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 .
Claims 11-22 and 26-28 (20-22 withdrawn), are currently pending in U.S. Patent Application No. 18/649,967 and an Office action on the merits follows.
Election/Restrictions
Applicant’s election without traverse of Group I, claims 11-19 and 26-28 (see requirement mailed 03/10/2026), in the reply filed on 05/10/2026 is acknowledged. Applicant may also see MPEP § 818.01 which indicates that an absence of any statement indicating whether the requirement to restrict is traversed or the failure to provide reasons for traverse will be treated as an election without traverse. Applicant’s election response requests the cancellation of non-elected claims 20-22, however claims 20-22 are instead withdrawn from further consideration pursuant to 37 CFR 1.142(b). Applicant may cancel the claims in question if desired, in any subsequently filed claim set conforming with the requirements set forth in MPEP § 714.
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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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 www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 11-19 and 26-28 (all those pending and not withdrawn) are rejected on the grounds of nonstatutory double patenting as being unpatentable and/or obvious over one or more claims of:
1) U.S. Patent No. 11,908,185 to parent Application No. 17/854,294.
The abovementioned instant claims are alternatively/further provisionally rejected on the grounds of nonstatutory double patenting as being unpatentable and/or obvious over one or more claims of:
2) pending child Application No. 19/369,274, which is a CON of the instant application.
The abovementioned instant claims are additionally rejected on the grounds of nonstatutory double patenting as being obvious over one or more claims of:
3) U.S. Patent No. 11,978,221 to Application No. 17/854,304, to which the parent ‘294 application is linked via Terminal Disclaimer (filed 09/29/2023). See also MPEP 804.02. For the case of 3) reference may be made to that modification and supporting rationale, in view of the teachings of e.g. Brouard et al. (US 10,528,812), as identified in the Non-Final Rejection mailed 06/30/2023 (e.g. page 5) and Final rejection mailed 02/13/2023 for the ‘294 Application. Examiner further notes the relevance of any superseding decision (not yet issued as of the time of this action) from the Appeals Review Panel (ARP) regarding Ex Parte Baurin, Appeal 2024-002920, App. No. 17/135,529 – however the ‘221 Patent shares the same EFD (06/30/2022).
Although the claims at issue are not identical, they are not patentably distinct from each other because claims of reference anticipate and/or render obvious one or more claim(s). The conflicting claims are also not patentably distinct from each other for at least the following reasons:
• Instant claims and claims of reference recite common subject matter, and recite the open ended transitional phrase “comprising” which does not preclude any additional elements recited by claims of reference;
• Language/terminology of instant claim(s) constituting minor/slight variations from the claims of reference, if/where present, (e.g. causing an image to be sent as input vs. receiving an image as input (requiring a sending for receiving)) require interpretations under Broadest Reasonable Interpretation and/or plain meaning definitions (MPEP 2173 and 2111) equivalent to/met by language of the reference claims in view of that corresponding/ shared Specification. While the disclosure of reference may not be used as prior art (Double Patenting concerns the claims of reference), portions of the specification which provide support for reference claims may also be examined and considered when addressing the scope of claim(s) of reference and the issue of whether an instant claim defines an obvious variation or falls within the scope of an invention claimed in the claim(s) of reference. See MPEP 804 with reference to In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970).
• Instant claims that differ merely in statutory category (computer implemented method vs. CRM vs. system comprising processor and/or memory combination(s)), if otherwise congruent in scope (e.g. instant claims 11(method)/26(CRM)), may be rejected in view of Obviousness type Double Patenting procedures as they relate to system/CRM/method claims of reference with congruent scopes.
• Language/terminology of instant claim(s) otherwise not explicitly recited in claim(s) of reference constitute limitations met in view of obvious modification to claims of reference for reasons same/similar to those presented in the prior art based rejection(s) below. It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify claims of reference in view of one or more of MPEP 2143 Example Rationales (A)-(G) and/or those presented in the prior-art based rejections below, and in a manner further characterized by a reasonable expectation of success.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 11-19 and 26-28 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception, in particular an Abstract Idea falling under the (c) mental processes grouping (concepts performable in the human mind including an observation, evaluation, judgement, opinion), not ‘integrated into a practical application’ at Prong Two of Step 2A and without ‘significantly more’ at Step 2B.
