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 .
Notice to Applications
This communication is in response to the Application filed on November 22, 2023.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March 01, 2024 is in compliance with the provisions of 27 CFR 1.97. Accordingly, the information disclosure statement is being considered and attached by the examiner.
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 claims at issue 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); and 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 a nonstatutory double patenting ground provided the reference application or patent either is shown to be commonly owned with this 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 §§ 706.02(l)(1) - 706.02(l)(3) 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to http://www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of provisional nonstatutory obviousness-type double patenting as being unpatentable over claims 1, 2-14, and 16-20 of Application no. 18/517,364. Although the claims at issue are not identical, they are not patentably distinct from each other. (see Claim-Comparison Table below for claims 1 and 2 of the instant application against claim 1 of the reference application no. 18/517,364).
Claim
Instant Application (18/517,372)
Claim
Reference Application (18/517,364)
1
A geographic prediction system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:
1
A geographic prediction system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to:
1
receive one or more hyperspectral image frames corresponding to at least a portion of a geographic region;
1
receive one or more hyperspectral image frames corresponding to at least a portion of a geographic region;
1
generate, using one or more machine learning models, one or more landcover predictions for the geographic region based at least in part on the one or more hyperspectral image frames, wherein:
1
generate, using one or more machine learning models, one or more landcover predictions for the geographic region based at least in part on the one or more hyperspectral image frames, wherein:
1
(i) the one or more landcover predictions correspond to one or more geographic portions within the geographic region, and
1
(i) the one or more landcover predictions correspond to one or more geographic portions within the geographic region,
1
(ii) the one or more machine learning models are trained using at least a plurality of historical hyperspectral image frames that correspond to one or more labeled geographic portions within one or more geographic regions; and
1
(ii) the one or more machine learning models are trained using at least a plurality of historical hyperspectral image frames that correspond to one or more labeled geographic portions within one or more geographic regions, and
2
receive, through the interactive user interface, user input indicative of a selection of at least one of the one or more geographic portions within the geographic region; and generate, using the one or more machine learning models, the one or more landcover predictions for the geographic region in response to a determination that the at least one geographic portion is an unlabeled geographic portion.
1
(iii) the one or more landcover predictions are generated by (a) receiving, through an interactive user interface, user input indicative of a selection of at least one geographic portion of the one or more geographic portions within the geographic region and generating, using the one or more machine learning models, the one or more landcover predictions for the geographic region in response to a determination that the at least one geographic portion is an unlabeled geographic portion; and
1
initiate, through an interactive user interface, a presentation of the one or more landcover predictions to a user.
1
initiate, through the interactive user interface, a presentation of the one or more landcover predictions to a user.
Claims 1 of the instant application, and 1 of the reference application no. 18/517,364, are nearly identical. Therefore, it would have been obvious to one of ordinary skill in the art at the date of filing to substitute the system, method, and computer program product steps of the reference application with that of the instant application, which would produce known results with a reasonable expectation for success.
Independent claims 14 and 20 of the instant application recite similar limitations to claim 1 but in the form of a method and computer program product respectively. Therefore, claims 14 and 20 of the instant application are rejected over claims 14 and 20 of the reference application no. 18/517,364 for similar rationale and reasoning to claim 1 (see the Claim-Comparison Table above for claims 1 and 2 of the instant application against claim 1 of the reference application no. 18/517,364).
Dependent claims 3-13 and 16-19 of the instant application are equivalent in scope with claims 3-13 and 16-19 of the reference application no. 18/517,364 respectively.
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable of Guillo et al., US 20240005656 A1, (hereinafter “Guillo”) in view of Brumby et al., US 20220415022 A1, (hereinafter “Brumby”).
