Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments have persuasively overcome the 112 rejection regarding “geographical area.”
The remaining issues are addressed below.
Interpretation
Applicant argues:
Thus, regardless of how broadly the underlying terms are construed, the claims require that damage-index data and damage- index images exist as intermediate representations that are generated prior to, and used as inputs to, object-level damage detection.
Examiner responds:
MPEP 2111.01(II) states “Altiris Inc. v. Symantec Corp., 318 F.3d 1363, 1371, 65 USPQ2d 1865, 1869-70 (Fed. Cir. 2003) (Although the specification discussed only a single embodiment, the court held that it was improper to read a specific order of steps into method claims where, as a matter of logic or grammar, the language of the method claims did not impose a specific order on the performance of the method steps, and the specification did not directly or implicitly require a particular order).”
The claim does not require that the various steps A) happen in a particular order or B) that the steps cannot be simultaneously performed.
Applicant argues:
Rather, the claims require that damage-index images be generated first and then used to guide, condition, or control how damage detection is performed on objects in the image data.
Examiner responds:
The examiner has not found this requirement in the claims.
112a
Applicant argues:
As a threshold matter, MPEP §2173.05(g) addresses indefiniteness under § 112(b), not written description under § 112(a).
Examiner responds:
MPEP §2173.05(g) discusses both, and explicitly authorizes 112(a) rejections.
Applicant argues:
Thus, "obtaining damage-index data" is not a bare statement of result, but is supported by detailed disclosure of specific data sources and processing techniques sufficient to demonstrate possession.
Examiner responds:
Specification, [03] states that the use of “using supplemental data, also referred to as damage-index data” is used to “improve detection and estimation of damage.” The examiner finds that obtaining damage index data is not a known step in the art because, as per Applicant’s Specification, this is a new technique to improve results. Applicant also argued “damage-index data is obtained as information indicating potential damage affecting a geographical area.” This means that Applicant is using expansive language to describe a result. As explained by MPEP 2173.05(g) “For instance, a single means claim covering every conceivable means for achieving the stated result was held to be invalid under 35 U.S.C. 112, first paragraph because the court recognized that the specification, which disclosed only those means known to the inventor, was not commensurate in scope with the claim.”
Narrowing the claim to the disclosed techniques (and any genuses for which a representative number of species are disclosed) is expected to overcome this rejection.
Applicant argues:
The Office Action similarly asserts that "detecting damage to an object" is unlimited functional claiming.
Examiner responds:
It appears that the claim encompasses what the Annual Report termed “proprietary analytics” (p. 16). Are those analytics disclosed in this specification? There are many ways to detect damage beyond what is presently disclosed, but the claims encompass those as well.
Applicant argues:
the Specification explains that object detection may be performed using learning-machine-based object detection models or filtering-based techniques
Examiner responds:
The rate at which new machine learning techniques are being developed is dizzying, and thus claims should not encompass future developments.
Applicant argues:
Written description does not require disclosure of every conceivable species within a genus, particularly in predictable arts such as image processing and remote sensing. Disclosure of representative species and explanation of their operation is sufficient. See Ariad, 598 F.3d at 1351. The Specification plainly meets this standard.
Examiner responds:
The examiner agrees that only a representative number of species is needed. However, machine learning is not entirely predictable. For example, improving the performance of a model by, say, 10% or 20% is often not a trivial task. Thus, if, for example, one explains how to create and train a convolutional neural network to recognize pictures of cats with 70% accuracy, improving the accuracy to 95% likely involves undue experimentation. Further, improving accuracy by changing to a different neural architecture likely also involves undue experimentation (similarly, trying to estimate how different training data will impact accuracy is an active area of research).
Applicant argues:
The Specification explicitly discloses detecting objects using both learning-based and filtering- based techniques,
Examiner responds:
As per above, the examiner is concerned with the scope of machine learning claims.
Applicant argues:
which is consistent with practical image- analysis systems
Examiner responds:
If Applicant submits a timely IDS with details of such systems, the examiner will consider them.
Applicant argues:
This assertion conflates written description with indefiniteness.
Examiner responds:
Applicant’s arguments are directed to the expected/believed likelihood of damage, but the claim reads on the actual, potentially unknown, likelihood of damage. Amending the claim to recite a projected/expected likelihood (or similar language) is expected to overcome this rejection.
112b
Applicant argues:
and are routinely upheld as definite when read in context.
Examiner responds:
If Applicant submits case law, the examiner expects to consider it, but presently there are no citations to support the idea that these are “upheld.”
