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 .
DETAILED ACTION
Status of the Application
The following is a Final Office Action in response to communication received on 4/10/2026. Claims 1, 3-6, and 8-20 have been examined in this application.
Subject Matter Overcoming the Prior Art of Record
Again, as detailed in the previous office action, prior art has not been applied to claim 14 however, claim 14 has been rejected under other grounds as detailed in the office action below.
Response to Amendment
Applicant’s amendments to claims 1, 3-4, 6, 8-11, 14-16, 18-20 are acknowledged. Applicant’s cancellation of claims 2 and 7 are acknowledged.
Response to Arguments
Applicant’s arguments in the response filed 4/10/2026 are acknowledged, the rejections have been updated below to reflect Applicant’s amendments.
Specifically with respect to the 101 rejection on pages 7-12 of Remarks, the 101 rejection has been updated to reflect Applicant’s amendments to the claims rendering most of Applicant’s arguments with respect to the 101 moot. However with respect to Applicant’s arguments with respect to improvements to the functioning of a computer, Applicant argues improving visual machine learning property analysis and cites paragraphs 0020, 0024, 0102, 0124, and 0135 to support this argument, however the Examiner respectfully disagrees. Here the cited paragraphs and arguments discuss improvements to the business process rather than improvements to the functioning of a computer (see Trading Technologies, MPEP 2106.05(a)). Trading Technologies makes it clear that improvements to the business proves do not improve computers or technology, see MPEP 2106.05 (a) cited herein “However, it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology.” As here the additional elements of “training” and the vectors being “feature” vectors merely recite apply it and generally linking it to the field of computers, which is not a practical application or significantly more as detailed in MPEP 2106.05(f) and MPEP 2106.05(h) and the 101 rejection below.
With respect to the 112 a/1st rejection on remarks pages 12-13 with respect to claim 14 Applicant argues the claims rejection under 112 a/1st paragraph, amends the claims, and cites paragraphs 0042, 0070-0072, 0115-0119 and 0138. Here Applicant’s specification and arguments merely disclose a type of model that can possibly be used to perform the calculations, not how the calculations are actually performed which is the basis of the rejection. Therefore the Examiner does not agree and has updated the 112 1st/a rejection below to reflect Applicant’s amendments (specifically the 112 first/a rejection has been updated to reflect a claim not interpreted under 112 sixth/f still lacking the algorithm).
With respect to the 112 b rejection and Applicant’s remarks on page 13, the Examiner has withdrawn the rejection in view of the no longer being interpreted under 112 sixth/f in view of Applicant’s amendments.
With respect to the double patenting rejection, Applicant argues the double patenting rejection is moot in view of Applicant’s amendments (See Remarks pages 13-14),the Examiner respectfully disagrees. The previous double patenting rejection has been updated in view of Applicant’s amendments.
With respect to the prior art, Applicant argues Applicant’s amendments. The Examiner has applied a new reference in view of Applicant’s amendments, see Yin et al.(United States Patent Application Publication Number: US 2022/0092227) rendering Applicant’s arguments moot.
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.
6. Claims 1, 3-6, 8-13 and 15-20 are rejected on the ground of nonstatutory double
patenting as being unpatentable over claims 1, 9, 11-12, 17-19, and 22 of U.S. Patent
No. 12, 229,845 further in view of Yin et al. (United States Patent Application Publication Number: US 2022/0092227).
As per claim 1, U.S. Patent No. 12, 229,845 (claim 1) teaches all the elements of the claim except (1) determining an image depicting a property, (2) wherein the parcel comprises a geometric boundary of land depicted within the image, (3) extracting one or more property attributes from the building segment as a feature vector, and (4) determining whether the property is part of a group based the parcel class and a distance between the feature vector of the building segment and a feature vector of a buildings segment associated with an adjacent parcel within a feature space.
However, Yin et al. (United States Patent Application Publication Number: US 2022/0092227) teaches these elements, specifically:
(1) determining an image depicting a property, (2) wherein the parcel comprises a geometric boundary of land depicted within the image, (see paragraph 0012, Examiner’s note: determining an adjacency graph where the adjacency graph includes internal information like room layout and exterior information associated with other structures on the same property like garage, shed, pool house, etc.).