Regarding claim 1, Guillo teaches a geographic prediction system comprising memory and one or more processors communicatively coupled to the memory, the one or more processors configured to ([0072] “By such programming, the computer-readable code, computer-readable instructions, computer-executable instructions, or “software” instruct one or more processors of the computer or computing device to carry out the operations and commands of the application. Inputted locations or geographic areas can be stored in the computer or computing device's memory. The memory can be implemented through non-transitory computer-readable storage media such as RAM. As used in the context of this specification, a “non-transitory computer-readable storage medium (or media)” may include any kind of computer memory, including magnetic storage media, optical storage media, nonvolatile memory storage media, and volatile memory.”):
receive one or more hyperspectral image frames corresponding to at least a portion of a ([0048] “The imagery can be obtained through aerial remote sensing platforms including but not limited to drones, aircraft, and satellites. The imagery can include images types including monochromatic, panchromatic, multispectral, hyperspectral, binary, grayscale, indexed, infrared, RGB, radar or any combination thereof.”) ([0012] “the instructions programmed to cause the one or more processors to select a geographic area of interest on a map or satellite or aerial image, cause a satellite or aerial image or portion thereof corresponding to the geographic area of interest to be sent as input”);
generate, using one or more machine learning models, one or more landcover predictions for the ([0009] “the instructions programmed to cause the one or more processors to select a geographic area of interest on a map or satellite or aerial image, cause a satellite or aerial image or portion thereof corresponding to the geographic area of interest to be sent as input for a machine learned model trained with a set of satellite or aerial images having features characteristic of housing at one or more stages of construction and corresponding labels representing such stages, and receive one or more output from the machine learned model, the output including one or more predictions of the stages of construction determined for the features in the satellite or aerial image or portion thereof.”), wherein:
the one or more landcover predictions correspond to one or more geographic portions within the ([0057] “When the user defines the geographic area of interest to their satisfaction, the user interface 20 allows the user to lock and select the shape or image for input into a location storage. An example of a selected shape which overlays a portion of a satellite image is shown in FIG. 2. The interface indicates selection of the shape by colorizing the interior of the shape. While the example image in FIG. 2 has some houses in it already in differing stages of construction, the user can input an area that represents an empty plot of land so construction or grading and road building activity can be tracked from the start and the onset and progress of such activity can be updated with predictions as new imagery becomes available.”), and
the one or more machine learning models are trained using at least a plurality of historical hyperspectral image frames that correspond to one or more labeled geographic portions within one or more ([0051] “One implementation provides a method of identifying a predefined status of a building structure in an optimized target image. The method includes training a neural network receiving training images and outline labels (slab, foundation, under construction, completed), thus creating a classifier (CNN) configured to identify the predefined state of each building structure in an optimized target image by dividing the image into regions using multiple proposals, and converting the outputs to a globally accepted data format for ingestion in geographic information systems.”); and
initiate, through an interactive user interface, a presentation of the one or more landcover predictions to a user ([0057] “When the user defines the geographic area of interest to their satisfaction, the user interface 20 allows the user to lock and select the shape or image for input into a location storage. An example of a selected shape which overlays a portion of a satellite image is shown in FIG. 2. The interface indicates selection of the shape by colorizing the interior of the shape. While the example image in FIG. 2 has some houses in it already in differing stages of construction, the user can input an area that represents an empty plot of land so construction or grading and road building activity can be tracked from the start and the onset and progress of such activity can be updated with predictions as new imagery becomes available.”).
Guillo does not specifically disclose a geographic region.
However, Brumby teaches a geographic region ([0047] “At 130, the mapping system may generate a map of a geographic area associated with the plurality of overhead images input to the trained machine learning model at 124, based on the plurality of overhead images and on the mapping categories determined at 128.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the geographic aerial prediction machine learning model of Guillo to geographic regions of Brumby to offer a wider variety and more accurate geographic data for landcover predictions.