Applicant argues:
Here, the Specification explains that
Examiner responds:
The examples from the specification appear definite, but the examiner is not importing these limitations from the specification. In other words, if the claims recite the algorithms in the specification, the rejections are expected to be overcome.
Applicant argues:
The entire thrust of the invention is distinguishing likelihood or probability of damage from definitive damage assessment.
Examiner responds:
While the examiner appreciates the distinction that Applicant is making, this differs from the claims. For example, amended claim 10 recites “likelihood of occurrence of damage,” i.e., whether damage will occur in the future (as opposed to a probabilistic assessment of whether damage has already occurred).
Applicant argues:
The Specification explicitly states that damage-index images (also referred to as damage- index maps) are spatially aligned raster representations of damage-index data [009], [075].
Examiner responds:
Paragraph [075] (that discusses specific representations) says “for example,” and thus is insufficient to limit the claim.
Applicant argues:
A person of ordinary skill would readily understand "learning machine" to mean a machine-learning-based model trained from data
Examiner responds:
“Learning machine” is not a term of art. The plain meaning of “learning machine” suggests hardware (i.e., a “machine’) whereas Applicant’s proposal (a model) appears to be software.
Applicant argues:
The Specification repeatedly explains how regions are defined, including grid-based partitioning, clustering (e.g., k-means), segmentation outputs, and contiguous regions derived from index values [030]-[039], [091].
Examiner responds:
But if one person thinks the regions are defined as grid-based, but the other person uses clustering, then each person can have a different determination as to whether there is overlap or not.
Applicant argues:
It simply denotes a one-to-one correspondence between elements, which is clear in context and consistent with Federal Circuit precedent.
Examiner responds:
Given, for example, a group of images and a group of types of damage, the claim does not specify how to match a particular image to a particular type of damage. In other words, Applicant’s one to one correspondence is what the examiner meant by comparing two lists.
Applicant argues:
the terms are used interchangeably in the art
Examiner responds:
If the intent is that the terms are the same, then it would appear that they should recite the same language.
Applicant argues:
are expressly defined in the Specification as allowing reliance on the stated factor alone or in combination with others [0119], eliminating any ambiguity.
Examiner responds:
Specification [0119] does not address “according to” and the definition to “based on” is circular.
Applicant argues:
The Examiner raises concerns regarding grammatical structure, exemplary clauses, and phrases such as "in response to determining," "normal," "currently received," and "particular."
Examiner responds:
See above.
102
Applicant argues:
There is no disclosure of first obtaining damage-index data that indicates potential damage independently of object detection
Examiner responds:
Applicant appears to be implicitly arguing for a negative claim limitation. However, the examiner believes that the broadest reasonable interpretation, in light of the specification, allows for creation of damage indices (including images) based on damage assessments. The examiner believes that the claim only requires that the damage indices (including images) are used to detect damage, but the claims do not require, for example, that this is the first detection of damage.
Applicant argues:
Damage-index data, as recited and described in the specification, represents probabilistic, indicative, or contextual information about potential damage affecting a geographical area.
Examiner responds:
Bertoluzza’s information indicates that damage occurred.
Applicant argues:
In other words, damage-index images function as an intermediate representation that encodes where damage is likely, not what specific objects are damaged.
Examiner responds:
At the beginning of the remarks, Applicant argued “Even under a Broadest Reasonable Interpretation that Applicant does not concede in which damage-index data encompasses any data indicating damage, and damage-index images encompass any image representing such data.” Thus, damage-index images are not as limited as Applicant argues here.
Applicant’s remaining prior art arguments all rely on the above point (i.e., the assertion that Bertoluzza lacks damage index images). Those arguments are unpersuasive for the above reasons.
101
Applicant argues:
These representations are not mental impressions; they are structured data artifacts produced by algorithmic processing of multispectral imagery, models, or geospatial inputs.
Examiner responds:
A person can look at a photo of a neighborhood and compare that with a historical flood map. Recentive Analytics, Inc. v. Fox Corp., 134 F. 4th 1205 (Fed. Cir. 2025) shows that reciting machine learning is insufficient to become subject matter eligible.
Applicant argues:
The Federal Circuit has repeatedly held that claims are not directed to a mental process when they recite specific data structures and processing steps that improve how a computer or technical system performs a task, even if the task relates to information analysis. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1336-37 (Fed. Cir. 2016) (claims directed to a specific data structure that improves computer functionality are not abstract); McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15 (Fed. Cir. 2016) (claims reciting specific rules for automated processing are not mental processes).
Examiner responds:
Enfish was about a novel data structure, not just any data. McRO was based on a comparison of the algorithm recited in the claim as compared to how a person did this. The present claims lack an algorithm comparable to McRO or a new data structure as in Enfish.