Step 1: The claim(s) in question are directed to a computer implemented method for segmenting/classifying one or more of roads and/or grading from satellite/aerial/map imagery. (Step 1: Yes).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim. Representative claim(s) 11/26 explicitly recite(s), at a high level of generality, e.g.:
(1) “selecting a geographic area of interest on a map or satellite or aerial image” and
(2) receiving (generating by means of executing a ML model during inference) “predictions of roads and/or grading determined for the features in the satellite or aerial image or portion thereof”
(1) constitutes a user selection/choice, that may be made with or without the assistance of a generic computer, and (2) constitutes a prediction, as permissibly interpreted under Broadest Reasonable Interpretation in view of plain meaning definitions not inconsistent with Applicant’s Specification (MPEP 2173 and 2111), that may be performed mentally (mentally/visually evaluating imagery to detect roads and/or grading). As the July 17 2024 PEG makes clear, the fact that the claim recites this prediction as being accomplished by means of a generically recited ML model, does not preclude the prediction from being drawn under the exception. Reference may be made to the 2024 PEG, Example 47 claim 2, wherein using an ANN did not preclude that anomaly detection and analysis of step(s) (d) and (e) from being drawn under the mental processes grouping at Prong One. See pages 6-7 of: https://www.uspto.gov/sites/default/files/documents/2024-AI-SMEUpdateExamples47-49.pdf
Even if the recited ‘selecting’ step requires the use of a computer (e.g. a user navigating Google Earth in a web browser, ESA Copernicus Browser, or equivalent), such a pre-processing image acquisition step does not preclude the prediction itself from being performed mentally, and the recited ‘features’ are not of a level of complexity that would render the prediction impractical for mental evaluation. See MPEP 2106.04(a)(2) subsection C. A Claim that Requires a Computer May Still Recite a Mental Process. Examiner further notes that causing an image to be sent as input may be a user initiated/manually performed operation, suggesting that it may also be subsumed under/within the exception itself (a review of the art also suggests such an inputting/sending is frequently performed for the purposes of model training, testing/validation, and/or inference). Since it is pertinent to the considerations at Prong One, Applicant is also advised that the courts have declined to adopt the enumerated Abstract Idea groupings from the Office’s 2019 guidance – see footnote 2 at page 14, Rideshare Displays, Inc v. Lyft, Inc., No. 23-2033, (Fed. Cir. September 29, 2025) available at: https://www.cafc.uscourts.gov/opinions-orders/23-2033.OPINION.9-29-2025_2579953.pdf Dependent claims are similarly analyzed at least at Prong One since they inherit this/these same limitations identified for the case of independent claim(s). (Step 2A, Prong One: Yes).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception, distinct from the exception itself. This evaluation is performed by (1) identifying whether there are any ‘additional elements’ recited in the claim beyond the judicial exception, and (2) evaluating those ‘additional elements’ individually and in combination (weighed against the exception) to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). Examiner notes for consideration at Prong Two of 2A that MPEP 2106.05(a), (b), (c), and (e) generally concern elements that may be indicative of integration, whereas 2106.05(f), (g), and (h) generally concern elements that are not likely indicative of integration. As an additional note, ‘additional elements’ are generally limitations excluded from interpretation under the Abstract Idea groupings, and may comprise portions of limitations otherwise identified as falling under those Abstract Idea groupings of the 2019 PEG (e.g. any detection/determination/recognition that may be made mentally accompanied by the use of a neural network and/or generic computer hardware considered under the ‘apply it’ considerations of 2106.05(f)). Any ‘providing’/outputting broadly, and ‘collection’ of data (i.e. image acquisition(s)), be they images for training any learning model and/or data/images visually observable/ evaluated by a user/operator, also fail(s) to integrate at least in view of MPEP 2106.05(g) (extra-solution data gathering/output) and/or 2106.05(h) as ‘generally linking’ the exception to a field of use involving machine learning and/or imagery so acquired. Examiner also pre-emptively notes with respect to 2106.05(a), that ‘functioning of a computer’ (see fact pattern of Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016)) does not constitute operations that a general purpose computer may be programmed/configured to perform, since functioning of a computer instead concerns functions integral to the way computers operate (e.g. memory read-write for Enfish and virus scanning for Finjan). Regarding the claim(s) ‘as a whole’, the requirement for considering the claim as a whole stems from the fact that the judicial exception alone cannot provide the improvement, and any ‘additional elements’ are not evaluated in a vacuum separate from the weight of those directed to the exception (in further view of the Alice/Mayo’s roots in pre-emption). Consideration must be given to the degree/extent to which the apparent/disclosed improvement, as it is realized in recited claim language, is to the exception itself or otherwise distinct from it and captured by those limitations clearly serving as ‘additional elements’ after analysis at Prong One, in addition to how the ‘additional elements’ weigh in comparison to those limitations directed to the exception.