Regarding claim 2, Guillo in view of Brumby teaches the geographic prediction system of claim 1, wherein the one or more processors are further configured to:
receive, through the interactive user interface, user input indicative of a selection of at least one of the one or more geographic portions within the geographic region (Guillo - [0057] “When the user defines the geographic area of interest to their satisfaction, the user interface 20 allows the user to lock and select the shape or image for input into a location storage. An example of a selected shape which overlays a portion of a satellite image is shown in FIG. 2. The interface indicates selection of the shape by colorizing the interior of the shape. While the example image in FIG. 2 has some houses in it already in differing stages of construction, the user can input an area that represents an empty plot of land so construction or grading and road building activity can be tracked from the start and the onset and progress of such activity can be updated with predictions as new imagery becomes available.”) (Brumby - [0047] “At 130, the mapping system may generate a map of a geographic area associated with the plurality of overhead images input to the trained machine learning model at 124, based on the plurality of overhead images and on the mapping categories determined at 128.”); and
generate, using the one or more machine learning models, the one or more landcover predictions for the geographic region in response to a determination that the at least one geographic portion is an unlabeled geographic portion (Guillo - [0009] “the instructions programmed to cause the one or more processors to select a geographic area of interest on a map or satellite or aerial image, cause a satellite or aerial image or portion thereof corresponding to the geographic area of interest to be sent as input for a machine learned model trained with a set of satellite or aerial images having features characteristic of housing at one or more stages of construction and corresponding labels representing such stages, and receive one or more output from the machine learned model, the output including one or more predictions of the stages of construction determined for the features in the satellite or aerial image or portion thereof.”) (Brumby - [0047] “At 130, the mapping system may generate a map of a geographic area associated with the plurality of overhead images input to the trained machine learning model at 124, based on the plurality of overhead images and on the mapping categories determined at 128.”) (Brumby - [0058] “In some embodiments, the mapping system may employ a “no data” class for unlabeled pixels, and images having more than a predefined amount (e.g., 15%, or any other suitable predefined amount) of the “no data” class may be excluded from training, and/or pixels may be predicted as “no data” if the probability of other classes is below a certain threshold.”).
The motivation for combining Guillo and Brumby is the same motivation as used for claim 1.
Regarding claim 3, Guillo in view of Brumby teaches the geographic prediction system of claim 1, wherein the interactive user interface comprises one or more selectable overlay icons and the presentation of the one or more landcover predictions is initiated in response to a selection of at least one of the one or more selectable overlay icons (Guillo - [0057] “When the user defines the geographic area of interest to their satisfaction, the user interface 20 allows the user to lock and select the shape or image for input into a location storage. An example of a selected shape which overlays a portion of a satellite image is shown in FIG. 2. The interface indicates selection of the shape by colorizing the interior of the shape. While the example image in FIG. 2 has some houses in it already in differing stages of construction, the user can input an area that represents an empty plot of land so construction or grading and road building activity can be tracked from the start and the onset and progress of such activity can be updated with predictions as new imagery becomes available.”) (Guillo - [0064] “Alternatively, or in addition, the data from iPoints Storage can be outputted to any standard Geographic Information System (GIS) application. When the iPoint data is outputted to the user interface, it is overlaid over the image that was used to define the geographic area of interest and corresponding imagery used by the Machine Learned Model 90 to make the predictions. This image layer can be switched on/off as per the user's convenience. FIG. 21 shows model predictions overlaid on a satellite image of the original location defined by the user and FIG. 22 shows a zoomed in view of the model predictions in the satellite image, where different colours indicate different stages of construction.”).
The motivation for combining Guillo and Brumby is the same motivation as used for claim 1.
Regarding claim 4, Guillo in view of Brumby teaches the geographic prediction system of claim 1, wherein a geographic portion of the geographic region comprises a geographic polygon or a georeferenced datapoint within the geographic region (Guillo - [0057] “The user interface includes a draw tool which allows the user to capture a segment with the geographic area of interest. The draw tool can be controlled by a mouse, stylus, or other input device to define a boundary surrounding an area which the user has an interest in determining the stages and extent of housing construction within the area. The draw tool can be controlled by the user to define a rectilinear shape (i.e., polygon), a curvilinear shape, or any combination of rectilinear or curvilinear shapes, that encompass areas of sizes of less than an acre to hundreds of acres or more.”) (Brumby - [0047] “At 130, the mapping system may generate a map of a geographic area associated with the plurality of overhead images input to the trained machine learning model at 124, based on the plurality of overhead images and on the mapping categories determined at 128.”).
The motivation for combining Guillo and Brumby is the same motivation as used for claim 1.
