image (Zhang, [0076] a combination of one or more images may be displayed, [0084] in processing a damaged region from an image may be displayed, further the client may select a new damaged region, and send that information to the server for processing, since all damaged portions from photographs as displayed at different points in the processing, it would be inherent that when a user selects a new photograph, this would be displayed at a later time in place of the first image. ).
Regarding claim 10 Zhang discloses; The method as recited in claim 9, the method further comprising:
based on the quality metrics, determining that the second image has a higher rank as compared to the other images associated with the second point of view classification (Zhang, [0010] the plurality video image frames are classified and a ratio or value is generated for each, [0011] the images are then ordered in descending order (highest rank first) based on this classification, therefore there is inherently a step of determining which images in the set have the higher rank or score or classification).
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.
2. Claims 3-6 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20200050867 A1) in view of Holzer (US 20200234488A1).
Regarding claim 3 Zhang does not teach; The method as recited in claim 2,
wherein the plurality of quality parameters define an object access parameter, the object access parameter indicative of a degree to which the first component is covered, such that the first finding is blocked in the respective image from view by the human eye.
However, in the same field of endeavor of region classification Holzer teaches;
wherein the plurality of quality parameters define an object access parameter (Holzer, [0154] the damage detection operation may be used to identify an object or component in an image, [0071]-[0073] the system determines whether the images suitable to detect object damage, and then multiple methods and parameters are used to determine the damage (quality paraments), [0204]-[0206] the object is mapped on a grid and then a determination can be made of whether the region is covered or in view (object access parameter)),
the object access parameter indicative of a degree to which the first component is covered (Holzer, [0210] the system determines using an evaluation a degree of coverage, which helps determine if a specific grid portion is covered or shown in the image),
such that the first finding is blocked in the respective image from view by the human eye (Holzer, [0204]-[0206] and figure 8, the system determines how much of the object is covered or if the object is sufficiently covered, [0234] points can be determined as visible or invisible from the human eye using the coverage analysis.).
The combination of Zhang and Holzer would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that when detecting defects or other regions of interest in an image, the ability to verify that regions or components of interest are visible or not visible in the image would allow for accurate assessment, as well as for the user to determine if more images are required. (Holzer, [0200]-[0215] and [0234])
Regarding claim 4 the combination of Zhang and Holzer teaches; The method as recited in claim 3, wherein determining the quality metrics further comprises:
determining a weight associated with each of the plurality of quality parameters (Zhang, [0048] during training the parameters of the network are determined using the training images which have been marked, where mini batching is a method that uses weighting to determine model parameters based on training data. [0049] the parameters of the model are used to determine the damaged regions and the degree of certainty that the region is damaged), wherein the weight is based on the first component (Zhang, [0048] during training the parameters of the network are determined using the training images which have been marked, where mini batching is a method that uses weighting to determine model parameters based on training data. [0049] the parameters of the model are used to determine the damaged regions (components) and the degree of certainty that the region is damaged);
and aggregating the plurality of quality parameters in accordance with their respective weights (Zhang, [0049] the parameters of the model (determined based on the data indicating the components) are determined using a mini batch method where they are weighted, further [0048] the model is trained using the quality data, and the training process includes a step of aggregation and parameter determination, as well as weighting using the mini-batch step), so as to compute the quality metrics (Zhang, [0049] the parameters of the model (determined based on the data indicating the components) are determined using a mini batch method where they are weighted, further [0048] the model is trained using the quality data, and the training process includes a step of aggregation and parameter determination, as well as weighting using the mini-batch step).
Regarding claim 5 the combination of Zhang and Holzer teaches; The method as recited in claim 4, wherein the weight is further based on context information associated with the images, the context information indicating an environment of the first component when the images are captured (Zhang, [0048] the parameters of the model are determined using a mini-batch step which weights them, this is done during training which uses the image data including the components, where [0045] the region/location of the damaged portion (environment of the component/context information) are identified by the network as well when determining the parameters).
Regarding claim 6 the combination of Zhang and Holzer teaches; The methods recited in claim 3, wherein the quality threshold is based on context information associated with the images, the context information indicating an environment of the first component when the images are captured (Zhang, [0048] the parameters of the model are determined using a mini-batch step which weights them, this is done during training which uses the image data including the components, where [0045] the region/location of the damaged portion (environment of the component/context information) are identified by the network as well when determining the parameters, the model parameters generated, [0046] the network damage detection model is training using the above methods to be used for damage detection (quality metrics), therefore the determination of these metrics by the model would also be based on the weights and parameters generated during training).
Claims 11-12 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20200050867 A1) in view of Xu (EP 3321860).