Reference may be made to the 08/04/2025 memo affirming analysis set forth in the 2024 PEG (https://www.uspto.gov/sites/default/files/documents/memo-101-20250804.pdf) and consistent with guidance to date. The most recent SME Memo(s) are available at: https://www.uspto.gov/patents/laws/examination-policy/subject-matter-eligibility and more specifically: https://www.uspto.gov/sites/default/files/documents/memo-desjardins.pdf
For the case of Desjardins, the claim(s) explicitly recited a limitation not drawn under/subsumed by the identified exception at Prong One, and realizing an improvement to the technical field of machine learning (serving for integration accordingly in view of 2106.05(a) – reciting an improvement to the way machine learning models are trained). The ARP’s decision in Desjardins also did not disturb the Board’s Prong One finding. The instant claims are unlike Desjardins however (do not concern any improvement to the technical field of training machine learning models), and read much more akin to an instance of ‘applying’ machine learning techniques to perform a segmentation/classification of roads, grading, etc., that is (as recited at a high level of generality) otherwise and perhaps even conventionally/traditionally so, performed visually/mentally. Even if the recited prediction/classification with respect to roads and/or grading is in itself useful/ practical – the utility (distinct inquiry MPEP 2107) of the exception itself does not serve for integration into a ‘practical application’ (see MPEP 2106.04(d)). Additional elements for the instant claim(s) include the use of a ML model (2106.05(f)), operations for one or more image acquisitions as necessary pre-processing step (2106.05(g)), and limitations linking to a field of use wherein a trained ML model is implemented (2106.05(h)), and fail to serve for integration accordingly. No additional elements outside of those directed to the exception itself, appear to explicitly/ specifically capture/recite any disclosed improvement in any technology and/or technical field (MPEP 2106.05(a)), but instead read akin to an automation, via the use of a ML model, of a prediction that may otherwise be performed visually/mentally. Applicant is advised that there is a growing corpus of jurisprudence (much of which is dated after Applicant’s effective filing date) describing how result oriented functional claiming that fails to recite “how” e.g. a prediction occurs (broad ‘use’ of a model being insufficient), is unlikely to be determined/ruled eligible subject matter. With reference to MPEP 2106.05(a):
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981))
Even when viewed in combination, the ‘additional elements’ present do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: No; Revised Step 2A: Yes [Wingdings font/0xE0] Step 2B).
Examiner requests Applicant’s assistance in providing a competing and compelling eligibility analysis, at Prong Two of Step 2A in particular, that explicitly identifies the improvement associated with Applicant’s invention (explicit or implied) (see MPEP 2106.05(a) sub-section II Improvements to any other technology or technical field – since the instant application does not concern “functioning of a computer” (see above, and see also e.g. TJTM Technologies v Google, Appeal No. 2025-1218 (Fed. Cir. May 5, 2026) at page 5 citing Enfish)), and how said improvement is realized by the claim – i.e. by explicitly identifying which limitations serve as the ‘additional elements’ realizing the improvement, and how these elements are not themselves subsumed under/within the identified exception.
