DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed June 11, 2026 have been fully considered but they are not persuasive. Regarding the rejection of claims 1, 11 and 20 under 35 U.S.C. 102 as being anticipated by Bellaish, Applicant argues that Bellaish does not teach or suggest “wherein the confidence value threshold range is selected based on a risk level associated with .
The threshold value range of Bellaish is not chosen in a vacuum, but is chosen based on the type of construction component, based on what constitutes a high-quality or low-quality installation of the component and based on the severity of the installation error that is determined by comparing the quality values to the threshold range because quality and severity are reflective of the risk level associated with the different installations. Therefore, the threshold values are chosen based at least in part on risk level.
Applicant argues that “the severity discussed in Bellaish cannot be equated with ‘a risk level’ recited in the pending claims.” It should be noted that claim 1 does not recite any particular type of risk level that the selection of threshold range is based on, but only that it “is selected based on a risk level associated with each of the one or more installed components”.
Applicant further argues that paras. [0043], [0044] of the present specification state “that the threshold range for the confidence value can be different for high and low risk installations, and the selection of the confidence value threshold range recited in claim 1 provides a customization to ensure accuracy in identifying improper installations and a means for additional oversight in high risk installations.” Applicant also refers to an example described in the present specification: “[a]s a non-limiting example, the subject application states that a bottom threshold value can be higher for a low risk installation (e.g., too many or missing fasteners) as compared to a higher bottom threshold value for a high risk installation (e.g., missing or incorrectly installed window flashing). See id. The confidence value threshold range customization therefore guarantees accuracy in detection of installation errors.” However, these details are not recited in claim 1. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Furthermore, there is additional language in Bellaish that indicates that the threshold range is selected based at least in part based on a risk level. Para. [0127] of Bellaish discloses that the threshold range may be selected based on “a legal requirement, an ordinance requirement, a regulative requirement, an industry standard requirement, etc.” Since laws, regulations and industry standards are adopted to reduce various types of risk levels, such as public safety risks, for example, selecting a threshold range based on them constitutes selecting a threshold range based at least in part on risk level.
Nevertheless, the rejection of claims 1-3, 5-6, 9-13, 15-16, 18-21 and 23 under 35 U.S.C. 102 as being anticipated by Bellaish is withdrawn in view the amendments to independent claims 1, 11 and 20. However, a new ground of rejection of these claims is set forth below.
Regarding the rejection of claim 24 under 35 U.S.C. 103 as being unpatentable over Bellaish in view of Li, Applicant argues that Li focuses on merging of marked regions that are identified as the same lung nodule and that one of ordinary skill in the art would understand from Li that if the nodules are not the same, the marked areas are not merged. The examiner disagrees. Li discloses that regardless of whether both marked regions correspond to the same nodule, the system of Li merges the marked areas into a single marked area if it determines that the amount of overlap of the marked areas is above a threshold amount of overlap. In Li, a large amount of overlap indicates that nodules that have been marked by different doctors are the same nodule and that the markings can therefore be merged. This meets the BRI for this claim limitation based on para. [0077] of the present disclosure, which reads: “[a]lthough the system may initially generate multiple bounding boxes 534 for a single installation, the system can determine that each of the boxes 534 in reality is associated with a single installation and a single defect. The system can therefore update the image to convert/merge the multiple bounding boxes 534 into a single bounding box 536.”
Therefore, Li discloses that “if an overlap of two or more of the initial markers is greater than a predetermined threshold value, the processing device is configured to merge the two or more of the initial markers to generate the marker,” as recited in claim 24.
Applicant also argues that Li is non-analogous art. The examiner disagrees. While the present disclosure and Li are addressing issues in the construction field and in the field of medicine, respectively, they are both directed to the field of image analysis and both systems acquire images, use machine learning techniques to extract and classify features detected in the images, mark the classified features, determine whether markings overlap by more than a predetermined amount, and if so, merge the markings.
