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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 12050994. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of instant application are anticipated by claims of U.S. Patent No. 12050994.
Claim 1 of instant application
Claim 1 of U.S. Patent No. 12050994
A method, comprising: determining a set of images depicting a component of a property, the set of images comprising a time series of at least two images; for each image in the set of images, extracting a feature vector from the image; and using a neural network, determining a state of the component of the property based on the feature vectors.
A method for feature change detection comprising: receiving a set of time-series images depicting a common feature; generating at least two feature vectors for the common feature based on the set of time-series images, wherein the at least two feature vectors are associated with the common feature at different timestamps; and determining a change in the common feature based on the at least two feature vectors, using a neural network model, wherein the neural network model receives as input the at least two feature vectors.
Claim 11 of instant application
Claim 12 of U.S. Patent No. 12050994
A method, comprising: determining a time series of images depicting a property; generating at least two feature vectors for the property based on the time series of images, wherein the at least two feature vectors are associated with the property at different timestamps; and using a machine learning model, determining a score for the property based on the at least two feature vectors.
A system for feature change detection, comprising a computer processor configured to: receive a set of time-series images depicting a shared feature; generate at least two feature vectors for the shared feature based on the set of time-series images, wherein the at least two feature vectors are associated with the shared feature at different timestamps; and determine a change in the shared feature based on the at least two feature vectors, using a neural network model.
Claims 2-10 and 12-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being dependent upon a rejected base claim, but would be withdrawn from the rejection if their base claims overcome the rejection by the timely filing of a terminal disclaimer.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims of U.S. Patent No. 11210552. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims of instant application are anticipated by claims of U.S. Patent No. 11210552.
Claim 1 of instant application
Claim 1 of U.S. Patent No. 11210552
A method, comprising: determining a set of images depicting a component of a property, the set of images comprising a time series of at least two images; for each image in the set of images, extracting a feature vector from the image; and using a neural network, determining a state of the component of the property based on the feature vectors.
A method of detecting a change in a feature in remotely sensed time series images, the method comprising: receiving, at a server, a plurality of remotely sensed time series images; extracting, at the server, a feature from the plurality of remotely sensed time series images; generating, at the server, at least two time series feature vectors based on the feature, wherein the at least two time series feature vectors correspond to the feature at two different times; creating, at the server, a neural network model configured to predict a change in the feature at a specified time; and determining, at the server using the neural network model, the change in the feature at the specified time based on a change between the at least two time series feature vectors, wherein the neural network model receives as input the at least two time series feature vectors.
Claim 11 of instant application
Claim 1 of U.S. Patent No. 12050994
A method, comprising: determining a time series of images depicting a property; generating at least two feature vectors for the property based on the time series of images, wherein the at least two feature vectors are associated with the property at different timestamps; and using a machine learning model, determining a score for the property based on the at least two feature vectors.
A method of detecting a change in a feature in remotely sensed time series images, the method comprising: receiving, at a server, a plurality of remotely sensed time series images; extracting, at the server, a feature from the plurality of remotely sensed time series images; generating, at the server, at least two time series feature vectors based on the feature, wherein the at least two time series feature vectors correspond to the feature at two different times; creating, at the server, a neural network model configured to predict a change in the feature at a specified time; and determining, at the server using the neural network model, the change in the feature at the specified time based on a change between the at least two time series feature vectors, wherein the neural network model receives as input the at least two time series feature vectors.
Claims 2-10 and 12-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being dependent upon a rejected base claim, but would be withdrawn from the rejection if their base claims overcome the rejection by the timely filing of a terminal disclaimer.
Claim Rejections - 35 USC § 102
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)(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.
Claim(s) 1, 2, 11, 12, 14, 16, and 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Greene (U.S. PG-PUB NO. 2018/0046910).
-Regarding claim 1, Greene discloses a method, comprising: determining a set of images depicting a component of a property, the set of images comprising a time series of at least two images (plurality of images can include a first image and a second image that depict the geographic area, where the first image and the second image are different from each other in at least one of the following attributes: time of capture, paragraph 100); for each image in the set of images, extracting a feature vector from the image (the object detection model can output one or more object feature vectors that respectively describe one or more attributes of one or more respective objects included in a geographic area depicted by the input imagery, paragraph 45); and using a neural network, determining a state of the component of the property based on the feature vectors (The one or more of the object feature vectors (or other object attribute data representations) can then be provided to a downstream model (e.g., a downstream condition prediction neural network included in the condition prediction model) for use in generating a prediction regarding the occurrence of the adverse condition, paragraph 46).
-Regarding claim 2, Greene further discloses determining the state of the component comprises: determining a set of scores based on the feature vectors using the neural network (confidence score, paragraph 74-75); and determining the state of the component based on the set of scores (a plurality of confidence scores that respectively describe a confidence of the adverse condition respectively occurring at the first structural asset, paragraph 108).