And (3) extracting one or more property attributes from the building segment as a feature vector, and (4) determining whether the property is part of a group based the parcel class and a distance between the feature vector of the building segment and a feature vector of a buildings segment associated with an adjacent parcel within a feature space (see paragraphs 0016-0018, Examiner’s note: determining distance between embedding vectors to determine other buildings similar to the current building (for example floorplan) (see paragraphs 01016-0018). Teaches where this is performed by a trained neural network using labeled or unlabeled data (see paragraph 0018)
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified U.S. Patent No. 12, 229,845 with the aforementioned teachings from Yin with the motivation of using a specific known type of calculations to determine similarity between objects expressed as vectors (see Yin paragraphs 0016-0018) as well as the collected measurement information being images of properties (see Yin paragraph 0012), when expressing the collected information as vectors (see U.S. Patent No. 12, 229,845 claims 8 and 18), the collected information comprising building information and aerial imagery (see U.S. Patent No. 12, 229,845 claim 8 and 20) and determining if a property is part of a group based on parcel class and building segment are all known (see U.S. Patent No. 12, 229,845 claim 8 and 20)
As per claim 3, US Patent No 12229845 teaches all the elements of the claim (see claim 1)
As per claim 4, further US Patent No 12229845 teaches all the elements of the claim (see claims 11 and 18)
As per claim 5, further US Patent No 12229845 teaches all the elements of the claim (see claims 11 and 22)
As per claim 6, US Patent No 12229845 does not expressly teach determining a neighboring property located within a neighboring parcel, wherein the neighboring parcel is separated from a second parcel associated with a second property less than a threshold distance, wherein the second property is determined to be a part of the group, and determining whether the neighboring property is part of the group based on a comparison between a feature vector of the neighboring property and feature vector of the second property.
However, Yin teaches this (see paragraph 0096, Examiner’s note: match or similar (e.g. neighboring) based on smallest determined distance).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified US Patent No 12229845 with the aforementioned teachings from Yin which the motivation of determining the collected information should be grouped together based on distances between vectors (see Yin paragraph 0096), when determining groups based on satisfying thresholds and that these may be feature vectors (see US Patent No 1222984 claims 7-8) are both known.
As per claim 8, US Patent No 12229845 teaches this in at least claims 9-10, except expressing information as feature vectors.
However, Yin teaches information collected or extracted information be expressed as feature vectors (see paragraphs 0016-0018, Examiner’s note: determining distance between embedding vectors to determine other buildings similar to the current building (for example floorplan) (see paragraphs 01016-0018). Teaches where this is performed by a trained neural network using labeled or unlabeled data (see paragraph 0018)
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified U.S. Patent
No. 12, 229,845 with the aforementioned teachings from Yin with the using a specific known type of a known way to express collected or extracted information as vectors (see Yin paragraphs 0016-0018), when expressing the building information as feature vectors (see No. 12, 229,845 claim 8 and 20) is known.
As per claim 9, US Patent No 12229845 does not expressly teach wherein properties within the merged group are associated with a common entity. However, Yin teaches this (see paragraph 0096, Examiner’s note: selecting one or more above a defined threshold like a smallest determination distance values (e.g. highest similarity to one or more of the indicated buildings)).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified US Patent No 12229845 with the aforementioned teachings from Yin which the motivation of providing a way to provide information related to shared features in the final group (see Yin paragraph 0018 and 0096), when providing information related to shared features is known ( see US 1229845 claims 5-6)
As per claim 10, US Patent No 12229845 does not expressly teach wherein the merged group is formed by merging the group with the second group when a distance between the average appearance feature vector of the group and the average appearance feature vector of the second group is less than a threshold distance within the feature space. However, Yin teaches this (see paragraph 0096, Examiner’s note: selecting one or more above a defined threshold like a smallest determination distance values (e.g. highest similarity to one or more of the indicated buildings)).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified U.S. Patent
No. 12, 229,845 with the aforementioned teachings from Yin with the motivation of using a specific known type of calculations to determine similarity between objects expressed as vectors (see Yin paragraphs 0016-0018), when expressing the building information as feature vectors is known (see No. 12, 229,845 claim 8 and 20)
As per claim 11, US Patent No 12229845 (claim 11) teaches all of the elements of the claim except for (1) each parcel comprising a geometric boundary of land encompassing a property and (2) limitations of feature vectors. It is noted that "extract a parcel feature" maps to an extract building segment.
However, Yin teaches (1) each parcel comprising a geometric boundary of land encompassing a property (see paragraph 0012, Examiner’s note: determining an adjacency graph where the adjacency graph includes internal information like room layout and exterior information associated with other structures on the same property like garage, shed, pool house, etc.).
And (2) limitations of feature vectors (see paragraphs 0016-0018, Examiner’s note: determining distance between embedding vectors to determine other buildings similar to the current building (for example floorplan) (see paragraphs 01016-0018). Teaches where this is performed by a trained neural network using labeled or unlabeled data (see paragraph 0018)
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified U.S. Patent
No. 12, 229,845 with the aforementioned teachings from Yin with the motivation of using a specific known type of way to express objects like as vectors (see Yin paragraphs 0016-0018) as well as a parcel including land (see Yin paragraph 0012), when expressing the building information as feature vectors (see No. 12, 229,845 claim 8 and 20) and the parcel including a building expressed as an aerial image (see No. 12, 229,845 claims 11 and 18) are both known.