Applicant argues:
A recognized technical problem in remote sensing and computer vision is that uniform detection thresholds and uniform detector behavior perform poorly in spatially heterogeneous disaster environments, leading to false positives in low-risk regions and missed detections in high- risk regions.
Examiner responds:
If Applicant submits timely evidence to support this, the examiner will consider it.
Applicant argues:
This is not an abstract idea of "interpreting data," but a concrete improvement in how an automated system processes remote-sensing imagery.
Examiner responds:
Here, the improvements are to the correctness of the result (i.e., interpreting data), they are not applicable to other approaches to processing insurance damage information (i.e., not an improvement to systems generally).
Applicant argues:
Even assuming arguendo that the claims implicate an abstract idea, they recite significantly more.
Examiner responds:
As per above, the examiner believes that the claims are much broader than Applicant asserts.
Applicant argues:
Applicant respectfully requests that consideration of these rejections be held in abeyance pending resolution of the substantive rejections.
Examiner responds:
The examiner cannot hold a rejection (or an objection) in abeyance.
Claim Objections
Claim 19 is objected to because of the following informalities:
Claim 19 recites “and and” (the third and fourth lines from the end of the claim)
Appropriate correction is required.
Examiner Notes
The listed assignee, Spark Insights, Inc. (Boston) was acquired by Concirrus (London) in 2021. According to the substitute oaths, the applicant (i.e., Concirrus) was unable to locate the inventors of this application (see the substitute oaths filed February 9, 2024). Because consulting with the inventors can be beneficial to prosecution, as a courtesy to applicant, the examiner notes that named co-inventors Ira Scharf, Paul Cummer and Vidyavathy Renganathan appear to be reachable via LinkedIn.com, and that the application data sheet submitted on August 26, 2025 in application 18/025,420 (now U.S. Patent 12,429,432) lists home addresses for named co-inventors Ira Scharf, Xiang Wen, Paul Cummer and Feng Pan.
Attached is the Annual Report for Allied Minds plc for the year 2019 (“Annual Report”) because it suggests the that present technology may have been on sale/publicly available more than one year before Applicant’s earliest asserted priority date. Applicant is invited to comment. The following quotes are all from the Annual Report:
“Allied Minds plc (Allied Minds or the Company or the Group) is an IP commercialisation company primarily focused on early stage company development within the technology sector.” (p. 3)
“In April 2019, Allied Minds subscribed to a $3.2 million preferred share financing in Spark Insights, with $1.2 million invested in April and the remaining $2.0 million invested in September 2019 due upon the achievement of certain technical milestones. As a result, Allied Minds holds a 70.59% ownership interest.” (p. 147)
There is a discussion of Spark Insights at pages 16-17.
The discussion of Spark Insights’ product at pages 16-17 sounds to be what is presently being patented. The Annual Report states that additional investment was made “upon the achievement of certain technical milestones.” The examiner believes that achievement of these technical milestones shows that the invention was ready for patenting. The examiner has not found case law as to whether the sale of equities (such as the present preferred share financing) triggers the on sale bar, but believes that MPEP 2133.03(c)(III) suggests that the financing might not trigger the bar. Here again, Applicant’s comments are welcome. However, the Annual Report statement that a “2020 key operational management objective” is to “Engage with pilot customers in the insurance and reinsurance industries” appears to be evidence that the product was on sale once the technical milestones were achieved.
Should Applicant submit the above Annual Report on an Information Disclosure Statement in other cases, Applicant may wish to also submit a copy of this Office Action because the relevance of the Annual Report on its own may not be apparent.
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 1, 3, 4, 8-12, 14-21, 33, and 36 (all claims) are rejected on the ground of nonstatutory double patenting as being unpatentable over the claims of U.S. Patent No. US 12429432 in view of the prior art as applied below.
Claims 1, 3, 4, 8-12, 14-21, 33, and 36 (all claims) are provisionally rejected on the ground of nonstatutory double patenting as being unpatentable over the claims of copending Application No. 18/275,558.
Both the pending claims and the conflicting patent and application are all directed to identifying damage in areas based on images. Therefore, the conflicting patent and application are directed to the same problem as the present application. Further, any differences between the present claims and the claims in any of the conflicting patents are obvious in view of the prior art as applied below. It would h,ave been obvious to one of ordinary skill in the art, before the effective filing date, to combine the below prior art with any of the conflicting patents in for implementation details (especially as the patent claims lack implementation details). Based on the findings herein, this is an example of “(A) Combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 3, 4, 8-12, 14-21, 33, and 36 (all claims) are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1, 10, 19, and 33 recite “obtaining damage-index data … ,” but this is unlimited functional claiming. MPEP 2173.05(g).