Furthermore, the disclosure of Li is reasonably pertinent to the problem faced by the inventors of the present disclosure. Para. [0077] of the present disclosure discusses how merging overlapping bounding boxes provides “easier and more convenient visualization” of the construction site. Li discloses merging marked areas when the amount by which the marked areas overlap exceeds a threshold amount of overlap, which makes it easier for doctors to view the relevant portions of the final image for diagnoses. Therefore, Li and the present disclosure address the same problem in the field of image analysis.
The examiner’s findings in this regard are consistent with the Supreme Court's findings that "[w]hen a work is available in one field of endeavor, design incentives and other market forces can prompt variations of it, either in the same field or a different one." KSR Int'l Co. v. Teleflex Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). The Federal Circuit has interpreted KSR as "direct[ing] us to construe the scope of analogous art broadly" because "familiar items may have obvious uses beyond their primary purposes, and a person of ordinary skill often will be able to fit the teachings of multiple patents together like pieces of a puzzle." Wyers v. Master Lock Co., 616 F.3d 1231, 1238, 95 USPQ2d 1525, 1530 (Fed. Cir. 2010) (quoting KSR, 550 U.S. at 402, 127 S. Ct. at 1727).
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation (BRI) using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The BRI of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
In the following, some of the terms in the claims have been given BRIs in light of the specification. These BRIs are used for purposes of searching for prior art and examining the claims, but cannot be incorporated into the claims. Should Applicant believe that different interpretations are appropriate, Applicant should point to the portions of the specification that clearly support a different interpretation.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 5-6, 9-13, 15-16, 18-21 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publ. Appl. No. 2020/0151833 of Bellaish et al. (hereinafter referred to as “Bellaish”) in view of Korean Patent No. 20110058399 A to Wan (hereinafter referred to as “Wan”)..
Regarding claim 1, Bellaish discloses a system for determining construction installation accuracy (Fig. 11, para. [0186] disclose detecting “an object installed incorrectly”), the system comprising:
a database configured to electronically store data, the data including an
installation detection model trained based on historical construction installation data (the BRI for historical construction data, based on para. 0041] of the present disclosure, is that it means data of accurate or inaccurate construction installations used to trans a model; para. [0123] of Bellaish discloses training an object detection machine learning model using images of construction installments that are labeled with quality indications that indicate a degree of correctness of the construction installation depicted in the image; regarding the recited database and the installation detection model, Fig. 6, memory 600, para. [0091] discusses the database of Bellaish and para. [0111] of Bellaish discusses the installation detection model: “[i]n some embodiments, analyzing image data, for example by Step 720, Step 730, Step 930, Step 1120, Step 1320, Step 1520, Step 1530, etc., may comprise analyzing the image data and/or the preprocessed image data using one or more rules, functions, procedures, artificial neural networks, object detection algorithms, face detection algorithms, visual event detection algorithms, action detection algorithms, motion detection algorithms, background subtraction algorithms, inference models, and so forth. Some examples of such inference models may include: an inference model preprogrammed manually; a classification model; a regression model; a result of training algorithms, such as machine learning algorithms and/or deep learning algorithms, on training examples, where the training examples may include examples of data instances, and in some cases, a data instance may be labeled with a corresponding desired label and/or result; and so forth”); and
a processing device in communication with the database (paras. [0084]-[0091], Fig. 5, computational node 500 includes memory unit 210 that can be part of the database memory 600), the processing device configured to:
receive as input an image associated with a construction installation (para. [0104], Fig. 7, step 710, apparatus 200 with image sensor 260);
electronically detect one or more components installed in the construction installation depicted in the image (para. [0112]: “[i]n some embodiments, analyzing the image data to identify a region depicting an object of an object type and made of concrete (Step 720) may comprise analyzing image data (such as image data captured from a construction site using at least one image sensor and obtained by Step 710) and/or preprocessed image data to identify a region of the image data depicting at least part of an object, wherein the object is of an object type and made, at least partly, of concrete. In one example, multiple regions may be identified, depicting multiple such objects of a single object type and made, at least partly, of concrete”);