As per claim 12, US Patent No 12229845 teaches all of the elements of the claim (see claim 12)
As per claim 13, US Patent No 12229845 teaches all of the elements of the claim (see claim 11), note "wherein the parcel feature set is extracted based on a parcel boundary" maps to at least" determine a set of measurements depicting each of a set of properties; extract a building segment for each property of the set of properties from the set of measurements using a segmentation model comprising"
As per claim 15, US Patent No 12229845 does not expressly teach wherein the at least one processor is further configured to iteratively determine parcel classes and group properties of the set of properties to determine a final group. However, Yin teaches this (see paragraphs 0018 and 0096, Examiner’s note: determining similarity between different graphs based on iterative methods(see paragraph 0018). Further teaches ranks and then determines one or more bet matches based on ranking this is also iterative (see paragraph 0096).
Before the effective filing date of the claimed invention it would have been
obvious for one of ordinary skill in the art to have modified US 1229845 with the
aforementioned teachings from Yin with the motivation of providing a way to determine a final group by comparing multiple different pieces of information (see Yin paragraphs 0018 and 0096), when merging groups together based on similarity is known (see US Patent No 12229845 claim 10 and 19)
As per claim 16, US Patent No 12229845 does not expressly teach wherein the at least one processor is further configured to determine a confidence score for the final group based on properties within the final group and a set of auxiliary features associated with the properties of within the final group. However, Yin teaches this (see paragraph 0097, Examiner’s note: a probability or other likelihood (which is interpreted as a confidence score) that the buildings have a similarity above a threshold).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified US 1229845 with the aforementioned teachings from Yin with the motivation of providing a way to provide information on the confidence of the calculation or estimation (see Yin paragraphs 0162 and 0168), when making an estimation is known (e.g. classifying according to a model)(see US 1229845 claim 1)
As per claim 17, US Patent No 12229845 teaches all of the elements of the claim (see claim 19)
As per claim 18, US Patent No 12229845 teaches all of the elements of the claim (see claim 17)
As per claim 19, US Patent No 12229845 teaches all of the elements of the claim in at least claims 9-10, except expressing information as feature vectors.
However, Yin teaches information can be expressed as feature vectors (see paragraphs 0016-0018, Examiner’s note: determining distance between embedding vectors to determine other buildings similar to the current building (for example floorplan) (see paragraphs 01016-0018). Teaches where this is performed by a trained neural network using labeled or unlabeled data (see paragraph 0018)
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified U.S. Patent
No. 12, 229,845 with the aforementioned teachings from Yin with the motivation of using a specific known type of way to express collected information like vectors (see Yin paragraphs 0016-0018), when expressing the building information as feature vectors (see No. 12, 229,845 claim 8 and 20) is known.
As per claim 20, US Patent No 12229845 does not teach determining a neighboring property adjacent to a property of a group; and determine whether a comparison metric based on geometry feature vectors extracted from a parcel associated with the neighboring property and a parcel associated with the property of the group satisfies a threshold; and associate the neighboring property with the group based on a determination that the comparison metric satisfies the threshold. However, Yin teaches this (see paragraph 0096, Examiner’s note: determining whether a building of a set of buildings is a match based on smallest determined distance).
Before the effective filing date of the claimed invention it would have been obvious for one of ordinary skill in the art to have modified US Patent No 12229845 with the aforementioned teachings from Yin which the motivation of determining whether collected information is part of a group based on distances between vectors (see Yin paragraph 0096), when determining groups based on satisfying thresholds and that these may be expressed as feature vectors (see US Patent No 1222984 claims 7-8) are both known.
Claim Interpretation
Claims 1, 3-6, 8-10 are interpreted as a process as the claims recite a method. Claims 11-20 are interpreted as a machine as the claims recite a system with a processor performing operations.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1, 3-6, and 8-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claim(s) recite(s) the idea of collecting information, analyzing it, and displaying results of the collection and analysis, where the specific information relates to property information.
The claims are recited at such a high level of generality the claims recite observations, evaluations, judgements, and opinions that could be performed in the human mind or with use of a physical aid (e.g. pen and paper) to perform the claim limitation therefore the claims recite a mental process.
Further the idea of collecting information, analyzing it and displaying results of the collection and analysis, where the specific information relates to property information is a fundamental economic principle or practice, which is a certain method of organizing human activity.
Further the specific type of analyzing of vector information to determine a result, like distances or averages could additionally or alternatively be mathematical calculations, which are in the enumerated groupings of mathematical concepts.