Claim 21 recites corresponding language and is likewise rejected.
Claims 1, 10, 19, and 33 recite “detecting damage to an object… ,” but this is unlimited functional claiming. MPEP 2173.05(g).
Claim 21 recites corresponding language and is likewise rejected.
Claims 8, 10, 11, 18, 19, and 36 recite “damage detection model,” but the specification has not demonstrated support for the entire genus of damage detection models.
Claim 9 recites “detecting at least some of the one or more objects in the received image data” and claim 20 recites “detecting at least some of the one or more objects in the received image data,” both of which are unlimited functional claiming. MPEP 2173.05(g).
Claim 11 recites “based on likelihood of occurrence of damage,” but this is unlimited functional claiming because there are many different types of damage and even more different ways to determine this likelihood. MPEP 2173.05(g).
Dependent claims are likewise rejected.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 3, 4, 8-12, 14-21, 33, and 36 (all claims) are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 9, 10, 19, 21 and 33 recite “representative,” but this is a subjective term because opinions can differ as to whether something is representative. MPEP 2173.05(b)(IV).
Claims 1, 10, 13, 19, 21 and 33 recite “indicating” or “indicates,” but these are subjective terms. MPEP 2173.05(b)(IV). Claim 16 is not rejected because claim 16 recites a determination.
Claims 1, 10, 19, 21 and 33 recite “potential damage,” but this is a relative term (i.e., potential is understood as a likelihood of damage occurring, but there is not an objective standard for what likelihood is needed to be “potential”). MPEP 2173.05(b).
Claims 1, 3, 10, 17-19, 21 and 33 recite “potential …” or “potentially …” connected to “affecting” or “affected.” “Affecting” or “affected” refer to something that is or has actually occurred, but “potential” is something that might happen. As with “potential,” there is not an objective standard to determine if something will potentially be affected.
Claims 1, 3, 8-10 and 17-19 recite “damage index images,” but this is new terminology. MPEP 2173.05(a). The examiner’s review suggests that “damage index” is a term of art for a numeric value of how much damage has occurred, but it is not clear what “damage index images” would be. Note that specification [09] states that “damage index images” may be maps, and not images at all.
Claims 3, 8, 9, 10, 17-19, and 33 recite “respective,” but not specify an ordered list. Because the claims instead recite unordered groupings (e.g., types of damages, clusters), it is not possible to determine if the various items are matched with the “respective” second item or not.
Claims 8, 18, and 19 recite “learning machine,” but this is new terminology. MPEP 2173.05(a).
Claims 8, 18, 19, and 33 recite “associated with,” but this is subjective. MPEP 2173.05(b)(IV).
Claim 9 recites “overlap between … regions,” but does not define how the regions are determined, and thus different borders of the “region” can lead to different results. MPEP 2173.05(b)(IV).
Claims 10, 11, 16, and 36 raise a similar issue for their recitations of “regions” because one needs to determine where the unspecified regions are to determine if the claim has been met.
Claims 10, 11, 33 and 36 recite “according to,” but this is subjective. MPEP 2173.05(b)(IV).
Claims 10-12, 15, 16, 33, and 36 variously recite “sensitivity level,” “sensitivity value,” and “sensitivity.” It is not clear what difference, if any, exists between these three.
Claim 10 recites “based on a portion,” but this is indefinite because whether the claim is met can turn on which portion was selected. MPEP 2173.05(b)(IV).
Claim 10 recites “corresponding to,” but this is subjective. MPEP 2173.05(b)(IV).
Claims 11 and 36 recite “receiver operation curve (ROC) representation,” but it is not clear what the metes and bounds of the claimed “representation” are. Does a ROC curve need to have been plotted? Can the representation include just a portion of the information from the ROC curve?
Claim 11 recites a clause with a series of commas and then an “or,” but it is not grammatically clear which claim language is operated on by the “or”.
Claims 11, 16 and 36 recite “particular” but this is subjective. MPEP 2173.05(b)(IV).
Claim 11 recites “or adjust a detection sensitivity value for the detection model within the region,” but this appears to be strictly broader than the first clause. This can be understood as the first clause being exemplary. MPEP 2173.05(d).
Claim 10 compares two likelihoods, but does not recite something to the effect of “all else being equal,” and thus is it not clear how to determine whether one likelihood is higher than the other.
Claim 16 recites “in response to determining,” but it is not clear if the “determining” is a required step of the method or not.