Regarding claim 5, Guillo in view of Brumby teaches the geographic prediction system of claim 4, wherein the geographic polygon comprises a closed geographic area within the geographic region and a landcover prediction for the geographic polygon is indicative of an object class physically located within the closed geographic area (Guillo - [0057] “The user interface includes a draw tool which allows the user to capture a segment with the geographic area of interest. The draw tool can be controlled by a mouse, stylus, or other input device to define a boundary surrounding an area which the user has an interest in determining the stages and extent of housing construction within the area. The draw tool can be controlled by the user to define a rectilinear shape (i.e., polygon), a curvilinear shape, or any combination of rectilinear or curvilinear shapes, that encompass areas of sizes of less than an acre to hundreds of acres or more.”) (Brumby - [0047] “At 130, the mapping system may generate a map of a geographic area associated with the plurality of overhead images input to the trained machine learning model at 124, based on the plurality of overhead images and on the mapping categories determined at 128.”) (Brumby - [0046] “For example, trained machine learning model 124 may be configured to output respective probabilities that a particular pixel belongs to a particular class (e.g., from among clouds, snow/ice, grass, trees, cropland, built area, bare ground, flooded vegetation, water, and/or scrub/shrub, or any other suitable mapping class, or any combination thereof). Based on such probabilities, a particular class may be selected by the mapping system.”) (Brumby - [0053] “User device 206 may comprise processing circuitry 230, input/output circuitry 232, communications circuitry 234, storage 236, memory 728, which may be implemented in a similar manner as processing circuitry 210, input/output circuitry 212, communications circuitry 214, storage 216 and memory 218, respectively, of server 202. In some embodiments, input/output circuitry 232 and/or communications circuitry 234 may be configured to receive images of various geographical areas over a predefined period of time (e.g., one year), and may receive input (e.g., from human editors) labeling pixels of the received images as belonging to a particular mapping category.”).
The motivation for combining Guillo and Brumby is the same motivation as used for claim 1.
Regarding claim 6, Guillo in view of Brumby teaches the geographic prediction system of claim 5, wherein the object class is one or more of a type of vegetation species or a type of geographic environment (Brumby - [0046] “For example, trained machine learning model 124 may be configured to output respective probabilities that a particular pixel belongs to a particular class (e.g., from among clouds, snow/ice, grass, trees, cropland, built area, bare ground, flooded vegetation, water, and/or scrub/shrub, or any other suitable mapping class, or any combination thereof). Based on such probabilities, a particular class may be selected by the mapping system.”) (Brumby - [0053] “User device 206 may comprise processing circuitry 230, input/output circuitry 232, communications circuitry 234, storage 236, memory 728, which may be implemented in a similar manner as processing circuitry 210, input/output circuitry 212, communications circuitry 214, storage 216 and memory 218, respectively, of server 202. In some embodiments, input/output circuitry 232 and/or communications circuitry 234 may be configured to receive images of various geographical areas over a predefined period of time (e.g., one year), and may receive input (e.g., from human editors) labeling pixels of the received images as belonging to a particular mapping category.”).
The motivation for combining Guillo and Brumby is the same motivation as used for claim 1.
Regarding claim 7, Guillo in view of Brumby teaches the geographic prediction system of claim 1, wherein the interactive user interface reflects the geographic region and initiating the presentation of the one or more landcover predictions to the user comprises:
applying a landcover overlay over the one or more geographic portions within the geographic region and the landcover overlay comprises one or more masks that are fitted to the one or more geographic portions (Guillo - [0057] “When the user defines the geographic area of interest to their satisfaction, the user interface 20 allows the user to lock and select the shape or image for input into a location storage. An example of a selected shape which overlays a portion of a satellite image is shown in FIG. 2. The interface indicates selection of the shape by colorizing the interior of the shape. While the example image in FIG. 2 has some houses in it already in differing stages of construction, the user can input an area that represents an empty plot of land so construction or grading and road building activity can be tracked from the start and the onset and progress of such activity can be updated with predictions as new imagery becomes available.” wherein one or more masks is the overlayed shapes wherein the interior of the shape is colored) (Brumby - [0047] “At 130, the mapping system may generate a map of a geographic area associated with the plurality of overhead images input to the trained machine learning model at 124, based on the plurality of overhead images and on the mapping categories determined at 128.”).