Mental processes, certain methods of organizing human activity, and or mathematical concepts are in the groupings of enumerated abstracts ideas, and hence the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims merely recite limitations that are not indicative of integration into a practical application in that the claims merely recite:
(1) Adding the words "apply it" ( or an equivalent) with the judicial exception, or
mere instructions to implement an abstract idea on a computer, or merely uses a
computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and or (2)
Generally linking the use of the judicial exception to a particular technological
environment or field of use (see MPEP 2106.05(h)).
Specifically as recited in the claims:
As per claim 1, the claims recite mental process and or human activity steps
Specifically determining buildings from an image, determining a parcel associated with a property where the parcel includes a geometric boundary of land depicted within the image, extracting a building segment associated with the property using a segmentation model, extracting one or more property attributes from the building segment as a vector, determining a parcel class for the parcel associated with a property using a classification model, where the parcel includes land encompassing the property and the classification model is used to predict a parcel class for each of the set of parcels associated with the properties based on known or previous information like features extracted from parcel information for each of the set of parcels using qualitative labels, and determining whether the property is part of a group based on the parcel class and a distance between vectors within a feature space. This is part of the abstract idea.
Further the specific type of analyzing of vector information, like distances between vectors, could additionally or alternatively be mathematical calculations (which is a mathematical concept), and therefore part of the abstract idea.
The additional elements that the model is "trained" and the known or previous
information is "training data" and that the vectors are “feature vectors” merely results apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further here the claim recites only the idea of a solution or outcome but fails to recite details of how a solution to a problem is accomplished. Rather here Applicant recites a result oriented solution and lack details as to how the computer performs the modifications which is equivalent to the words apply it.
Further the additional elements that the model is "trained" and the known or
previous information is "training data" and that the vectors are “feature vectors” merely results in generally linking it to the field of computers.
As per claim 3, the claims further define the segmentation that as discussed
above is part of the abstract idea. The additional element that further defines the
segmentation model is a neural network merely results in apply it. Specifically here the
claim invokes computers or other machinery merely as a tool to perform an existing
process. Use of a computer or other machinery in its ordinary capacity for economic or
other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose
computer or computer components after the fact to an abstract idea does not integrate a
judicial exception into a practical application or provide significantly more. Further here
the claim recites only the idea of a solution or outcome but fails to recite details of how a
solution to a problem is accomplished. Rather here Applicant recites a result oriented solution and lack details as to how the computer performs the modifications which is
equivalent to the words apply it.
Further the additional element that that the model is a "neural network" merely
results in generally linking it to the field of computers.
As per claim 4, the claims recite the measurement is from an aerial image. This
is a mental process and or human activity step given the broad recitation in the claim.
There are no additional elements beyond those previously discussed above.
As per claim 5, the claims define the different type of parcel classes such are
mental process and or human activity steps. There are no additional elements beyond
those previously discussed above.
As per claim 6, the claims recite mental process and or human activity steps of
determining neighboring property located within a neighboring parcel, wherein the neighboring parcel is separated from a second parcel associated with a second property by less than a threshold distance, wherein the second property is determined to be part of the group, and determining whether the neighboring property is part of the group based on a comparison between a vector of the neighboring property and a vector of a second property. This is part of the abstract idea.
Further the specific type of comparing of vector information, like distances between vectors, could additionally or alternatively be mathematical calculations (which is a mathematical concept), and therefore part of the abstract idea.
The additional element that the vectors are “feature vectors” merely results in apply it or generally linking it to the field of computers as discussed above.
As per claim 8, the claims recite mental process and or human activity steps of determining averages of vectors and merging based on averages of vectors. This is part of the abstract idea.
Further the specific type of analyzing of vector information, like averages between vectors, could additionally or alternatively be mathematical calculations (which is a mathematical concept), and therefore part of the abstract idea.
The additional element that the vectors are “feature” vectors merely results in apply it or generally linking it to the field of computers as discussed above.
As per claim 9, the claims recite mental process and or human activity steps of
properties within the final group are associated with a common entity. There are no
additional elements beyond those previously discussed above.
As per claim 10, the claims recite mental process and or human activity steps of the merged groups is formed by merging the group with the second group when the distance between the average vector of the group and the average appearance vector of the second group is less than a threshold distance within the feature space. This is part of the abstract idea.
Further the specific type of analyzing of vector information, like averages between vectors and distances, could additionally or alternatively be mathematical calculations (which is mathematical concepts), and therefore part of the abstract idea.
The additional element that the vectors are “feature vectors” merely results in apply it or generally linking it to the field of computers as discussed above.