Claim 16 recites “normal,” but this is a relative term. MPEP 2173.05(b).
Claim 19 recites “composite damage index map,” but this is new terminology. MPEP 2173.05(a). Additionally, claim 19 recites “based on one or more,” but it is unclear how to interpret “composite” if the map is only based on a single type of information (i.e., it is not a composite of two or more).
Claim 20 recites “currently received,” but this is relative terminology. MPEP 2173.05(b).
Claim 20 recites two clauses for detecting objects based on, but the second clause’s recitation of “the currently received image data,” requires the first clause to occurs, leaving the second clause as exemplary. MPEP 2173.05(d).
Claim 21 recites “a communication interface,” but this term does not have a precise meaning (e.g., is this a network interface card, or could this just be software to load data?).
Dependent claims are likewise rejected.
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.
Claims 1, 3, 4, 8-12, 14-21, 33, and 36 (all claims) are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Step 1: Claims 1, 10, 19, and 33 (and their dependents) recite a method, and processes are eligible subject matter.
Claim 21 recites a system, and machines are eligible subject matter.
Step 2A, prong one: All of the elements of the claims are a mental process because a person can look at an image and decide if it shows damage to an object or region in the image. Further, the various models are also mental processes, see example 47, claim 2, element (d) (from the July 2024 AI subject matter eligibility examples). MPEP 2106.04(a)(2)(III)(C) explains that use of a generic computer or in a computer environment is still a mental process. In particular, this section begins by citing Gottschalk v. Benson, 409 US 63 (1972). “The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea.” In Benson the Supreme Court did not separately analyze the computer hardware at issue; the specifics of what hardware was claimed is only included in an appendix to the decision.
Because there are no additional elements, no further analysis is required for Step 2A, prong two or Step 2B.
Claim Rejections - 35 USC § 102
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 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 3, 4, 8, 9, 19-21 and 33 (all claims except those rejected under 103, below) are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bertoluzza, Manuel. "Novel Methods for Change Detection in Multitemporal Remote Sensing Images." (2019): 1-120 (“Bertoluzza”).
1. (Original) A method for detecting damage in a geographical area, the method comprising:
receiving image data for the geographical area, the image data containing data representative of one or more objects; (Bertoluzza, electronic page 5, “The unprecedented number of images enables a large number of applications related to the monitoring of the environment on a global and regional scale. A non-exhaustive list of applications contains climate change assessment, disaster monitoring and urban planning.” The citations are to the attached pdf page numbers, i.e., the fifth page of the pdf is the abstract.)
obtaining damage-index data comprising information indicating potential damage affecting the geographical area; and (Bertoluzza, electronic page 5, “In this context, this thesis focuses on Change Detection (CD) techniques capable of identifying areas within remote sensing images where the land-cover/land-use changed.”)
detecting damage to an object, from the one or more objects in the geographical area, based on the received image data for the geographical area and the damage-index data comprising the information indicating the potential damage affecting the geographical area, and (Bertoluzza, electronic pages 39-40, “The proposed framework is general and can be applied to change detection maps obtained by any possible binary CD technique present in the literature. Therefore it can be useful to analyze time series describing phenomena not only related to remote sensing (e.g., risk assessment, emergency response, damage assessment,”)
wherein detecting damage to the object based on the received image data for the geographical area and the damage-index data comprises:
generating one or more damage index images from the obtained damage index data; and (Bertoluzza, electronic page 76, Fig. 3.17, See also, from the Fig. 3.6(c) caption (electronic page 53), “Reference map of the changes … that shows the areas burned by a forest fire in black color.”)
detecting damage to the object based on the received image data and the one or more damage index images. (Bertoluzza, electronic pages 39-40, “The proposed framework is general and can be applied to change detection maps obtained by any possible binary CD technique present in the literature. Therefore it can be useful to analyze time series describing phenomena not only related to remote sensing (e.g., risk assessment, emergency response, damage assessment,”)
3. (Original) The method of claim 1, wherein the image data for the geographical area comprises multi-band geospatial data, and (Bertoluzza, electronic page 53, “a pair of real multispectral images characterized by an abrupt change.” Multipsectral teaches the claimed multiband, see, e.g., specification [060].)
wherein obtaining the damage-index data includes filtering the image data comprising the multi-band geospatial data to extract band data, for the geographical area, to identify one or more types of damage; and (Bertoluzza, electronic page 54, “In the experiments, the red and the near-infrared were used since they demonstrated to be highly sensitive to the presence of the vegetation, thus widely used in many vegetation indexes, and among the most significant multispectral bands characterized by the highest contrast for the change of interest.”)
wherein generating the one or more damage-index images comprises generating, based on the extracted band data, the one or more damage index images identifying portions within each of the one or more damage-index images that potentially are affected by the respective one or more types of damage. (Bertoluzza, electronic page 76, Fig. 3.17, See also, from the Fig. 3.6(c) caption (electronic page 53), “Reference map of the changes … that shows the areas burned by a forest fire in black color.”)