The motivation for combining Guillo and Brumby is the same motivation as used for claim 1.
Regarding claim 8, Guillo in view of Brumby teaches the geographic prediction system of claim 7, wherein the one or more geographic portions comprise one or more geographic polygons, the landcover overlay comprises one or more polygon masks that are fitted to the one or more geographic polygons, and each of the one or more polygon masks reflect an object class corresponding to a respective landcover prediction (Guillo - [0057] “The user interface includes a draw tool which allows the user to capture a segment with the geographic area of interest. The draw tool can be controlled by a mouse, stylus, or other input device to define a boundary surrounding an area which the user has an interest in determining the stages and extent of housing construction within the area. The draw tool can be controlled by the user to define a rectilinear shape (i.e., polygon), a curvilinear shape, or any combination of rectilinear or curvilinear shapes, that encompass areas of sizes of less than an acre to hundreds of acres or more…When the user defines the geographic area of interest to their satisfaction, the user interface 20 allows the user to lock and select the shape or image for input into a location storage. An example of a selected shape which overlays a portion of a satellite image is shown in FIG. 2. The interface indicates selection of the shape by colorizing the interior of the shape.” wherein one or more polygon masks are the user defined shapes that are colorized on their interior) (Brumby - [0046] “For example, trained machine learning model 124 may be configured to output respective probabilities that a particular pixel belongs to a particular class (e.g., from among clouds, snow/ice, grass, trees, cropland, built area, bare ground, flooded vegetation, water, and/or scrub/shrub, or any other suitable mapping class, or any combination thereof). Based on such probabilities, a particular class may be selected by the mapping system.”) (Brumby - [0053] “User device 206 may comprise processing circuitry 230, input/output circuitry 232, communications circuitry 234, storage 236, memory 728, which may be implemented in a similar manner as processing circuitry 210, input/output circuitry 212, communications circuitry 214, storage 216 and memory 218, respectively, of server 202. In some embodiments, input/output circuitry 232 and/or communications circuitry 234 may be configured to receive images of various geographical areas over a predefined period of time (e.g., one year), and may receive input (e.g., from human editors) labeling pixels of the received images as belonging to a particular mapping category.”).
The motivation for combining Guillo and Brumby is the same motivation as used for claim 1.
Regarding claim 9, Guillo in view of Brumby teaches the geographic prediction system of claim 7, wherein each of the one or more masks comprises a class-specific color reflective of the object class corresponding to the respective landcover prediction (Guillo - [0057] “When the user defines the geographic area of interest to their satisfaction, the user interface 20 allows the user to lock and select the shape or image for input into a location storage. An example of a selected shape which overlays a portion of a satellite image is shown in FIG. 2. The interface indicates selection of the shape by colorizing the interior of the shape. While the example image in FIG. 2 has some houses in it already in differing stages of construction, the user can input an area that represents an empty plot of land so construction or grading and road building activity can be tracked from the start and the onset and progress of such activity can be updated with predictions as new imagery becomes available.” wherein one or more masks is the overlayed shapes wherein the interior of the shape is colored) (Brumby - [0046] “For example, trained machine learning model 124 may be configured to output respective probabilities that a particular pixel belongs to a particular class (e.g., from among clouds, snow/ice, grass, trees, cropland, built area, bare ground, flooded vegetation, water, and/or scrub/shrub, or any other suitable mapping class, or any combination thereof). Based on such probabilities, a particular class may be selected by the mapping system.”) (Brumby - [0053] “User device 206 may comprise processing circuitry 230, input/output circuitry 232, communications circuitry 234, storage 236, memory 728, which may be implemented in a similar manner as processing circuitry 210, input/output circuitry 212, communications circuitry 214, storage 216 and memory 218, respectively, of server 202. In some embodiments, input/output circuitry 232 and/or communications circuitry 234 may be configured to receive images of various geographical areas over a predefined period of time (e.g., one year), and may receive input (e.g., from human editors) labeling pixels of the received images as belonging to a particular mapping category.”) (Brumby - [0062] “As shown in legend 414 of FIG. 4B, each mapping category may be represented by a corresponding identifier or indicator (e.g., a color, a pattern, or any other suitable distinguishing feature, or any combination thereof) in labeled images 408, 410, 412.”).