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to ‘significantly more’ than the recited exception, i.e., whether any ‘additional element’, or combination of additional elements, adds an inventive concept to the claim. The considerations of Step 2A Prong 2 and Step 2B overlap, but differ in that 2B also requires considering whether the claims feature any “specific limitation(s) other than what is well-understood, routine, conventional activity in the field” (WURC) (MPEP 2106.05(d)). Such a limitation if specifically recited however, must still be excluded from interpretation under any of the Abstract Idea groupings. Step 2B further requires a re-evaluation of any additional elements drawn to extra-solution activity in Step 2A (e.g. gathering images) – however no limitations appear directed to any novel collection per se. For at least the case of representative claim 11, both the selecting and causing/sending (for model input) are generically recited, if not WURC – and potential subject matter directed to e.g. UI content, is not subject matter generally classified/ examined under USPC 382 but instead e.g. USPC 715 and/or 717 (requiring different field of search). With respect to jurisprudence broadly referenced by the Examiner above Applicant may also consider e.g. Longitude Licensing Ltd. v. Google LLC, No. 24-1202, (Fed. Cir. April 30, 2025) (available at https://www.cafc.uscourts.gov/opinions-orders/24-1202.OPINION.4-30-2025_2506816.pdf) (see e.g. pages 7-9). While it is the MPEP that governs Examination and not necessarily case law (2019 marking a shift away from analysis attempting to identify analogous case law), this opinion and those referenced therein (e.g. Recentive in particular - precedential) Recentive Analytics, Inc., v. Fox Corp., Appeal No. 2023-2437, (Fed. Cir. Apr. 18, 2025) available at https://www.cafc.uscourts.gov/opinions-orders/23-2437.OPINION.4-18-2025_2500790.pdf serve to illustrate the manner in which claims that seek to apply broad classes of machine learning to a field of use, and/or claim limitations that do not explain/capture how a purported inventive concept/ improvement is actually achieved, are not likely to be determined eligible/enforceable. (Step 2B: No).
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 of this title, 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.
1. Claims 11-14, 16-19 and 26-28 are rejected under 35 U.S.C. 103 as being unpatentable over Brouard et al. (US 10,528,812 B1) in view of Yang et al. (US 11,042,742 B1) and Jurick et al. (US 2022/0067856 A1).
As to claim 11, Brouard discloses a method comprising:
selecting a geographic area of interest on a map or satellite or aerial image (col 1 lines 50-60 “The system circuitry is first configured to receive, from the database, aerial images and land maps corresponding to a geographic region, divide the aerial images into a plurality of partially overlapping aerial blocks and divide the land maps into a plurality of partially overlapping map blocks corresponding to the aerial blocks”, Fig. 4 generation of 402/404, Fig. 2 pre-processing stages 214/114 and 204/104, etc., see also e.g. col 12 lines 50-55 “For example, the orthophotos 302 and the land maps 303 may be divided according to boundaries of irregularly shaped districts, such as school district”);
causing 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 (Abs “using one or more multilayer convolutional neural network (CNN) models trained”, CNN model col 17 lines 40-50, Fig. 1 model(s) 130) trained (Fig. 4 410-412, training dataset/training of col 1 lines 30-45, etc.,) with a set of satellite or aerial images (col 3 lines 50-60 “The system circuitry may be further configured to input the aerial blocks with overlapping regions into the multilayer landmark recognition and segmentation convolutional neural network to obtain a first set of predicted landmark boundary boxes for landmarks in the aerial blocks with corresponding landmark labels, wherein each landmark is of one of a predetermined set of landmark types and each boundary box is labeled with one of the predetermined set of landmark types”, col 10 lines 1-10 “CNN model 130 to process the image blocks 120 (including but not limited to image blocks 122, 124, and 126) to generate labeled image blocks 140 including but not limited to labeled image blocks 142, 144, and 146, each labeled with recognized and segmented objects. The labels for the objects may include but are not limited to locations of the objects in the image blocks (e.g., pixel locations), boundaries of the objects, and types of objects”) having features characteristic of roads and/or grading (Fig. 6 610, col 11 lines 45-55 “For example, in an application used by an administrative district to monitor and enforce land use regulations, tax codes, and environmental policies, the set of types of landmarks of interest may be defined as including but not limited to buildings (e.g., as indicated by arrows 306), swimming pools (e.g., as indicated by arrows 304), roads (e.g., as indicated by arrow 308)”, col 18 line 60 – col 19 line 5, etc.,; Examiner notes ‘having features characteristic of roads and/or grading’ (the Office routinely interprets ‘A and/or B’ as ‘A or B or (A and B)’) further limits the nature of the images sent for input (arguably less any model, what it detects/classifies, or how it operates) – Official Notice is taken (see MPEP 2144.03) that acquired satellite imagery is typically characterized by features characteristic of roads and/or grading, given the prevalence of roads as fundamental civil infrastructure and the essential nature of ‘grading’/ land/ground/soil leveling/reshaping to meet desired design elevations/slopes, prevent erosion, re-direct water etc., as an initial phase for a plurality of frequently performed construction projects) and one or more corresponding labels (col 10 lines 1-10 “The labels for the objects may include but are not limited to locations of the objects in the image blocks (e.g., pixel locations), boundaries of the objects, and types of objects”); and
receiving one or more output from the machine learned model (receipt of labels/output at 240 and also 206, e.g. Fig. 2, as received by e.g. Land Registration Analytics 270, col 12 lines 55-65 “data pipeline 210 may include using a trained multilayer landmark recognition and segmentation CNN model 230 to process the aerial blocks 220 (including but not limited to aerial blocks 222, 224, and 226) to generate labeled aerial blocks 240 including but not limited to labeled aerial blocks 242, 244, and 246, each labeled with recognized and segmented landmarks. The labels for the landmarks may include but are not limited to locations of the landmarks in the aerial blocks (e.g., pixel locations), boundaries of the landmarks, and types of landmarks”, col 16 lines 55-60 “The final CNN model may then be deployed for landmark recognition and segmentation of unlabeled aerial blocks”, etc.,), the output comprising one or more predictions related to roads and/or grading determined for the features in the satellite or aerial image or portion thereof (col 2 lines 30-35 “For example, the predicted landmark labels for the unlabeled aerial blocks may be filtered by identifying roads in the unlabeled aerial blocks”, col 14 lines 55-65 ‘predicted landmarks’ in view of col 13 lines 5-10, etc.,).
In response to any assertion that the ‘identified’ roads of Brouard are not predictions themselves because they are identified instead for the purposes of correcting false positives for predicted landmarks that are e.g. buildings/roofs, swimming pools and/or solar panels, Examiner asserts that permissible modification to that road identification of Brouard, would include modification such that the output of the one or more models comprises one or more predictions ‘of’ (as compared to ‘related to’) roads and/or grading.
Numerous references of record evidence the obvious nature of ML model output comprising predictions of roads and/or grading (particularly as recited in the alternative), however at least Yang evidences the obvious nature of receiving one or more output from a machine learned model, the output comprising one or more predictions of roads and/or grading (Abs “Disclosed are convolutional neural network-based road detecting apparatus and method and a convolutional neural network-based road detecting method according to an exemplary embodiment”, col 1 lines 30-35 “Referring to FIG. 1, the semantic segmentation technique is utilized to search a given satellite image (a) in pixel units to output a result (b) of labeling a region corresponding to a road”, col 4 lines 5-10 “FIGS. 6A to 6C are views illustrating comparison of the final segmentation map derived by a convolutional neural network-based road detecting apparatus”, etc.,).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to modify the system and method of Brouard such that ML model derived prediction outputs further comprise one or more predictions of roads and/or grading as taught/suggested by Yang, the motivation as similarly taught/suggested therein that such a prediction output may serve as an alternative to (particularly if unavailable in Brouard’s map blocks 440/502), and/or validation of, road identification otherwise determined from e.g. road-specific/isolated map layers (Brouard col 11 lines 60-66).