4. (Original) The method of claim 3, wherein filtering the image data comprising the multi-band geospatial data comprises:
filtering the image data to extract one or more of RGB band data for the image, (Bertoluzza, electronic page 54, “In the experiments, the red and the near-infrared were used since they demonstrated to be highly sensitive to the presence of the vegetation, thus widely used in many vegetation indexes, and among the most significant multispectral bands characterized by the highest contrast for the change of interest.”)
false color composite data comprising near infrared data for the image plus red component and green component data for the image, or (Bertoluzza, electronic page 54, “In the experiments, the red and the near-infrared were used since they demonstrated to be highly sensitive to the presence of the vegetation, thus widely used in many vegetation indexes, and among the most significant multispectral bands characterized by the highest contrast for the change of interest.” Bertoluzza, electronic page 54 also teaches “false colors.”)
RGB band data for the image plus infrared band data for the image. (Bertoluzza, electronic page 54, “In the experiments, the red and the near-infrared were used since they demonstrated to be highly sensitive to the presence of the vegetation, thus widely used in many vegetation indexes, and among the most significant multispectral bands characterized by the highest contrast for the change of interest.”)
5. - 7. (Cancelled)
8. (Original) The method of claim 1, wherein generating the one or more damage index images comprises:
applying a damage detection model, implemented on a learning machine, to the received image data for the geographical area to detect regions in the received image data associated with respective one or more types of damage-causing events. (Bertoluzza, electronic page 76, Fig. 3.17, See also, from the Fig. 3.6(c) caption (electronic page 53), “Reference map of the changes … that shows the areas burned by a forest fire in black color.” Bertoluzza’s change determination teaches the claimed learning machine, see, e.g., specification [076].)
9. (Original) The method of claim 1, wherein detecting damage to the object based on the one or more damage index images comprises:
detecting at least some of the one or more objects in the received image data; and (Bertoluzza, electronic page 55, “The change event corresponds to the areas burned by a wildfire occurred on July 2nd, 2016 with an extension approximately of 2312 of hectares.” Bertoluzza’s areas teach the claimed objects.)
determining overlap between the detected at least some of the one or more objects in the received image data and regions of the one or more generated damage-index images comprising data representative of respective types of damage. (Bertoluzza, electronic page 76, Fig. 3.17)
19. (Original) <The beginning of claim 19 is mapped as per claim 1>
wherein obtaining the damage-index data comprises performing one or more of:
i) receiving prior-knowledge data including one or more of
historical wind speed information in the geographical area,
historical flood information,
historical storm path information for the geographical area,
historical flood map information for the geographical area,
burn-index information,
vegetation index, or
weather reports for the geographical area;
ii) applying a damage detection model, implemented on a learning machine, to the received image data for the geographical area to generate segmented data representative of one or more segmented regions in the received image data associated with respective one or more types of damage-causing events; or (Bertoluzza, electronic page 76, Fig. 3.17, See also, from the Fig. 3.6(c) caption (electronic page 53), “Reference map of the changes … that shows the areas burned by a forest fire in black color.” Bertoluzza’s change determination teaches the claimed learning machine, see, e.g., specification [076].)
iii) receiving multi-band geospatial data, and filtering the multi-band geospatial data to extract band data, for the geographical area, to generate one or more multi-band indices; and and (Bertoluzza, electronic page 53, “a pair of real multispectral images characterized by an abrupt change.” Multipsectral teaches the claimed multiband, see, e.g., specification [060].)
wherein detecting damage to the object comprises generating a composite damage index map based on one or more of: the prior-knowledge data, the generated segmented data, or the one or more multi-band indices. (Bertoluzza, electronic page 76, Fig. 3.17, See also, from the Fig. 3.6(c) caption (electronic page 53), “Reference map of the changes … that shows the areas burned by a forest fire in black color.”)
20. (Original) The method of claim 19, wherein detecting damage to the object, from the one or more objects in the geographical area, comprises:
detecting the one or more objects in the geographical area based on one or more of:
currently received image data obtained subsequent to occurrence of a damage-causing event affecting the geographical area, or (Bertoluzza, electronic page 76, Fig. 3.17)
earlier received image data obtained prior to the occurrence of the damage-causing event affecting the geographical area with the earlier received image data being aligned to the currently received image data. (Bertoluzza, electronic page 76, Fig. 3.17)
Claim 21 is rejected as per claim 1. Additionally, Bertoluzza, electronic page 69, Table 3.4 teaches the use of the claimed hardware.