The motivation for combining Guillo and Brumby is the same motivation as used for claim 1.
Regarding claim 10, Guillo in view of Brumby teaches the geographic prediction system of claim 1, wherein the one or more processors are further configured to:
receive, through the interactive user interface, user input indicative of a landcover label for a geographic portion within the geographic region (Guillo - [0057] “A user 10 interacts with a user interface 20 which first provides an input screen which allows the user to define a geographic area or location of interest where potential residential housing construction may be occurring at various stages or may be beginning, or where grading has occurred, or roads have been laid in. The input screen can be a map or image (aerial, satellite) that allows a user to zoom in and out of or move through a geographic area either through control of a cursor or through input of data associated with the location such as city, county, state, zip code, geographic coordinates (latitude and longitude), or tax parcel number. The map or image can be provided from a service such as Google Maps, Bing Maps, or other internet service map/image provider. The user interface includes a draw tool which allows the user to capture a segment with the geographic area of interest.”) (Brumby - [0046] “For example, trained machine learning model 124 may be configured to output respective probabilities that a particular pixel belongs to a particular class (e.g., from among clouds, snow/ice, grass, trees, cropland, built area, bare ground, flooded vegetation, water, and/or scrub/shrub, or any other suitable mapping class, or any combination thereof). Based on such probabilities, a particular class may be selected by the mapping system.”) (Brumby - [0053] “User device 206 may comprise processing circuitry 230, input/output circuitry 232, communications circuitry 234, storage 236, memory 728, which may be implemented in a similar manner as processing circuitry 210, input/output circuitry 212, communications circuitry 214, storage 216 and memory 218, respectively, of server 202. In some embodiments, input/output circuitry 232 and/or communications circuitry 234 may be configured to receive images of various geographical areas over a predefined period of time (e.g., one year), and may receive input (e.g., from human editors) labeling pixels of the received images as belonging to a particular mapping category.”) (Brumby - [0047] “At 130, the mapping system may generate a map of a geographic area associated with the plurality of overhead images input to the trained machine learning model at 124, based on the plurality of overhead images and on the mapping categories determined at 128.”); and
in response to the user input, update the one or more labeled geographic portions within the one or more geographic regions (Guillo - [0057] “While the example image in FIG. 2 has some houses in it already in differing stages of construction, the user can input an area that represents an empty plot of land so construction or grading and road building activity can be tracked from the start and the onset and progress of such activity can be updated with predictions as new imagery becomes available. For example, FIG. 3 shows a user interface with drawing tools enabled, with a Location Point tool 130 shown. The user drops a marker 140 at a location of interest as shown in FIG. 4 and saves the location by selecting the save icon 150 shown in FIG. 5. Another example is shown in FIGS. 6-9 with respect to a potential area of road construction, where FIG. 6 shows a location polygon/outline drawing tool 160, FIG. 7 shows the location polygon/outline tool activated 170, FIG. 8 shows a polygon 180 drawn over an area of interest, and FIG. 9 shows a save icon 190 for saving the location.”) (Brumby - [0046] “For example, trained machine learning model 124 may be configured to output respective probabilities that a particular pixel belongs to a particular class (e.g., from among clouds, snow/ice, grass, trees, cropland, built area, bare ground, flooded vegetation, water, and/or scrub/shrub, or any other suitable mapping class, or any combination thereof). Based on such probabilities, a particular class may be selected by the mapping system.”) (Brumby - [0053] “User device 206 may comprise processing circuitry 230, input/output circuitry 232, communications circuitry 234, storage 236, memory 728, which may be implemented in a similar manner as processing circuitry 210, input/output circuitry 212, communications circuitry 214, storage 216 and memory 218, respectively, of server 202. In some embodiments, input/output circuitry 232 and/or communications circuitry 234 may be configured to receive images of various geographical areas over a predefined period of time (e.g., one year), and may receive input (e.g., from human editors) labeling pixels of the received images as belonging to a particular mapping category.”) (Brumby - [0047] “At 130, the mapping system may generate a map of a geographic area associated with the plurality of overhead images input to the trained machine learning model at 124, based on the plurality of overhead images and on the mapping categories determined at 128.”).