execute the installation detection model to determine an installation accuracy of the installed one or more components (para. [0111] cited above discloses one or more installation detection models/algorithms that are executed to detect objects; para. [0186] discloses analyzing the captured images of the construction site in step 1120 of Fig. 11 to “identify an object installed incorrectly”; para. [0099] defines low quality concrete as a construction error);
generate a confidence value associated with the installation accuracy determined for each of the one or more installed components (paras. [0122]-[0125] of Bellaish disclose using an inference model to generate quality indication values for installations of different types of concrete components (e.g., walls, ceilings, stairs, etc.) based on images of the concrete components; the quality indication values constitute confidence values associated with installation accuracy; for example, para. [0099] defines construction errors to include, for example, “concrete of low quality”, which would be indicated by the quality indication value generated by the inference model);
if the confidence value is within a confidence value threshold range for a respective one or more installed components, identify the respective installed one or more installed components as being improperly installed, wherein the confidence value threshold range is selected based on a risk level associated with each of the one or more installed components (Bellaish discloses that the threshold values or ranges are selected for the respective type of concrete component and that if the quality indication is within a quality value threshold range for the respective concrete component, the component is identified as improperly installed. Para. [0125] discusses selecting thresholds for the respective concrete component types. Para. [0133] discusses comparing the quality indication values to the respective thresholds. Para. [0127] discloses that the threshold value for the different types of component can be based on a construction plan that identifies “minimal quality indication requirements” for the concrete components. The comparison of the quality indication value with the respective threshold value or a range delineated by the threshold, the installation is deemed to be improper or in error. Para. [0123] discusses training the inference model to determine the quality indication values for the concrete objects based on images of the objects that are in the ordinary condition and based on images of the objects having known defects (“segregation of the concrete, discoloration of the concrete, scaling of the concrete, crazing of the concrete, cracking of the concrete, curling of the concrete, etc.”). Since the purpose of identifying the quality indication values is so that they can be compared to threshold ranges that are selected to identify such defects, the threshold ranges are selected based on the type of defect to be identified in the different components; however, Bellaish does not explicitly disclose that the threshold range is based on a risk level associated with each of the one or more installed components); and
generate a marker in the image to identify the one or more installed components having the confidence value within the confidence value threshold range as an improperly installed component (the BRI for “marker”, based on para. [0045] of the present disclosure, is that it means any designation on the image identifying a region or item in the image, such as, for example, a bounding box, circle, oval, arrow, or the like; Fig. 18, para. [0253] of Bellaish discloses overlaying the image containing the construction error with a visual indication of the construction error, “wherein the overlay may include a visual indication of the construction error, and the visual indication may be based on the type of the construction error”; para. [0259] discloses that the visual indication marker may include “an arrow pointing on the location, with a polygon around the location”).
As indicated above, Bellaish does not explicitly disclose that the threshold is based on a risk level associated with each of the one or more installed components. Wan, in the same field of endeavor, discloses a system for managing a shape of cable of a rod bridge using a location based system to rapidly deal with a risk element associated with the tension of the cable. The location based system sensor measures a tension according to an angular position of the rod bridge cable and generates cable topology data. A cable form information collector collects cable topology data from at least one LBS sensor. A cable form information analyzer analyzes the risk of the rod bridge cable on the real time basis (Abstract). A risk tolerance threshold associated with the tension of the cable is monitored and compared to the threshold to determine the risk associated with the tension, as described in the seventh full paragraph on page 7: “[t]he cable tension DB (241) is measured in the at least one LBS sensor stored cable location-specific tension value which a real-time monitoring, in particular, the cable tension DB (242) is to predict the risk of the cable tension Real-time monitoring of the position by tension, and the weather, season, the tension data of the time is stored. That is, since the tension of the cable will vary depending onweather, season, time, tension Taking this into consideration, the threshold data can be changed.” See also the first full paragraph on page 11: “[r]isk tolerances are stored corresponding to the threshold of the position of the Pole Star tension cable bridge D.”