Claim 33 is rejected as per claim 10. Note that the broadest reasonable interpretation of claim 33 includes the various clusters all having the same damage probability value.
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.
Claims 10 and 14-18 are rejected under 35 U.S.C. 103 as being unpatentable over Bertoluzza, Manuel. "Novel Methods for Change Detection in Multitemporal Remote Sensing Images." (2019): 1-120 (“Bertoluzza”) in view of CN100585656C (“Tan”).
10. (Original) <The beginning of claim 10 is mapped as per claim 1>
wherein detecting damage to the object comprises:
dividing the image data into multiple portions according to the damage index data; and (Bertoluzza, electronic page 53, “The spectral signatures of burned areas extracted from another image were inserted into the first image [59].” Bertoluzza’s burned areas teach the claimed portions.)
for each of the multiple portions of the divided image data, applying a damage detection model with an adjustable detection sensitivity level, including controlling the adjustable detection sensitivity level based on a portion of the damage-index data corresponding to the respective portions of the divided image data. (Bertoluzza, electronic page 51, “Threshold 7 can assume values in the range [0, M] and its value tunes the sensitivity of the proposed mechanism to unreliable samples.”)
Bertoluzza teaches the method of claim 10, but is not relied on for the below claim language.
However, Tan teaches wherein applying the detection model comprises:
adjusting the detection sensitivity levels for a first region and for a second region in the geographical area so that a likelihood of identifying a first object in the first region as being damaged is higher than a likelihood of identifying a second object in the second region as being damaged, (Tan, p. 4 “The results obtained from the background segmentation are subjected to differential thresholding” Tan’s differential thresholding teaches the claimed adjusting a sensitivity value because Tan’s threshold is a sensitivity value.)
when the damage-index data indicates that a likelihood of occurrence of damage in the first region is higher than a likelihood of occurrence of the damage in the second region. (Tan, p. 2 “Step S21: Thresholding the results obtained by background segmentation to obtain candidate targets” Tan’s candidate targets teach the claimed higher likelihood of occurrence.)
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 teachings of Tan to the teachings of Bertoluzza’s such that Tan’s image segmenting and analysis is applied to Bertoluzza’s images for the purpose of better compensating for complex field environments and other interference. Tan, pp. 1-2.
Based on the above, this is an example of “combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
14. (Original) The method of claim 10, wherein applying the detection model includes applying a detection model implemented as a binary classifier. (Bertoluzza, electronic page 7, “Binary Change Detection Maps”)
15. (Original) The method of claim 14, wherein controlling the adjustable detection sensitivity level comprises adjusting a discrimination threshold of the binary classifier. (Bertoluzza, electronic page 51, “Threshold 7 can assume values in the range [0, M] and its value tunes the sensitivity of the proposed mechanism to unreliable samples.”)
16. (Original) The method of claim 10, wherein controlling the adjustable detection sensitivity level comprises:
increasing the sensitivity level for a particular region in response to determining that the portion of damage-index data for the particular region indicates higher than normal likelihood relative to a baseline likelihood for the geographic are of occurrence of a damage-causing event. (Tan, p. 4 “First, the image sequence collected by the camera is background segmented to obtain the correct foreground, then the obtained foreground is subjected to target detection to obtain the object to be monitored” Tan’s foreground teaches the claimed region with higher than normal likelihood of the target event (e.g., Bertoluzza’s fire damage).)
17. (Original) The method of claim 10, wherein the image data of the geographical area comprises multi-band geospatial data, and (Bertoluzza, electronic page 53, “a pair of real multispectral images characterized by an abrupt change.” Multipsectral teaches the claimed multiband, see, e.g., specification [060].)
wherein obtaining the damage-index data comprises:
filtering the image data comprising the multi-band geospatial data to extract band data, for the geographical area, to identify one or more types of damage; and (Bertoluzza, electronic page 54, “In the experiments, the red and the near-infrared were used since they demonstrated to be highly sensitive to the presence of the vegetation, thus widely used in many vegetation indexes, and among the most significant multispectral bands characterized by the highest contrast for the change of interest.”)
generating based on the extracted band data one or more resultant damage index images identifying portions within each of the one or more resultant damage index images that potentially are affected by the respective one or more types of damage. (Bertoluzza, electronic page 76, Fig. 3.17, See also, from the Fig. 3.6(c) caption (electronic page 53), “Reference map of the changes … that shows the areas burned by a forest fire in black color.”)