The motivation for combining Guillo and Brumby is the same motivation as used for claim 1.
Regarding claim 11, Guillo in view of Brumby teaches the geographic prediction system of claim 10, wherein the user input is reflective of (i) a selection of a mask fitted over the geographic portion and (ii) a modification to a shape or an object class for the geographic portion (Guillo - [0057] “The draw tool can also allow a user to input an overlay of a variety of geographic shape templates onto the map which the user can manipulate by selecting various points on the outline of the shape and dragging them on the input screen. The draw tool can also allow selection of various points on the map to define a boundary and connect them to define the area. As such, an area of any size and shape can be captured on the input screen by the user. In other implementations, the user inputs the area as a point of geographic coordinates which represents the center of the geographic area of interest. When the user defines the geographic area of interest to their satisfaction, the user interface 20 allows the user to lock and select the shape or image for input into a location storage. An example of a selected shape which overlays a portion of a satellite image is shown in FIG. 2. The interface indicates selection of the shape by colorizing the interior of the shape. While the example image in FIG. 2 has some houses in it already in differing stages of construction, the user can input an area that represents an empty plot of land so construction or grading and road building activity can be tracked from the start and the onset and progress of such activity can be updated with predictions as new imagery becomes available.” wherein one or more masks is the overlayed shapes wherein the interior of the shape is colored).
The motivation for combining Guillo and Brumby is the same motivation as used for claim 1.
Regarding claim 12, Guillo in view of Brumby teaches the geographic prediction system of claim 11, wherein receiving, through the interactive user interface, the user input indicative of the landcover label for the geographic portion within the geographic region comprises:
receiving, through the interactive user interface, a drawing input reflective of a geographic polygon (Guillo - [0057] “The user drops a marker 140 at a location of interest as shown in FIG. 4 and saves the location by selecting the save icon 150 shown in FIG. 5. Another example is shown in FIGS. 6-9 with respect to a potential area of road construction, where FIG. 6 shows a location polygon/outline drawing tool 160, FIG. 7 shows the location polygon/outline tool activated 170, FIG. 8 shows a polygon 180 drawn over an area of interest, and FIG. 9 shows a save icon 190 for saving the location.”); and
receiving, through the interactive user interface, a labeling input reflective of an object class for the geographic polygon (Guillo - [0057] “The user drops a marker 140 at a location of interest as shown in FIG. 4 and saves the location by selecting the save icon 150 shown in FIG. 5. Another example is shown in FIGS. 6-9 with respect to a potential area of road construction, where FIG. 6 shows a location polygon/outline drawing tool 160, FIG. 7 shows the location polygon/outline tool activated 170, FIG. 8 shows a polygon 180 drawn over an area of interest, and FIG. 9 shows a save icon 190 for saving the location.”) (Brumby - [0046] “For example, trained machine learning model 124 may be configured to output respective probabilities that a particular pixel belongs to a particular class (e.g., from among clouds, snow/ice, grass, trees, cropland, built area, bare ground, flooded vegetation, water, and/or scrub/shrub, or any other suitable mapping class, or any combination thereof). Based on such probabilities, a particular class may be selected by the mapping system.”) (Brumby - [0053] “User device 206 may comprise processing circuitry 230, input/output circuitry 232, communications circuitry 234, storage 236, memory 728, which may be implemented in a similar manner as processing circuitry 210, input/output circuitry 212, communications circuitry 214, storage 216 and memory 218, respectively, of server 202. In some embodiments, input/output circuitry 232 and/or communications circuitry 234 may be configured to receive images of various geographical areas over a predefined period of time (e.g., one year), and may receive input (e.g., from human editors) labeling pixels of the received images as belonging to a particular mapping category.”).
The motivation for combining Guillo and Brumby is the same motivation as used for claim 1.