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the system and method of Bellaish based on the teachings of Wan to base the threshold ranges for each of the installed components on a risk level associated with the installed component in the same way that Wan teaches basing the threshold range on a risk level associated with the installed bridge cable. One of ordinary skill in the art would have been motivated to make the modification to ensure that installed components that do not meet safety requirements are flagged to allow remedial action to be taken. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (modifying the software executed by the system of Bellaish to determine select threshold values for the installed components that take into account risk levels associated with the installed components).
Regarding claim 2, Bellaish discloses that the installation detection model is trained based on at least one of manufacturer or industry construction standards (para. [0261] discloses that the detected construction error can be an incorrect rating of the installed object, such as an incorrect fire rating, soundproofing rating, etc.; such ratings are governed by manufacturer and/or industry standards; see also para. [0127] discussing determining whether construction site installations meet industry standards).
Regarding claim 3, Bellaish discloses that the system comprises a user electronic device in communication with the processing device, the user electronic device capable of electronically transmitting the image as the input to the processing device via a communication interface, wherein the processing device is configured to transmit an updated image to the user electronic device via the communication interface, the updated image including the marker generated in the image to identify the improperly installed component (para. [0072] discloses capturing the images of the construction site via one or more image sensors 260 of a user device and transmitting the images via one or more communications modules 230 to other computerized devices such as communication node 500, which constitutes the processing device; para. [0103] discloses that the image sensor 260 can be, for example, a various types of wearable image sensors worn by a person at the construction site; Fig. 18, para. [0253] of Bellaish discloses overlaying the image containing the construction error with a visual indication of the construction error, “wherein the overlay may include a visual indication of the construction error, and the visual indication may be based on the type of the construction error”; para. [0259] discloses that the visual indication marker may include “an arrow pointing on the location, with a polygon around the location”; para. [0080] discloses transmitting visual information to the user via user electronic visual output devices; as indicated above, Fig. 18, para. [0253] discloses overlaying the image containing the construction error with a visual indication of the construction error; the image overlayed with the visual indication of the construction error constitutes the updated image that is transmitted to the user electronic device).
Regarding claim 5, Bellaish discloses that the processing device (computational node 500) is configured to transmit to the user electronic device instructions on how to correctly install the one or more components (para. [0263] discloses transmitting to the user the image with the overlaid visual indication indicating that the object is installed at an incorrect orientation along with a visual indication of the correct orientation of the installed object based on the desired orientation in a construction plan; this constitutes transmitting instructions to the user on how to correctly install the component).
Regarding claim 6, Bellaish discloses that the one or more components installed in the construction installation depicted in the image are of different types of installations, the processing device is configured to detect, identify and classify each type of the different types of installations (paras. [0259]-[0267] disclose that the one or more components are different types of installations, e.g., the object installed at an incorrect location at the construction site (para. [0259]), an object of an incorrect type installed at the construction site (para. [0260]), an object of an incorrect rating installed at construction site (para. [0261]), an incorrect quantity of an element installed at the construction site (para. [0262]), an incorrect orientation of an object at the construction site (para. [0263]), an object of incorrect dimensions installed at the construction site (para. [0264]), an object missing from the construction site (para. [0265]), an object installed in the construction site that is unnecessary according to a construction plan (para. [0266]), an incorrect performance order of actions, e.g., a performance of a first action in the construction site before a performance of a second action (para. [0267]), an incorrect spacing between objects installed in the construction site (para. [0268]); paras. [0259]-[0267] disclose that the type of error is detected, identified and classified as, for example, an incorrect location of installment, an incorrect type of object installed, an object of an incorrect rating installed, etc.).