18. (Original) The method of claim 10, wherein obtaining the damage-index data comprises:
applying a damage detection model, implemented on a learning machine, to the received image data for the geographical area to detect regions in the received image data associated with respective one or more types of damage; and (Bertoluzza, electronic page 76, Fig. 3.17, See also, from the Fig. 3.6(c) caption (electronic page 53), “Reference map of the changes … that shows the areas burned by a forest fire in black color.” Bertoluzza’s change determination teaches the claimed learning machine, see, e.g., specification [076].)
generating, based on the detected regions in the received image data associated with the respective one or more types of damage-causing events, one or more resultant damage index images identifying portions within each of the one or more resultant damage index images that potentially are affected by the respective one or more types of damage. (Bertoluzza, electronic page 76, Fig. 3.17, See also, from the Fig. 3.6(c) caption (electronic page 53), “Reference map of the changes … that shows the areas burned by a forest fire in black color.”)
Claims 11, 12 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Bertoluzza, Manuel. "Novel Methods for Change Detection in Multitemporal Remote Sensing Images." (2019): 1-120 (“Bertoluzza”) in view of CN100585656C (“Tan”) further in view of Ali, Sardar, et al. "Progressive Differential Thresholding for Network Anomaly Detection." 2011 IEEE International Conference on Communications (ICC). IEEE, 2011 (“Ali”).
11. (Original) The method of claim 10, … adjust a detection sensitivity value for the detection model within the region. (Tan, p. 4 “The results obtained from the background segmentation are subjected to differential thresholding” Tan’s differential thresholding teaches the claimed adjusting a sensitivity value because Tan’s threshold is a sensitivity value.)
The combination of Bertoluzza and Tan is not relied on for the below claim language.
However, Ali teaches wherein the adjustable sensitivity level is controlled according to a receiver operation curve (ROC) representation defining a relationship between true-positive and false-positive rates, and (Ali, p. 4, right column, “We performed all our analysis by using a range of differential thresholds and plotting their correponding Receiver Operating Curves (ROCs).”)
wherein applying the damage detection model comprises:
dynamically tuning an operation point for the ROC for a particular portion of the image data based on likelihood of occurrence of damage, determined based on the damage-index data, in a region within the geographical area to adjust a false-positive detection rate of the detection model, or (Ali, abstract, “In this paper, we propose a Progressive Differential Thresholding (PDT) framework for coordinated network anomaly detection. Under the proposed framework, nodes present on a packet’s path progressively encode their opinion (malicious or benign) inside a packet. Subsequent nodes on the path use the encoded opinion as side-information to adapt their
anomaly detection thresholds and in turn improve their classification accuracies”)
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 teachings of Ali to the teachings of the combination of Bertoluzza and Tan such that Ali’s differential thresholding is used with the combination of Bertoluzza and Tan’s differential thresholding for the purpose of implementation details on analyzing the thresholds. See, e.g., Bertoluzza, electronic page 96 at the bottom discussing different sensitivity thresholds (i.e., the ‘m’ values) as compared with Ali’s statement that these can be plotted on a receiver operating curve (see cited section above). Further, one of ordinary skill in the art before the effective filing date would have appreciated that the analytical techniques that Ali uses to set detection thresholds for packets are the same as those used by each of Tan and Bertoluzza to set thresholds for detecting in images or segments of images.
Based on the above, this is an example of “combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
12. (Original) The method of claim 11, wherein dynamically tuning the operation point for the ROC comprises one of:
decreasing the detection sensitivity value for the detection model to cause a decrease in the false-positive detection rate within the region; or (Ali, p. 4, right column, “We performed all our analysis by using a range of differential thresholds and plotting their correponding Receiver Operating Curves (ROCs).”)
increasing the detection sensitivity for the detection model to cause an increase in the false-positive detection rate within the region. (Ali, p. 4, right column, “We performed all our analysis by using a range of differential thresholds and plotting their correponding Receiver Operating Curves (ROCs).”)
Claim 36 is rejected as per claim 11.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US10664702B2 – “Method and system for crop recognition and boundary delineation”
US10217169B2 – “FIELD Embodiments relate to insurance processing systems and methods. More particularly, embodiments relate to the calculation, modeling and/or provision of a safety score associated with a user location.” Col. 1, ll. 24-29.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID ORANGE whose telephone number is (571)270-1799. The examiner can normally be reached Mon-Fri, 9-5.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DAVID ORANGE/ Primary Examiner, Art Unit 2663
Prosecution Insights