Regarding claim 11 Zhang discloses; A train computing system comprising:
[a plurality of cameras configured to capture a plurality of images defining different components of a train, from a plurality of points of view;]
a monitor configured to display the images and data associated with the images to an operator (Zhang, [0014] a terminal device is used to display the image of the region of damage);
a processor (Zhang, [0018] and [0019] the system has processors to execute the code);
and a memory storing instructions that, when executed by the processor, cause the train computing system to (Zhang, [0018] and [0019] the system has processors to execute the code which are connected to memories):
based on the plurality of images, detect a plurality of findings associated with at least one of the components (Zhang, [0039] the client obtains video data and the data is sent to the server, [0040] further clarifies that the server processes frames of images in the video data, therefore there are multiple images obtains in the video data, [0042] the video/image data is captures from multiple angles to show the same damaged portion of the vehicle body, indicating multiple points of view, [0044] the damaged portion of the vehicle (components) is identified and classified with its location and size);
determine a first component associated with a first finding of the plurality of findings (Zhang, [0043]-[0044] the server detects video images in captured video data to identify the damaged portion of the of the vehicle in the images, [0048] the network takes the image as input, the output of the network is a selection of multiple damaged regions and there confidences, where the confidence is a parameter indicating the degree of authenticity of the identified region, [0055] during analysis the video frames are analyzed to determine whether a vehicle component is damaged. [0056] a close up image set including images displaying the damaged portion and a component image set including images displaying a vehicle component to which the damaged portion belongs, [0057] the classifications including the damage type is included);
identify a set of images of the plurality of images, each image in the set of images including the first finding (Zhang, [0054] the server classifies the video images based on the detected damaged portion to determine the candidate image classification sets of the damaged portion. [0056] the determined candidate image classification sets may include a close up set of images displaying the damaged portion and a component image set including images displaying the damaged portion and a component image set including the vehicle component where the damaged portion occurs. [0057] the images may be classified into 3 difference categories/types);
determine quality metrics associated with each of the images in the set of images (Zhang, [0061] the damage portions are determined as specific type based upon how large of an area the damage is over, this is compared using a threshold, [0096] the server detects whether or not the clarity of an image (quality metric) is sufficient using a threshold);
make a comparison of the quality metric to a quality threshold associated with the first component (Zhang, [0061] the damage portions are determined as specific type based upon how large of an area the damage is over, this is compared using a threshold, [0096] the server detects whether or not the clarity of an image (quality metric) is sufficient using a threshold);
and based on the comparison, select a subset of images for display to an operator associated with the system(Zhang, [0075] states the server uses a vehicle loss assessment image to classify the images based on a present condition, [00139] where the region where the damage occurs is displayed and identified through detection of the region in the video images), wherein each image in the subset of images defines the first finding (Zhang, , [00083] the served send the region of the damaged portion to the client to display in real time, where the damage portion may be identified and displayed, and the use of an operate may facilitate observation), and the respective quality metric of each image in the subset meets or exceeds the quality threshold (Zhang, [0075] states the server uses a vehicle loss assessment image to classify the images based on a present condition).
Zhang does not disclose; a plurality of cameras configured to capture a plurality of images defining different components of a train, from a plurality of points of view;
However, Xu teaches;
a plurality of cameras configured to capture a plurality of images defining different components of a train, from a plurality of points of view (Xu, figure 2, step s202 generate partial images of a train, [0046] the system may use a camera to capture multiple partial images of the train);
The combination of Zhang and Xu would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. Zhang teaches the method of determining components of images and quality metrics thereof, but not that these images are of trains. Xu teaches this deficiency, motivation for using the system of Zhang on images of trains would be to assist in ranking and assessing images using quality metrics as taught in paragraphs [0001]-[0005] of the applicant’s specification.
Regarding claim 12 The combination of Zhang and Xu teaches; The computing system as recited in claim 11, the memory further storing instructions that, when executed by the processor, further cause the train computing system to (Zhang, [0018] and [0019] the system has processors to execute the code which are connected to memories):
assign a value related to each of a plurality of quality parameters Zhang, [0061] the damage portions are determined as specific type based upon how large of an area the damage is over, this is compared using a threshold, where the size, width or area of the damage is compared to a threshold to determine the type of damage, further [0062]-[0068] discusses multiple value based metrics which can be assigned to each damage type including a ratio of the area of the damage, or a value corresponding to the pixels in the damaged region or span of the damage), each quality parameter representative of a feature of the images that can be distinguished by a human eye (Zhang, [0061]-[0068] the regions of damage are visible in the images and being classified based on size and pixel number, where each damaged regions (features) being assessed using parameters are visible to a human).
Regarding claim 15 The combination of Zhang and Xu teaches; The computing system as recited in claim 11, the memory further storing instructions that, when executed by the processor, further cause the train computing system to (Zhang, [0018] and [0019] the system has processors to execute the code which are connected to memories):
based on the quality metrics, rank each image in the subset of images so as to define a first image having the highest quality metric (Zhang, [0090] multiple images which have the highest clarity are selected to be used for damage assessment);
and the monitor is further configured to display the first image having the highest quality metric (Zhang, [0090] multiple images which have the highest clarity are selected to be used for damage assessment, where the images selected to the display (first images) hive the best quality of the batch).