Regarding claim 9, Bellaish discloses if the confidence value is equal to or greater than a bottom threshold value of the confidence value threshold range, the processing device is configured to transmit an updated image to a user electronic device via a communication interface, the updated image including the identified improperly installed component (para. [0132] discloses that the quality indication is compared to at least a bottom threshold (para. [0127], the minimal quality indication value for the component indicated by the construction plan) of the quality indication value threshold range, and in response to the result of the comparison, transmitting an updated image overlaid with a visual indication presenting information to the user based on the quality indication; para. [0080] discloses transmitting visual information such as the updated image with the overlay to the user via user electronic visual output devices; paras. [0063]-[0064] disclose various communication interfaces for transmitting information from the user to the system and vice versa).
Regarding claim 10, Bellaish discloses wherein if the confidence value is less than the bottom threshold value, the processing device is configured to transmit an updated image to a reviewer electronic device via a communication interface to request confirmation of the identification of the improperly installed component, and wherein validation of the updated image by the reviewer trains the installation detection model and improves performance of the installation detection model (para. [0132] discloses determining whether a quality indication is or is not greater than the bottom threshold value and causing an action to be taken based on the comparison, where the action can be presenting the result of the comparison to a user; paras. [0127] and [0135] disclose that the threshold may be set to a minimal quality indication requirement specified in the construction plan, in which case a determination that the quality indication is less than the threshold would indicate that the installation does not meet specifications and therefore constitutes an improperly installed component; para. [0132] discloses that the indications provided to the user can be “provided as a: visual output, audio output, tactile output, any combination of the above, and so forth. In some examples, the amount of indications provided to the user, the events triggering the indications provided to the user, the content of the indications provided to the user, the nature of the indications provided to the user, etc., may be configurable. The indications provided to the user may be provided: by the apparatus detecting the events, through another apparatus (such as a mobile device associated with the user, mobile phone 111, tablet 112, and personal computer 113, etc.), and so forth”; paras. [0218]-[0219] disclose that when the confidence with which an object at the construction site is detected and recognized is low, the image data and associated progress records are presented to the user with a query asking the user to identify the object by inputting information to a user interface; when the user answers the query, step 1340 of Fig. 13 receives the feedback; para. [0123] discloses using training examples, which can include these images and the corresponding user feedback, to train the installation detection model to improve the performance of the model).
Regarding claim 11, the rejection of claim 1 applies mutatis mutandis to claim 11.
Regarding claims 12 and 13, the rejections of claims 3 and 5 apply mutatis mutandis to claims 12 and 13.
Regarding claim 15, the rejection of claim 2 applies mutatis mutandis to claim 15.
Regarding claim 16, the rejection of claim 6 applies mutatis mutandis to claim 16.
Regarding claim 18, the rejection of claim 9 applies mutatis mutandis to claim 18.
Regarding claim 19, the rejection of claim 10 applies mutatis mutandis to claim 19.
Regarding claim 20, to the extent that claims 1 and 20 recite the same limitations, the rejection of claim 1 applies mutatis mutandis to claim 20. The only limitation that is recited in claim 20 that are not also recited in claim 1 is the non-transitory computer-readable medium storing instructions for performing the operations recited in claims 1 and 20. Bellaish discloses a non-transitory computer-readable medium storing instructions for performing those operations (para. [0027]).
Regarding claim 21, Bellaish discloses that the risk level identified for the installed component is a high risk installation or a low risk installation (the BRI for the terms “low risk” and “high risk”, based on para. [0043] of the present specification, is that it means the risk that the defect has on building envelope performance, such as missing fasteners compared to missing window flashing. Bellaish discloses determining the levels of severity of identified defects and assigning different severities to the defect based on the comparison of the error with a threshold (para. [0026]). A defect assigned a higher severity would correspond to a high-risk installation whereas a defect assigned a lower severity would correspond to a low-risk installation).
