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 .
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.
101
Although, the claim recites an abstract idea (mental processes). However, it is integrated into a practical application that improves the functioning of a model in a specific, real-world technical environment, and uses specific hardware and targeted retraining techniques.
Claim Objections
Claim 9 is objected to because of the following informalities: claim lines 3, appears to be a typographical error, lines include the word “geographi” appears it should be “geographic”. Appropriate correction is required.
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.
Claims 6-21 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Tal et al (11,481,991).
Regarding claims 6 and 16 Tal discloses,
Generating a set of image data for a plurality of sub-regions of an identified geographic location (note col. 3 lines 52-55 and col. 4 lines 11-16, digital image from a second section different from first section and col. 5 lines 65-67, geolocation sensors) the set of image data including a texture map, a grayscale image, and color image for the plurality of sub-regions (note col. 10 lines 34-41, pixel image may be color intensity or grey level)
for individual sub-regions, processing the generated set of image data according to a machine learned algorithm to define material defects in paved materials (note col. 6 lines 42-55, neural network identifies incidents/objects i.e cracks of roads examiner interprets as paved materials), wherein the definition of material defects corresponds to the machine learned algorithm utilizing the generated set of image data as inputs (note col. 20 lines 58-65, col. 26 lines 42-45, neural network accept images, thus images input in neural network) and generating an output defining a severity of material defects according to hierarchical categories of material defects (note col. 24 lines 44-59, segment/ classifiers cites estimate surface area severity),
c; identify at least one remedial action for the sub-region based on the definition of material defects (note col. 26 lines 59-60, cites road repairs include crack sealing) ; and generate a processing result corresponding to the definition of material defects and the identified at least one remedial action (note col. 4 lines 9-21, lines describe generating processing result of the invention defining material defects).
Regarding claims 7 and 17 Tal discloses,
Processing aggregated remedial actions for a plurality of sub-regions to generate at least one additional remedial recommendation (note col. 26 lines 59-60, lines describe multiple remedial actions).
Regarding claim 8 Tal discloses,
Wherein processing the aggregated remedial actions include applying a threshold associated with individual remedial actions for a plurality of sub-regions and defining at least one additional remedial action based on exceeding the applied threshold (note col. 26 lines 59-60, remedial action).
Regarding claims 9 and 18 Tal discloses,
Processing the set of image data to conduct at least one additional processing associated with the identified geographic location (note col. 23 lines 38-45, processed using the image processing instructions, the system outputs the processed object data, further, software is configured to construct the object data by associating the sensor information 17 (e.g. including geo coordinate data) for each of the objects 12 of interest.23 lines 38-45,
Regarding claim 10 Tal discloses,
Processing the set of image data to conduct at least one additional processing includes identifying organic material in at least one sub-region (note col. 11 lines 1-9, identifying object of interest).
Regarding claim 11 Tal discloses,
Processing the set of image data to conduct at least one additional processing characterizing compliance with at least one regulation (note col. 17 lines 1-15, addresses regulations).
Regarding claim 12 Tal discloses,
Wherein the characterization corresponds to the machine learned algorithm utilizing the generated set of image data as inputs and generating an output defining a severity of material defects according to one of six hierarchical categories of material defects (note col. 23 lines 55-63 and col. 24 lines 7-27).
Regarding claim 13 Tal discloses,
Wherein processing the generated set of image data according to a machine learned algorithm to characterize material defects in paved materials includes processing the generated set of image data according to a plurality of machine learned algorithms to characterize material defects in paved materials, wherein the plurality of machine learned algorithms corresponds to individual hierarchical categories (note col. 24 lines 60 – col. 25 lines 14).
Regarding claims 14 and 20 Tal discloses,
Wherein the identified at least one remedial action includes an automated estimation of materials associated with the identified at least one remedial action based on the characterization (note col. 26 lines 59-60, identifies remedial action).
Regarding claims 15 and 19 Tal discloses,
Wherein the automated estimation of materials includes at least one of an amount of material or an estimated financial cost (note col. 3 lines 45-50, lines cite system of automated identification).
Regarding claim 21 Tal discloses,
Wherein the automated estimation includes information from at least one external resource (note col. 23 lines 34-40 and 50-54, automated identifying and server).
Allowable Subject Matter
Claims 1-5 are allowed.
The following is an examiner’s statement of reasons for allowance for independent claims 1. Prior art could not be found for the features identify at least one geofence encompassing at least a portion of the identified region to be processed; identify an image collection flight pattern for a controllable image device drone as a function of the at least one geofence, wherein the flight pattern traverses an ordered set of sub-regions encompassing the at least one geofence; generating a set of image data for a plurality of sub-regions of an identified geographic location, the set of image data including a texture map, a grayscale image, and color image for the plurality of sub-regions; for individual sub-regions, processing the generated set of image data according to a machine learned algorithm to define material defects in paved materials, wherein the definition of material defects corresponds to the machine learned algorithm utilizing the generated set of image data as inputs and generating an output defining a severity of material defects according to hierarchical categories of material defects; identify at least one remedial action for the sub-region based on the definition of material defects. These features in combination with other features could not be found in the prior art. Claims 2-5 depend on claims 1. Therefore are also allowable.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Related Prior Art
Agosta et al ( 9129161) generating a set of image data for a plurality of sub-regions of an identified geographic location (note fig. 3, block 304, partition region).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GREGORY M DESIRE whose telephone number is (571)272-7449. The examiner can normally be reached Monday-Friday 6:30am-3:00pm.
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G.D.
April 28, 2026
/GREGORY M DESIRE/Primary Examiner, Art Unit 2676
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