In view of any assertion that permissible interpretation of the recited ‘selecting’ requires a specific if not recited degree of user interaction, Jurick further evidences the obvious nature of predicting, by way of a machine learned model and based on image features corresponding to development activity, roads and/or grading based at least in part on a user facilitated geographic area selection ([0003] “For example, multiple factors may be required for properly siting a new construction project”, [0007] “features of the plurality of land parcels based on at least one of a land parcel size, type, slope, or proximity”, [0020], [0069] “In some non-limiting embodiments or aspects, a map of a geographic location includes one or more routes that include one or more roadways. In some nonlimiting embodiments or aspects, map data associated with a map of the geographic location associates each land parcel, object, or road”, [0100] “A land parcel proximity score (e.g., a first land proximity score, land parcel slope score, etc.) is assigned to each parcel utilizing an overlay and attribute information as stored in data stores 106 or region database 108”, [0103] “siting platform 104 determines a land parcel slope score by correlating a maximum percent slope determined for the land parcel from a GIS slope layer with a site slope from the land parcel request. A maximum (e.g., high score, etc.) land parcel slope score is assigned for a land parcel”, [0104], [0106], [0022], [0030], [0133] “platform 104 displays computer generated land parcels determined to address a siting request. The computer-generated land parcels may be displayed to a user of the land parcel siting interface 102. For example, the computer-generated land parcels generated to fit a user's request, may be displayed as a layer of a map, where one or more candidate land parcels satisfy the requirements of a user and are selectable. For example, a computer-generated land parcel may include a selection capability or an information display function. Information may include a display of the subordinate land parcels that comprise the selectable land parcel”, [0045], etc.,). Examiner further submits to Applicant that, the state of the art concerning ML models (e.g. availability of pretrained models, techniques regarding transfer learning, etc.,), and level of skill in the art, are at least in the aggregate such that training a model to make predictions of roads, grading, and/or any number of comparable/related class/feature based determinations, as of the time of Applicant’s EFD, would be obvious provided sufficient supervised/ labeled (manually or otherwise) training samples can be generated/ procured (see MPEP 2141 with respect to the basic factual inquires of Graham v John Deere Co.).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the system and method of Brouard in view of Yang such that the ‘selecting’ recited is accomplished by means of various embodiments involving user interaction/input as taught/suggested by Jurick, the motivation being as similarly taught/suggested therein that such a user selection enables the system/method to more precisely serve (in terms of specific areas of interest) associated user interests related to one or more of e.g. market research, developer acquisition, zoning/restriction compliance verification, etc..
As to claim 12, Brouard in view of Yang and Jurick teaches/suggests the method of claim 11.
Brouard in view of Yang and Jurick teaches/suggests the method further comprising storing the geographic area of interest (Brouard suggests storing associated blocks 404/402 at 120/220 even if in RAM and for the purposes of subsequent processing, discloses associated geographic areas stored prior to selection (e.g. col 12 lines 1-15), and further discloses e.g. updating/adding landmark registry entries at 716/718, Brouard col 19 line 55 “The storage 809 may be used to store various initial, intermediate, or final data or model for object/landmark recognition, segmentation, and further data analytics. The storage 809 may further store training aerial images, land maps, and land registry data used for training and deployment of the object/landmark recognition and segmentation models, and various data analytics based on the output of the models. The storage 809 may be centralized or distributed. For example, it may be hosted remotely by a cloud computing service provider”; see also e.g. [0064] of Jurick).
As to claim 13, Brouard in view of Yang and Jurick teaches/suggests the method of claim 11.