-Regarding claim 11, Greene discloses a method, comprising: determining a time series of images depicting a property (plurality of images can include a first image and a second image that depict the geographic area, where the first image and the second image are different from each other in at least one of the following attributes: time of capture, paragraph 100); generating at least two feature vectors for the property based on the time series of images, wherein the at least two feature vectors are associated with the property at different timestamps (the object detection model can output one or more object feature vectors that respectively describe one or more attributes of one or more respective objects included in a geographic area depicted by the input imagery, paragraph 45); and using a machine learning model, determining a score for the property based on the at least two feature vectors (The one or more of the object feature vectors (or other object attribute data representations) can then be provided to a downstream model (e.g., a downstream condition prediction neural network included in the condition prediction model) for use in generating a prediction regarding the occurrence of the adverse condition, paragraph 46).
-Regarding claim 12, Greene further discloses the score for the property comprises a score for a condition of the property (confidence score, paragraph 74-75).
-Regarding claim 14, Greene further discloses the machine learning model comprises a neural network (neural network, abstract).
-Regarding claim 16, Greene further discloses the score for the property is associated with a timestamp (paragraph 26).
-regarding claim 20, Greene further discloses the set of images comprises aerial images (paragraph 20).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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) 3, 4, 10, 13, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Greene (U.S. PG-PUB NO. 2018/0046910) in view of Wang (U.S. PG-PUB NO. 2017/0236024).
-Regarding claim 3, Greene is silent to teaching that the component comprises a roof. However, the claimed limitation is well known in the art as evidenced by Wang.
In the same field of endeavor, Wang teaches the component comprises a roof (roof, paragraph 104).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Greene with the teaching of Wang in order to provide more cost effective than manual inspection while providing pertinent information for assessment of roofing projects.
-Regarding claim 4, the combination further discloses the state of the component comprises a condition of the roof (Wang, analyzing a roof of a building, paragraph 42).
-Regarding claim 10, the combination further discloses the state of the component comprises at least one of: roof damage or new roof installation (Greene, adverse conditions at structural assets, paragraph 19; Wang, roof, paragraph 104).
-Regarding claim 13, the combination further discloses the score for the condition of the property comprises a score for a condition of a roof of the property (Greene, confidence score, paragraph 74-75; Wang, roof, paragraph 104).
-Regarding claim 19, the combination further discloses the machine learning model is trained to detect at least one of: shingle conditions, shingle displacement, missing shingles, streaking, or spots (Greene, adverse conditions at structural assets, paragraph 19; Wang, roof, paragraph 104).
Claim(s) 5 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Greene (U.S. PG-PUB NO. 2018/0046910) in view of Huo (CN 109002771 A).
-Regarding claim 5, Greene is silent to teaching that the neural network comprises a recurrent neural network. However, the claimed limitation is well known in the art as evidenced by B.
In the same field of endeavor, Huo teaches the neural network comprises a recurrent neural network (recurrent neural network, abstract).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Greene with the teaching of Huo in order to provide multi-spectrum and high-spectrum optical remote sensing image, extracting the space structure information, and spectral information for image classification, to obtain better classifying result.
-Regarding claim 15, the combination further discloses the neural network comprises a recurrent neural network (Huo, recurrent neural network, abstract).
Claim(s) 6-9, 17 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Greene (U.S. PG-PUB NO. 2018/0046910) in view of Bjorn (JP 2017-62776 A).
-Regarding claim 6, Greene is silent to teaching that the neural network is trained to be invariant to temporary features. However, the claimed limitation is well known in the art as evidenced by Bjorn.
In the same field of endeavor, Bjorn the neural network is trained to be invariant to temporary features (Neural networks train to be indifferent to image differences that do not depend on changes to the structure, paragraph 7).
Therefore, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to combine the teaching of Greene with the teaching of Bjorn in order to detect fewer abnormal changes with fewer false positives.
-Regarding claim 7, the combination further discloses temporary features comprise shadows (Bjorn, different shadows in different images, paragraph 11).
-Regarding claim 8, the combination further discloses the state of the component is associated with a first timestamp, the method further comprising determining a second state of the component of the property associated with a second timestamp (Bjorn, a change mask that indicates the presence / absence of changes to the structure between the first and second time periods, step S316).
-Regarding claim 9, the combination further discloses determining a change in the component based on the state of the component and the second state of the component (Bjorn, a change mask that indicates the presence / absence of changes to the structure between the first and second time periods, step S316).
-Regarding claim 17, the combination further discloses the machine learning model is trained to be invariant to temporary features (Bjorn, Neural networks train to be indifferent to image differences that do not depend on changes to the structure, paragraph 7).
-Regarding claim 18, the combination further discloses temporary features comprise shadows (Bjorn, different shadows in different images, paragraph 11).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PING Y HSIEH whose telephone number is (571)270-3011. The examiner can normally be reached Monday-Friday, 9am-4pm.
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/PING Y HSIEH/ Primary Examiner, Art Unit 2664
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