As per claim 11, the claims recite mental process and or human activity steps
Specifically determining a set of properties, each comprising one or more buildings, determine a parcel for each property of the set of properties, each parcel comprising a geometric boundary of land encompassing a property, extracting a parcel feature for each parcel as a vector, determine a parcel class for the parcel property of the set of properties based on a respective feature set, using a classification model, wherein the classification model is used to predict parcel classes based on previous or known data including vectors extracted associated with a set of properties qualitative labels and group properties of the set of properties based on a relationship between the parcel classes. This is part of the abstract idea.
The additional element that the model is "trained" and the known or previous
information is "training data" and the vectors are “feature vectors” merely results apply it or generally linking it to the field of computers as discussed above in claim 1.
The additional element that these limitations are being performed by a computer
"at least one memory; and at least one processor coupled to the at least one memory" merely results apply it. Specifically here the claim invokes computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g. to receive, store or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea does not integrate a judicial exception into a practical application or provide significantly more. Further here the claim recites only the idea of a solution or outcome but fails to recite details of how a solution to a problem is accomplished.
Further the additional elements that these human activities or mental process
steps are being performed by a computer "at least one memory; and at least one processor coupled to the at least one memory" merely results in generally linking it
to the field of computers.
The additional element that the vectors are “feature” vectors merely results in apply it or generally linking it to the field of computers as discussed above.
As per claim 12, the claims recite mental process and or human activity steps of
the parcel class is determined based on a relationship between the parcel and a
building segment associated with the property. This is part of the abstract idea. There are no additional elements beyond those previously discussed above.
As per claim 13, the claims recite mental process and or human activity steps of
the parcel feature set is extracted based on a parcel boundary. This is part of the abstract idea. There are no additional elements beyond those previously discussed above.
As per claim 14, the claims recite mental process and or human activity steps of combining parcels of two or more properties to form a combined parcel comprising a combined parcel boundary, determining whether a convexity of the combined parcel boundary is greater than a convexity of a parcel boundary of each parcel independently using a group determination model, and grouping the properties of the set of properties based on a determination that the convexity of the combined parcel boundary is greater than the convexity of the parcel boundary of each parcel independently. This is part of the abstract idea.
The additional element that this is being performed by the processor merely results in apply it or generally linking it to the field of computers as discussed above in claim 11.
As per claim 15, the claims recite mental process and or human activity steps of iteratively determine parcel classes and group properties of the set of properties to determine a final group. This is part of the abstract idea.
The additional element that this is being performed by the processor merely results in apply it or generally linking it to the field of computers as discussed above in claim 11.
As per claim 16, the claims recite mental process and or human activity steps of
determine a confidence score for the final group based on properties within the final group and a set of auxiliary features associated with the properties within the final group. This is part of the abstract idea.
The additional element that this is being performed by the processor merely results in apply it or generally linking it to the field of computers as discussed above in claim 11.
As per claim 17, the claims recite mental process and or human activity steps of
the final group is determined by merging a first group with a second group. This is part of the abstract idea. There are no additional elements beyond those previously discussed above
As per claim 18, the claims recite mental process and or human activity steps of
the first and second group are merged based on a comparison between a first and
second summary descriptive parameters for the first and second groups. This is part of the abstract idea. There are no additional elements beyond those previously discussed above.
As per claim 19, the claims recite mental process and or human activity steps of
wherein the summary descriptive parameter comprises an average vector determined by extracting property attributes for each property of the first group and the second group as vectors, averaging the vectors of the first group to generate an average vector of the first group, and averaging the vectors of the second group to generate an average vector of the second group. This is part of the abstract idea.
Further the specific type of analyzing of vector information, like averages between vectors, could additionally or alternatively be mathematical calculations (which are mathematical concepts), and therefore part of the abstract idea.
The additional element that the vectors are “feature vectors” merely results in apply it or generally linking it to the field of computers as discussed previously above.
As per claim 20, the claims recite mental process and or human activity steps of determine a neighboring property adjacent to a property of a group, and determine whether a comparison metric based on a geometry vector extracted from a parcel associated with the neighboring property and a parcel associated with the property of the group satisfies a threshold, and associated the neighboring property with the group based on a determination that the comparison metric satisfies a threshold. This is part of the abstract idea.
Further the specific type of analyzing of vector information, comparing vectors, could additionally or alternatively be mathematical calculations (which is a mathematical concept), and therefore part of the abstract idea.
The additional element that the vectors are “feature vectors” merely results in apply it or generally linking it to the field of computers as discussed previously above.