Regarding claim 13, Guillo in view of Brumby teaches the geographic prediction system of claim 1, wherein generating the one or more landcover predictions comprises:
inputting the one or more hyperspectral image frames to a feature reduction model to generate one or more three-channel image frames from the one or more hyperspectral image frames (Guillo - [0061] “RGB images include data representing color composed of red, green, and blue channels. The image data can also include two dimensions representing the relative location of the pixel in the image. The pixel data can be represented in common data types such as bytes and unsigned integers. Using machine learning algorithms, the model is trained by associating pixel data representing various construction or road and grading features with a classification or label (e.g., slab, foundation, under construction, completed; new construction present or absent; or roads, grading, or none). These features are represented as edges or geometric patterns within the pixel data where contrast with the background of the image occurs…The Machine Learned Model then can output a classification or label as a prediction for features in a new image that was not seen by the model (i.e., not used to train the model) which includes a probability that the classification is correct.” wherein a feature reduction model is the machine learning algorithm and the one more three-channel image frames are the red, green, and blue channels); and
inputting the one or more three-channel image frames to a machine learning classification model to generate the one or more landcover predictions (Guillo - [0061] “RGB images include data representing color composed of red, green, and blue channels. The image data can also include two dimensions representing the relative location of the pixel in the image. The pixel data can be represented in common data types such as bytes and unsigned integers. Using machine learning algorithms, the model is trained by associating pixel data representing various construction or road and grading features with a classification or label (e.g., slab, foundation, under construction, completed; new construction present or absent; or roads, grading, or none). These features are represented as edges or geometric patterns within the pixel data where contrast with the background of the image occurs…The Machine Learned Model then can output a classification or label as a prediction for features in a new image that was not seen by the model (i.e., not used to train the model) which includes a probability that the classification is correct.” wherein a feature reduction model is the machine learning algorithm and the one more three-channel image frames are the red, green, and blue channels).
The motivation for combining Guillo and Brumby is the same motivation as used for claim 1.
Regarding claim 14, the claim recites similar limitations to claim 1 but in the form of a method. Therefore, claim 14 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Regarding claim 15, the claim recites similar limitations to claim 2 but in the form of a method. Therefore, claim 15 recites similar limitations to claim 2 and is rejected for similar rationale and reasoning (see the analysis for claim 2 above).
Regarding claim 16, the claim recites similar limitations to claim 3 but in the form of a method. Therefore, claim 16 recites similar limitations to claim 3 and is rejected for similar rationale and reasoning (see the analysis for claim 3 above).
Regarding claim 17, the claim recites similar limitations to claim 4 but in the form of a method. Therefore, claim 17 recites similar limitations to claim 4 and is rejected for similar rationale and reasoning (see the analysis for claim 4 above).
Regarding claim 18, the claim recites similar limitations to claim 5 but in the form of a method. Therefore, claim 18 recites similar limitations to claim 5 and is rejected for similar rationale and reasoning (see the analysis for claim 5 above).
Regarding claim 19, the claim recites similar limitations to claim 6 but in the form of a method. Therefore, claim 19 recites similar limitations to claim 6 and is rejected for similar rationale and reasoning (see the analysis for claim 6 above).
Regarding claim 20, the claim recites similar limitations to claim 1 but in the form of a computer program product comprising a non-transitory computer readable medium having computer program instructions stored therein, the computer program instructions when executed by one or more processors, cause the one or more processors to carry out the system of claim 1 (Guillo - [0072] “The memory can be implemented through non-transitory computer-readable storage media such as RAM. As used in the context of this specification, a “non-transitory computer-readable storage medium (or media)” may include any kind of computer memory, including magnetic storage media, optical storage media, nonvolatile memory storage media, and volatile memory. Non-limiting examples of non-transitory computer-readable storage media include floppy disks, magnetic tape, conventional hard disks, CD-ROM, DVD-ROM, BLU-RAY, Flash ROM, memory cards, optical drives, solid state drives, flash drives, erasable programmable read only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), non-volatile ROM, and RAM.”). Therefore, claim 20 recites similar limitations to claim 1 and is rejected for similar rationale and reasoning (see the analysis for claim 1 above).
Conclusion
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/AMANDA H PEARSON/Examiner, Art Unit 2666
/MING Y HON/Primary Examiner, Art Unit 2666
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