Brouard in view of Yang and Jurick teaches/suggests the method wherein the selecting and causing are based upon input from a user interface (Brouard at least suggests (even if implicitly) a method that is as a whole at least initiated based at least in part upon user input (even if at an initial execution of associated software), in further view of Brouard col 19 lines 25-40; Jurick further evidences the obvious nature of user initiated analysis in response to a user request/query, [0133] “a computer-generated land parcel may include a selection capability or an information display function. Information may include a display of the subordinate land parcels that comprise the selectable land parcel”, [0138] “A user selects a siting location of interest within the geographical region. The location of interest has a corresponding geographical location (e.g., geographic coordinates, etc.). The graphical map is created in a manner such that it includes, and optionally, may be centered on (e.g., includes the surroundings, a range of surroundings, etc.), the geographical location of the user-selected siting location of interest or a combination of locations of interest”, etc., in further view of that modification/rationale as presented above for the case of claim 11).
As to claim 14, Brouard in view of Yang and Jurick teaches/suggests the method of claim 13.
Brouard in view of Yang and Jurick teaches/suggests the method wherein the input comprises one or more location information chosen from information comprising city, county, state, zip code, geographic coordinates and tax parcel number (Brouard e.g. col 18 line 55 “The geo-coordinate information may be used as key to identify tax identifications from land registry data 712 in process 714”; Examiner also notes, as written the recited information appear to include information embodiments that may be ‘chosen’/considered mentally by the user; see also Jurick Figs. 2A-2C, [0069], [0089] “Siting platform 104 determines a subset of GIS parcels within a region based on the siting request by the user (e.g., a user selected state, county, etc.) is created”, etc.,).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the system and method of Brouard in view of Yang and Jurick such area of interest selection criteria includes any of those recited, since each constitutes known/established and/or “Obvious to Try” area identifier alternatives, readily implemented with a reasonable expectation of success (see MPEP 2143 Rationale (E)).
As to claim 16, Brouard in view of Yang and Jurick teaches/suggests the method of claim 13.
Brouard in view of Yang and Jurick teaches/suggests the method wherein the one or more output is received by the user interface (Brouard col 19 lines 25-30 “Each computer 801 may include communication interfaces 802, system circuitry 804, input/output (I/O) interfaces 806, storage 809, and display circuitry 808 that generates machine interfaces 810 locally or for remote display”; see also Jurick [0045], [0133] “Siting platform 104 transmits response data representing the updated site selection. Upon completion, one or more sites are created with or associating related parcel information (i.e., assessed value, owner, address, infrastructure, zoning, etc.) to the map output. For example, siting platform 104 displays computer generated land parcels determined to address a siting request. The computer-generated land parcels may be displayed to a user of the land parcel siting interface 102”, etc.,).
As to claim 17, Brouard in view of Yang and Jurick teaches/suggests the method of claim 16.
Brouard in view of Yang and Jurick teaches/suggests the method wherein the one or more output is chosen from information comprising text indicating roads, grading, or none, a grid, and geographic coordinates corresponding to the grid (Brouard col 10 lines 1-15 “CNN model 130 to process the image blocks 120 (including but not limited to image blocks 122, 124, and 126) to generate labeled image blocks 140 including but not limited to labeled image blocks 142, 144, and 146, each labeled with recognized and segmented objects. The labels for the objects may include but are not limited to locations of the objects in the image blocks (e.g., pixel locations), boundaries of the objects, and types of objects. The locations of the objects, for example, may comprise centers-of-pixels of the recognized objects. The boundaries of the objects, depending on the complexity of the multilayer CNN model 130, may comprise simple boundary boxes with polygonal or other geometric shapes for approximating the object boundaries, or may comprise more elaborated contours of the objects”, in further view of “geographical coordinate” disclosure of Brouard in association with grid/blocks 310-336, e.g. col 3 lines 15-30 “For example, the system circuitry may be further configured to convert locations in the predicted labels to geographical coordinates based on the unlabeled aerial blocks and meta data associated with the unlabeled aerial blocks, to determine a subset of labels among the de-duplicated predicted labels that correspond to entries in a digital landmark registry database based on the geographical coordinates; and to determine tax non-conformity based on boundary box information of the de-duplicated predicted labels and size information contained in the digital landmark registry database”).
As to claim 18, Brouard in view of Yang and Jurick teaches/suggests the method of claim 11.