The additional element that this is being performed by the processor merely results in apply it or generally linking it to the field of computers as discussed above in claim 11.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims merely recite limitations that are not indicative of an inventive concept (“significantly more”) in that the claims merely recite:
(1) Adding the words "apply it" ( or an equivalent) with the judicial exception, or
mere instructions to implement an abstract idea on a computer, or merely uses a
computer as a tool to perform an abstract idea (see MPEP 2106.05(f)) and or (2)
Generally linking the use of the judicial exception to a particular technological
environment or field of use (see MPEP 2106.05(h)), as detailed above with respect to the practical application step.
Claim Rejections - 35 USC § 112
7. 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.
8. Claim 14 is 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.
As per claim 14, Applicant's specification does not disclose the algorithm for
claim 14 specifically the limitation of “determining whether a convexity of the combined parcel boundary is greater than a convexity of the combined parcel boundary is greater than a convexity of a parcel boundary of each parcel independently using a group determination model; and grouping the properties of the set of properties based on a determination that the convexity of the combined parcel boundary is greater than the convexity of the parcel boundary of each parcel independently.”
MPEP 2161 states the following: “When examining computer-implemented functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor possessed the claimed subject matter at the time of filing” and “It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See, e.g., Vasudevan Software, Inc. v. MicroStrategy, Inc., 782 F.3d 671, 681-683, 114 USPQ2d 1349, 1356, 1357 (Fed. Cir. 2015) (reversing and remanding the district court’s grant of summary judgment of invalidity for lack of adequate written description where there were genuine issues of material fact regarding "whether the specification show[ed] possession by the inventor of how accessing disparate databases is achieved"). If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made.”
The specification does not recite the algorithm for performing this function.
Specifically, Applicant merely recites results not a series of steps for how the claimed
determination is made. See MPEP 2161, cited herein: " An algorithm is defined, for example, as "a finite sequence of steps for solving a logical or mathematical problem or performing a task." Microsoft Computer Dictionary (5th ed., 2002). Applicant may "express that algorithm in any understandable terms including as a mathematical formula, in prose, or as a flow chart, or in any other manner that provides sufficient structure."
Specifically here Applicant does not disclose how the processing device or
computer determines the convexity of the parcel boundary for the combined parcel and
the convexity of the parcel boundary for each parcel independently to then perform the
determination. Applicant as amended merely recites this is being performed by a “group determination model”, which merely describes a type of model being used. This does not disclose how the processing device or computer determines the convexity of the parcel boundary for the combined parcel and the convexity of the parcel boundary for each parcel independently to then perform the determination.
No sequence of steps, flowchart, example, etc. is provided as to how these calculations are being performed. Rather Applicant merely discloses the results of the determination, and what do with the results of the determination. One of ordinary skill in the art would not understand the sequence of steps that perform the function claimed, and therefore convey that the inventors at the time the application was filed had possession of the claimed invention. Therefore Applicant does not recite the algorithm for performing the claimed function, and therefore the claims do not comply with the written description requirement and must be rejected under 112 a/first.
Examiner notes below the sections, where convexity is cited in Applicant's specification
for reference.
Paragraph 0042- One or more property features can be extracted from the property
information (and/or a set thereof). A feature can represent aspects of the information
itself (e.g., aspects of the measurement). Features can be independent (e.g., do not
carry information about and/or are not dependent on the values of other features) or
dependent (e.g., determined based on another feature, dependent upon another
feature, etc.). Examples of features that can be determined include: geometric features
(e.g., aspects of a geometric measurement), appearance-based features (e.g., aspects
of an image or appearance measurement), interaction-based features (e.g., how
geometries interact with each other, how attributes interact with each other, etc.), and/or
other features. Examples of features that can be extracted can include: color
components, length, area, circularity, gradient magnitude, gradient direction, points,
edges, measurement unit intensity values, convexity gain (e.g., whether the total
convexity of two combined geometries is higher than the convexities of the geometries
alone), and/or other features. Features can be determined using: image processing,
point cloud processing, machine learning techniques (ex. extracted using an encoder,
extracted by a neural network or an intermediate layer thereof, etc.), SIFT, using a
Gaussian, edge detection, corner detection, blob detection, ridge detection, edge
direction, changing intensity, autocorrelation, thresholding, blob extraction, template
matching, Hough transform, etc.), and/or any other suitable set of methodologies.
Paragraph 0115- In a fourth embodiment of the second variant, a parcel can be
classified as a unit parcel when the overall convexity of the parcel, fit against another
parcel (e.g., an adjacent parcel), has a higher convexity than the parcels' individual
convexity.
Paragraph 0116- In a fifth embodiment of the second variant, a parcel can be classified
based on the feature values and/or attribute values (e.g., using heuristics, a classifier,
etc.).
Paragraph 0117- However, the parcel can be otherwise determined based on rules
and/or heuristics.
Paragraph 0118- However, the one or more parcels can be otherwise classified.