Regarding claim 23, to the extent that claim 23 recites limitations concerning the marker that are recited in claims 1 and 3, the rejections of claims 1 and 3 apply mutatis mutandis to claim 23. Bellaish discloses that the processing device generates multiple initial markers for each respective different element improperly installed and associated with the improperly installed component (paras. [0253]-[0272] discuss multiple markers overlaid on the image to identify multiple types of construction errors, such as, for example, an installed object having incorrect dimensions, an installed object having an incorrect rating, an installed object being in an incorrect location on the construction site, an installed door or window being open on the construction site, etc.).
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over Bellaish in view of Wan as applied to claims 1-3, 5-6, 9-13, 15-16, 18-21 and 23 and further in view of Chinese Patent Publication CN 112819747 A to Li (hereinafter referred to as “Li”).
Bellaish does not explicitly disclose that if an overlap of two or more of the initial markers is greater than a predetermined threshold value, the processing device merges the two or more of the initial markers to generate the marker. Li, in the same field of endeavor, discloses that if an overlap of two markers that mark lung nodule areas is greater than a predetermined threshold value (e.g., 60%), the processing device merges the two markers (page 8, last paragraph: “When multiple doctors are marking the same CT sequence or the area marked by the same doctor overlaps, calculate the overlap of the coordinates of the overlap area (the ratio of the coordinates of the overlap area to the coordinates of the marked area of each lung nodule), If the repetition is greater than the set threshold (60%), the two lung nodules will be merged into the same lung nodule for processing, otherwise, if the repetition is less than the set threshold, they will be processed separately”).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the system and method of Bellaish based on the teachings of Li to merge markers if they overlap by greater than a predetermined threshold amount as taught by Li. One of ordinary skill in the art would have been motivated to make the modification to prevent the final images from appearing cluttered or confusing due to multiple markers designating the same defect. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (modifying the software executed by the system of Bellaish to determine the amount of overlap between markers and merge markers that have a large overlap).
Claim 25 is rejected under 35 U.S.C. 103 as being unpatentable over Bellaish in view of Wan as applied to claims 1-3, 5-6, 9-13, 15-16, 18-21 and 23 and further in view of U.S. Publ. Appl. No. 2025/0174354 A1 to Kavusi (hereinafter referred to as “Kavusi”).
Bellaish does not explicitly disclose that the processing device automatically rejects the image if the image has insufficient quality to detect the one or more components and generates a notification requesting input of an additional image having sufficient quality to detect the one or more components. Kavusi, in the same field of endeavor, discloses a diagnostic platform (Fig. 6, 602) that automatically rejects an image if the image has insufficient quality to detect the one or more components (structures of the human eye) and generates a notification requesting input of an additional image having sufficient quality to detect the one or more components (if the platform 602 performing the quality check 702 determines that the image is of insufficient quality for diagnosis, the platform causes a notification to be sent to a recipient that prompts the recipient to generate another image (paras. [0065]-[0066]).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the present disclosure, to modify the system and method of Bellaish based on the teachings of Kavusi to determine whether the image captured by the user device is of sufficient quality to analyze for improper installations, and if so, to send a request to the user to capture another image of the construction site. One of ordinary skill in the art would have been motivated to make the modification to improve the accuracy of the defect detection algorithm and to reduce computational overhead associated with performing the analysis on images of poor quality that may lead to erroneous results. The modification could have been made by one of ordinary skill in the art before the effective filing date of the present disclosure with a reasonable expectation of success because making the modification merely involves combining prior art elements according to known methods to yield predictable results (modifying the software executed by the system of Bellaish to check image quality before performing the analysis and send a message to the user to capture another image if the image is of poor quality).
Allowable Subject Matter
Claim 22 is 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.
The following is a statement of reasons for the indication of allowable subject matter: none of the prior art taken alone or in combination teaches or suggests that if the risk level associated with each of the installed components is a high risk installation, the confidence value threshold range has a bottom threshold value smaller than the bottom threshold value for the low risk installation.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL J SANTOS whose telephone number is (571)272-2867. The examiner can normally be reached M-F 9-5.
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/DANIEL J SANTOS/Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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