Regarding claim 16 The combination of Zhang and Xu teaches; The computing system as recited in claim 15, the memory further storing instructions that, when executed by the processor, further cause the train computing system to (Zhang, [0018] and [0019] the system has processors to execute the code which are connected to memories):
identify a point of view associated with each image in the subset of images (Zhang, [0078] the angle of the photographer (angle of the image capture) can be determined), the point of view defined by a direction from which the first component is viewable in the respective image (Zhang, [0078] the angle of the photographer (angle of the image capture) can be determined, [0081] the angle of view and location of filming/image capture may change, and this may be determined by the server), so as define multiple point of view classifications (Zhang, [0041] multiple videos are captured of the same component from multiple angles to obtain multiple points of view);
and based on the quality metrics, rank each image in the subset of images with respect each of the multiple point of view classifications (Zhang, [0090] multiple images which have the highest clarity and multiple filming angles are selected to be used for damage assessment, where the images selected to the display (first images) hive the best quality of the batch).
Regarding claim 17 The combination of Zhang and Xu teaches; The computing system as recited in claim 16, the memory further storing instructions that, when executed by the processor, further cause the train computing system to (Zhang, [0018] and [0019] the system has processors to execute the code which are connected to memories):
receive a user actuation (Zhang, [0050] the user may input confirmation into the client device);
and responsive to the user actuation, select a second image from a second point of view classification that is different than the first point of view classification (Zhang, [0051] location regions in the image may be selected, [0057] multiple image frames may be selected and processed, [0076] multiple images may be selected for having good clarity and multiple angles),
wherein the monitor is further configured to, responsive to the user actuation, display the second image instead of the first image (Zhang, [0076] a combination of one or more images may be displayed, [0084] in processing a damaged region from an image may be displayed, further the client may select a new damaged region, and send that information to the server for processing, since all damaged portions from photographs as displayed at different points in the processing, it would be inherent that when a user selects a new photograph, this would be displayed at a later time in place of the first image.).
Claims 13-14 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 20200050867 A1) in view of Xu (EP 3321860) and in further view of Holzer (US 20200234488 A1).
Regarding claim 13 the combination of Zhang and Xu fails to teach; The computing system as recited in claim 12,
wherein the plurality of quality parameters define an object access parameter, the object access parameter indicative of a degree to which the first component is covered, such that the first finding is blocked in the respective image from view by a human eye.
However, in the same field of endeavor of region classification Holzer teaches;
wherein the plurality of quality parameters define an object access parameter (Holzer, [0154] the damage detection operation may be used to identify an object or component in an image, [0071]-[0073] the system determines whether the images suitable to detect object damage, and then multiple methods and parameters are used to determine the damage (quality paraments), [0204]-[0206] the object is mapped on a grid and then a determination can be made of whether the region is covered or in view (object access parameter)),
the object access parameter indicative of a degree to which the first component is covered (Holzer, [0210] the system determines using an evaluation a degree of coverage, which helps determine if a specific grid portion is covered or shown in the image),
such that the first finding is blocked in the respective image from view by the human eye (Holzer, [0204]-[0206] and figure 8, the system determines how much of the object is covered or if the object is sufficiently covered, [0234] points can be determined as visible or invisible from the human eye using the coverage analysis.).
The combination of Zhang, Xu and Holzer would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The motivation for the combination lies in that when detecting defects or other regions of interest in an image, the ability to verify that regions or components of interest are visible or not visible in the image would allow for accurate assessment, as well as for the user to determine if more images are required. (Holzer, [0200]-[0215] and [0234])
Regarding claim 14 the combination of Zhang, Xu and Holzer teaches; The computing system as recited in claim 13, the memory further storing instructions that, when executed by the processor, further cause the train computing system to (Zhang, [0018] and [0019] the system has processors to execute the code which are connected to memories):
determine a weight associated with each of the plurality of quality parameters, wherein the weight is based on the first component (Zhang, [0048] during training the parameters of the network are determined using the training images which have been marked, where mini batching is a method that uses weighting to determine model parameters based on training data. [0049] the parameters of the model are used to determine the damaged regions and the degree of certainty that the region is damaged), wherein the weight is based on the first component (Zhang, [0048] during training the parameters of the network are determined using the training images which have been marked, where mini batching is a method that uses weighting to determine model parameters based on training data. [0049] the parameters of the model are used to determine the damaged regions (components) and the degree of certainty that the region is damaged);
and aggregate the plurality of quality parameters in accordance with their respective weights, so as to compute the quality metrics (Zhang, [0049] the parameters of the model (determined based on the data indicating the components) are determined using a mini batch method where they are weighted, further [0048] the model is trained using the quality data, and the training process includes a step of aggregation and parameter determination, as well as weighting using the mini-batch step), so as to compute the quality metrics (Zhang, [0049] the parameters of the model (determined based on the data indicating the components) are determined using a mini batch method where they are weighted, further [0048] the model is trained using the quality data, and the training process includes a step of aggregation and parameter determination, as well as weighting using the mini-batch step).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Prior art made of record but not relied by the examiner can be found on the attached PTO-892 Notice of References Cited form.
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/J.M.E./Examiner, Art Unit 2666
/EMILY C TERRELL/Supervisory Patent Examiner, Art Unit 2666