Paragraph 0119- S 100 can optionally include determining parcel information for one or
more parcels. The parcel information can be determined for a single parcel, for each
parcel of multiple parcels, for combined multiple parcels (e.g., geometric interactions of multiple parcels such as combined shape of multiple parcels), and/or otherwise
determined. The parcel information can be associated with one parcel, multiple parcels,
and/or any other suitable number of parcels. Parcels can be adjacent to each other or
not be adjacent to each other. Parcel information can include: parcel boundary, parcel
position, parcel area, parcel shape, parcel perimeter, parcel convexity, other parcel
geometry, and/or any other suitable information. In examples, the parcel information
includes a convexity of a boundary for a single parcel and/or a convexity of boundary for
combined parcels.
Paragraph 0138- In a sixth variant, S300 can include identifying other properties within
the group based on parcel information for parcels. In an example, given two parcels (a
parcel associated with the property and a parcel associated with a different property), if
a convexity of the boundary for the combined parcels is greater than a convexity of the
boundary for each parcel independently, the properties associated with the parcels are
determined to be part of the same group. In another example, parcel subgroups can be
combined when the convexity of the combined parcel subgroups is greater than the
convexity of each parcel subgroup independently.
Claim Rejections - 35 USC § 102
9. 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.
10. 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
11. Claim(s) 1, 3-6, 8-13, and 15-20 are rejected under 35 U.S.C. 102(a)(1) and or 102(a)(2) as being unpatentable over Yin et al. (United States Patent Application Publication Number: US 2022/0092227).
As per claim 1, Yin teaches A method, comprising: (see abstract, Examiner’s note: method to determine similar floor plans).
determining an image depicting a property, wherein the property comprises one or more buildings; determining a parcel associated with the property, wherein the parcel comprises a geometric boundary of land depicted within the image; extracting a building segment associated with the property using a segmentation model; (see paragraph 0012, Examiner’s note: determining an adjacency graph where the adjacency graph includes internal information like room layout and exterior information associated with other structures on the same property like garage, shed, pool house, etc.).
extracting one or more property attributes from the building segment as a feature vector; determining a parcel class for the parcel associated with the property using a classification model, wherein the parcel includes land encompassing the property, and wherein the classification model is trained to predict a training parcel class for each of a set of training parcels associated with a set of training properties based on features extracted from parcel information for each of the set of training parcels using qualitative labels; and determining whether the property is part of a group based on the parcel class and a distance between the feature vector of the building segment and a feature vector of a building segment associated with an adjacent parcel within a feature space (see paragraphs 0016-0018, Examiner’s note: determining distance between embedding vectors to determine other buildings similar to the current building (for example floorplan) (see paragraphs 01016-0018). Teaches where this is performed by a trained neural network using labeled or unlabeled data (see paragraph 0018) ).
As per claim 3, Yin teaches
wherein the segmentation model comprises a neural network (see paragraph 0018, Examiner’s note: teaches this is done by a neural network).
As per claim 4, Yin teaches
wherein the image comprises an aerial image (see paragraph 0029, Examiner’s note: teaches this can be an aerial image as this can be captured from an aerial drone).
As per claim 5, Yin teaches
wherein the parcel class comprises at least one of a unit parcel, a surrounding parcel, or a stand-alone parcel (see paragraph 0012, Examiner’s note: teaches capturing information within a same property)
As per claim 6, Yin teaches
further comprising: determining a neighboring property located within a neighboring parcel, wherein the neighboring parcel is separated from neighboring a second parcel associated with a second property that is by less than a threshold distance, wherein the second property is determined to be a part of the group; and determining whether the neighboring property is part of the group based on a comparison between feature vector of the neighboring property and a feature vector of the second property (see paragraph 0096, Examiner’s note: match or similar (e.g. neighboring) based on smallest determined distance).
As per claim 8, Yin teaches
further comprising: determining an average appearance feature vector of the group; determining an average appearance feature vector of a second group; (see paragraphs 0017, 0018, 0096, Examiner’s note: comparing distances to average vectors).
and merging the group and the second group to form merged group comprising properties determined to be part of a same group based on a comparison of the average appearance feature vectors (see paragraph 0096, Examiner’s note: selecting one or more above a defined threshold like a smallest determination distance values (e.g. highest similarity to one or more of the indicated buildings)).
As per claim 9, Yin teaches
wherein properties within the merged group are associated with a common entity. (see paragraph 0096, Examiner’s note: selecting one or more above a defined threshold like a smallest determination distance values (e.g. highest similarity to one or more of the indicated buildings)).
As per claim 10, Yin teaches
wherein the merged group is formed by merging the group with the second group when a distance between the average appearance feature vector of the group and the average appearance feature vector of the second group is less than a threshold distance within the feature space. (see paragraph 0096, Examiner’s note: selecting one or more above a defined threshold like a smallest determination distance values (e.g. highest similarity to one or more of the indicated buildings)).