Brouard in view of Yang and Jurick teaches/suggests the method wherein the machine learned model is a trained Convolutional Neural Network (Brouard CNN(s) 130, col 6 lines 45-60 “data analytics for object recognition and segmentation may be based on, for example, one or more convolutional neural network (CNN) models containing a collection of, e.g., convolution, pooling, rectification, and fully connected layers. Connection between the elements, or neurons, within each layer and between the layers of a CNN may be represented by model parameters in the form of convolutional kernels or features, weights, biases, and other types of model parameters. These model parameters may be determined during a training process of the CNN model based on a set of training data. The training data may include training images labeled with locations and boundary boxes for various objects in the image that are pre-identified via other means. The training data are preferably domain specific to the types of images for which the CNN model will be deployed. Once the CNN model is trained with the training parameters determined, it may be deployed to process unlabeled images to intelligently recognize and segment objects therein”).
As to claim 19, Brouard in view of Yang and Jurick teaches/suggests the method of claim 11.
Brouard in view of Yang and Jurick teaches/suggests the method wherein the selecting, causing, and receiving are performed by one or more processors (Fig. 8, Brouard col 20 lines 10-15 “The system circuitry 804 may further include specific processors 818 (such as GPUs and other artificial intelligence processors) for implementing the CNN models”, lines 20-25 “The methods, devices, processing, and logic described above may be implemented in many different ways and in many different combinations of hardware and software. For example, all or parts of the implementations may be circuitry that includes an instruction processor, such as a Central Processing Unit (CPU), microcontroller, or a microprocessor; an Application Specific Integrated Circuit (ASIC), Programmable Logic Device (PLD), or Field Programmable Gate Array (FPGA); or circuitry that includes discrete logic or other circuit components, including analog circuit components, digital circuit components or both; or any combination thereof”, etc.,).
As to claim 26, this claim is the CRM claim corresponding to the method of claim 11 and is rejected accordingly.
As to claim 27, this claim is the CRM claim corresponding to the method of claim 18 and is rejected accordingly.
As to claim 28, this claim is the CRM claim corresponding to the method of claim 17 and is rejected accordingly.
2. Claims 15 are rejected under 35 U.S.C. 103 as being unpatentable over Brouard et al. (US 10,528,812 B1) in view of Yang et al. (US 11,042,742 B1), Jurick et al. (US 2022/0067856 A1) and Kanaujia et al. (US 2024/0331375 A1).
As to claim 15, Brouard in view of Yang and Jurick teaches/suggests the method of claim 13.
Brouard fails to explicitly disclose the method wherein the input comprises providing an outline surrounding the geographic area of interest.
Kanaujia evidences the obvious nature of an area selection based on user input comprising an outline surrounding the geographic area of interest (Fig. 4A, [0028] “FIG. 4A illustrates the selection of an area of interest”, [0065] “FIG. 4A, a selected geospatial image 400 typically comprises an area considerably greater than the area of interest 405 that the user wishes to monitor for detection of selected objects. The user establishes the boundaries of the area of interest by geofencing by any convenient means such as entry of coordinates, selection of vertexes on a map, and so on”, etc., while in the context of a user selection post-analysis, see also Kanaujia [0110]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date, to further modify the system and method of Brouard in view of Yang and Jurick such that input comprises providing an outline surrounding the geographic area of interest as taught/suggested by Kanaujia, the motivation as similarly taught/ suggested therein that such an input may serve as a convenient means facilitating user satisfaction and/or generally accommodating user desires in terms of incorporating vs not incorporating elements surrounding an area of interest, and/or shape characteristics associated with an area of interest (e.g. areas other than those that are square/ rectangular in nature – analogous to the school district disclosure of Brouard).
Additional References
Prior art made of record and not relied upon that is considered pertinent to applicant's disclosure:
Additionally cited references (see attached PTO-892) otherwise not relied upon above have been made of record in view of the manner in which they evidence the general state of the art, and/or at least teach/suggest one or more limitations in isolation. Justus et al. US 2022/0327722 A1, e.g. [0032] further discloses a user guided selection of a geographic area of interest.
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/IAN L LEMIEUX/Primary Examiner, Art Unit 2669
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