As per claim 11, Yin teaches A system, comprising: (see paragraph 0006, Examiner’s note: system for implementing the present disclosure).
at least one memory; and at least one processor coupled to the at least one memory processing system, and configured to: (see paragraph 0069 and 0392-0393, Examiner’s note: computer implementing instructions to perform functions).
determine a set of properties, each property comprising one or more buildings;
determine a parcel for each property of the set of properties, each parcel comprising a geometric boundary of land encompassing a property; (see paragraph 0012, Examiner’s note: determining an adjacency graph where the adjacency graph includes internal information like room layout and exterior information associated with other structures on the same property like garage, shed, pool house, etc.).
extract a parcel feature set for each parcel as a feature vector; determine a parcel class for the parcel for each property of the set of properties based on the respective parcel feature set, using a classification model, wherein the classification model is trained, to predict training parcel classes based on the feature vectors extracted from training parcels associated with a set of training properties qualitative labels; and group properties of the set of properties based on a relationship between the respective parcel classes. (see paragraphs 0016-0018, Examiner’s note: determining distance between embedding vectors to determine other buildings similar to the current building (for example floorplan) (see paragraphs 01016-0018). Teaches where this is performed by a trained neural network using labeled or unlabeled data (see paragraph 0018)).
As per claim 12, Yin teaches
wherein the parcel class is determined based on a relationship between the parcel and a building segment associated with the property. (see paragraphs 0016-0018, Examiner’s note: determining distance between embedding vectors to determine other buildings similar to the current building (for example floorplan) (see paragraphs 01016-0018). Teaches where this is performed by a trained neural network using labeled or unlabeled data (see paragraph 0018)
As per claim 13, Yin teaches
wherein the parcel feature set is extracted based on a parcel boundary. (see paragraph 0012, Examiner’s note: determining an adjacency graph where the adjacency graph includes internal information like room layout and exterior information associated with other structures on the same property like garage, shed, pool house, etc.).
As per claim 15, Yin teaches
wherein the at least one processor is further configured to by iteratively determine parcel classes and group properties of the set of properties to determine a final group (see paragraphs 0018 and 0096, Examiner’s note: determining similarity between different graphs based on iterative methods(see paragraph 0018). Further teaches ranks and then determines one or more best matches based on ranking this is also iterative (see paragraph 0096)).
As per claim 16, Yin teaches
wherein the at least one processor is further configured to determine a confidence score for the final group based on properties within the final group and a set of auxiliary features associated with the properties within the final group (see paragraph 0097, Examiner’s note: a probability or other likelihood (which is interpreted as a confidence score) that the buildings have a similarity above a threshold).
As per claim 17, Yin teaches
wherein the final group is determined by merging a first group with a second group (see paragraph 0096, Examiner’s note: select one or more best matches).
As per claim 18, Yin teaches
wherein the first group and the second group are merged based on a comparison between a first summary descriptive parameter for the first group and a second summary descriptive parameter for the second group. (see paragraph 0096, Examiner’s note: select one or more best matches based on a similarity degree value).
As per claim 19, Yin teaches
wherein the first summary descriptive parameter and the second summary descriptive parameter comprise an average feature vector determined by: extracting one or more property attributes from each property of the first group and the second group as feature vectors; averaging the feature vectors of the first group to generate an average feature vector of the first group; and averaging the feature vectors of the second group to generate an average feature vector of the second group. (see paragraphs 0017, 0018, and 0096, Examiner’s note: comparing distances to average vectors).
As per claim 20, Yin teaches
wherein the processing system at least one processor is further configured to: (see paragraph 0069 and 0392-0393, Examiner’s note: computer implementing instructions to perform functions).
determine a neighboring property adjacent to a property of a group; and determine whether a comparison metric based on geometry feature vectors extracted from a parcel associated with the neighboring property and a parcel associated with the property of the group satisfies a threshold; and associate include the neighboring property as part of the with the group when based on a determination that the comparison metric satisfies the threshold(see paragraph 0096, Examiner’s note: determining whether a building of a set of buildings is a match based on smallest determined distance).
Conclusion
12. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
13. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
a. Barajas Hernandez et al. (United States Patent Application Publication Number: US 2019/0130641) teaches capturing digital aerial images and clustering the information according to models to generate model of a site where this may relate to buildings (see abstract and paragraph 0002)
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to KIERSTEN SUMMERS whose telephone number is (571)272-6542. The examiner can normally be reached Monday - Friday 7am-3:30pm.
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/KIERSTEN V SUMMERS/Primary Examiner, Art Unit 3626
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